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• Types of Interviews in Research | Guide & Examples

Types of Interviews in Research | Guide & Examples

Published on March 10, 2022 by Tegan George . Revised on June 22, 2023.

An interview is a qualitative research method that relies on asking questions in order to collect data . Interviews involve two or more people, one of whom is the interviewer asking the questions.

There are several types of interviews, often differentiated by their level of structure.

• Structured interviews have predetermined questions asked in a predetermined order.
• Unstructured interviews are more free-flowing.
• Semi-structured interviews fall in between.

Interviews are commonly used in market research, social science, and ethnographic research .

Table of contents

What is a structured interview, what is a semi-structured interview, what is an unstructured interview, what is a focus group, examples of interview questions, advantages and disadvantages of interviews, other interesting articles, frequently asked questions about types of interviews.

Structured interviews have predetermined questions in a set order. They are often closed-ended, featuring dichotomous (yes/no) or multiple-choice questions. While open-ended structured interviews exist, they are much less common. The types of questions asked make structured interviews a predominantly quantitative tool.

Asking set questions in a set order can help you see patterns among responses, and it allows you to easily compare responses between participants while keeping other factors constant. This can mitigate   research biases and lead to higher reliability and validity. However, structured interviews can be overly formal, as well as limited in scope and flexibility.

• You feel very comfortable with your topic. This will help you formulate your questions most effectively.
• You have limited time or resources. Structured interviews are a bit more straightforward to analyze because of their closed-ended nature, and can be a doable undertaking for an individual.
• Your research question depends on holding environmental conditions between participants constant.

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Semi-structured interviews are a blend of structured and unstructured interviews. While the interviewer has a general plan for what they want to ask, the questions do not have to follow a particular phrasing or order.

Semi-structured interviews are often open-ended, allowing for flexibility, but follow a predetermined thematic framework, giving a sense of order. For this reason, they are often considered “the best of both worlds.”

However, if the questions differ substantially between participants, it can be challenging to look for patterns, lessening the generalizability and validity of your results.

• You have prior interview experience. It’s easier than you think to accidentally ask a leading question when coming up with questions on the fly. Overall, spontaneous questions are much more difficult than they may seem.
• Your research question is exploratory in nature. The answers you receive can help guide your future research.

An unstructured interview is the most flexible type of interview. The questions and the order in which they are asked are not set. Instead, the interview can proceed more spontaneously, based on the participant’s previous answers.

Unstructured interviews are by definition open-ended. This flexibility can help you gather detailed information on your topic, while still allowing you to observe patterns between participants.

However, so much flexibility means that they can be very challenging to conduct properly. You must be very careful not to ask leading questions, as biased responses can lead to lower reliability or even invalidate your research.

• You have a solid background in your research topic and have conducted interviews before.
• Your research question is exploratory in nature, and you are seeking descriptive data that will deepen and contextualize your initial hypotheses.
• Your research necessitates forming a deeper connection with your participants, encouraging them to feel comfortable revealing their true opinions and emotions.

A focus group brings together a group of participants to answer questions on a topic of interest in a moderated setting. Focus groups are qualitative in nature and often study the group’s dynamic and body language in addition to their answers. Responses can guide future research on consumer products and services, human behavior, or controversial topics.

Focus groups can provide more nuanced and unfiltered feedback than individual interviews and are easier to organize than experiments or large surveys . However, their small size leads to low external validity and the temptation as a researcher to “cherry-pick” responses that fit your hypotheses.

• Your research focuses on the dynamics of group discussion or real-time responses to your topic.
• Your questions are complex and rooted in feelings, opinions, and perceptions that cannot be answered with a “yes” or “no.”
• Your topic is exploratory in nature, and you are seeking information that will help you uncover new questions or future research ideas.

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Depending on the type of interview you are conducting, your questions will differ in style, phrasing, and intention. Structured interview questions are set and precise, while the other types of interviews allow for more open-endedness and flexibility.

Here are some examples.

• Semi-structured
• Unstructured
• Focus group
• Do you like dogs? Yes/No
• Do you associate dogs with feeling: happy; somewhat happy; neutral; somewhat unhappy; unhappy
• If yes, name one attribute of dogs that you like.
• If no, name one attribute of dogs that you don’t like.
• What feelings do dogs bring out in you?
• When you think more deeply about this, what experiences would you say your feelings are rooted in?

Interviews are a great research tool. They allow you to gather rich information and draw more detailed conclusions than other research methods, taking into consideration nonverbal cues, off-the-cuff reactions, and emotional responses.

However, they can also be time-consuming and deceptively challenging to conduct properly. Smaller sample sizes can cause their validity and reliability to suffer, and there is an inherent risk of interviewer effect arising from accidentally leading questions.

Here are some advantages and disadvantages of each type of interview that can help you decide if you’d like to utilize this research method.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of 4 types of interviews .

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

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Much of what we learned in the previous chapter on survey research applies to quantitative interviews as well. In fact, quantitative interviews are sometimes referred to as survey interviews because they resemble survey-style question-and-answer formats. They might also be called standardized interviews . The difference between surveys and standardized interviews is that questions and answer options are read to respondents in a standardized interview, rather than having respondents complete a survey on their own. As with surveys, the questions posed in a standardized interview tend to be closed-ended. There are instances in which a quantitative interviewer might pose a few open-ended questions as well. In these cases, the coding process works somewhat differently than coding in-depth interview data. We will describe this process in the following section.

In quantitative interviews, an interview schedule is used to guide the researcher as he or she poses questions and answer options to respondents. An interview schedule is usually more rigid than an interview guide. It contains the list of questions and answer options that the researcher will read to respondents. Whereas qualitative researchers emphasize respondents’ roles in helping to determine how an interview progresses, in a quantitative interview, consistency in the way that questions and answer options are presented is very important. The aim is to pose every question-and-answer option in the very same way to every respondent. This is done to minimize interviewer effect, or possible changes in the way an interviewee responds based on how or when questions and answer options are presented by the interviewer.

Quantitative interviews may be recorded, but because questions tend to be closed-ended, taking notes during the interview is less disruptive than it can be during a qualitative interview. If a quantitative interview contains open-ended questions, recording the interview is advised. It may also be helpful to record quantitative interviews if a researcher wishes to assess possible interview effect. Noticeable differences in responses might be more attributable to interviewer effect than to any real respondent differences. Having a recording of the interview can help a researcher make such determinations.

Quantitative interviewers are usually more concerned with gathering data from a large, representative sample. Collecting data from many people via interviews can be quite laborious. In the past, telephone interviewing was quite common; however, growth in the use of mobile phones has raised concern regarding whether or not traditional landline telephone interviews and surveys are now representative of the general population (Busse & Fuchs, 2012). Indeed, there are other drawbacks to telephone interviews. Aside from the obvious problem that not everyone has a phone (mobile or landline), research shows that phone interview respondents were less cooperative, less engaged in the interview, and more likely to express dissatisfaction with the length of the interview than were face-to-face respondents (Holbrook, Green, & Krosnick, 2003, p. 79). Holbrook et al.’s research also demonstrated that telephone respondents were more suspicious of the interview process and more likely than face-to-face respondents to present themselves in a socially desirable manner.

Research Methods, Data Collection and Ethics Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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500+ Quantitative Research Titles and Topics

Table of Contents

Quantitative research involves collecting and analyzing numerical data to identify patterns, trends, and relationships among variables. This method is widely used in social sciences, psychology , economics , and other fields where researchers aim to understand human behavior and phenomena through statistical analysis. If you are looking for a quantitative research topic, there are numerous areas to explore, from analyzing data on a specific population to studying the effects of a particular intervention or treatment. In this post, we will provide some ideas for quantitative research topics that may inspire you and help you narrow down your interests.

Quantitative Research Titles

Quantitative Research Titles are as follows:

Business and Economics

• “Statistical Analysis of Supply Chain Disruptions on Retail Sales”
• “Quantitative Examination of Consumer Loyalty Programs in the Fast Food Industry”
• “Predicting Stock Market Trends Using Machine Learning Algorithms”
• “Influence of Workplace Environment on Employee Productivity: A Quantitative Study”
• “Impact of Economic Policies on Small Businesses: A Regression Analysis”
• “Customer Satisfaction and Profit Margins: A Quantitative Correlation Study”
• “Analyzing the Role of Marketing in Brand Recognition: A Statistical Overview”
• “Quantitative Effects of Corporate Social Responsibility on Consumer Trust”
• “Price Elasticity of Demand for Luxury Goods: A Case Study”
• “The Relationship Between Fiscal Policy and Inflation Rates: A Time-Series Analysis”
• “Factors Influencing E-commerce Conversion Rates: A Quantitative Exploration”
• “Examining the Correlation Between Interest Rates and Consumer Spending”
• “Standardized Testing and Academic Performance: A Quantitative Evaluation”
• “Teaching Strategies and Student Learning Outcomes in Secondary Schools: A Quantitative Study”
• “The Relationship Between Extracurricular Activities and Academic Success”
• “Influence of Parental Involvement on Children’s Educational Achievements”
• “Digital Literacy in Primary Schools: A Quantitative Assessment”
• “Learning Outcomes in Blended vs. Traditional Classrooms: A Comparative Analysis”
• “Correlation Between Teacher Experience and Student Success Rates”
• “Analyzing the Impact of Classroom Technology on Reading Comprehension”
• “Gender Differences in STEM Fields: A Quantitative Analysis of Enrollment Data”
• “The Relationship Between Homework Load and Academic Burnout”
• “Assessment of Special Education Programs in Public Schools”
• “Role of Peer Tutoring in Improving Academic Performance: A Quantitative Study”

Medicine and Health Sciences

• “The Impact of Sleep Duration on Cardiovascular Health: A Cross-sectional Study”
• “Analyzing the Efficacy of Various Antidepressants: A Meta-Analysis”
• “Patient Satisfaction in Telehealth Services: A Quantitative Assessment”
• “Dietary Habits and Incidence of Heart Disease: A Quantitative Review”
• “Correlations Between Stress Levels and Immune System Functioning”
• “Smoking and Lung Function: A Quantitative Analysis”
• “Influence of Physical Activity on Mental Health in Older Adults”
• “Antibiotic Resistance Patterns in Community Hospitals: A Quantitative Study”
• “The Efficacy of Vaccination Programs in Controlling Disease Spread: A Time-Series Analysis”
• “Role of Social Determinants in Health Outcomes: A Quantitative Exploration”
• “Impact of Hospital Design on Patient Recovery Rates”
• “Quantitative Analysis of Dietary Choices and Obesity Rates in Children”

Social Sciences

• “Examining Social Inequality through Wage Distribution: A Quantitative Study”
• “Impact of Parental Divorce on Child Development: A Longitudinal Study”
• “Social Media and its Effect on Political Polarization: A Quantitative Analysis”
• “The Relationship Between Religion and Social Attitudes: A Statistical Overview”
• “Influence of Socioeconomic Status on Educational Achievement”
• “Quantifying the Effects of Community Programs on Crime Reduction”
• “Public Opinion and Immigration Policies: A Quantitative Exploration”
• “Analyzing the Gender Representation in Political Offices: A Quantitative Study”
• “Impact of Mass Media on Public Opinion: A Regression Analysis”
• “Influence of Urban Design on Social Interactions in Communities”
• “The Role of Social Support in Mental Health Outcomes: A Quantitative Analysis”
• “Examining the Relationship Between Substance Abuse and Employment Status”

Engineering and Technology

• “Performance Evaluation of Different Machine Learning Algorithms in Autonomous Vehicles”
• “Material Science: A Quantitative Analysis of Stress-Strain Properties in Various Alloys”
• “Impacts of Data Center Cooling Solutions on Energy Consumption”
• “Analyzing the Reliability of Renewable Energy Sources in Grid Management”
• “Optimization of 5G Network Performance: A Quantitative Assessment”
• “Quantifying the Effects of Aerodynamics on Fuel Efficiency in Commercial Airplanes”
• “The Relationship Between Software Complexity and Bug Frequency”
• “Machine Learning in Predictive Maintenance: A Quantitative Analysis”
• “Wearable Technologies and their Impact on Healthcare Monitoring”
• “Quantitative Assessment of Cybersecurity Measures in Financial Institutions”
• “Analysis of Noise Pollution from Urban Transportation Systems”
• “The Influence of Architectural Design on Energy Efficiency in Buildings”

Quantitative Research Topics

Quantitative Research Topics are as follows:

• The effects of social media on self-esteem among teenagers.
• A comparative study of academic achievement among students of single-sex and co-educational schools.
• The impact of gender on leadership styles in the workplace.
• The correlation between parental involvement and academic performance of students.
• The effect of mindfulness meditation on stress levels in college students.
• The relationship between employee motivation and job satisfaction.
• The effectiveness of online learning compared to traditional classroom learning.
• The correlation between sleep duration and academic performance among college students.
• The impact of exercise on mental health among adults.
• The relationship between social support and psychological well-being among cancer patients.
• The effect of caffeine consumption on sleep quality.
• A comparative study of the effectiveness of cognitive-behavioral therapy and pharmacotherapy in treating depression.
• The relationship between physical attractiveness and job opportunities.
• The correlation between smartphone addiction and academic performance among high school students.
• The impact of music on memory recall among adults.
• The effectiveness of parental control software in limiting children’s online activity.
• The relationship between social media use and body image dissatisfaction among young adults.
• The correlation between academic achievement and parental involvement among minority students.
• The impact of early childhood education on academic performance in later years.
• The effectiveness of employee training and development programs in improving organizational performance.
• The relationship between socioeconomic status and access to healthcare services.
• The correlation between social support and academic achievement among college students.
• The impact of technology on communication skills among children.
• The effectiveness of mindfulness-based stress reduction programs in reducing symptoms of anxiety and depression.
• The relationship between employee turnover and organizational culture.
• The correlation between job satisfaction and employee engagement.
• The impact of video game violence on aggressive behavior among children.
• The effectiveness of nutritional education in promoting healthy eating habits among adolescents.
• The relationship between bullying and academic performance among middle school students.
• The correlation between teacher expectations and student achievement.
• The impact of gender stereotypes on career choices among high school students.
• The effectiveness of anger management programs in reducing violent behavior.
• The relationship between social support and recovery from substance abuse.
• The correlation between parent-child communication and adolescent drug use.
• The impact of technology on family relationships.
• The effectiveness of smoking cessation programs in promoting long-term abstinence.
• The relationship between personality traits and academic achievement.
• The correlation between stress and job performance among healthcare professionals.
• The impact of online privacy concerns on social media use.
• The effectiveness of cognitive-behavioral therapy in treating anxiety disorders.
• The relationship between teacher feedback and student motivation.
• The correlation between physical activity and academic performance among elementary school students.
• The impact of parental divorce on academic achievement among children.
• The effectiveness of diversity training in improving workplace relationships.
• The relationship between childhood trauma and adult mental health.
• The correlation between parental involvement and substance abuse among adolescents.
• The impact of social media use on romantic relationships among young adults.
• The effectiveness of assertiveness training in improving communication skills.
• The relationship between parental expectations and academic achievement among high school students.
• The correlation between sleep quality and mood among adults.
• The impact of video game addiction on academic performance among college students.
• The effectiveness of group therapy in treating eating disorders.
• The relationship between job stress and job performance among teachers.
• The correlation between mindfulness and emotional regulation.
• The impact of social media use on self-esteem among college students.
• The effectiveness of parent-teacher communication in promoting academic achievement among elementary school students.
• The impact of renewable energy policies on carbon emissions
• The relationship between employee motivation and job performance
• The effectiveness of psychotherapy in treating eating disorders
• The correlation between physical activity and cognitive function in older adults
• The effect of childhood poverty on adult health outcomes
• The impact of urbanization on biodiversity conservation
• The relationship between work-life balance and employee job satisfaction
• The effectiveness of eye movement desensitization and reprocessing (EMDR) in treating trauma
• The correlation between parenting styles and child behavior
• The effect of social media on political polarization
• The impact of foreign aid on economic development
• The relationship between workplace diversity and organizational performance
• The effectiveness of dialectical behavior therapy in treating borderline personality disorder
• The correlation between childhood abuse and adult mental health outcomes
• The effect of sleep deprivation on cognitive function
• The impact of trade policies on international trade and economic growth
• The relationship between employee engagement and organizational commitment
• The effectiveness of cognitive therapy in treating postpartum depression
• The correlation between family meals and child obesity rates
• The effect of parental involvement in sports on child athletic performance
• The impact of social entrepreneurship on sustainable development
• The relationship between emotional labor and job burnout
• The effectiveness of art therapy in treating dementia
• The correlation between social media use and academic procrastination
• The effect of poverty on childhood educational attainment
• The impact of urban green spaces on mental health
• The relationship between job insecurity and employee well-being
• The effectiveness of virtual reality exposure therapy in treating anxiety disorders
• The correlation between childhood trauma and substance abuse
• The effect of screen time on children’s social skills
• The impact of trade unions on employee job satisfaction
• The relationship between cultural intelligence and cross-cultural communication
• The effectiveness of acceptance and commitment therapy in treating chronic pain
• The correlation between childhood obesity and adult health outcomes
• The effect of gender diversity on corporate performance
• The impact of environmental regulations on industry competitiveness.
• The impact of renewable energy policies on greenhouse gas emissions
• The relationship between workplace diversity and team performance
• The effectiveness of group therapy in treating substance abuse
• The correlation between parental involvement and social skills in early childhood
• The effect of technology use on sleep patterns
• The impact of government regulations on small business growth
• The relationship between job satisfaction and employee turnover
• The effectiveness of virtual reality therapy in treating anxiety disorders
• The correlation between parental involvement and academic motivation in adolescents
• The effect of social media on political engagement
• The impact of urbanization on mental health
• The relationship between corporate social responsibility and consumer trust
• The correlation between early childhood education and social-emotional development
• The effect of screen time on cognitive development in young children
• The impact of trade policies on global economic growth
• The relationship between workplace diversity and innovation
• The effectiveness of family therapy in treating eating disorders
• The correlation between parental involvement and college persistence
• The effect of social media on body image and self-esteem
• The impact of environmental regulations on business competitiveness
• The relationship between job autonomy and job satisfaction
• The effectiveness of virtual reality therapy in treating phobias
• The correlation between parental involvement and academic achievement in college
• The effect of social media on sleep quality
• The impact of immigration policies on social integration
• The relationship between workplace diversity and employee well-being
• The effectiveness of psychodynamic therapy in treating personality disorders
• The correlation between early childhood education and executive function skills
• The effect of parental involvement on STEM education outcomes
• The impact of trade policies on domestic employment rates
• The relationship between job insecurity and mental health
• The effectiveness of exposure therapy in treating PTSD
• The correlation between parental involvement and social mobility
• The effect of social media on intergroup relations
• The impact of urbanization on air pollution and respiratory health.
• The relationship between emotional intelligence and leadership effectiveness
• The effectiveness of cognitive-behavioral therapy in treating depression
• The correlation between early childhood education and language development
• The effect of parental involvement on academic achievement in STEM fields
• The impact of trade policies on income inequality
• The relationship between workplace diversity and customer satisfaction
• The effectiveness of mindfulness-based therapy in treating anxiety disorders
• The correlation between parental involvement and civic engagement in adolescents
• The effect of social media on mental health among teenagers
• The impact of public transportation policies on traffic congestion
• The relationship between job stress and job performance
• The effectiveness of group therapy in treating depression
• The correlation between early childhood education and cognitive development
• The effect of parental involvement on academic motivation in college
• The impact of environmental regulations on energy consumption
• The relationship between workplace diversity and employee engagement
• The effectiveness of art therapy in treating PTSD
• The correlation between parental involvement and academic success in vocational education
• The effect of social media on academic achievement in college
• The impact of tax policies on economic growth
• The relationship between job flexibility and work-life balance
• The effectiveness of acceptance and commitment therapy in treating anxiety disorders
• The correlation between early childhood education and social competence
• The effect of parental involvement on career readiness in high school
• The impact of immigration policies on crime rates
• The relationship between workplace diversity and employee retention
• The effectiveness of play therapy in treating trauma
• The correlation between parental involvement and academic success in online learning
• The effect of social media on body dissatisfaction among women
• The impact of urbanization on public health infrastructure
• The relationship between job satisfaction and job performance
• The effectiveness of eye movement desensitization and reprocessing therapy in treating PTSD
• The correlation between early childhood education and social skills in adolescence
• The effect of parental involvement on academic achievement in the arts
• The impact of trade policies on foreign investment
• The relationship between workplace diversity and decision-making
• The effectiveness of exposure and response prevention therapy in treating OCD
• The correlation between parental involvement and academic success in special education
• The impact of zoning laws on affordable housing
• The relationship between job design and employee motivation
• The effectiveness of cognitive rehabilitation therapy in treating traumatic brain injury
• The correlation between early childhood education and social-emotional learning
• The effect of parental involvement on academic achievement in foreign language learning
• The impact of trade policies on the environment
• The relationship between workplace diversity and creativity
• The effectiveness of emotion-focused therapy in treating relationship problems
• The correlation between parental involvement and academic success in music education
• The effect of social media on interpersonal communication skills
• The impact of public health campaigns on health behaviors
• The relationship between job resources and job stress
• The effectiveness of equine therapy in treating substance abuse
• The correlation between early childhood education and self-regulation
• The effect of parental involvement on academic achievement in physical education
• The impact of immigration policies on cultural assimilation
• The relationship between workplace diversity and conflict resolution
• The effectiveness of schema therapy in treating personality disorders
• The correlation between parental involvement and academic success in career and technical education
• The effect of social media on trust in government institutions
• The impact of urbanization on public transportation systems
• The relationship between job demands and job stress
• The correlation between early childhood education and executive functioning
• The effect of parental involvement on academic achievement in computer science
• The effectiveness of cognitive processing therapy in treating PTSD
• The correlation between parental involvement and academic success in homeschooling
• The effect of social media on cyberbullying behavior
• The impact of urbanization on air quality
• The effectiveness of dance therapy in treating anxiety disorders
• The correlation between early childhood education and math achievement
• The effect of parental involvement on academic achievement in health education
• The impact of global warming on agriculture
• The effectiveness of narrative therapy in treating depression
• The correlation between parental involvement and academic success in character education
• The effect of social media on political participation
• The impact of technology on job displacement
• The relationship between job resources and job satisfaction
• The effectiveness of art therapy in treating addiction
• The correlation between early childhood education and reading comprehension
• The effect of parental involvement on academic achievement in environmental education
• The impact of income inequality on social mobility
• The relationship between workplace diversity and organizational culture
• The effectiveness of solution-focused brief therapy in treating anxiety disorders
• The correlation between parental involvement and academic success in physical therapy education
• The effect of social media on misinformation
• The impact of green energy policies on economic growth
• The relationship between job demands and employee well-being
• The correlation between early childhood education and science achievement
• The effect of parental involvement on academic achievement in religious education
• The impact of gender diversity on corporate governance
• The relationship between workplace diversity and ethical decision-making
• The correlation between parental involvement and academic success in dental hygiene education
• The effect of social media on self-esteem among adolescents
• The impact of renewable energy policies on energy security
• The effect of parental involvement on academic achievement in social studies
• The impact of trade policies on job growth
• The relationship between workplace diversity and leadership styles
• The correlation between parental involvement and academic success in online vocational training
• The effect of social media on self-esteem among men
• The impact of urbanization on air pollution levels
• The effectiveness of music therapy in treating depression
• The correlation between early childhood education and math skills
• The effect of parental involvement on academic achievement in language arts
• The impact of immigration policies on labor market outcomes
• The effectiveness of hypnotherapy in treating phobias
• The effect of social media on political engagement among young adults
• The impact of urbanization on access to green spaces
• The relationship between job crafting and job satisfaction
• The effectiveness of exposure therapy in treating specific phobias
• The correlation between early childhood education and spatial reasoning
• The effect of parental involvement on academic achievement in business education
• The impact of trade policies on economic inequality
• The effectiveness of narrative therapy in treating PTSD
• The correlation between parental involvement and academic success in nursing education
• The effect of social media on sleep quality among adolescents
• The impact of urbanization on crime rates
• The relationship between job insecurity and turnover intentions
• The effectiveness of pet therapy in treating anxiety disorders
• The correlation between early childhood education and STEM skills
• The effect of parental involvement on academic achievement in culinary education
• The impact of immigration policies on housing affordability
• The relationship between workplace diversity and employee satisfaction
• The effectiveness of mindfulness-based stress reduction in treating chronic pain
• The correlation between parental involvement and academic success in art education
• The effect of social media on academic procrastination among college students
• The impact of urbanization on public safety services.

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Principles of Sociological Inquiry

Chapter 9: interviews: qualitative and quantitative approaches, chapter 9 interviews: qualitative and quantitative approaches, why interview research.

Today’s young men are delaying their entry into adulthood. That’s a nice way of saying they are “totally confused”; “cannot commit to their relationships, work, or lives”; and are “obsessed with never wanting to grow up.” These quotes come from a summary of reviews on the website dedicated to Kimmel’s book, Guyland : http://www.guyland.net . But don’t take my word for it. Take sociologist Michael Kimmel’s word. He interviewed 400 young men, ages 16 to 26, over the course of 4 years across the United States to learn how they made the transition from adolescence into adulthood. Since the results of Kimmel’s research were published in 2008, Kimmel, M. (2008). Guyland: The perilous world where boys become men . New York, NY: Harper Collins. his book has made quite a splash. Featured in news reports, on blogs, and in many book reviews, some claim Kimmel’s research “could save the humanity of many young men,” This quote from Gloria Steinem is provided on the website dedicated to Kimmel’s book, Guyland : http://www.guyland.net . while others suggest that its conclusions can only be applied to “fraternity guys and jocks.” This quote comes from “Thomas,” who wrote a review of Kimmel’s book on the following site: http://yesmeansyesblog.wordpress.com/2010/03/12/review-guyland . Whatever your take on Kimmel’s research, one thing remains true: We surely would not know nearly as much as we now do about the lives of many young American men were it not for interview research.

9.1 Interview Research: What Is It and When Should It Be Used?

Learning objectives.

• Define interviews from the social scientific perspective.
• Identify when it is appropriate to employ interviews as a data-collection strategy.

Knowing how to create and conduct a good interview is one of those skills you just can’t go wrong having. Interviews are used by market researchers to learn how to sell their products, journalists use interviews to get information from a whole host of people from VIPs to random people on the street. Regis Philbin (a sociology major in college This information comes from the following list of famous sociology majors provided by the American Sociological Association on their website: http://www.asanet.org/students/famous.cfm . ) used interviews to help television viewers get to know guests on his show, employers use them to make decisions about job offers, and even Ruth Westheimer (the famous sex doctor who has an MA in sociology Read more about Dr. Ruth, her background, and her credentials at her website: http://www.drruth.com . ) used interviews to elicit details from call-in participants on her radio show. Interested in hearing Dr. Ruth’s interview style? There are a number of audio clips from her radio show, Sexually Speaking , linked from the following site: http://www.cs.cmu.edu/~chuck/ruthpg . Warning: some of the images and audio clips on this page may be offensive to some readers. It seems everyone who’s anyone knows how to conduct an interview.

From the social scientific perspective, interviews A method of data collection that involves two or more people exchanging information through a series of questions and answers. are a method of data collection that involves two or more people exchanging information through a series of questions and answers. The questions are designed by a researcher to elicit information from interview participant(s) on a specific topic or set of topics. Typically interviews involve an in-person meeting between two people, an interviewer and an interviewee. But as you’ll discover in this chapter, interviews need not be limited to two people, nor must they occur in person.

The question of when to conduct an interview might be on your mind. Interviews are an excellent way to gather detailed information. They also have an advantage over surveys; with a survey, if a participant’s response sparks some follow-up question in your mind, you generally don’t have an opportunity to ask for more information. What you get is what you get. In an interview, however, because you are actually talking with your study participants in real time, you can ask that follow-up question. Thus interviews are a useful method to use when you want to know the story behind responses you might receive in a written survey.

Interviews are also useful when the topic you are studying is rather complex, when whatever you plan to ask requires lengthy explanation, or when your topic or answers to your questions may not be immediately clear to participants who may need some time or dialogue with others in order to work through their responses to your questions. Also, if your research topic is one about which people will likely have a lot to say or will want to provide some explanation or describe some process, interviews may be the best method for you. For example, I used interviews to gather data about how people reach the decision not to have children and how others in their lives have responded to that decision. To understand these “how’s” I needed to have some back-and-forth dialogue with respondents. When they begin to tell me their story, inevitably new questions that hadn’t occurred to me from prior interviews come up because each person’s story is unique. Also, because the process of choosing not to have children is complex for many people, describing that process by responding to closed-ended questions on a survey wouldn’t work particularly well.

In sum, interview research is especially useful when the following are true:

• You wish to gather very detailed information
• You anticipate wanting to ask respondents for more information about their responses
• You plan to ask questions that require lengthy explanation
• The topic you are studying is complex or may be confusing to respondents
• Your topic involves studying processes

Key Takeaways

• Understanding how to design and conduct interview research is a useful skill to have.
• In a social scientific interview, two or more people exchange information through a series of questions and answers.
• Interview research is often used when detailed information is required and when a researcher wishes to examine processes.
• Think about a topic about which you might wish to collect data by conducting interviews. What makes this topic suitable for interview research?

9.2 Qualitative Interview Techniques and Considerations

• Identify the primary aim of in-depth interviews.
• Describe what makes qualitative interview techniques unique.
• Define the term interview guide and describe how to construct an interview guide.
• Outline the guidelines for constructing good qualitative interview questions.
• Define the term focus group and identify one benefit of focus groups.
• Identify and describe the various stages of qualitative interview data analysis.
• Identify the strengths and weaknesses of qualitative interviews.

Qualitative interviews are sometimes called intensive or in-depth interviews A semistructured meeting between a researcher and respondent in which the researcher asks a series of open-ended questions; questions may be posed to respondents in slightly different ways or orders. . These interviews are semistructured; the researcher has a particular topic about which he or she would like to hear from the respondent, but questions are open ended and may not be asked in exactly the same way or in exactly the same order to each and every respondent. In in-depth interviews, the primary aim is to hear from respondents about what they think is important about the topic at hand and to hear it in their own words. In this section, we’ll take a look at how to conduct interviews that are specifically qualitative in nature, analyze qualitative interview data, and use some of the strengths and weaknesses of this method. In Section 9.4 “Issues to Consider for All Interview Types” , we return to several considerations that are relevant to both qualitative and quantitative interviewing.

Conducting Qualitative Interviews

Qualitative interviews might feel more like a conversation than an interview to respondents, but the researcher is in fact usually guiding the conversation with the goal in mind of gathering information from a respondent. A key difference between qualitative and quantitative interviewing is that qualitative interviews contain open-ended questions Questions for which a researcher does not provide answer options; questions that require respondents to answer in their own words. . The meaning of this term is of course implied by its name, but just so that we’re sure to be on the same page, I’ll tell you that open-ended questions are questions that a researcher poses but does not provide answer options for. Open-ended questions are more demanding of participants than closed-ended questions, for they require participants to come up with their own words, phrases, or sentences to respond.

In a qualitative interview, the researcher usually develops a guide in advance that he or she then refers to during the interview (or memorizes in advance of the interview). An interview guide A list of topics or questions that an interviewer hopes to cover during the course of an interview. is a list of topics or questions that the interviewer hopes to cover during the course of an interview. It is called a guide because it is simply that—it is used to guide the interviewer, but it is not set in stone. Think of an interview guide like your agenda for the day or your to-do list—both probably contain all the items you hope to check off or accomplish, though it probably won’t be the end of the world if you don’t accomplish everything on the list or if you don’t accomplish it in the exact order that you have it written down. Perhaps new events will come up that cause you to rearrange your schedule just a bit, or perhaps you simply won’t get to everything on the list.

Interview guides should outline issues that a researcher feels are likely to be important, but because participants are asked to provide answers in their own words, and to raise points that they believe are important, each interview is likely to flow a little differently. While the opening question in an in-depth interview may be the same across all interviews, from that point on what the participant says will shape how the interview proceeds. This, I believe, is what makes in-depth interviewing so exciting. It is also what makes in-depth interviewing rather challenging to conduct. It takes a skilled interviewer to be able to ask questions; actually listen to respondents; and pick up on cues about when to follow up, when to move on, and when to simply let the participant speak without guidance or interruption.

I’ve said that interview guides can list topics or questions. The specific format of an interview guide might depend on your style, experience, and comfort level as an interviewer or with your topic. I have conducted interviews using different kinds of guides. In my interviews of young people about their experiences with workplace sexual harassment, the guide I used was topic based. There were few specific questions contained in the guide. Instead, I had an outline of topics that I hoped to cover, listed in an order that I thought it might make sense to cover them, noted on a sheet of paper. That guide can be seen in Figure 9.4 “Interview Guide Displaying Topics Rather Than Questions” .

Figure 9.4 Interview Guide Displaying Topics Rather Than Questions

In my interviews with child-free adults, the interview guide contained questions rather than brief topics. One reason I took this approach is that this was a topic with which I had less familiarity than workplace sexual harassment. I’d been studying harassment for some time before I began those interviews, and I had already analyzed much quantitative survey data on the topic. When I began the child-free interviews, I was embarking on a research topic that was entirely new for me. I was also studying a topic about which I have strong personal feelings, and I wanted to be sure that I phrased my questions in a way that didn’t appear biased to respondents. To help ward off that possibility, I wrote down specific question wording in my interview guide. As I conducted more and more interviews, and read more and more of the literature on child-free adults, I became more confident about my ability to ask open-ended, nonbiased questions about the topic without the guide, but having some specific questions written down at the start of the data collection process certainly helped. The interview guide I used for the child-free project is displayed in Figure 9.5 “Interview Guide Displaying Questions Rather Than Topics” .

Figure 9.5 Interview Guide Displaying Questions Rather Than Topics

As you might have guessed, interview guides do not appear out of thin air. They are the result of thoughtful and careful work on the part of a researcher. As you can see in both of the preceding guides, the topics and questions have been organized thematically and in the order in which they are likely to proceed (though keep in mind that the flow of a qualitative interview is in part determined by what a respondent has to say). Sometimes qualitative interviewers may create two versions of the interview guide: one version contains a very brief outline of the interview, perhaps with just topic headings, and another version contains detailed questions underneath each topic heading. In this case, the researcher might use the very detailed guide to prepare and practice in advance of actually conducting interviews and then just bring the brief outline to the interview. Bringing an outline, as opposed to a very long list of detailed questions, to an interview encourages the researcher to actually listen to what a participant is telling her. An overly detailed interview guide will be difficult to navigate through during an interview and could give respondents the misimpression that the interviewer is more interested in her questions than in the participant’s answers.

When beginning to construct an interview guide, brainstorming is usually the first step. There are no rules at the brainstorming stage—simply list all the topics and questions that come to mind when you think about your research question. Once you’ve got a pretty good list, you can begin to pare it down by cutting questions and topics that seem redundant and group like questions and topics together. If you haven’t done so yet, you may also want to come up with question and topic headings for your grouped categories. You should also consult the scholarly literature to find out what kinds of questions other interviewers have asked in studies of similar topics. As with quantitative survey research, it is best not to place very sensitive or potentially controversial questions at the very beginning of your qualitative interview guide. You need to give participants the opportunity to warm up to the interview and to feel comfortable talking with you. Finally, get some feedback on your interview guide. Ask your friends, family members, and your professors for some guidance and suggestions once you’ve come up with what you think is a pretty strong guide. Chances are they’ll catch a few things you hadn’t noticed.

In terms of the specific questions you include on your guide, there are a few guidelines worth noting. First, try to avoid questions that can be answered with a simple yes or no, or if you do choose to include such questions, be sure to include follow-up questions. Remember, one of the benefits of qualitative interviews is that you can ask participants for more information—be sure to do so. While it is a good idea to ask follow-up questions, try to avoid asking “why” as your follow-up question, as this particular question can come off as confrontational, even if that is not how you intend it. Often people won’t know how to respond to “why,” perhaps because they don’t even know why themselves. Instead of “why,” I recommend that you say something like, “Could you tell me a little more about that?” This allows participants to explain themselves further without feeling that they’re being doubted or questioned in a hostile way.

Also, try to avoid phrasing your questions in a leading way. For example, rather than asking, “Don’t you think that most people who don’t want kids are selfish?” you could ask, “What comes to mind for you when you hear that someone doesn’t want kids?” Or rather than asking, “What do you think about juvenile delinquents who drink and drive?” you could ask, “How do you feel about underage drinking?” or “What do you think about drinking and driving?” Finally, as noted earlier in this section, remember to keep most, if not all, of your questions open ended. The key to a successful qualitative interview is giving participants the opportunity to share information in their own words and in their own way.

Even after the interview guide is constructed, the interviewer is not yet ready to begin conducting interviews. The researcher next has to decide how to collect and maintain the information that is provided by participants. It is probably most common for qualitative interviewers to take audio recordings of the interviews they conduct.

Recording interviews allows the researcher to focus on her or his interaction with the interview participant rather than being distracted by trying to take notes. Of course, not all participants will feel comfortable being recorded and sometimes even the interviewer may feel that the subject is so sensitive that recording would be inappropriate. If this is the case, it is up to the researcher to balance excellent note-taking with exceptional question asking and even better listening. I don’t think I can understate the difficulty of managing all these feats simultaneously. Whether you will be recording your interviews or not (and especially if not), practicing the interview in advance is crucial. Ideally, you’ll find a friend or two willing to participate in a couple of trial runs with you. Even better, you’ll find a friend or two who are similar in at least some ways to your sample. They can give you the best feedback on your questions and your interview demeanor.

All interviewers should be aware of, give some thought to, and plan for several additional factors, such as where to conduct an interview and how to make participants as comfortable as possible during an interview. Because these factors should be considered by both qualitative and quantitative interviewers, we will return to them in Section 9.4 “Issues to Consider for All Interview Types” after we’ve had a chance to look at some of the unique features of each approach to interviewing.

Although our focus here has been on interviews for which there is one interviewer and one respondent, this is certainly not the only way to conduct a qualitative interview. Sometimes there may be multiple respondents present, and occasionally more than one interviewer may be present as well. When multiple respondents participate in an interview at the same time, this is referred to as a focus group Multiple respondents participate in an interview at the same time. . Focus groups can be an excellent way to gather information because topics or questions that hadn’t occurred to the researcher may be brought up by other participants in the group. Having respondents talk with and ask questions of one another can be an excellent way of learning about a topic; not only might respondents ask questions that hadn’t occurred to the researcher, but the researcher can also learn from respondents’ body language around and interactions with one another. Of course, there are some unique ethical concerns associated with collecting data in a group setting. We’ll take a closer look at how focus groups work and describe some potential ethical concerns associated with them in Chapter 12 “Other Methods of Data Collection and Analysis” .

Analysis of Qualitative Interview Data

Analysis of qualitative interview data typically begins with a set of transcripts of the interviews conducted. Obtaining said transcripts requires having either taken exceptionally good notes during an interview or, preferably, recorded the interview and then transcribed it. Transcribing interviews is usually the first step toward analyzing qualitative interview data. To transcribe Creating a complete, written copy of a recorded interview by playing the recording back and typing in each word that is spoken on the recording, noting who spoke which words. an interview means that you create, or someone whom you’ve hired creates, a complete, written copy of the recorded interview by playing the recording back and typing in each word that is spoken on the recording, noting who spoke which words. In general, it is best to aim for a verbatim transcription, one that reports word for word exactly what was said in the recorded interview. If possible, it is also best to include nonverbals in an interview’s written transcription. Gestures made by respondents should be noted, as should the tone of voice and notes about when, where, and how spoken words may have been emphasized by respondents.

If you have the time (or if you lack the resources to hire others), I think it is best to transcribe your interviews yourself. I never cease to be amazed by the things I recall from an interview when I transcribe it myself. If the researcher who conducted the interview transcribes it himself or herself, that person will also be able to make a note of nonverbal behaviors and interactions that may be relevant to analysis but that could not be picked up by audio recording. I’ve seen interviewees roll their eyes, wipe tears from their face, and even make obscene gestures that spoke volumes about their feelings but that could not have been recorded had I not remembered to include these details in their transcribed interviews.

The goal of analysis The process of arriving at some inferences, lessons, or conclusions by condensing large amounts of data into relatively smaller bits of understandable information. is to reach some inferences, lessons, or conclusions by condensing large amounts of data into relatively smaller, more manageable bits of understandable information. Analysis of qualitative interview data often works inductively (Glaser & Strauss, 1967; Charmaz, 2006). For an additional reminder about what an inductive approach to analysis means, see Chapter 2 “Linking Methods With Theory” . If you would like to learn more about inductive qualitative data analysis, I recommend two titles: Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research . Chicago, IL: Aldine; Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis . Thousand Oaks, CA: Sage. To move from the specific observations an interviewer collects to identifying patterns across those observations, qualitative interviewers will often begin by reading through transcripts of their interviews and trying to identify codes. A code A shorthand representation of some more complex set of issues or ideas. is a shorthand representation of some more complex set of issues or ideas. In this usage, the word code is a noun. But it can also be a verb. The process of identifying codes in one’s qualitative data is often referred to as coding . Coding involves identifying themes across interview data by reading and rereading (and rereading again) interview transcripts until the researcher has a clear idea about what sorts of themes come up across the interviews.

Qualitative researcher and textbook author Kristin Esterberg (2002) Esterberg, K. G. (2002). Qualitative methods in social research . Boston, MA: McGraw-Hill. describes coding as a multistage process. Esterberg suggests that there are two types of coding: open coding and focused coding. To analyze qualitative interview data, one can begin by open coding The first stage of developing codes in qualitative data; involves reading data with an open mind and jotting down themes or categories that various bits of data seem to suggest. transcripts. This means that you read through each transcript, line by line, and make a note of whatever categories or themes seem to jump out to you. At this stage, it is important that you not let your original research question or expectations about what you think you might find cloud your ability to see categories or themes. It’s called open coding for a reason—keep an open mind. Open coding will probably require multiple go-rounds. As you read through your transcripts, it is likely that you’ll begin to see some commonalities across the categories or themes that you’ve jotted down. Once you do, you might begin focused coding.

Focused coding A later stage of developing codes in qualitative data; occurs after open coding and involves collapsing or narrowing themes and categories identified in open coding, succinctly naming them, describing them, and identifying passages of data that represent them. involves collapsing or narrowing themes and categories identified in open coding by reading through the notes you made while conducting open coding. Identify themes or categories that seem to be related, perhaps merging some. Then give each collapsed/merged theme or category a name (or code), and identify passages of data that fit each named category or theme. To identify passages of data that represent your emerging codes, you’ll need to read through your transcripts yet again (and probably again). You might also write up brief definitions or descriptions of each code. Defining codes is a way of making meaning of your data and of developing a way to talk about your findings and what your data mean. Guess what? You are officially analyzing data!

As tedious and laborious as it might seem to read through hundreds of pages of transcripts multiple times, sometimes getting started with the coding process is actually the hardest part. If you find yourself struggling to identify themes at the open coding stage, ask yourself some questions about your data. The answers should give you a clue about what sorts of themes or categories you are reading. In their text on analyzing qualitative data, Lofland and Lofland (1995) Lofland, J., & Lofland, L. H. (1995). Analyzing social settings: A guide to qualitative observation and analysis (3rd ed.) Belmont, CA: Wadsworth. identify a set of questions that I find very useful when coding qualitative data. They suggest asking the following:

• Of what topic, unit, or aspect is this an instance?
• What question about a topic does this item of data suggest?
• What sort of answer to a question about a topic does this item of data suggest (i.e., what proposition is suggested)?

Asking yourself these questions about the passages of data that you’re reading can help you begin to identify and name potential themes and categories.

Still feeling uncertain about how this process works? Sometimes it helps to see how interview passages translate into codes. In Table 9.1 “Interview Coding Example” , I present two codes that emerged from the inductive analysis of transcripts from my interviews with child-free adults. I also include a brief description of each code and a few (of many) interview excerpts from which each code was developed.

Table 9.1 Interview Coding Example

As you might imagine, wading through all these data is quite a process. Just as quantitative researchers rely on the assistance of special computer programs designed to help with sorting through and analyzing their data, so, too, do qualitative researchers. Where quantitative researchers have SPSS and MicroCase (and many others), qualitative researchers have programs such as NVivo ( http://www.qsrinternational.com ) and Atlasti ( http://www.atlasti.com ). These are programs specifically designed to assist qualitative researchers with organizing, managing, sorting, and analyzing large amounts of qualitative data. The programs work by allowing researchers to import interview transcripts contained in an electronic file and then label or code passages, cut and paste passages, search for various words or phrases, and organize complex interrelationships among passages and codes.

In sum, the following excerpt, from a paper analyzing the workplace sexual harassment interview data I have mentioned previously, summarizes how the process of analyzing qualitative interview data often works:

All interviews were tape recorded and then transcribed and imported into the computer program NVivo. NVivo is designed to assist researchers with organizing, managing, interpreting, and analyzing non-numerical, qualitative data. Once the transcripts, ranging from 20 to 60 pages each, were imported into NVivo, we first coded the data according to the themes outlined in our interview guide. We then closely reviewed each transcript again, looking for common themes across interviews and coding like categories of data together. These passages, referred to as codes or “meaning units” (Weiss, 2004), Weiss, R. S. (2004). In their own words: Making the most of qualitative interviews. Contexts, 3 , 44–51. were then labeled and given a name intended to succinctly portray the themes present in the code. For this paper, we coded every quote that had something to do with the labeling of harassment. After reviewing passages within the “labeling” code, we placed quotes that seemed related together, creating several sub-codes. These sub-codes were named and are represented by the three subtitles within the findings section of this paper. Our three subcodes were the following: (a) “It’s different because you’re in high school”: Sociability and socialization at work; (b) Looking back: “It was sexual harassment; I just didn’t know it at the time”; and (c) Looking ahead: New images of self as worker and of workplace interactions. Once our sub-codes were labeled, we re-examined the interview transcripts, coding additional quotes that fit the theme of each sub-code. (Blackstone, Houle, & Uggen, 2006) Blackstone, A., Houle, J., & Uggen, C. “At the time, I thought it was great”: Age, experience, and workers’ perceptions of sexual harassment. Presented at the Annual Meeting of the American Sociological Association, Montreal, QC, August 2006. Currently under review.

Strengths and Weaknesses of Qualitative Interviews

As the preceding sections have suggested, qualitative interviews are an excellent way to gather detailed information. Whatever topic is of interest to the researcher employing this method can be explored in much more depth than with almost any other method. Not only are participants given the opportunity to elaborate in a way that is not possible with other methods such as survey research, but they also are able share information with researchers in their own words and from their own perspectives rather than being asked to fit those perspectives into the perhaps limited response options provided by the researcher. And because qualitative interviews are designed to elicit detailed information, they are especially useful when a researcher’s aim is to study social processes, or the “how” of various phenomena. Yet another, and sometimes overlooked, benefit of qualitative interviews that occurs in person is that researchers can make observations beyond those that a respondent is orally reporting. A respondent’s body language, and even her or his choice of time and location for the interview, might provide a researcher with useful data.

Of course, all these benefits do not come without some drawbacks. As with quantitative survey research, qualitative interviews rely on respondents’ ability to accurately and honestly recall whatever details about their lives, circumstances, thoughts, opinions, or behaviors are being asked about. As Esterberg (2002) puts it, “If you want to know about what people actually do, rather than what they say they do, you should probably use observation [instead of interviews].” Esterberg, K. G. (2002). Qualitative methods in social research . Boston, MA: McGraw-Hill. Further, as you may have already guessed, qualitative interviewing is time intensive and can be quite expensive. Creating an interview guide, identifying a sample, and conducting interviews are just the beginning. Transcribing interviews is labor intensive—and that’s before coding even begins. It is also not uncommon to offer respondents some monetary incentive or thank-you for participating. Keep in mind that you are asking for more of participants’ time than if you’d simply mailed them a questionnaire containing closed-ended questions. Conducting qualitative interviews is not only labor intensive but also emotionally taxing. When I interviewed young workers about their sexual harassment experiences, I heard stories that were shocking, infuriating, and sad. Seeing and hearing the impact that harassment had had on respondents was difficult. Researchers embarking on a qualitative interview project should keep in mind their own abilities to hear stories that may be difficult to hear.

• In-depth interviews are semistructured interviews where the researcher has topics and questions in mind to ask, but questions are open ended and flow according to how the participant responds to each.
• Interview guides can vary in format but should contain some outline of the topics you hope to cover during the course of an interview.
• NVivo and Atlas.ti are computer programs that qualitative researchers use to help them with organizing, sorting, and analyzing their data.
• Qualitative interviews allow respondents to share information in their own words and are useful for gathering detailed information and understanding social processes.
• Drawbacks of qualitative interviews include reliance on respondents’ accuracy and their intensity in terms of time, expense, and possible emotional strain.
• Based on a research question you have identified through earlier exercises in this text, write a few open-ended questions you could ask were you to conduct in-depth interviews on the topic. Now critique your questions. Are any of them yes/no questions? Are any of them leading?
• Read the open-ended questions you just created, and answer them as though you were an interview participant. Were your questions easy to answer or fairly difficult? How did you feel talking about the topics you asked yourself to discuss? How might respondents feel talking about them?

9.3 Quantitative Interview Techniques and Considerations

• Define and describe standardized interviews.
• Describe how quantitative interviews differ from qualitative interviews.
• Describe the process and some of the drawbacks of telephone interviewing techniques.
• Describe how the analysis of quantitative interview works.
• Identify the strengths and weaknesses of quantitative interviews.

Quantitative interviews are similar to qualitative interviews in that they involve some researcher/respondent interaction. But the process of conducting and analyzing findings from quantitative interviews also differs in several ways from that of qualitative interviews. Each approach also comes with its own unique set of strengths and weaknesses. We’ll explore those differences here.

Conducting Quantitative Interviews

Much of what we learned in the previous chapter on survey research applies to quantitative interviews as well. In fact, quantitative interviews are sometimes referred to as survey interviews because they resemble survey-style question-and-answer formats. They might also be called standardized interviews Interviews during which the same questions are asked of every participant in the same way, and survey-style question-and-answer formats are utilized. . The difference between surveys and standardized interviews is that questions and answer options are read to respondents rather than having respondents complete a questionnaire on their own. As with questionnaires, the questions posed in a standardized interview tend to be closed ended. See Chapter 8 “Survey Research: A Quantitative Technique” for the definition of closed ended. There are instances in which a quantitative interviewer might pose a few open-ended questions as well. In these cases, the coding process works somewhat differently than coding in-depth interview data. We’ll describe this process in the following subsection.

In quantitative interviews, an interview schedule A document containing the list of questions and answer options that quantitative interviewers read to respondents. is used to guide the researcher as he or she poses questions and answer options to respondents. An interview schedule is usually more rigid than an interview guide. It contains the list of questions and answer options that the researcher will read to respondents. Whereas qualitative researchers emphasize respondents’ roles in helping to determine how an interview progresses, in a quantitative interview, consistency in the way that questions and answer options are presented is very important. The aim is to pose every question-and-answer option in the very same way to every respondent. This is done to minimize interviewer effect Occurs when an interviewee is influenced by how or when questions and answer options are presented by an interviewer. , or possible changes in the way an interviewee responds based on how or when questions and answer options are presented by the interviewer.

Quantitative interviews may be recorded, but because questions tend to be closed ended, taking notes during the interview is less disruptive than it can be during a qualitative interview. If a quantitative interview contains open-ended questions, however, recording the interview is advised. It may also be helpful to record quantitative interviews if a researcher wishes to assess possible interview effect. Noticeable differences in responses might be more attributable to interviewer effect than to any real respondent differences. Having a recording of the interview can help a researcher make such determinations.

Quantitative interviewers are usually more concerned with gathering data from a large, representative sample. As you might imagine, collecting data from many people via interviews can be quite laborious. Technological advances in telephone interviewing procedures can assist quantitative interviewers in this process. One concern about telephone interviewing is that fewer and fewer people list their telephone numbers these days, but random digit dialing (RDD) takes care of this problem. RDD programs dial randomly generated phone numbers for researchers conducting phone interviews. This means that unlisted numbers are as likely to be included in a sample as listed numbers (though, having used this software for quantitative interviewing myself, I will add that folks with unlisted numbers are not always very pleased to receive calls from unknown researchers). Computer-assisted telephone interviewing (CATI) programs have also been developed to assist quantitative survey researchers. These programs allow an interviewer to enter responses directly into a computer as they are provided, thus saving hours of time that would otherwise have to be spent entering data into an analysis program by hand.

Conducting quantitative interviews over the phone does not come without some drawbacks. Aside from the obvious problem that not everyone has a phone, research shows that phone interviews generate more fence-sitters than in-person interviews (Holbrook, Green, & Krosnick, 2003). Holbrook, A. L., Green, M. C., & Krosnick, J. A. (2003). Telephone versus face-to-face interviewing of national probability samples with long questionnaires: Comparisons of respondent satisficing and social desirability response bias. Public Opinion Quarterly, 67 , 79–125. Responses to sensitive questions or those that respondents view as invasive are also generally less accurate when data are collected over the phone as compared to when they are collected in person. I can vouch for this latter point from personal experience. While conducting quantitative telephone interviews when I worked at a research firm, it was not terribly uncommon for respondents to tell me that they were green or purple when I asked them to report their racial identity.

Analysis of Quantitative Interview Data

As with the analysis of survey data, analysis of quantitative interview data usually involves coding response options numerically, entering numeric responses into a data analysis computer program, and then running various statistical commands to identify patterns across responses. Section 8.5 “Analysis of Survey Data” of Chapter 8 “Survey Research: A Quantitative Technique” describes the coding process for quantitative data. But what happens when quantitative interviews ask open-ended questions? In this case, responses are typically numerically coded, just as closed-ended questions are, but the process is a little more complex than simply giving a “no” a label of 0 and a “yes” a label of 1.

In some cases, quantitatively coding open-ended interview questions may work inductively, as described in Section 9.2.2 “Analysis of Qualitative Interview Data” . If this is the case, rather than ending with codes, descriptions of codes, and interview excerpts, the researcher will assign a numerical value to codes and may not utilize verbatim excerpts from interviews in later reports of results. Keep in mind, as described in Chapter 1 “Introduction” , that with quantitative methods the aim is to be able to represent and condense data into numbers. The quantitative coding of open-ended interview questions is often a deductive process. The researcher may begin with an idea about likely responses to his or her open-ended questions and assign a numerical value to each likely response. Then the researcher will review participants’ open-ended responses and assign the numerical value that most closely matches the value of his or her expected response.

Strengths and Weaknesses of Quantitative Interviews

Quantitative interviews offer several benefits. The strengths and weakness of quantitative interviews tend to be couched in comparison to those of administering hard copy questionnaires. For example, response rates tend to be higher with interviews than with mailed questionnaires (Babbie, 2010). Babbie, E. (2010). The practice of social research (12th ed.). Belmont, CA: Wadsworth. That makes sense—don’t you find it easier to say no to a piece of paper than to a person? Quantitative interviews can also help reduce respondent confusion. If a respondent is unsure about the meaning of a question or answer option on a questionnaire, he or she probably won’t have the opportunity to get clarification from the researcher. An interview, on the other hand, gives the researcher an opportunity to clarify or explain any items that may be confusing.

As with every method of data collection we’ve discussed, there are also drawbacks to conducting quantitative interviews. Perhaps the largest, and of most concern to quantitative researchers, is interviewer effect. While questions on hard copy questionnaires may create an impression based on the way they are presented, having a person administer questions introduces a slew of additional variables that might influence a respondent. As I’ve said, consistency is key with quantitative data collection—and human beings are not necessarily known for their consistency. Interviewing respondents is also much more time consuming and expensive than mailing questionnaires. Thus quantitative researchers may opt for written questionnaires over interviews on the grounds that they will be able to reach a large sample at a much lower cost than were they to interact personally with each and every respondent.

• Unlike qualitative interviews, quantitative interviews usually contain closed-ended questions that are delivered in the same format and same order to every respondent.
• Quantitative interview data are analyzed by assigning a numerical value to participants’ responses.
• While quantitative interviews offer several advantages over self-administered questionnaires such as higher response rates and lower respondent confusion, they have the drawbacks of possible interviewer effect and greater time and expense.
• The General Social Survey (GSS), which we’ve mentioned in previous chapters, is administered via in-person interview, just like quantitative interviewing procedures described here. Read more about the GSS at http://www.norc.uchicago.edu/GSS+Website .
• Take a few of the open-ended questions you created after reading Section 9.2 “Qualitative Interview Techniques and Considerations” on qualitative interviewing techniques. See if you can turn them into closed-ended questions.

9.4 Issues to Consider for All Interview Types

• Identify the main issues that both qualitative and quantitative interviewers should consider.
• Describe the options that interviewers have for balancing power between themselves and interview participants.
• Describe and define rapport.
• Define the term probe and describe how probing differs in qualitative and quantitative interviewing.

While quantitative interviews resemble survey research in their question/answer formats, they share with qualitative interviews the characteristic that the researcher actually interacts with her or his subjects. The fact that the researcher interacts with his or her subjects creates a few complexities that deserve attention. We’ll examine those here.

First and foremost, interviewers must be aware of and attentive to the power differential between themselves and interview participants. The interviewer sets the agenda and leads the conversation. While qualitative interviewers aim to allow participants to have some control over which or to what extent various topics are discussed, at the end of the day it is the researcher who is in charge (at least that is how most respondents will perceive it to be). As the researcher, you are asking someone to reveal things about themselves they may not typically share with others. Also, you are generally not reciprocating by revealing much or anything about yourself. All these factors shape the power dynamics of an interview.

A number of excellent pieces have been written dealing with issues of power in research and data collection. Feminist researchers in particular paved the way in helping researchers think about and address issues of power in their work (Oakley, 1981). Oakley, A. (1981). Interviewing women: A contradiction in terms. In H. Roberts (Ed.), Doing feminist research (pp. 30–61). London, UK: Routledge & Kegan Paul. Suggestions for overcoming the power imbalance between researcher and respondent include having the researcher reveal some aspects of her own identity and story so that the interview is a more reciprocal experience rather than one-sided, allowing participants to view and edit interview transcripts before the researcher uses them for analysis, and giving participants an opportunity to read and comment on analysis before the researcher shares it with others through publication or presentation (Reinharz, 1992; Hesse-Biber, Nagy, & Leavy, 2007). Reinharz, S. (1992). Feminist methods in social research . New York, NY: Oxford University Press; Hesse-Biber, S. N., & Leavy, P. L. (Eds.). (2007). Feminist research practice: A primer . Thousand Oaks, CA: Sage. On the other hand, some researchers note that sharing too much with interview participants can give the false impression that there is no power differential, when in reality researchers retain the ability to analyze and present participants’ stories in whatever way they see fit (Stacey, 1988). Stacey, J. (1988). Can there be a feminist ethnography? Women’s Studies International Forum, 11 , 21–27.

However you feel about sharing details about your background with an interview participant, another way to balance the power differential between yourself and your interview participants is to make the intent of your research very clear to the subjects. Share with them your rationale for conducting the research and the research question(s) that frame your work. Be sure that you also share with subjects how the data you gather will be used and stored. Also, be sure that participants understand how their privacy will be protected including who will have access to the data you gather from them and what procedures, such as using pseudonyms, you will take to protect their identities. Many of these details will be covered by your institutional review board’s informed consent procedures and requirements, but even if they are not, as researchers we should be attentive to how sharing information with participants can help balance the power differences between ourselves and those who participate in our research.

There are no easy answers when it comes to handling the power differential between the researcher and researched, and even social scientists do not agree on the best approach for doing so. It is nevertheless an issue to be attentive to when conducting any form of research, particularly those that involve interpersonal interactions and relationships with research participants.

Location, Location, Location

One way to balance the power between researcher and respondent is to conduct the interview in a location of the participants’ choosing, where he or she will feel most comfortable answering your questions. Interviews can take place in any number of locations—in respondents’ homes or offices, researchers’ homes or offices, coffee shops, restaurants, public parks, or hotel lobbies, to name just a few possibilities. I have conducted interviews in all these locations, and each comes with its own set of benefits and its own challenges. While I would argue that allowing the respondent to choose the location that is most convenient and most comfortable for her or him is of utmost importance, identifying a location where there will be few distractions is also important. For example, some coffee shops and restaurants are so loud that recording the interview can be a challenge. Other locations may present different sorts of distractions. For example, I have conducted several interviews with parents who, out of necessity, spent more time attending to their children during an interview than responding to my questions (of course, depending on the topic of your research, the opportunity to observe such interactions could be invaluable). As an interviewer, you may want to suggest a few possible locations, and note the goal of avoiding distractions, when you ask your respondents to choose a location.

Of course, the extent to which a respondent should be given complete control over choosing a location must also be balanced by accessibility of the location to you, the interviewer, and by your safety and comfort level with the location. I once agreed to conduct an interview in a respondent’s home only to discover on arriving that the living room where we conducted the interview was decorated wall to wall with posters representing various white power organizations displaying a variety of violently racist messages. Though the topic of the interview had nothing to do with the topic of the respondent’s home décor, the discomfort, anger, and fear I felt during the entire interview consumed me and certainly distracted from my ability to carry on the interview. In retrospect, I wish I had thought to come up with some excuse for needing to reschedule the interview and then arranged for it to happen in a more neutral location. While it is important to conduct interviews in a location that is comfortable for respondents, doing so should never come at the expense of your safety.

Researcher-Respondent Relationship

Finally, a unique feature of interviews is that they require some social interaction, which means that to at least some extent, a relationship is formed between interviewer and interviewee. While there may be some differences in how the researcher-respondent relationship works depending on whether your interviews are qualitative or quantitative, one essential relationship element is the same: R-E-S-P-E-C-T. You should know by now that I can’t help myself. If you, too, now have Aretha Franklin on the brain, feel free to excuse yourself for a moment to enjoy a song and dance: http://www.youtube.com/watch?v=z0XAI-PFQcA . A good rapport between you and the person you interview is crucial to successful interviewing. Rapport The sense of connection a researcher establishes with a participant. is the sense of connection you establish with a participant. Some argue that this term is too clinical, and perhaps it implies that a researcher tricks a participant into thinking they are closer than they really are (Esterberg, 2002). Esterberg, K. G. (2002). Qualitative methods in social research . Boston, MA: McGraw-Hill. While it is unfortunately true that some researchers might adopt this misguided approach to rapport, that is not the sense in which I use the term here nor is that the sort of rapport I advocate researchers attempt to establish with their subjects. Instead, as already mentioned, it is respect that is key.

There are no big secrets or tricks for how to show respect for research participants. At its core, the interview interaction should not differ from any other social interaction in which you show gratitude for a person’s time and respect for a person’s humanity. It is crucial that you, as the interviewer, conduct the interview in a way that is culturally sensitive. In some cases, this might mean educating yourself about your study population and even receiving some training to help you learn to effectively communicate with your research participants. Do not judge your research participants; you are there to listen to them, and they have been kind enough to give you their time and attention. Even if you disagree strongly with what a participant shares in an interview, your job as the researcher is to gather the information being shared with you, not to make personal judgments about it. In case you still feel uncertain about how to establish rapport and show your participants respect, I will leave you with a few additional bits of advice.

Developing good rapport requires good listening. In fact, listening during an interview is an active, not a passive, practice. Active listening Occurs when an interviewer demonstrates that he or she understands what an interview participant has said; requires probes or follow-up questions that indicate such understanding. means that you, the researcher, participate with the respondent by showing that you understand and follow whatever it is that he or she is telling you (Devault, 1990). For more on the practice of listening, especially in qualitative interviews, see Devault, M. (1990). Talking and listening from women’s standpoint: Feminist strategies for interviewing and analysis. Social Problems, 37 , 96–116. The questions you ask respondents should indicate that you’ve actually heard what they’ve just said. Active listening probably means that you will probe the respondent for more information from time to time throughout the interview. A probe A request, on the part of an interviewer, for more information from an interview participant. is a request for more information. Both qualitative and quantitative interviewers probe respondents, though the way they probe usually differs. In quantitative interviews, probing should be uniform. Often quantitative interviewers will predetermine what sorts of probes they will use. As an employee at the research firm I’ve mentioned before, our supervisors used to randomly listen in on quantitative telephone interviews we conducted. We were explicitly instructed not to use probes that might make us appear to agree or disagree with what respondents said. So “yes” or “I agree” or a questioning “hmmmm” were discouraged. Instead, we could respond with “thank you” to indicate that we’d heard a respondent. We could use “yes” or “no” if, and only if, a respondent had specifically asked us if we’d heard or understood what they had just said.

In some ways qualitative interviews better lend themselves to following up with respondents and asking them to explain, describe, or otherwise provide more information. This is because qualitative interviewing techniques are designed to go with the flow and take whatever direction the respondent goes during the interview. Nevertheless, it is worth your time to come up with helpful probes in advance of an interview even in the case of a qualitative interview. You certainly do not want to find yourself stumped or speechless after a respondent has just said something about which you’d like to hear more. This is another reason that practicing your interview in advance with people who are similar to those in your sample is a good idea.

• While there are several key differences between qualitative and quantitative interviewing techniques, all interviewers using either technique should take into consideration the power differential between themselves and respondents, should take care in identifying a location for an interview, and should take into account the fact that an interview is, to at least some extent, a form of relationship.
• Feminist researchers paved the way for helping interviewers think about how to balance the power differential between themselves and interview participants.
• Interviewers must always be respectful of interview participants.
• Imagine that you will be conducting interviews. What are some possible locations in your area you think might be good places to conduct interviews? What makes those locations good?
• What do you think about the suggestions for balancing power between interviewers and interviewees? How much of your own story do you think you’d be likely to share with interview participants? Why? What are the possible consequences (positive and negative) of revealing information about yourself when you’re the researcher?
• Principles of Sociological Inquiry: Qualitative and Quantitative Methods. Provided by : Saylor Academy. Located at : https://saylordotorg.github.io/text_principles-of-sociological-inquiry-qualitative-and-quantitative-methods/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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Quantitative Interview Techniques & Considerations

61 Conducting Quantitative Interviews

Much of what we learned in the previous chapter on survey research applies to quantitative interviews as well. In fact, quantitative interviews are sometimes referred to as survey interviews because they resemble survey-style question-and-answer formats. They might also be called standardized interviews . The difference between surveys and standardized interviews is that questions and answer options are read to respondents in a standardized interview, rather than having respondents complete a survey on their own. As with surveys, the questions posed in a standardized interview tend to be closed ended. There are instances in which a quantitative interviewer might pose a few open-ended questions as well. In these cases, the coding process works somewhat differently than coding in-depth interview data. We will describe this process in the following section.

In quantitative interviews, an interview schedule is used to guide the researcher as he or she poses questions and answer options to respondents. An interview schedule is usually more rigid than an interview guide. It contains the list of questions and answer options that the researcher will read to respondents. Whereas qualitative researchers emphasize respondents’ roles in helping to determine how an interview progresses, in a quantitative interview, consistency in the way that questions and answer options are presented is very important. The aim is to pose every question-and-answer option in the very same way to every respondent. This is done to minimize interviewer effect, or possible changes in the way an interviewee responds based on how or when questions and answer options are presented by the interviewer.

Quantitative interviews may be recorded, but because questions tend to be closed ended, taking notes during the interview is less disruptive than it can be during a qualitative interview. If a quantitative interview contains open-ended questions, recording the interview is advised. It may also be helpful to record quantitative interviews if a researcher wishes to assess possible interview effect. Noticeable differences in responses might be more attributable to interviewer effect than to any real respondent differences. Having a recording of the interview can help a researcher make such determinations.

Quantitative interviewers are usually more concerned with gathering data from a large, representative sample. As you might imagine, collecting data from many people via interviews can be quite laborious. In the past, telephone interviewing was quite common; however, the growth in use mobile phones has raised concern regarding whether or not traditional landline telephone interviews and surveys are now representative of the general population (Busse & Fuchs, 2012). Indeed, there are other drawbacks to telephone interviews. Aside from the obvious problem that not everyone has a phone (mobile or landline), research shows that phone interview respondents were less cooperative, less engaged in the interview, and more likely to express dissatisfaction with the length of the interview than were face-to-face respondents (Holbrook, Green, & Krosnick, 2003, p. 79). Holbrook et al.’s research also demonstrated that telephone respondents were more suspicious of the interview process and more likely than face-to-face respondents to present themselves in a socially desirable manner.

Text Attributions

• This chapter is an adaptation of Chapter 9.3 in Principles of Sociological Inquiry , which was adapted by the Saylor Academy without attribution to the original authors or publisher, as requested by the licensor. © Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License .

An Introduction to Research Methods in Sociology Copyright © 2019 by Valerie A. Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Top 20 Quantitative Interview Questions & Answers

Master your responses to Quantitative related interview questions with our example questions and answers. Boost your chances of landing the job by learning how to effectively communicate your Quantitative capabilities.

Embarking on a career in the quantitative field means immersing yourself in a world where numbers, data analysis, and algorithmic thinking are paramount. Whether you’re aiming for a role in finance, research, or another sector that relies heavily on quantitative skills, it’s imperative to demonstrate not just your technical acumen but also your ability to apply complex mathematical concepts to real-world problems.

During an interview for a quantitative position, expect to encounter questions designed to probe your expertise in statistical methods, your experience with programming languages, and your knack for critical thinking under pressure. To help you navigate these challenges and convey your quantitative prowess effectively, we’ve curated a selection of typical interview questions tailored for the quantitatively inclined professional, coupled with strategic advice on formulating compelling responses.

Common Quantitative Interview Questions

1. how would you validate a statistical model you’ve developed for predicting market trends.

Understanding the validation of a statistical model is essential, particularly when predicting market trends. It’s not just about accuracy but also about the model’s adaptability to changing conditions and the robustness of the underlying data. Candidates should be prepared to discuss their experience with statistical validation techniques and their ability to foresee potential issues.

When responding, highlight your approach to model validation, which might include split-sample testing, cross-validation, or out-of-time validation. Discuss the importance of using relevant and high-quality data, as well as the need to test the model against unseen data to gauge its generalizability. Emphasize your understanding of key performance metrics—like R-squared, RMSE, or MAE—that can help quantify the model’s accuracy. Show that you are also mindful of the model’s limitations and the importance of continuous monitoring and updating to maintain its predictive relevance over time.

Example: “ To validate a statistical model designed for predicting market trends, I would employ a combination of split-sample testing and cross-validation techniques to ensure the model’s robustness and generalizability. Initially, I would partition the dataset into training and testing subsets, using the training set to build the model and the testing set to evaluate its predictive power. This approach helps to mitigate overfitting and provides an initial assessment of the model’s performance on unseen data.

Subsequently, I would implement k-fold cross-validation, which further refines the validation process by averaging the model’s performance across different partitions of the data, offering a more comprehensive view of its predictive capabilities. Throughout this process, I would closely monitor key performance metrics such as R-squared for assessing the proportion of variance explained by the model, and RMSE or MAE for quantifying the prediction errors, adjusting the model accordingly to optimize these metrics.

It’s crucial to recognize that model validation is not a one-time task but an ongoing process. As market conditions evolve, the model should be periodically revalidated using fresh, out-of-time data to ensure its continued relevance. Moreover, I would remain vigilant about the quality of data inputs, as the predictive accuracy of the model is inherently dependent on the relevance and integrity of the data it is trained on. Continuous monitoring and updating of the model are essential to account for new patterns or structural changes in the market, ensuring the model’s long-term predictive validity.”

2. Describe an experience where you had to interpret complex data sets without clear patterns.

For roles that require a high degree of analytical rigor, candidates must be adept at interpreting ambiguous data. This question probes the candidate’s persistence, creativity, and ability to communicate complex findings to non-technical stakeholders.

When responding, candidates should outline the steps they took to analyze the data, including any specific methodologies or tools they employed. They should discuss how they identified variables of interest, dealt with missing or noisy data, and the reasoning behind the particular analytical approaches they chose. It’s also beneficial to explain how they validated their conclusions and the impact of their findings on the decision-making process. A story that conveys the complexity of the situation, the approach taken, and the eventual outcome or learning experience will demonstrate their competence in handling such challenges.

Example: “ In an experience dealing with a multifaceted data set lacking apparent patterns, I approached the analysis through a combination of exploratory data analysis (EDA) and advanced statistical techniques. Initially, I employed EDA methods, such as visualizing the data through histograms, boxplots, and scatter plots, to identify any underlying structures or anomalies. This preliminary step was crucial in formulating hypotheses about potential relationships within the data.

Subsequently, I used dimensionality reduction techniques like Principal Component Analysis (PCA) to distill the data into its most informative components. This was followed by implementing machine learning algorithms, including clustering methods like K-means and DBSCAN, to detect any subtle groupings or trends that were not immediately obvious. To address missing or noisy data, I applied imputation methods and robust statistical measures to minimize bias.

Throughout the analysis, I rigorously validated the findings using cross-validation techniques and sensitivity analysis to ensure the robustness of the results. The insights gained from this comprehensive approach led to the identification of several key drivers that were previously obscured. These findings informed strategic decisions, resulting in optimized processes and a significant improvement in the overall efficiency of the project. The experience underscored the importance of a methodical and iterative approach to data analysis, especially when faced with complex and initially inscrutable data sets.”

3. What metrics would you prioritize when assessing the financial health of a tech startup?

A tech startup’s financial health is not just about current profitability. Candidates should be ready to discuss how they evaluate a startup’s potential for growth, scalability, and other industry-specific KPIs that reflect the unique challenges of the technology sector.

When responding to this question, candidates should focus on a balanced set of metrics such as cash flow, runway, customer acquisition cost (CAC), customer lifetime value (CLV), monthly recurring revenue (MRR), and churn rate. It’s important to articulate why each metric is relevant and how it would influence strategic decisions. For instance, a high CAC relative to CLV could signal unsustainable growth, while a short runway might necessitate immediate fundraising or cost-cutting measures. Demonstrating an understanding of how these metrics interplay can show your analytical skills and your ability to guide a startup towards financial stability and growth.

Example: “ When evaluating the financial health of a tech startup, I would prioritize a mix of liquidity, profitability, and growth metrics. Cash flow is paramount as it indicates the company’s ability to sustain operations and grow without external financing. I would assess the runway by comparing the current burn rate with available cash to estimate the time before additional capital is required. This metric is critical for understanding the immediacy of fundraising needs or the necessity for cost optimization.

Simultaneously, I would analyze customer-centric metrics such as CAC and CLV to gauge the efficiency of the startup’s growth strategies. A lower CAC relative to CLV suggests a sustainable acquisition strategy and a healthy potential for long-term profitability. MRR and churn rate offer insights into the recurring revenue stability and customer retention, respectively. High MRR growth coupled with low churn rates often indicates product-market fit and a loyal customer base, which are strong indicators of a startup’s upward trajectory. These metrics collectively provide a comprehensive view of the startup’s financial performance and can inform strategic decisions to ensure both short-term survival and long-term success.”

4. Outline your process for conducting a Monte Carlo simulation in portfolio risk assessment.

Conducting a Monte Carlo simulation is a key skill for effective portfolio risk assessment. Candidates should be prepared to explain their approach to this stochastic technique and how it helps them understand the impact of risk and uncertainty in financial models.

When responding, you should clearly outline the key steps: defining a domain of possible inputs, generating inputs randomly from a probability distribution that reflects the risk or uncertainty being modeled, running a deterministic computation with those inputs, and aggregating the results to get a probability distribution of the output. It’s important to detail your experience with relevant software or programming languages, explain how you ensure the accuracy and reliability of the data, and discuss how you interpret the results to inform risk management decisions. Demonstrating an understanding of the limitations and assumptions of the model will also show depth of knowledge.

Example: “ In conducting a Monte Carlo simulation for portfolio risk assessment, I begin by defining the domain of possible inputs, which typically includes historical return distributions for each asset class, correlations, and volatilities. I ensure these inputs are based on robust statistical analysis and are reflective of current market conditions. Using these distributions, I generate a large number of random scenarios for future returns, employing a pseudo-random number generator or a quasi-random sequence for better convergence properties.

Next, I run deterministic computations for each scenario, which involves calculating the portfolio returns and risk metrics such as VaR (Value at Risk) or CVaR (Conditional Value at Risk). This is done through a simulation engine that I either code in a language like Python or R, or by using specialized software such as @RISK or Crystal Ball, depending on the complexity and the specific requirements of the task.

The aggregation of results is critical; I analyze the output distribution to assess the risk profile of the portfolio, looking at the range of outcomes and the likelihood of extreme losses. I interpret these results within the context of the portfolio’s investment strategy and risk tolerance, providing actionable insights for risk management decisions.

Throughout the process, I am mindful of the assumptions and limitations inherent in the model, such as the assumption of a static correlation structure or the potential for model risk due to input uncertainty. I conduct sensitivity analyses to understand how changes in the inputs affect the outcomes, ensuring that the final recommendations are robust and account for a range of possible market conditions.”

5. In what ways have you utilized machine learning algorithms to enhance quantitative analysis?

In modern finance, marketing, or data science, machine learning is increasingly important. Candidates should be able to discuss how they use machine learning algorithms to enhance decision-making and drive innovation.

When responding to this question, a candidate should highlight specific projects or experiences where machine learning algorithms directly impacted the analysis. Discuss the type of algorithms used—such as decision trees, neural networks, or clustering techniques—and the outcomes they achieved. Explain the problem-solving process, including how the algorithm was selected, the data preparation involved, and how the results were validated and interpreted. It’s essential to articulate the value added through these techniques, such as increased accuracy of predictions, time saved, or improved profitability.

Example: “ In a recent project, I leveraged Random Forest algorithms to enhance the predictive accuracy of a quantitative trading strategy. By integrating a multitude of decision trees, the model was trained on historical market data to identify complex patterns and interactions between various financial indicators. The ensemble approach not only improved the robustness of the predictions against overfitting but also increased the out-of-sample Sharpe ratio significantly, leading to a more profitable and risk-adjusted return profile.

Furthermore, I employed neural networks for time-series forecasting, specifically LSTM (Long Short-Term Memory) models, to capture the temporal dependencies in asset price movements. The LSTM’s ability to remember information over extended periods was crucial for understanding the momentum and mean-reversion effects in the markets. The model’s forecasts were instrumental in optimizing trade execution and managing dynamic portfolio allocations, resulting in a marked decrease in slippage costs and enhanced overall portfolio performance. Validation of the models’ effectiveness was conducted through rigorous backtesting and forward performance monitoring, ensuring that the machine learning applications provided tangible benefits to the quantitative analysis framework.”

6. Detail a scenario where you significantly improved a model’s accuracy; what changes did you make?

Model improvement is a critical skill in quantitative roles. Candidates should be ready to showcase their problem-solving skills and their ability to enhance existing systems, demonstrating a deep understanding of data science methodologies.

When responding, candidates should outline the situation clearly, describing the model in question, the specific issues with its accuracy, and the steps taken to address them. They should discuss the data analysis performed, any algorithm adjustments, feature engineering, or cross-validation techniques employed. Articulating the reasoning behind each change and how it contributed to the overall improvement in accuracy will show a thoughtful and methodical approach to model optimization.

Example: “ In one scenario, the model’s performance was hindered by overfitting due to a high dimensionality of features relative to the number of observations. To address this, I implemented a combination of feature selection and regularization techniques. I started by applying a variance threshold to remove features with minimal variance, as they were unlikely to contribute significantly to the model’s predictive power. Then, I used a recursive feature elimination process with cross-validation (RFECV) to identify and retain the most impactful features.

Subsequently, I incorporated L1 regularization (Lasso) into the model to penalize the magnitude of the coefficients and encourage sparsity, effectively reducing the complexity of the model. This regularization technique not only helped in feature selection but also improved the model’s generalization by discouraging overfitting. The changes led to a more parsimonious model with a better balance between bias and variance, resulting in a significant uplift in out-of-sample accuracy, as confirmed by a stratified K-fold cross-validation approach.”

7. Walk us through a time when you had to explain quantitative findings to a non-technical audience.

Translating complex quantitative data into digestible insights is a valuable skill. Candidates should be prepared to discuss how they make complex numerical information accessible and actionable for those without a technical background.

When responding, begin by outlining the context of the quantitative findings you were dealing with, emphasizing the audience’s lack of technical expertise. Describe the steps you took to simplify the data, such as using analogies, visual aids, or breaking down the methodology into layman’s terms. Highlight your ability to engage with the audience, asking questions to gauge their understanding, and adjusting your explanation accordingly. Conclude by reflecting on the outcome—how your communication facilitated a better understanding and led to informed decisions or actions.

Example: “ In a recent project, I was tasked with presenting the results of a complex regression analysis that identified key factors affecting customer retention rates. The audience comprised department heads with varied backgrounds, most of whom lacked statistical training. To bridge the gap, I distilled the findings into the core message: which factors were most influential and by how much. I used a simple analogy, comparing the statistical model to a recipe where each ingredient’s quantity affects the final taste, to convey the idea of coefficients impacting the outcome.

I supplemented this with a clear, intuitive visualization—a bar chart showing the relative importance of each factor, avoiding technical jargon like “p-values” or “confidence intervals.” During the presentation, I engaged with the audience by asking if the visual representation made sense and if they could relate the findings to their experiences. This interaction helped me tailor the explanation further, ensuring clarity.

The outcome was a productive discussion that led to actionable strategies. The department heads grasped the key takeaways and were able to brainstorm targeted initiatives to improve customer retention, demonstrating that the quantitative findings had been successfully communicated and understood.”

8. Which quantitative research publication has most influenced your work and why?

The influence of quantitative research on your work can demonstrate scholarly rigor and breadth of knowledge. Candidates should be ready to discuss how they integrate complex data from research publications into their thought process.

To respond effectively, you should select a publication that is not only reputable but also closely related to your field of work or the position you are applying for. Discuss the publication’s findings or methodologies and clearly articulate how it has shaped your approach to data analysis or problem-solving. Be prepared to explain the publication’s impact on your thought process or professional practices, providing concrete examples of how you have applied its insights to your work.

Example: “ The publication that has most influenced my work is “The Econometrics of Financial Markets” by John Y. Campbell, Andrew W. Lo, and A. Craig MacKinlay. This seminal work provided a comprehensive framework for analyzing financial markets using econometric methods, which has been invaluable in both my approach to modeling market behaviors and in developing predictive analytics for asset pricing.

The rigorous treatment of topics such as time-series analysis, the Capital Asset Pricing Model (CAPM), and the Efficient Market Hypothesis (EMH) within this text has been particularly impactful. It has honed my ability to critically evaluate model assumptions and to integrate econometric techniques with machine learning algorithms for enhanced forecasting accuracy. For instance, leveraging the concepts from this publication, I’ve successfully implemented vector autoregression models to predict stock prices, accounting for the dynamic interplay between multiple economic indicators. The book’s empirical focus also encouraged a data-driven mindset that ensures the robustness and validity of my analyses, a principle that I’ve upheld in all my quantitative endeavors.”

9. What approach do you take to ensure compliance with data protection regulations during analysis?

Data protection is a critical concern, especially when handling sensitive information. Candidates should be prepared to discuss their knowledge of data protection laws and how they incorporate compliance into their daily work processes.

To respond effectively, outline specific steps you take to ensure compliance, such as anonymizing data, implementing access controls, and conducting regular audits. You may also mention staying updated on the latest regulations, attending training sessions, and collaborating with legal or compliance teams. It’s essential to demonstrate a proactive approach to data protection, showcasing that you prioritize ethical considerations alongside analytical rigor.

Example: “ In ensuring compliance with data protection regulations during analysis, my approach is both proactive and systematic. Initially, I conduct a thorough data inventory to classify the sensitivity of the data sets and understand the specific compliance requirements associated with each. Following this, I employ data minimization techniques, ensuring that only the necessary data for the analysis is processed, and anonymize or pseudonymize personal data to mitigate any privacy risks.

I implement robust access controls, restricting data access to authorized personnel and applying the principle of least privilege. To maintain the integrity of these measures, I regularly perform audits and monitor data access logs to detect any unauthorized attempts or non-compliance issues. Furthermore, I stay abreast of the evolving regulatory landscape by attending advanced training sessions and actively engaging with legal or compliance teams to ensure that my analysis practices are aligned with the latest data protection standards. This ongoing dialogue allows for the anticipation of regulatory changes and the seamless integration of new compliance requirements into the analytical workflow.”

10. Share an example of how you’ve used hypothesis testing to inform a business decision.

Hypothesis testing is a fundamental statistical method in decision-making. Candidates should be ready to explain how they conduct hypothesis tests and how they use the results to inform business strategies.

When responding to this question, articulate a clear scenario where hypothesis testing was central to resolving a business question or challenge. Describe the hypothesis you tested, the data you used, and the statistical method you employed. Most importantly, discuss the results of the test and how you interpreted them to make a well-informed business decision. Highlight how your analysis had a tangible impact on the business, such as improving a product, optimizing a process, or enhancing customer satisfaction. This will showcase your ability to bridge the gap between data analysis and practical business outcomes.

Example: “ In a recent project, we were faced with the challenge of optimizing the pricing strategy for a new product line. The hypothesis was that by increasing the price point by 10%, we would not significantly affect the sales volume, aiming to increase overall revenue without deterring customers. To test this hypothesis, we conducted an A/B test, where Group A was exposed to the current pricing and Group B to the increased price.

Utilizing a t-test for the difference in means between the two groups, we analyzed sales data over a four-week period. The results indicated that there was no statistically significant difference in sales volume (p > 0.05), suggesting that the price increase did not negatively impact sales. Based on this analysis, we advised the business to implement the new pricing strategy across all markets, which ultimately led to a 9% rise in revenue without a drop in sales volume. This decision was further supported by monitoring post-implementation sales data, confirming the sustainability of the new pricing model.”

11. When building predictive models, how do you address multicollinearity in your independent variables?

Multicollinearity can complicate the interpretation of predictive models. Candidates should be prepared to discuss how they diagnose and resolve issues of multicollinearity to ensure the accuracy of their models.

When responding to this question, you should demonstrate a clear understanding of the concept of multicollinearity and its implications for predictive modeling. Outline the methods you employ to detect multicollinearity, such as examining correlation matrices or variance inflation factors (VIF). Then, explain the strategies you apply to address it, which might include removing highly correlated predictors, combining them into a single variable, or using regularization techniques like Lasso or Ridge regression that can penalize coefficients for correlated variables and reduce overfitting. Your answer should convey a methodical and informed approach to maintaining the integrity of your predictive models.

Example: “ To address multicollinearity among independent variables in predictive modeling, I first employ diagnostic tools like correlation matrices and Variance Inflation Factor (VIF) thresholds to detect the presence and severity of multicollinearity. A VIF above 5 or 10, depending on the context and domain-specific thresholds, usually signals a multicollinearity issue that needs to be addressed.

Once identified, I tackle multicollinearity using a combination of domain knowledge and statistical techniques. If two variables are highly correlated and one is deemed less important based on domain knowledge, I might remove it to reduce redundancy. Alternatively, I might combine correlated variables into a single feature through principal component analysis or factor analysis, which preserves the information while mitigating the multicollinearity effect. In situations where variable selection is crucial, I might apply regularization methods like Lasso regression, which is designed to perform both variable selection and parameter shrinkage, effectively handling multicollinearity by penalizing the coefficients of correlated predictors and driving some to zero. This approach ensures the model remains robust and generalizable without compromising the predictive power.”

12. Describe a situation where you integrated qualitative insights into your quantitative analysis.

Blending quantitative and qualitative insights can lead to more effective analysis. Candidates should be ready to discuss how they incorporate qualitative factors like consumer behavior and market trends into their data analysis.

When responding, candidates should recount a specific scenario where they identified the need for qualitative input to complement their quantitative findings. They might discuss how they gathered qualitative data, such as customer feedback or expert opinions, and the methods used to integrate this information with quantitative results. The aim is to illustrate their thought process, the challenges they faced, and how the integration of both data types led to a more informed decision or recommendation. It’s important to convey adaptability, analytical depth, and a recognition that numbers tell a part of the story, but not the whole.

Example: “ In a recent analysis of customer churn, I recognized that while the quantitative data highlighted trends in cancellations, it didn’t provide the underlying reasons for customer dissatisfaction. To address this gap, I conducted a series of in-depth interviews and focus groups to gain qualitative insights. I used thematic analysis to identify common reasons for churn that weren’t apparent in the numerical data, such as perceived value and customer service interactions.

Integrating these qualitative insights with the quantitative data involved creating a framework that allowed for a holistic view of the factors influencing churn. By overlaying sentiment analysis on the churn rate across different customer segments, I was able to pinpoint specific service touchpoints that required improvement. This mixed-methods approach not only enriched the analysis but also led to targeted strategies that reduced churn by 15% over the next quarter. The process highlighted the importance of blending qualitative nuances with quantitative rigor to derive actionable intelligence.”

13. How do you determine the right sample size for a statistically significant survey study?

Determining the right sample size is crucial for survey studies. Candidates should be prepared to discuss their approach to calculating sample size and ensuring the reliability and validity of their research findings.

When responding to this question, one should discuss the factors that influence sample size determination, such as the desired confidence level, the margin of error, the population size, and the expected effect size or variability in the data. It’s important to articulate a systematic approach to sample size calculation, perhaps referencing specific statistical formulas or software used to make these estimations. Offering an example from past experience where you determined sample size for a study can showcase your practical application of these concepts, along with any lessons learned from the outcome of that research.

Example: “ Determining the right sample size for a statistically significant survey study hinges on balancing precision and practicality. Initially, I establish the desired confidence level, typically 95% for conventional standards, which sets the Z-score used in calculations. The margin of error, reflecting the maximum expected difference between the sample statistic and the population parameter, is chosen based on the level of precision required for the study’s objectives. For a general population survey, a 5% margin is common, but this might be tightened for more sensitive analyses.

I then consider the population size, especially relevant when dealing with finite populations, to apply the finite population correction if necessary. The expected effect size or variability in the data, informed by prior research or pilot studies, guides my estimation of the standard deviation, which is crucial for calculating the required sample size. Utilizing formulas for sample size calculation or software like G*Power or nQuery, I incorporate these parameters to yield a sample size that balances statistical rigor with resource constraints. For instance, in a previous study estimating the prevalence of a health behavior, I used a conservative estimate of variability to ensure the sample size was sufficient to detect even small prevalence rates, which was vital for the subsequent health intervention planning. The study’s success underscored the importance of meticulous sample size calculation in achieving meaningful and actionable results.”

14. What is your strategy for staying current with advancements in quantitative methods and tools?

Keeping up with the latest trends and tools is essential in the quantitative field. Candidates should be ready to discuss their commitment to continuous learning and how they stay current with new technologies and methodologies.

When responding to this question, a candidate should highlight their proactive approach to professional development. This could include subscribing to industry journals, attending workshops and conferences, participating in online courses, or being part of professional forums and networks. Demonstrating a systematic approach to integrating new knowledge and tools into one’s work routine will reassure employers that the candidate is both technically proficient and strategically prepared to contribute to the organization’s success.

Example: “ To stay abreast of the latest advancements in quantitative methods and tools, I maintain a disciplined approach to continuous learning and professional development. I regularly subscribe to and read key industry journals such as the “Journal of Quantitative Analysis” and “Quantitative Finance,” which provide insights into cutting-edge research and applications. Additionally, I leverage online platforms like Coursera and edX to enroll in relevant courses that sharpen my skills and deepen my understanding of new techniques.

I also prioritize attending annual conferences and workshops, which not only offer exposure to innovative methodologies but also provide opportunities to engage with thought leaders and practitioners in the field. This engagement is complemented by active participation in professional forums and networks, such as the Quantitative Finance Professional Group on LinkedIn, where I can exchange ideas and discuss practical challenges with peers. By systematically integrating new knowledge into my existing framework, I ensure that my quantitative analysis remains robust, relevant, and aligned with the state-of-the-art in the field.”

15. Provide an instance where you had to adapt your analytical approach due to unexpected data limitations.

Agility in problem-solving is key when data is incomplete or unexpected. Candidates should be prepared to discuss how they handle such challenges while maintaining data integrity and accuracy.

When responding, recount a specific scenario where you encountered data limitations, emphasizing how you evaluated the situation, identified the constraints, and decided on an alternative approach. Outline the steps you took to ensure the new approach still provided valuable insights, and if possible, share the outcome of your analysis. This will illustrate your adaptability, problem-solving skills, and commitment to delivering results despite challenges.

Example: “ In a project where I was modeling customer churn, I encountered a situation where the historical data was far less comprehensive than initially anticipated. The data lacked granularity on customer interactions and product usage, which were critical for building a robust predictive model. I quickly realized that traditional regression techniques would not yield the predictive power needed due to the sparsity of data.

To adapt, I pivoted to a survival analysis approach, leveraging what we did have—customer tenure and churn events. This allowed me to model churn risk over time without the need for detailed interaction data. I also employed bootstrapping methods to enhance the stability of the model given the limited dataset. By focusing on the time-to-event aspect, I could still provide the business with meaningful insights into customer retention patterns and identify key time intervals for intervention. The outcome was a strategic shift in customer engagement, informed by the survival model, which ultimately led to a measurable reduction in churn.”

16. In your view, what is the biggest challenge facing quantitative analysts in the finance sector today?

The finance sector’s constant evolution requires quantitative analysts to be adaptable. Candidates should be ready to discuss how they integrate new data sources and technologies into their financial models.

When responding to this question, candidates should articulate their understanding of the current financial landscape, highlighting specific challenges such as the need for real-time data analysis, cybersecurity threats, or the implications of global economic events. They should also discuss their approach to continuous learning and adaptation, perhaps by mentioning their engagement with new analytical tools, professional development courses, or forums that discuss emerging trends in finance. Demonstrating awareness of these challenges and a proactive approach to overcoming them will show interviewers that the candidate is both informed and forward-thinking.

Example: “ The most pressing challenge for quantitative analysts in the finance sector today is the rapid evolution of machine learning and artificial intelligence technologies. These advancements are constantly reshaping the landscape of data analysis and predictive modeling. The integration of AI into quantitative finance not only requires analysts to maintain a robust understanding of new algorithms and computational methods but also to ensure that these tools are employed without introducing systemic risk. As financial markets become increasingly complex and automated, the potential for AI-driven strategies to create feedback loops or unforeseen market dynamics grows, necessitating a deep understanding of both the financial instruments involved and the underlying technology.

To navigate this challenge, I actively engage with the latest research and developments in AI and machine learning, applying a critical eye to how these innovations can be leveraged responsibly in financial contexts. This involves not only staying abreast of the technical aspects but also understanding the broader economic implications and regulatory frameworks. By maintaining a dialogue with industry peers through forums and professional development opportunities, I ensure that my approach to quantitative analysis is both cutting-edge and grounded in a risk-aware perspective.”

17. How do you balance the need for timely results with the rigor of thorough quantitative analysis?

Delivering accurate data analysis promptly is essential in quantitative roles. Candidates should be prepared to discuss how they balance the need for speed with the need for precision in their work.

When responding to this question, candidates should articulate a structured approach to quantitative tasks that showcases their time management skills. They could mention specific methodologies or tools they use to streamline analysis, such as automating repetitive tasks, employing statistical software, or breaking projects into phases to allow for preliminary insights to be shared in advance of full analysis completion. It’s also important to communicate an understanding of when depth is crucial versus when an executive summary will suffice, and to provide examples from past experiences where this balance was successfully achieved.

Example: “ Balancing timeliness with rigor in quantitative analysis is a matter of prioritizing efficiency without compromising on the integrity of the results. I employ a phased approach, where I initially focus on exploratory data analysis to quickly identify patterns, outliers, and potential areas of interest. This allows for the generation of preliminary insights that can guide subsequent, more detailed investigations. I leverage statistical software and scripting to automate routine data processing tasks, which significantly cuts down on the time required for data cleaning and manipulation.

When deeper analysis is necessary, I judiciously apply sampling techniques or model-based approaches to extrapolate findings without having to crunch every data point, which can be time-consuming. I’m always conscious of the trade-off between accuracy and speed, and I communicate these considerations transparently with stakeholders. For instance, in a past project involving predictive modeling, I used ensemble methods to quickly generate a robust model, then iteratively refined it as time allowed, ensuring that stakeholders had a functional tool at their disposal while I worked on enhancing its precision.”

18. Illustrate how you would conduct a cost-benefit analysis on implementing new technology within an organization.

Conducting a cost-benefit analysis requires strategic thinking. Candidates should be ready to discuss how they evaluate the tangible and intangible factors that contribute to an organization’s ROI.

To respond, you should outline a structured approach, starting with defining the scope of the analysis and identifying all associated costs, such as purchase price, implementation expenses, training, and maintenance. Next, articulate how you would measure the anticipated benefits, which might include increased efficiency, revenue growth, or improved customer satisfaction. Explain how you would consider the time value of money in your analysis, possibly utilizing net present value (NPV) or internal rate of return (IRR) calculations. Then, discuss how you would weigh these factors against each other, possibly including a scenario analysis to account for uncertainty. Finally, describe how you would present your findings, emphasizing clear communication of the potential risks and rewards to stakeholders.

Example: “ In conducting a cost-benefit analysis for new technology implementation, I would begin by meticulously defining the scope and identifying all relevant costs, including upfront capital expenditure, operational costs, and any indirect costs such as potential downtime during the transition. I would then forecast the tangible benefits, such as productivity gains and cost savings, as well as intangible benefits like customer satisfaction and competitive advantage, quantifying these where possible.

To assess the financial viability, I would calculate the net present value (NPV) of the project, ensuring that future cash flows are discounted appropriately to reflect the time value of money. I would also consider the internal rate of return (IRR) to understand the project’s profitability relative to the cost of capital. To address uncertainty, I would perform sensitivity and scenario analyses, varying key assumptions to gauge the robustness of the project under different conditions. My findings would be synthesized into a clear, data-driven recommendation, articulating the strategic rationale and potential impact on the organization’s bottom line, ensuring that decision-makers are fully informed of the risks and potential returns.”

19. What techniques do you apply to forecast demand for a product with little historical data available?

Forecasting demand with limited historical data is challenging. Candidates should be prepared to discuss their approach to making informed predictions in such scenarios.

When responding, emphasize your systematic approach to the problem. You might start by explaining how you gather qualitative insights from market research, expert opinions, and competitive analysis to form initial hypotheses. Then, detail how you use quantitative methods such as time series analysis, regression models, or even newer techniques like machine learning to extrapolate from available data. Highlight any specific tools or software you are proficient with, and describe a past scenario where you successfully forecasted under similar constraints, focusing on the process and the outcome.

Example: “ To forecast demand for a product with limited historical data, I employ a combination of qualitative assessments and exploratory quantitative methods. Initially, I gather insights through market research, including customer surveys, focus groups, and Delphi method sessions with industry experts to establish a foundational understanding of potential demand drivers and market dynamics. Concurrently, I perform a competitive analysis to benchmark against similar products or services, which can provide valuable clues about the market’s response to analogous offerings.

Quantitatively, I lean towards employing Bayesian methods that allow for the incorporation of prior knowledge and expert opinion into the statistical model, which is particularly useful when data is scarce. This approach, coupled with bootstrapping techniques, helps in constructing confidence intervals around forecasts even with small datasets. Additionally, I utilize machine learning algorithms, such as random forests or gradient boosting machines, which can capture complex nonlinear relationships and interactions between variables, even when historical data is not robust. Tools like R or Python, with their extensive libraries for statistical and machine learning methods, are instrumental in this process.

A specific instance where this approach proved successful was when forecasting the demand for a niche technological product entering a new market. By synthesizing market research insights with a Bayesian model that integrated expert assessments, I was able to provide a demand estimate that was later validated to be within 10% of actual sales in the first quarter post-launch. This accuracy was pivotal in optimizing the supply chain and marketing strategy, ultimately contributing to a successful product introduction.”

20. Tell us about a project where you leveraged network analysis to uncover insights into customer behavior.

Network analysis can reveal strategic business insights. Candidates should be ready to discuss how they use network analysis to inform decisions on marketing, product development, and customer engagement.

When responding to this question, candidates should outline a specific project they worked on, detailing the objectives, the nature of the data, the network analysis techniques employed, and the software or tools used. It’s crucial to articulate the unique customer behavior insights gained from the analysis and how these insights led to actionable strategies or decisions within the project. Quantify the impact whenever possible, such as increased customer retention rates or improved product recommendations, to underscore the value of your analytical contributions.

Example: “ In a recent project, the objective was to understand the interconnectedness of customers within a subscription-based service to identify influential users and predict churn. Utilizing a combination of transactional data and social interaction metrics, I constructed a weighted undirected graph where nodes represented customers and edges signified the frequency and quality of interactions between them.

Employing network analysis techniques such as centrality measures and community detection algorithms, I identified clusters of highly interconnected users and pinpointed those with high eigenvector centrality as potential influencers. These insights were instrumental in developing targeted retention strategies. By engaging these key influencers with personalized incentives, we observed a 15% reduction in churn rate within their respective clusters. Additionally, the analysis of structural holes within the network revealed opportunities for cross-selling, leading to a 10% increase in uptake of additional services among identified bridging users. The project leveraged Python’s NetworkX library for the analysis, which facilitated a robust and scalable examination of the network’s properties.”

Top 20 Product Strategy Interview Questions & Answers

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Quantitative Interview Techniques and Considerations

LEARNING OBJECTIVES

• Define and describe standardized interviews.
• Describe how quantitative interviews differ from qualitative interviews.
• Describe the process and some of the drawbacks of telephone interviewing techniques.
• Describe how the analysis of quantitative interview works.
• Identify the strengths and weaknesses of quantitative interviews.

Quantitative interviews are similar to qualitative interviews in that they involve some researcher/respondent interaction. But the process of conducting and analyzing findings from quantitative interviews also differs in several ways from that of qualitative interviews. Each approach also comes with its own unique set of strengths and weaknesses. We’ll explore those differences here.

• Conducting Quantitative Interviews
• Analysis of Quantitative Interview Data
• Strengths and Weaknesses of Quantitative Interviews
• Relevance, Balance, and Accessibility
• Different Sources of Knowledge
• Ontology and Epistemology KEY TAKEAWAYS EXERCISES
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• Design and Goals of This Text LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
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• Deductive Approaches and Some Examples
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• Some Specific Examples KEY TAKEAWAYS EXERCISES
• Feasibility
• Field Trip: Visit Your Library KEY TAKEAWAYS EXERCISES
• Exploration, Description, Explanation
• Idiographic or Nomothetic?
• Applied or Basic? KEY TAKEAWAYS EXERCISES
• Units of Analysis and Units of Observation
• Hypotheses KEY TAKEAWAYS EXERCISES
• Triangulation LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Searching for Literature
• Reviewing the Literature
• Additional Important Components KEY TAKEAWAYS EXERCISES
• What Do Social Scientists Measure?
• How Do Social Scientists Measure? KEY TAKEAWAYS EXERCISE
• Concepts and Conceptualization
• A Word of Caution About Conceptualization KEY TAKEAWAYS EXERCISES
• Putting It All Together KEY TAKEAWAYS EXERCISE
• Reliability
• Validity KEY TAKEAWAYS EXERCISES
• Levels of Measurement
• Indexes, Scales, and Typologies KEY TAKEAWAYS EXERCISES
• Populations Versus Samples LEARNING OBJECTIVE KEY TAKEAWAYS EXERCISES
• Nonprobability Sampling
• Types of Nonprobability Samples KEY TAKEAWAYS EXERCISES
• Probability Sampling
• Types of Probability Samples KEY TAKEAWAYS EXERCISES
• Who Sampled, How Sampled, and for What Purpose? KEY TAKEAWAYS EXERCISES
• Survey Research: What Is It and When Should It Be Used? LEARNING OBJECTIVES KEY TAKEAWAY EXERCISE
• Strengths of Survey Method
• Weaknesses of Survey Method KEY TAKEAWAYS EXERCISES
• Administration KEY TAKEAWAYS EXERCISES
• Asking Effective Questions
• Response Options
• Designing Questionnaires KEY TAKEAWAYS EXERCISES
• From Completed Questionnaires to Analyzable Data
• Identifying Patterns KEY TAKEAWAYS EXERCISES
• Interview Research: What Is It and When Should It Be Used? LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
• Conducting Qualitative Interviews
• Analysis of Qualitative Interview Data
• Strengths and Weaknesses of Qualitative Interviews KEY TAKEAWAYS EXERCISES
• Strengths and Weaknesses of Quantitative Interviews KEY TAKEAWAYS EXERCISES
• Location, Location, Location
• Researcher-Respondent Relationship KEY TAKEAWAYS EXERCISES
• Field Research: What Is It and When to Use It? LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Strengths of Field Research
• Weaknesses of Field Research KEY TAKEAWAYS EXERCISES
• Choosing a Site
• Choosing a Role KEY TAKEAWAYS EXERCISES
• Writing in the Field
• Writing out of the Field KEY TAKEAWAYS EXERCISE
• From Description to Analysis KEY TAKEAWAYS EXERCISE
• Unobtrusive Research: What Is It and When to Use It? LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Strengths of Unobtrusive Research
• Weaknesses of Unobtrusive Research KEY TAKEAWAYS EXERCISES
• Content Analysis
• Indirect Measures
• Analysis of Unobtrusive Data Collected by You KEY TAKEAWAYS EXERCISES
• Analyzing Others’ Data LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Reliability in Unobtrusive Research LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
• Focus Groups LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Experiments LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Ethnomethodology and Conversation Analysis LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
• Sharing It All: The Good, the Bad, and the Ugly
• Knowing Your Audience KEY TAKEAWAYS EXERCISE
• Presenting Your Research LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Writing Up Research Results LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Disseminating Findings LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Reading Reports of Sociological Research LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Being a Responsible Consumer of Research LEARNING OBJECTIVE KEY TAKEAWAY EXERCISE
• Media Reports of Sociological Research LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Sociological Research: It’s Everywhere LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
• Evaluation Research
• Market Research
• Policy and Other Government Research KEY TAKEAWAY EXERCISE
• Doing Research for a Cause LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
• Public Sociology LEARNING OBJECTIVE KEY TAKEAWAYS EXERCISES
• Transferable Skills
• Understanding Yourself, Your Circumstances, and Your World KEY TAKEAWAYS EXERCISES
•  Back Matter

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Jessie Ball duPont Library

Quantitative and qualitative interviewing, overview of quantitative research methods, questionnaire/survey methods, quantitative analysis, suggested books for further research.

• What are Quantitative Methods?
• Qualitative Interviewing
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Common Quantitative Terms

• ANOVA (Analysis of Variance)
• Experimental Design
• Quasi-Experimental Design
• Pre-Testing
• Small n Design
• Random Control Trial
• Correlational Design
• Descriptive Statistics
• Partial Correlaton
• Linear Regression
• Repeated Measures ANOVA
• Effect Size
• Overview Video Broad-based overview of the quantitative process, not limited to interviews
• Using Structured Interview Techniques In-depth document covering every aspect of the Quantitative Interview

Survey - data collection tool for gather information from a group of individuals. Data can be factual or opinions. Administration options includes structured interview or participant/respondent completing survey on their own.  

• Questionnaire (face-to-face or telephone) - structured questions for individuals to obtain information
• Market survey
• Explanation of Questionnaires/surveys
• Example of the results of a Questionnaire/survey 
• Using EXCEL How do you analyze your quantitative data? You might try using Microsoft EXCEL.

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4.5 Data Collection Methods

Choosing the most appropriate and practical data collection method is an important decision that must be made carefully. It is important to recognise that the quality of data collected in a qualitative manner is a direct reflection of the skill and competence of the researcher. Advanced interpersonal skills are required, especially the ability to accurately interpret and respond to subtle participant behavior in a variety of situations. Interviews, focus groups and observations are the primary methods of data collection used in qualitative healthcare research (Figure 4.7). 62

Interviews can be used to explore individual participants’ views, experiences, beliefs and motivations. There are three fundamental types of research interviews: structured, semi-structured and unstructured.

Structured interviews, also known as standardised open-ended interviews, are carefully prepared ahead of time, and each participant is asked the same question in a certain sequence. 63 A structured interview is essentially an oral questionnaire in which a pre-determined list of questions is asked, with little or no variation and no room for follow-up questions to answers that require further clarification. 63 Structured interviews are relatively quick and easy to develop and use and are especially useful when you need clarification on a specific question. 63 However, by its very nature, it allows only a limited number of participant responses, so it is of little use if “depth” is desired. This approach resists improvisation and the pursuit of intuition but can promote consistency among participants. 63

Semi-structured interviews, also known as the general interview guide approach, include an outline of questions to ensure that all pertinent topics are covered. 63 A semi-structured interview consists of a few key questions that help define the area to be explored but also allow the interviewer or respondent to diverge and explore ideas or responses in more detail. 64 This interview format is used most frequently in healthcare, as it provides participants with some guidance about what to talk about. The flexibility of this approach, especially when compared to structured interviews, is that it allows participants to discover or refine important information that may not have been previously considered relevant by the research team. 63

Unstructured interviews, also known as informal conversational interviews, consist of questions that are spontaneously generated in the natural flow of conversation, reflect no preconceptions or ideas, and have little or no organisation. 65 Such conversations can easily start with an opening question such as, “Can you tell me about your experience at the clinic?” It then proceeds primarily based on the initial response. Unstructured interviews tend to be very lengthy (often hours), lack pre-set interview questions, and provide little guidance on what to talk about, which can be difficult for participants. 63

As a result, they are often considered only when great “depth” is required, little is known about the subject, or another viewpoint on a known issue is requested. 63 Significant freedom in unstructured interviews allows for more intuitive and spontaneous exchanges between the researcher and the participants. 63. .

Advantages and Disadvantages

Interviews can be conducted via Phone, Face-to-Face or Online, depending on participants’ preferences and availability. Often participants are flattered to be asked and they make the time to speak with you and they reward you with candour. 66 Usually, interviews provide flexibility to schedule sessions at the convenience of the interviewees. 66 It also provides less observer or participant bias as other participants’ experiences or opinions do not influence the interviewee. Interviews also provide enough talk time for interviewees and spare them from spending time listening to others. Additionally, the interviewer can observe the non-verbal behaviour of the interviewee and potentially record it as data. 66

Interviews also have inherent weaknesses. Conducting interviews can be very costly and time-consuming. 66 Interviews also provide less anonymity, which is usually a major concern for many respondents. 66 Nonetheless, qualitative interviews can be a valuable tool to help uncover meaning and understanding of phenomena. 66

With your understanding of interviews, watch this video clip and identify what you would do differently and provide your thoughts in the Padlet below.

Now watch the video clip below to see how a good interview should be conducted

After watching the video, reflect on the responses you provided in the Padlet and consider if there is anything you may have missed out or need to revise.

Focus group

Focus groups are group interviews that explore participants’ knowledge and experiences and how and why individuals act in various ways. 67   This method involves bringing a small group together to discuss a specific topic or issue. The groups typically include 6-8 participants and are conducted by an experienced moderator who follows a topic guide or interview guide. 67 The conversations can be audio or videotaped and then transcribed, depending on the researchers’ and participants’ preferences. In addition, focus groups can include an observer who records nonverbal parts of the encounter, potentially with the help of an observation guide. 67

Advantages and disadvantages

Focus groups effectively bring together homogenous groups of people with relevant expertise and experience on a specific issue and can offer comprehensive information. 67 They are often used to gather information about group dynamics, attitudes, and perceptions and can provide a rich source of data. 67

Disadvantages include less control over the process and a lower level of participation by each individual. 67 Also, focus group moderators, as well as those responsible for data processing, require prior experience. Focus groups are less suitable for discussing sensitive themes as some participants may be reluctant to express their opinions in a group environment. 67 Furthermore, it is important to watch for the creation of “groupthink” or dominance of certain group members, as group dynamics and social dynamics can influence focus groups. 67

Observation

Observations involve the researcher observing and recording the behaviour and interactions of individuals or groups in a natural setting. 67 Observations are especially valuable for gaining insights about a specific situation and real behaviour. They can be participant (the researcher participates in the activity) or non-participant (the researcher observes from a distance) in nature. 67 The observer in participant observations is a member of the observed context, such as a nurse working in an intensive care unit. The observer is “on the outside looking in” in non-participant observations, i.e. present but not a part of the scenario, attempting not to impact the environment by their presence. 67 During the observation, the observer notes everything or specific elements of what is happening around them, such as physician-patient interactions or communication between different professional groups. 67

The advantage of performing observations includes reducing the gap between the researcher and the study. Issues may be found that the researcher was unaware of and are relevant in gaining a greater understanding of the research. 67 However, observation can be time-consuming, as the researcher may need to observe the behaviour or interactions for an extended period to collect enough data. In addition, they can be influenced by the researcher’s biases, which can affect the accuracy and validity of the data collected. 68

An Introduction to Research Methods for Undergraduate Health Profession Students Copyright © 2023 by Faith Alele and Bunmi Malau-Aduli is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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9.3: Quantitative Interview Techniques and Considerations

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Learning Objectives

• Define and describe standardized interviews.
• Describe how quantitative interviews differ from qualitative interviews.
• Describe the process and some of the drawbacks of telephone interviewing techniques.
• Describe how the analysis of quantitative interview works.
• Identify the strengths and weaknesses of quantitative interviews.

Quantitative interviews are similar to qualitative interviews in that they involve some researcher/respondent interaction. But the process of conducting and analyzing findings from quantitative interviews also differs in several ways from that of qualitative interviews. Each approach also comes with its own unique set of strengths and weaknesses. We’ll explore those differences here.

Conducting Quantitative Interviews

Much of what we learned in the previous chapter on survey research applies to quantitative interviews as well. In fact, quantitative interviews are sometimes referred to as survey interviews because they resemble survey-style question-and-answer formats. They might also be called standardized interviews . The difference between surveys and standardized interviews is that questions and answer options are read to respondents rather than having respondents complete a questionnaire on their own. As with questionnaires, the questions posed in a standardized interview tend to be closed ended.See Chapter 8 for the definition of closed ended.There are instances in which a quantitative interviewer might pose a few open-ended questions as well. In these cases, the coding process works somewhat differently than coding in-depth interview data. We’ll describe this process in the following subsection.

In quantitative interviews, an interview schedule is used to guide the researcher as he or she poses questions and answer options to respondents. An interview schedule is usually more rigid than an interview guide. It contains the list of questions and answer options that the researcher will read to respondents. Whereas qualitative researchers emphasize respondents’ roles in helping to determine how an interview progresses, in a quantitative interview, consistency in the way that questions and answer options are presented is very important. The aim is to pose every question-and-answer option in the very same way to every respondent. This is done to minimize interviewer effect , or possible changes in the way an interviewee responds based on how or when questions and answer options are presented by the interviewer.

Quantitative interviews may be recorded, but because questions tend to be closed ended, taking notes during the interview is less disruptive than it can be during a qualitative interview. If a quantitative interview contains open-ended questions, however, recording the interview is advised. It may also be helpful to record quantitative interviews if a researcher wishes to assess possible interview effect. Noticeable differences in responses might be more attributable to interviewer effect than to any real respondent differences. Having a recording of the interview can help a researcher make such determinations.

Quantitative interviewers are usually more concerned with gathering data from a large, representative sample. As you might imagine, collecting data from many people via interviews can be quite laborious. Technological advances in telephone interviewing procedures can assist quantitative interviewers in this process. One concern about telephone interviewing is that fewer and fewer people list their telephone numbers these days, but random digit dialing (RDD) takes care of this problem. RDD programs dial randomly generated phone numbers for researchers conducting phone interviews. This means that unlisted numbers are as likely to be included in a sample as listed numbers (though, having used this software for quantitative interviewing myself, I will add that folks with unlisted numbers are not always very pleased to receive calls from unknown researchers). Computer-assisted telephone interviewing (CATI) programs have also been developed to assist quantitative survey researchers. These programs allow an interviewer to enter responses directly into a computer as they are provided, thus saving hours of time that would otherwise have to be spent entering data into an analysis program by hand.

Conducting quantitative interviews over the phone does not come without some drawbacks. Aside from the obvious problem that not everyone has a phone, research shows that phone interviews generate more fence-sitters than in-person interviews (Holbrook, Green, & Krosnick, 2003).Holbrook, A. L., Green, M. C., & Krosnick, J. A. (2003). Telephone versus face-to-face interviewing of national probability samples with long questionnaires: Comparisons of respondent satisficing and social desirability response bias. Public Opinion Quarterly, 67 , 79–125. Responses to sensitive questions or those that respondents view as invasive are also generally less accurate when data are collected over the phone as compared to when they are collected in person. I can vouch for this latter point from personal experience. While conducting quantitative telephone interviews when I worked at a research firm, it was not terribly uncommon for respondents to tell me that they were green or purple when I asked them to report their racial identity.

Analysis of Quantitative Interview Data

As with the analysis of survey data, analysis of quantitative interview data usually involves coding response options numerically, entering numeric responses into a data analysis computer program, and then running various statistical commands to identify patterns across responses. Section 8.5 of Chapter 8 describes the coding process for quantitative data. But what happens when quantitative interviews ask open-ended questions? In this case, responses are typically numerically coded, just as closed-ended questions are, but the process is a little more complex than simply giving a “no” a label of 0 and a “yes” a label of 1.

In some cases, quantitatively coding open-ended interview questions may work inductively, as described in Section 9.2.2 "Analysis of Qualitative Interview Data" . If this is the case, rather than ending with codes, descriptions of codes, and interview excerpts, the researcher will assign a numerical value to codes and may not utilize verbatim excerpts from interviews in later reports of results. Keep in mind, as described in Chapter 1, that with quantitative methods the aim is to be able to represent and condense data into numbers. The quantitative coding of open-ended interview questions is often a deductive process. The researcher may begin with an idea about likely responses to his or her open-ended questions and assign a numerical value to each likely response. Then the researcher will review participants’ open-ended responses and assign the numerical value that most closely matches the value of his or her expected response.

Strengths and Weaknesses of Quantitative Interviews

Quantitative interviews offer several benefits. The strengths and weakness of quantitative interviews tend to be couched in comparison to those of administering hard copy questionnaires. For example, response rates tend to be higher with interviews than with mailed questionnaires (Babbie, 2010).Babbie, E. (2010). The practice of social research (12th ed.). Belmont, CA: Wadsworth. That makes sense—don’t you find it easier to say no to a piece of paper than to a person? Quantitative interviews can also help reduce respondent confusion. If a respondent is unsure about the meaning of a question or answer option on a questionnaire, he or she probably won’t have the opportunity to get clarification from the researcher. An interview, on the other hand, gives the researcher an opportunity to clarify or explain any items that may be confusing.

As with every method of data collection we’ve discussed, there are also drawbacks to conducting quantitative interviews. Perhaps the largest, and of most concern to quantitative researchers, is interviewer effect. While questions on hard copy questionnaires may create an impression based on the way they are presented, having a person administer questions introduces a slew of additional variables that might influence a respondent. As I’ve said, consistency is key with quantitative data collection—and human beings are not necessarily known for their consistency. Interviewing respondents is also much more time consuming and expensive than mailing questionnaires. Thus quantitative researchers may opt for written questionnaires over interviews on the grounds that they will be able to reach a large sample at a much lower cost than were they to interact personally with each and every respondent.

KEY TAKEAWAYS

• Unlike qualitative interviews, quantitative interviews usually contain closed-ended questions that are delivered in the same format and same order to every respondent.
• Quantitative interview data are analyzed by assigning a numerical value to participants’ responses.
• While quantitative interviews offer several advantages over self-administered questionnaires such as higher response rates and lower respondent confusion, they have the drawbacks of possible interviewer effect and greater time and expense.
• The General Social Survey (GSS), which we’ve mentioned in previous chapters, is administered via in-person interview, just like quantitative interviewing procedures described here. Read more about the GSS at http://www.norc.uchicago.edu/GSS+Website .
• Take a few of the open-ended questions you created after reading Section 9.2 on qualitative interviewing techniques. See if you can turn them into closed-ended questions.

Quantitative Research Interview questions: Why they're important, and what to ask

In this blog post, we're sharing potential interview questions for assessing quantitative research candidates. These questions are designed to reveal analytical abilities, problem-solving skills, and suitability for roles where data analysis takes center stage.

Interview question 1: What statistical software or programming languages are you proficient in, and can you provide examples of projects where you've used them?

 Why this question is important: This question evaluates a candidate’s fit with the role's requirements. It also assesses adaptability, verifies resume claims (this is huge!), and gauges their ability to communicate technical concepts effectively. Look for answers that can explain multiple languages in depth, and can give realistic answers of how they’re applied.

Example answer: " I am experienced in Python, R, and MATLAB. In my previous role, I used Python for risk assessment, analyzing financial data. R was essential for predictive modeling and visualization of customer behavior. For engineering projects, I relied on MATLAB to design control algorithms, like the one for an autonomous vehicle prototype. I also have experience with SQL and distributed computing frameworks like ApacheSpark, making me well-equipped to tackle quantitative engineering challenges."

Interview question 2: How would you handle missing data in a dataset you're analyzing?

 Why this question is important: Asking quantitative research candidates about handling missing data is crucial because it assesses their technical competence, problem-solving skills, and understanding of quality assurance. Their response reveals if they can produce reliable results and adapt to specific project needs. Additionally, it highlights their commitment to ethics and transparency.- which is essential in data roles.

Example answer: “I would handle missing data in a dataset through careful assessment of the missing data pattern. If it's random or related to other variables, I'd consider imputation methods like mean, median, or regression-based imputation. If data loss is minimal, I'd consider deletion methods. I'd document my approach and conduct sensitivity analysis to validate the results, prioritizing accuracy and transparency.”

Interview question 3: Can you describe your experience with time series analysis and forecasting?

‍ Why this question is important: Asking quantitative researchers about their experience with time series analysis and forecasting  provides insights into a candidate's ability to analyze historical data, identify patterns, and make future predictions - demonstrating their proficiency in quantitative techniques.

Additionally, this question allows interviewers to assess a candidate's adaptability. Methods and tools for time series analysis and forecasting are constantly evolving, making it vital for engineers to stay up-to-date with the latest approaches and technologies.

Example answer: Answers here may vary, but look for responses that mention historical data, applying neural networks, and model evaluation. Also look for answers that address risk assessment and portfolio optimization – as this indicates that candidates make strong data-driven decisions.  

Interview question 4: Walk me through a quantitative research project you worked on from start to finish. What was the problem, and how did you approach it?

 Why this question is important: It's crucial for a quantitative researcher to answer the question about a past research project because it demonstrates their ability to apply their quantitative skills in a practical context. By outlining the project's problem, approach, and execution from start to finish, the candidate showcases their problem-solving capabilities and clear-headed thinking. Additionally, it offers the interviewer a clear understanding of the candidate's research process, which is crucial to know when evaluating fit.

Example answer: " I recently worked on a quantitative research project focused on analyzing customer churn in a subscription-based service. The problem was declining customer retention rates which was ultimately impacting company revenue. I started by gathering historical data, conducting exploratory data analysis to identify key factors influencing churn, and then built predictive models using logistic regression and decision trees. After analyzing the results, we successfully reduced churn rates by 15% within six months."  

Interview question 5: Tell me about a time when you encountered unexpected results in your analysis. How did you handle it, and what did you learn from the experience?

 Why this question is important: Things are always going to go wrong at some point in every job, but how you address it makes all the difference! Their response to this question showcases their analytical and critical-thinking skills and highlights how they manage unexpected findings, which can still lead to new insights or the refinement of research methodologies.

Example answer: "During a research project analyzing the impact of advertising campaigns on product sales, I encountered unexpected results when one campaign that was anticipated to have a significant positive effect showed a negative impact instead. To address this, I dove deeper into the data and discovered a data quality issue in the advertising spend records. I corrected the discrepancies, re-ran the analysis, and found that the campaign indeed had a positive effect, aligning with expectations.This experience taught me the importance of thorough data validation and the potential for data quality issues to lead to misleading conclusions in quantitative research."

These interview questions should help you assess a candidate's technical proficiency, problem-solving abilities, and their ability to work effectively in a quantitative research role. Learn more about Quantitative Research roles here .

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Qualitative Interviews vs. Quantitative Interviews: Which type for Deeper JTBD insights?

Published: 14 July, 2023

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Qualitative and quantitative interviews play a vital role in research and market analysis, providing invaluable insights into the intricate dynamics of Jobs To Be Done (JTBD) . Digital Leadership, a renowned digital strategy and execution firm, firmly believes in the transformative potential of emerging technologies and innovative business models to enhance customer service. This comprehensive article delves into the realms of qualitative and quantitative interviews, shedding light on their respective roles in unearthing profound JTBD insights job mapping , and solution design . By comprehending the power and significance of qualitative interviews in research, we can unlock invaluable JTBD insights that drive customer satisfaction, inform job mapping, and facilitate solution design, ultimately fostering heightened engagement.

Qualitative Interviews

Qualitative interviews, including qualitative research interviews, research interviews, qualitative research method interviewing, qualitative research techniques, and focus groups, are valuable for understanding the subjective experiences, perceptions, and behaviours of individuals. They gather detailed insights into experiences, perspectives, and behaviours, uncovering rich and diverse perspectives often overlooked by quantitative methods.

Qualitative interviews Definition

Qualitative interviews, as a form of customer research, are valuable for understanding the subjective experiences, perceptions, and behaviours of individuals. They provide rich and nuanced data that goes beyond surface-level understanding, enabling effective data interpretation. These interviews help researchers construct a deeper understanding of customers and their needs, informing business strategies and enhancing customer satisfaction.

By exploring the “why” behind customer actions, qualitative interviews uncover underlying motivations and contextual factors that influence their decision-making. They provide insights into not only the functional value of products or services but also the emotional and psychological aspects that drive customer behaviour. Overall, qualitative interviews contribute to informed decision-making by offering comprehensive insights into customers’ perspectives and enhancing the interpretation of collected data.

Types of Qualitative Interviews

To effectively conduct qualitative interviews, researchers must be aware of various interview types and their respective applications.

• Semi-Structured Interviews

Semi-structured interviews combine predefined questions with flexibility for probing and exploring emerging themes. They offer a balance between standardized data collection and the opportunity for participants to elaborate on their experiences and perspectives.

• Unstructured Interviews

Unstructured interviews provide participants with the freedom to express themselves without predefined questions. This approach allows for a more open-ended exploration of their experiences, opinions, and emotions, enabling deeper insights into their JTBD .

• In-Depth Interviews

In-depth interviews focus on exploring a specific topic or phenomenon in great detail. Researchers engage in in-depth conversations with participants, aiming to uncover underlying motivations, beliefs, and experiences related to the JTBD. These interviews provide comprehensive insights into individual perspectives.

Advantages and Disadvantages of Qualitative Interview

Importance of understanding the customer criteria using qualitative research in gathering insights for jtbd.

In the realm of Jobs-to-be-Done (JTBD) research , qualitative research plays a pivotal role in uncovering subjective perspectives and gaining an in-depth understanding of customer criteria. The use of interviews, coupled with effective interview techniques, is particularly valuable in capturing the nuanced insights necessary to understand customers’ JTBD and foster customer centricity .

Here are three main types of criteria to look out for:

• Functional criteria : Needing more or less of something, e.g., faster, simpler, cheaper etc.
• Emotional criteria : Fulfilling needs like reduced stress, increased safety, comfort, personal mastery or accomplishment
• Social criteria : Increased trust, openness, willingness to participate or connection

For comprehensive guidance on understanding customer criteria and qualitative research for JTBD, we recommend our latest book, “ How to Create Innovation .” This comprehensive resource delves into Jobs to be done and customer mindsets,  organizational structures , and strategies that empower you to innovate efficiently achieving maximum success by optimizing resource utilization.

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Let’s explore the key reasons why qualitative research and effective interview techniques are essential in this context:

• Uncovering Subjective Perspectives: JTBD research aims to understand customers’ underlying motivations, needs, and desired outcomes. Qualitative research, such as interviews, allows researchers to delve into the subjective perspectives of customers. By engaging in open-ended conversations, researchers can explore the reasons behind customers’ choices, experiences, and decision-making processes. This qualitative approach provides valuable insights into the emotional and psychological dimensions of customers’ JTBD.
• Probing for In-Depth Understanding: Effective interview techniques enable researchers to probe and explore customers’ experiences and perspectives in greater detail. Through probing, researchers can uncover the underlying drivers behind customers’ JTBD, gaining a deeper understanding of their desired outcomes, pain points, and unmet needs. By asking follow-up questions and encouraging customers to elaborate on their experiences, researchers can extract richer and more comprehensive insights.
• Contextual Understanding: Qualitative research allows researchers to capture the contextual information that shapes customers’ JTBD. By conducting interviews, researchers can explore the situational, social, and environmental factors that influence customers’ choices and behaviors. Understanding the context in which customers’ JTBD occur is crucial for developing effective strategies and solutions that address their unique circumstances.
• Uncovering Unconscious Needs: Effective interview techniques can help uncover customers’ unconscious or latent needs. Through open dialogue and probing, researchers can go beyond customers’ stated preferences and tap into their underlying desires and aspirations. This deeper exploration allows for the discovery of unmet needs that customers may not be fully aware of or unable to articulate explicitly. Such insights are invaluable for identifying innovative solutions and creating products or services that truly resonate with customers, leading to value creation .
• Iterative Learning and Improvement: Qualitative research, including interviews, allows for iterative learning and improvement. By conducting interviews with customers at different stages of the JTBD journey, researchers can gather feedback and refine their understanding. This iterative approach helps researchers uncover evolving patterns and emerging themes, leading to continuous learning and the ability to adapt strategies and solutions accordingly.

Understanding customer criteria is a crucial aspect of any business strategy aimed at delivering exceptional products and services. The Unite Jobs-to-be-Done template for defining customer criteria holds immense importance in the realm of quantitative interviews within the Jobs-to-be-Done (JTBD) framework. It allows us to uncover valuable insights into customer motivations and preferences, enabling us to optimize our product development, marketing strategies, and overall customer experience. you can download it now.

Your download is now available!

You can now access the complete Jobs to Be Done Package, including a full presentation, related models and instructions for use.

The UNITE Jobs-To-Be-Done Statement & Map

Steps to conduct a qualitative interview.

To conduct effective qualitative interviews, researchers must follow a systematic approach that ensures reliable and meaningful data collection.

• Developing Interview Questions

Crafting well-designed interview questions is crucial for obtaining the desired insights. Researchers should carefully consider the JTBD, research objectives, and the specific information they seek to gather. Open-ended questions that encourage participants to share detailed accounts and personal experiences are particularly effective.

• Selecting Participants and Sample Size

Selecting participants who possess relevant experiences and perspectives is essential to ensure the interview findings are representative and insightful. Researchers must consider demographic factors, Jobs to be done (JTBD) relevance, and diversity within the sample. The sample size should be determined based on data saturation, which occurs when additional interviews do not yield new insights.

• Interview Techniques and Approaches

Researchers employ various techniques and approaches during qualitative interviews to create a comfortable and engaging environment for participants. Active listening, effective probing, and empathy contribute to building rapport and trust, facilitating open and honest responses. Researchers should adopt a flexible interview style that adapts to participants’ preferences and communication styles.

Quantitative Interview

Quantitative interviews are a research method that utilizes structured questioning techniques to collect numerical data from participants. Unlike qualitative research which focuses on subjective understanding, quantitative interviews aim to measure specific variables, attitudes, opinions, or behaviours within a given population.

Quantitative interviews definition

Quantitative interviews refer to a research method that utilizes structured questioning techniques to collect numerical data from participants. In quantitative interviews, researchers ask standardized questions with predetermined response options, aiming to measure specific variables, attitudes, opinions, or behaviours within a given population. The data collected from quantitative interviews is typically analyzed using statistical techniques to identify patterns, relationships, and trends. Unlike qualitative interviews which emphasize subjective understanding and in-depth exploration, quantitative interviews focus on gathering data that can be quantified and analyzed statistically.

Q uantitative Interviews Types

Q uantitative interviews encompass various types that researchers can utilize depending on their research objectives and the nature of the data they seek to collect. Some common types of quantitative interviews include:

• Structured Interviews: Structured interviews involve pre-determined questions with fixed response options. Researchers follow a standardized protocol, asking the same set of questions to all participants. This method ensures consistency and comparability of data across respondents.
• Semi-Structured Interviews: Semi-structured interviews combine structured and open-ended questions. Researchers have a set of predetermined questions but also have the flexibility to probe further or ask follow-up questions based on participants’ responses. This approach allows for some level of exploration while maintaining a systematic framework.
• Phone or Online Surveys: Phone or online surveys involve conducting interviews remotely via telephone or online platforms. Researchers administer questionnaires to participants and collect their responses electronically. This method offers convenience and accessibility, enabling researchers to reach a large number of participants efficiently.
• Panel Interviews: Panel interviews involve conducting multiple interviews with the same group of participants over time. This longitudinal approach allows researchers to collect data at different points, observe changes, and track trends within the panel. It is particularly useful for studying phenomena that evolve over time.

Q uantitative Interviews Advantages and Disadvantages

Conducting Effective Quantitative Interviews

Conducting effective quantitative interviews requires attention to various factors to ensure the reliability and validity of the data collected. Some key considerations include:

• Designing a Structured Questionnaire: Developing a clear and well-structured questionnaire with appropriate question types, response options, and skip patterns is crucial. The questionnaire should be designed to elicit accurate and consistent responses from participants.
• Sampling Techniques: Selecting an appropriate sample is essential for the generalizability of quantitative interview findings. Researchers should employ random or representative sampling techniques to ensure that the selected participants represent the target population of interest.
• Standardizing Interview Administration: Maintaining consistency in interview administration is vital to minimize interviewer bias and ensure data integrity. Interviewers should receive proper training to follow standardized protocols, clarify any participant questions, and avoid introducing unintended influences.
• Data Management and Analysis: Collecting quantitative interview data requires proper data management and analysis. Researchers should ensure accurate data entry, validation, and cleaning processes. Statistical analysis techniques can then be employed to derive meaningful insights from the data.
• Ethical Considerations: Researchers must adhere to ethical guidelines when conducting quantitative interviews. Informed consent should be obtained from participants, and their privacy and confidentiality should be respected throughout the research process.

Qualitative Interviews Vs Quantitative Interviews for Jobs To Be Done Insights

Understanding customer needs and motivations is essential for any business seeking to create products and services that truly resonate with their target audience. Both qualitative and quantitative interviews play a pivotal role in uncovering the Job Statement in JTBD research. The Unite Jobs-to-be-Done (JTBD) Job Statement model, holds immense importance in both qualitative and quantitative interviews within the context of JTBD research. This model serves as a powerful tool for understanding customer needs, motivations, and desired outcomes when trying to accomplish specific jobs in their lives. you can now download it.

You can now access the complete Jobs to Be Done Customer’s Job Statement Package, including a full presentation, related models and instructions for use.

When conducting research to understand customer needs and preferences, two primary interview approaches come into play: Quantitative Interviews and Qualitative Interviews. Each method brings unique strengths and serves distinct purposes in the pursuit of gathering valuable insights. The table below provides a concise comparison between the two interview types:

Both qualitative and quantitative interviews have distinct advantages in uncovering deeper insights into Jobs-To-Be-Done (JTBD) . In JTBD research, qualitative interviews are especially important for capturing rich contextual information, exploring customers’ emotions and motivations, and uncovering hidden needs, they provide a comprehensive understanding of individual perspectives and contribute to informed decision-making. While Quantitative interviews offer the advantage of large sample sizes, statistical analysis, and generalizability, they provide a broader understanding of customer preferences, behaviours, and market trends.

Integrating qualitative and quantitative research methods is crucial for a holistic JTBD analysis. By combining these approaches, organizations can triangulate findings, validate qualitative insights, and make data-driven decisions. The importance of each type depends on the research objectives and the depth of insights required. It is essential to recognize the strengths of both approaches and employ them in a balanced manner to gain a comprehensive understanding of underserved customer needs and behaviors in the JTBD context.

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9.3 Quantitative Interview Techniques and Considerations

Learning objectives.

• Define and describe standardized interviews.
• Describe how quantitative interviews differ from qualitative interviews.
• Describe the process and some of the drawbacks of telephone interviewing techniques.
• Describe how the analysis of quantitative interview works.
• Identify the strengths and weaknesses of quantitative interviews.

Quantitative interviews are similar to qualitative interviews in that they involve some researcher/respondent interaction. But the process of conducting and analyzing findings from quantitative interviews also differs in several ways from that of qualitative interviews. Each approach also comes with its own unique set of strengths and weaknesses. We’ll explore those differences here.

Conducting Quantitative Interviews

Much of what we learned in the previous chapter on survey research applies to quantitative interviews as well. In fact, quantitative interviews are sometimes referred to as survey interviews because they resemble survey-style question-and-answer formats. They might also be called standardized interviews Interviews during which the same questions are asked of every participant in the same way, and survey-style question-and-answer formats are utilized. . The difference between surveys and standardized interviews is that questions and answer options are read to respondents rather than having respondents complete a questionnaire on their own. As with questionnaires, the questions posed in a standardized interview tend to be closed ended. See Chapter 8 "Survey Research: A Quantitative Technique" for the definition of closed ended. There are instances in which a quantitative interviewer might pose a few open-ended questions as well. In these cases, the coding process works somewhat differently than coding in-depth interview data. We’ll describe this process in the following subsection.

In quantitative interviews, an interview schedule A document containing the list of questions and answer options that quantitative interviewers read to respondents. is used to guide the researcher as he or she poses questions and answer options to respondents. An interview schedule is usually more rigid than an interview guide. It contains the list of questions and answer options that the researcher will read to respondents. Whereas qualitative researchers emphasize respondents’ roles in helping to determine how an interview progresses, in a quantitative interview, consistency in the way that questions and answer options are presented is very important. The aim is to pose every question-and-answer option in the very same way to every respondent. This is done to minimize interviewer effect Occurs when an interviewee is influenced by how or when questions and answer options are presented by an interviewer. , or possible changes in the way an interviewee responds based on how or when questions and answer options are presented by the interviewer.

Quantitative interviews may be recorded, but because questions tend to be closed ended, taking notes during the interview is less disruptive than it can be during a qualitative interview. If a quantitative interview contains open-ended questions, however, recording the interview is advised. It may also be helpful to record quantitative interviews if a researcher wishes to assess possible interview effect. Noticeable differences in responses might be more attributable to interviewer effect than to any real respondent differences. Having a recording of the interview can help a researcher make such determinations.

Quantitative interviewers are usually more concerned with gathering data from a large, representative sample. As you might imagine, collecting data from many people via interviews can be quite laborious. Technological advances in telephone interviewing procedures can assist quantitative interviewers in this process. One concern about telephone interviewing is that fewer and fewer people list their telephone numbers these days, but random digit dialing (RDD) takes care of this problem. RDD programs dial randomly generated phone numbers for researchers conducting phone interviews. This means that unlisted numbers are as likely to be included in a sample as listed numbers (though, having used this software for quantitative interviewing myself, I will add that folks with unlisted numbers are not always very pleased to receive calls from unknown researchers). Computer-assisted telephone interviewing (CATI) programs have also been developed to assist quantitative survey researchers. These programs allow an interviewer to enter responses directly into a computer as they are provided, thus saving hours of time that would otherwise have to be spent entering data into an analysis program by hand.

Conducting quantitative interviews over the phone does not come without some drawbacks. Aside from the obvious problem that not everyone has a phone, research shows that phone interviews generate more fence-sitters than in-person interviews (Holbrook, Green, & Krosnick, 2003). Holbrook, A. L., Green, M. C., & Krosnick, J. A. (2003). Telephone versus face-to-face interviewing of national probability samples with long questionnaires: Comparisons of respondent satisficing and social desirability response bias. Public Opinion Quarterly, 67 , 79–125. Responses to sensitive questions or those that respondents view as invasive are also generally less accurate when data are collected over the phone as compared to when they are collected in person. I can vouch for this latter point from personal experience. While conducting quantitative telephone interviews when I worked at a research firm, it was not terribly uncommon for respondents to tell me that they were green or purple when I asked them to report their racial identity.

Analysis of Quantitative Interview Data

As with the analysis of survey data, analysis of quantitative interview data usually involves coding response options numerically, entering numeric responses into a data analysis computer program, and then running various statistical commands to identify patterns across responses. Section 8.5 "Analysis of Survey Data" of Chapter 8 "Survey Research: A Quantitative Technique" describes the coding process for quantitative data. But what happens when quantitative interviews ask open-ended questions? In this case, responses are typically numerically coded, just as closed-ended questions are, but the process is a little more complex than simply giving a “no” a label of 0 and a “yes” a label of 1.

In some cases, quantitatively coding open-ended interview questions may work inductively, as described in Section 9.2.2 "Analysis of Qualitative Interview Data" . If this is the case, rather than ending with codes, descriptions of codes, and interview excerpts, the researcher will assign a numerical value to codes and may not utilize verbatim excerpts from interviews in later reports of results. Keep in mind, as described in Chapter 1 "Introduction" , that with quantitative methods the aim is to be able to represent and condense data into numbers. The quantitative coding of open-ended interview questions is often a deductive process. The researcher may begin with an idea about likely responses to his or her open-ended questions and assign a numerical value to each likely response. Then the researcher will review participants’ open-ended responses and assign the numerical value that most closely matches the value of his or her expected response.

Strengths and Weaknesses of Quantitative Interviews

Quantitative interviews offer several benefits. The strengths and weakness of quantitative interviews tend to be couched in comparison to those of administering hard copy questionnaires. For example, response rates tend to be higher with interviews than with mailed questionnaires (Babbie, 2010). Babbie, E. (2010). The practice of social research (12th ed.). Belmont, CA: Wadsworth. That makes sense—don’t you find it easier to say no to a piece of paper than to a person? Quantitative interviews can also help reduce respondent confusion. If a respondent is unsure about the meaning of a question or answer option on a questionnaire, he or she probably won’t have the opportunity to get clarification from the researcher. An interview, on the other hand, gives the researcher an opportunity to clarify or explain any items that may be confusing.

As with every method of data collection we’ve discussed, there are also drawbacks to conducting quantitative interviews. Perhaps the largest, and of most concern to quantitative researchers, is interviewer effect. While questions on hard copy questionnaires may create an impression based on the way they are presented, having a person administer questions introduces a slew of additional variables that might influence a respondent. As I’ve said, consistency is key with quantitative data collection—and human beings are not necessarily known for their consistency. Interviewing respondents is also much more time consuming and expensive than mailing questionnaires. Thus quantitative researchers may opt for written questionnaires over interviews on the grounds that they will be able to reach a large sample at a much lower cost than were they to interact personally with each and every respondent.

Key Takeaways

• Unlike qualitative interviews, quantitative interviews usually contain closed-ended questions that are delivered in the same format and same order to every respondent.
• Quantitative interview data are analyzed by assigning a numerical value to participants’ responses.
• While quantitative interviews offer several advantages over self-administered questionnaires such as higher response rates and lower respondent confusion, they have the drawbacks of possible interviewer effect and greater time and expense.
• The General Social Survey (GSS), which we’ve mentioned in previous chapters, is administered via in-person interview, just like quantitative interviewing procedures described here. Read more about the GSS at http://www.norc.uchicago.edu/GSS+Website .
• Take a few of the open-ended questions you created after reading Section 9.2 "Qualitative Interview Techniques and Considerations" on qualitative interviewing techniques. See if you can turn them into closed-ended questions.

How to conduct qualitative interviews (tips and best practices)

Last updated

18 May 2023

Reviewed by

Miroslav Damyanov

However, conducting qualitative interviews can be challenging, even for seasoned researchers. Poorly conducted interviews can lead to inaccurate or incomplete data, significantly compromising the validity and reliability of your research findings.

When planning to conduct qualitative interviews, you must adequately prepare yourself to get the most out of your data. Fortunately, there are specific tips and best practices that can help you conduct qualitative interviews effectively.

• What is a qualitative interview?

A qualitative interview is a research technique used to gather in-depth information about people's experiences, attitudes, beliefs, and perceptions. Unlike a structured questionnaire or survey, a qualitative interview is a flexible, conversational approach that allows the interviewer to delve into the interviewee's responses and explore their insights and experiences.

In a qualitative interview, the researcher typically develops a set of open-ended questions that provide a framework for the conversation. However, the interviewer can also adapt to the interviewee's responses and ask follow-up questions to understand their experiences and views better.

• How to conduct interviews in qualitative research

Conducting interviews involves a well-planned and deliberate process to collect accurate and valid data. 

Here’s a step-by-step guide on how to conduct interviews in qualitative research, broken down into three stages:

1. Before the interview

The first step in conducting a qualitative interview is determining your research question . This will help you identify the type of participants you need to recruit . Once you have your research question, you can start recruiting participants by identifying potential candidates and contacting them to gauge their interest in participating in the study. 

After that, it's time to develop your interview questions. These should be open-ended questions that will elicit detailed responses from participants. You'll also need to get consent from the participants, ideally in writing, to ensure that they understand the purpose of the study and their rights as participants. Finally, choose a comfortable and private location to conduct the interview and prepare the interview guide.

2. During the interview

Start by introducing yourself and explaining the purpose of the study. Establish a rapport by putting the participants at ease and making them feel comfortable. Use the interview guide to ask the questions, but be flexible and ask follow-up questions to gain more insight into the participants' responses. 

Take notes during the interview, and ask permission to record the interview for transcription purposes. Be mindful of the time, and cover all the questions in the interview guide.

3. After the interview

Once the interview is over, transcribe the interview if you recorded it. If you took notes, review and organize them to make sure you capture all the important information. Then, analyze the data you collected by identifying common themes and patterns. Use the findings to answer your research question. 

Finally, debrief with the participants to thank them for their time, provide feedback on the study, and answer any questions they may have.

• What kinds of questions should you ask in a qualitative interview?

Qualitative interviews involve asking questions that encourage participants to share their experiences, opinions, and perspectives on a particular topic. These questions are designed to elicit detailed and nuanced responses rather than simple yes or no answers.

Effective questions in a qualitative interview are generally open-ended and non-leading. They avoid presuppositions or assumptions about the participant's experience and allow them to share their views in their own words. 

In customer research , you might ask questions such as:

What motivated you to choose our product/service over our competitors?

How did you first learn about our product/service?

Can you walk me through your experience with our product/service?

What improvements or changes would you suggest for our product/service?

Have you recommended our product/service to others, and if so, why?

The key is to ask questions relevant to the research topic and allow participants to share their experiences meaningfully and informally. 

• How to determine the right qualitative interview participants

Choosing the right participants for a qualitative interview is a crucial step in ensuring the success and validity of the research . You need to consider several factors to determine the right participants for a qualitative interview. These may include:

Relevant experiences : Participants should have experiences related to the research topic that can provide valuable insights.

Diversity : Aim to include diverse participants to ensure the study's findings are representative and inclusive.

Access : Identify participants who are accessible and willing to participate in the study.

Informed consent : Participants should be fully informed about the study's purpose, methods, and potential risks and benefits and be allowed to provide informed consent.

You can use various recruitment methods, such as posting ads in relevant forums, contacting community organizations or social media groups, or using purposive sampling to identify participants who meet specific criteria.

• How to make qualitative interview subjects comfortable

Making participants comfortable during a qualitative interview is essential to obtain rich, detailed data. Participants are more likely to share their experiences openly when they feel at ease and not judged. 

Here are some ways to make interview subjects comfortable:

Explain the purpose of the study

Start the interview by explaining the research topic and its importance. The goal is to give participants a sense of what to expect.

Create a comfortable environment

Conduct the interview in a quiet, private space where the participant feels comfortable. Turn off any unnecessary electronics that can create distractions. Ensure your equipment works well ahead of time. Arrive at the interview on time. If you conduct a remote interview, turn on your camera and mute all notetakers and observers.

Build rapport

Greet the participant warmly and introduce yourself. Show interest in their responses and thank them for their time.

Use open-ended questions

Ask questions that encourage participants to elaborate on their thoughts and experiences.

Listen attentively

Resist the urge to multitask . Pay attention to the participant's responses, nod your head, or make supportive comments to show you’re interested in their answers. Avoid interrupting them.

Avoid judgment

Show respect and don't judge the participant's views or experiences. Allow the participant to speak freely without feeling judged or ridiculed.

Offer breaks

If needed, offer breaks during the interview, especially if the topic is sensitive or emotional.

Creating a comfortable environment and establishing rapport with the participant fosters an atmosphere of trust and encourages open communication. This helps participants feel at ease and willing to share their experiences.

• How to analyze a qualitative interview

Analyzing a qualitative interview involves a systematic process of examining the data collected to identify patterns, themes, and meanings that emerge from the responses. 

Here are some steps on how to analyze a qualitative interview:

1. Transcription

The first step is transcribing the interview into text format to have a written record of the conversation. This step is essential to ensure that you can refer back to the interview data and identify the important aspects of the interview.

2. Data reduction

Once you’ve transcribed the interview, read through it to identify key themes, patterns, and phrases emerging from the data. This process involves reducing the data into more manageable pieces you can easily analyze.

The next step is to code the data by labeling sections of the text with descriptive words or phrases that reflect the data's content. Coding helps identify key themes and patterns from the interview data.

4. Categorization

After coding, you should group the codes into categories based on their similarities. This process helps to identify overarching themes or sub-themes that emerge from the data.

5. Interpretation

You should then interpret the themes and sub-themes by identifying relationships, contradictions, and meanings that emerge from the data. Interpretation involves analyzing the themes in the context of the research question .

6. Comparison

The next step is comparing the data across participants or groups to identify similarities and differences. This step helps to ensure that the findings aren’t just specific to one participant but can be generalized to the wider population.

7. Triangulation

To ensure the findings are valid and reliable, you should use triangulation by comparing the findings with other sources, such as observations or interview data.

8. Synthesis

The final step is synthesizing the findings by summarizing the key themes and presenting them clearly and concisely. This step involves writing a report that presents the findings in a way that is easy to understand, using quotes and examples from the interview data to illustrate the themes.

• Tips for transcribing a qualitative interview

Transcribing a qualitative interview is a crucial step in the research process. It involves converting the audio or video recording of the interview into written text. 

Here are some tips for transcribing a qualitative interview:

Use transcription software

Transcription software can save time and increase accuracy by automatically transcribing audio or video recordings.

Listen carefully

When manually transcribing, listen carefully to the recording to ensure clarity. Pause and rewind the recording as necessary.

Use appropriate formatting

Use a consistent format for transcribing, such as marking pauses, overlaps, and interruptions. Indicate non-verbal cues such as laughter, sighs, or changes in tone.

Edit for clarity

Edit the transcription to ensure clarity and readability. Use standard grammar and punctuation, correct misspellings, and remove filler words like "um" and "ah."

Proofread and edit

Verify the accuracy of the transcription by listening to the recording again and reviewing the notes taken during the interview.

Use timestamps

Add timestamps to the transcription to reference specific interview sections.

Transcribing a qualitative interview can be time-consuming, but it’s essential to ensure the accuracy of the data collected. Following these tips can produce high-quality transcriptions useful for analysis and reporting.

• Why are interview techniques in qualitative research effective?

Unlike quantitative research methods, which rely on numerical data, qualitative research seeks to understand the richness and complexity of human experiences and perspectives. 

Interview techniques involve asking open-ended questions that allow participants to express their views and share their stories in their own words. This approach can help researchers to uncover unexpected or surprising insights that may not have been discovered through other research methods.

Interview techniques also allow researchers to establish rapport with participants, creating a comfortable and safe space for them to share their experiences. This can lead to a deeper level of trust and candor, leading to more honest and authentic responses.

• What are the weaknesses of qualitative interviews?

Qualitative interviews are an excellent research approach when used properly, but they have their drawbacks. 

The weaknesses of qualitative interviews include the following:

Subjectivity and personal biases

Qualitative interviews rely on the researcher's interpretation of the interviewee's responses. The researcher's biases or preconceptions can affect how the questions are framed and how the responses are interpreted, which can influence results.

Small sample size

The sample size in qualitative interviews is often small, which can limit the generalizability of the results to the larger population.

Data quality

The quality of data collected during interviews can be affected by various factors, such as the interviewee's mood, the setting of the interview, and the interviewer's skills and experience.

Socially desirable responses

Interviewees may provide responses that they believe are socially acceptable rather than truthful or genuine.

Conducting qualitative interviews can be expensive, especially if the researcher must travel to different locations to conduct the interviews.

Time-consuming

The data analysis process can be time-consuming and labor-intensive, as researchers need to transcribe and analyze the data manually.

Despite these weaknesses, qualitative interviews remain a valuable research tool . You can take steps to mitigate the impact of these weaknesses by incorporating the perspectives of other researchers or participants in the analysis process, using multiple data sources , and critically analyzing your biases and assumptions.

Mastering the art of qualitative interviews is an essential skill for businesses looking to gain deep insights into their customers' needs , preferences, and behaviors. By following the tips and best practices outlined in this article, you can conduct interviews that provide you with rich data that you can use to make informed decisions about your products, services, and marketing strategies. 

Remember that effective communication, active listening, and proper analysis are critical components of successful qualitative interviews. By incorporating these practices into your customer research, you can gain a competitive edge and build stronger customer relationships.

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16 Quantitative Research Analyst Interview Questions (With Example Answers)

It's important to prepare for an interview in order to improve your chances of getting the job. Researching questions beforehand can help you give better answers during the interview. Most interviews will include questions about your personality, qualifications, experience and how well you would fit the job. In this article, we review examples of various quantitative research analyst interview questions and sample answers to some of the most common questions.

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Common Quantitative Research Analyst Interview Questions

What motivated you to choose quantitative research as your area of focus, what do you think sets quantitative research apart from other research disciplines, what would you say is the most challenging aspect of quantitative research, what do you think is the most rewarding aspect of quantitative research, what do you think is the most important skill for a quantitative researcher to possess, what do you think is the most important attribute of successful quantitative research projects, what do you think is the most important factor to consider when designing a quantitative research study, what do you think is the most important element of data analysis in quantitative research, what do you think is the most important consideration when interpreting results from quantitative research studies, what do you think is the most important thing to remember when writing a report on quantitative research findings, what do you think is the most important advice you would give to someone who is new to conducting quantitative research, what do you think is the most important thing to keep in mind when working with clients or sponsors on quantitative research projects, what do you think is the most important factor to consider when planning a career in quantitative research, what do you think is the most important attribute of successful quantitative researchers, what do you think sets quantitative research apart from other types of research, what do you think is the most rewarding aspect of a career in quantitative research.

There are a few reasons why an interviewer might ask this question. First, they may be trying to gauge your interest in the field of quantitative research. Second, they may be trying to determine if you have the necessary skills and knowledge to be successful in this field. Finally, they may be trying to get a sense of your long-term career goals and how quantitative research fits into those goals.

It is important for the interviewer to know your motivation for choosing quantitative research as your area of focus because it will help them understand your level of commitment to the field and whether or not you are likely to stick with it for the long haul. Additionally, this question can give the interviewer some insight into your thought process and how you go about making decisions.

Example: “ I was motivated to choose quantitative research as my area of focus because it is a highly analytical and detail-oriented field that allows me to use my critical thinking skills to solve complex problems. Additionally, I am interested in the mathematical and statistical aspects of quantitative research, which makes this field even more appealing to me. ”

There are a few reasons why an interviewer might ask this question to a quantitative research analyst. First, it allows the interviewer to gauge the analyst's understanding of quantitative research methods. Second, it allows the interviewer to determine whether the analyst is familiar with the key differences between quantitative and other research disciplines. Finally, this question can help the interviewer to understand the analyst's thoughts on the strengths and weaknesses of quantitative research methods.

Quantitative research is a scientific approach to data collection and analysis that focuses on measuring and quantifying variables of interest. In contrast, qualitative research is a more exploratory and open-ended approach that emphasizes understanding and describing phenomena rather than measuring and quantifying them.

The key difference between quantitative and qualitative research lies in their respective goals. Quantitative research is typically used to test hypotheses or to answer questions about cause-and-effect relationships, while qualitative research is used to explore phenomena or to generate new hypotheses. Qualitative research is often more flexible and allows for more detailed data collection than quantitative methods, but it can be more difficult to draw clear and definitive conclusions from qualitative data.

Both quantitative and qualitative research play important roles in the scientific process, and each has its own strengths and weaknesses. Quantitative methods are often seen as more objective and rigorous, while qualitative methods are seen as more flexible and responsive to the complexities of real-world phenomena. Ultimately, the choice of which research method to use depends on the specific question being asked and the resources available.

Example: “ There are a few key things that set quantitative research apart from other research disciplines: 1. The focus on data and numbers. Quantitative researchers are interested in understanding relationships between variables using numerical data. This data can be collected through surveys, experiments, or other means. 2. The use of statistical methods. In order to analyze this data, quantitative researchers use statistical methods to identify patterns and relationships. 3. The use of formal models. Formal models are used to describe the relationships between variables and to make predictions about future behavior. 4. The focus on generalizability. One of the goals of quantitative research is to be able to generalize findings to a larger population. This requires careful design and analysis of data. ”

There are a few reasons why an interviewer might ask this question. First, they want to see if you are able to identify the challenges of quantitative research. This is important because it shows that you understand the limitations of this type of research and that you are aware of the potential difficulties that can arise. Second, they want to see how you would address these challenges if you were to encounter them in your work. This is important because it shows that you are proactive and that you have a plan for dealing with difficult situations. Finally, they want to see if you have a good understanding of the statistical methods that are used in quantitative research. This is important because it shows that you are knowledgeable about the topic and that you are able to apply these methods in a real-world setting.

Example: “ There are many challenges that can be faced when conducting quantitative research, but one of the most challenging is ensuring the data collected is accurate and representative of the population being studied. This can be difficult to achieve if the sample size is small or if there is a lot of variability in the data. Another challenge is designing experiments or surveys that accurately measure the phenomena being studied. This can be difficult if the phenomena are complex or if there are many variables that need to be considered. ”

There are a few reasons why an interviewer might ask this question to a quantitative research analyst. First, it allows the interviewer to gauge the analyst's understanding of the field of quantitative research. Second, it allows the interviewer to gauge the analyst's understanding of the benefits of quantitative research. Finally, it allows the interviewer to gauge the analyst's motivation for pursuing a career in quantitative research.

The most rewarding aspect of quantitative research is that it allows analysts to use their skills to help organizations make better decisions. Quantitative research provides organizations with data that can be used to improve policies, make more informed decisions, and allocate resources more effectively. By conducting quantitative research, analysts can have a direct and positive impact on the lives of people and organizations.

Example: “ There are many rewarding aspects of quantitative research, but I think the most rewarding is the ability to see the impact of your work on real-world problems. When you can see that your research is making a difference in the world, it is a very gratifying feeling. ”

Some possible reasons an interviewer might ask this question are to better understand the candidate's views on the role of a quantitative researcher, to gauge the candidate's level of experience, or to get a sense for how the candidate would approach problem-solving in this role. The most important skill for a quantitative researcher depends on the specific field or industry, but some essential skills might include the ability to effectively collect and analyze data, to develop hypotheses and test them using statistical methods, and to communicate findings clearly.

Example: “ There are many important skills that a quantitative researcher should possess, but some of the most important ones include: 1. Strong analytical and critical thinking skills: A quantitative researcher needs to be able to analyze data and identify patterns and trends. They also need to be able to think critically about the data and come up with hypotheses about what it might mean. 2. Strong math skills: A quantitative researcher needs to be able to understand and work with complex mathematical concepts. They need to be able to use statistical software to analyze data and draw conclusions from it. 3. Strong communication skills: A quantitative researcher needs to be able to communicate their findings clearly and concisely, both in writing and verbally. They need to be able to explain their findings to those who may not be familiar with the concepts involved. ”

There are many important attributes of successful quantitative research projects, but the most important attribute is probably methodological rigor. A rigorous quantitative research project is one that is carefully designed and executed, and which uses sound statistical methods to analyze the data. A rigorous quantitative research project can provide valuable insights into a wide variety of topics, and can help to improve decision-making in many different fields.

Example: “ There are a number of attributes that can contribute to the success of quantitative research projects, but some of the most important include: 1. A clear and concise research question that can be answered using quantitative methods. 2. A well-designed research plan that includes a detailed methodology and robust data collection and analysis procedures. 3. A commitment to rigorously following the research plan and ensuring that data is of high quality. 4. A willingness to iterate and refine the research design as needed in order to obtain accurate and meaningful results. 5. A thorough understanding of statistical methods and their application to the data at hand. 6. The ability to effectively communicate findings to both academic and non-academic audiences. ”

There are many factors to consider when designing a quantitative research study, but the most important factor is the research question. The research question should be clear and concise, and it should be possible to answer it with the data that is collected. Other important factors to consider include the population of interest, the sample size, and the type of data that is collected.

Example: “ The most important factor to consider when designing a quantitative research study is the research question. The research question should be clear and concise, and should be able to be answered by the data that is collected. Other important factors to consider when designing a quantitative research study include the population of interest, the sampling method, and the type of data that is collected. ”

The interviewer is likely looking for qualities that are important in a quantitative research analyst, such as attention to detail, strong mathematical skills, and the ability to draw conclusions from data. This question allows the interviewer to gauge the interviewee's understanding of the role of data analysis in quantitative research and their ability to articulate why it is important.

Example: “ There are many elements of data analysis in quantitative research, but I believe the most important element is accuracy. In order to produce accurate results, quantitative researchers need to have a strong understanding of statistics and be able to apply the proper statistical techniques to their data. They also need to be able to effectively communicate their findings to others. ”

There are a few reasons why an interviewer might ask this question to a quantitative research analyst. First, it is important to understand the limitations of quantitative research studies in order to properly interpret their results. Second, analysts need to be aware of potential sources of bias that can distort results. Finally, analysts need to understand how to effectively communicate results to those who may not be familiar with the technical details of the study.

The most important consideration when interpreting results from quantitative research studies is understanding the limitations of the study. Quantitative research studies are often limited in scope and cannot provide a complete picture of a phenomenon. For example, a quantitative study might only be able to measure a limited number of variables, or it might only be able to observe a phenomenon over a short period of time. As a result, analysts need to be careful not to overinterpret the results of a quantitative study.

Another important consideration when interpreting results from quantitative research studies is potential sources of bias. There are many potential sources of bias that can distort results, such as selection bias, measurement bias, and self-reporting bias. analysts need to be aware of these potential sources of bias and take them into account when interpreting results.

Finally, analysts need to understand how to effectively communicate results to those who may not be familiar with the technical details of the study. When presenting results from a quantitative study, analysts need to clearly explain the methodology used and the limitations of the study. They also need to provide context for the results, such as how the results compare to other studies on the same topic.

Example: “ There are a number of important considerations to take into account when interpreting results from quantitative research studies. Perhaps the most important consideration is the study's methodological quality. This includes factors such as the study's design, sample size, and statistical analysis. If a study has flaws in any of these areas, its results may not be accurate or reliable. Another important consideration is the context in which the study was conducted. This includes factors such as the population being studied, the setting in which the data was collected, and the specific research question that was being addressed. All of these factors can affect the results of a quantitative study and how they should be interpreted. Finally, it is also important to consider the implications of the results before drawing any conclusions. What do the results mean in terms of real-world applications? Are there any potential risks or benefits associated with implementing the findings? These are just some of the questions that need to be considered before making any decisions based on quantitative research results. ”

There are a few reasons why an interviewer might ask this question to a quantitative research analyst. First, it is important to remember that when writing a report on quantitative research findings, it is important to be clear and concise. The report should be easy to read and understand, and should not contain any superfluous information. Second, it is important to remember that the report should be objective and unbiased. The report should not be swayed by the researcher's personal opinions or biases. Third, the report should be accurate. All of the data and information included in the report should be accurate and up-to-date. Finally, the report should be well-organized. The information should be presented in a logical and easy-to-follow manner.

Example: “ There are a few things to keep in mind when writing a report on quantitative research findings: 1. Make sure to clearly state the research question that was being addressed in the study. 2. Present the data in a clear and concise manner, using tables and graphs as needed. 3. Be sure to discuss the implications of the findings and how they relate to the research question. 4. Finally, make sure to proofread the report carefully before submitting it. ”

There are a few reasons why an interviewer would ask this question to a quantitative research analyst. First, it allows the interviewer to gauge the analyst's level of experience and expertise in conducting quantitative research. Second, it allows the interviewer to understand the analyst's process for conducting quantitative research and how they go about acquiring data and analyzing it. Finally, it allows the interviewer to get a sense for the analyst's personal philosophies or methods for conducting research, which can be helpful in determining if they would be a good fit for the position.

Example: “ There are a few things to keep in mind when conducting quantitative research: 1. Make sure your data is of high quality. This means that it should be accurate, reliable, and representative of the population you are studying. 2. Choose the right statistical methods for your data and research question. There are many different statistical methods, and it is important to choose the one that is most appropriate for your data and question. 3. Be careful when interpreting results. Quantitative research is often complex, and it is easy to make mistakes when interpreting results. Make sure to carefully review your results before drawing any conclusions. ”

There are a few reasons why an interviewer might ask this question to a quantitative research analyst. Firstly, the interviewer wants to know if the analyst is aware of the importance of working closely with clients or sponsors on quantitative research projects. Secondly, the interviewer wants to know if the analyst has the ability to think critically about the project and identify the most important aspects that need to be considered. Finally, the interviewer wants to gauge the analyst's level of experience and knowledge in this area.

Quantitative research projects can be extremely complex, and it is crucial that analysts work closely with clients or sponsors in order to ensure that all of the necessary data is collected and analyzed correctly. Furthermore, analysts need to be able to identify the most important factors that will impact the results of the research in order to ensure that the project is successful. Therefore, it is essential that analysts have a strong understanding of both the quantitative research process and the specific needs of their clients or sponsors.

Example: “ There are a few things that are important to keep in mind when working with clients or sponsors on quantitative research projects: 1. It is important to clearly define the goals and objectives of the project from the outset. This will help to ensure that everyone is on the same page and that the project stays focused. 2. It is also important to be clear about who the target audience is for the research. This will help to ensure that the data collected is relevant and can be used to answer the research questions. 3. Another thing to keep in mind is that quantitative research can be expensive, so it is important to work with a budget in mind. This will help to ensure that the project stays within its financial constraints. 4. Finally, it is also important to keep in mind that quantitative research takes time. This means that it is important to plan for adequate time to collect and analyze data before presenting results. ”

There are many factors to consider when planning a career in quantitative research, but the most important factor is probably experience. The more experience you have in the field, the better equipped you will be to handle the challenges that come with it. Additionally, it is important to stay current on the latest methods and techniques used in quantitative research.

Example: “ There are many factors to consider when planning a career in quantitative research, but the most important factor is probably your own skills and interests. If you're not interested in the subject matter, it will be very difficult to succeed in this field. Likewise, if you don't have strong mathematical and analytical skills, you'll likely find it difficult to progress in this career. So, it's important to consider your own skills and interests when planning a career in quantitative research. ”

There are many important attributes of successful quantitative researchers, but some attributes are more important than others. The most important attribute of successful quantitative researchers is the ability to think critically and solve problems. Quantitative research is all about finding solutions to problems, so it is essential that quantitative researchers be able to think critically and solve problems. Other important attributes of successful quantitative researchers include the ability to communicate effectively, the ability to work independently, and the ability to work in a team.

Example: “ There are a few attributes that are important for successful quantitative researchers. Firstly, they need to be excellent at math and statistics. Secondly, they need to be able to think logically and solve problems efficiently. Thirdly, they need to be able to communicate their findings clearly and concisely. Lastly, they need to be able to work well under pressure and meet deadlines. ”

There are a few reasons why an interviewer might ask this question. First, it allows them to gauge the interviewee's understanding of quantitative research. Second, it allows them to see how the interviewee would explain the concept of quantitative research to someone who is not familiar with it. Finally, it allows the interviewer to get a sense of the interviewee's thought process and how they approach problem solving.

It is important for the interviewer to ask this question because it allows them to get a better understanding of the interviewee's skills and abilities. Additionally, it allows the interviewer to get a better sense of the interviewee's personality and whether or not they would be a good fit for the position.

Example: “ Quantitative research is a type of scientific research that focuses on the collection and analysis of numerical data. This data can be collected through surveys, experiments, or other methods of observation. Once collected, this data can be used to answer questions about the relationships between different variables, or to test hypotheses about how these variables interact with each other. One of the main things that sets quantitative research apart from other types of research is its focus on data. This data can be collected in a number of ways, but it must be numerical in order to be analyzed. This means that quantitative research is often more rigorous and objective than other types of research, as it relies on hard evidence rather than personal opinions or anecdotal evidence. Another thing that sets quantitative research apart is its focus on relationships between variables. This type of research is often used to test hypotheses about how different variables interact with each other. For example, a researcher might want to know if there is a relationship between income and happiness. By collecting data on both income and happiness levels, the researcher can test their hypothesis and see if there is a statistically significant relationship between the two variables. Overall, quantitative research is a powerful tool for understanding the world around us. By collecting and analyzing numerical data, we can ”

An interviewer might ask this question to gain insight into what motivates the research analyst and what they consider to be the most important part of their job. This can help the interviewer understand if the analyst is likely to be satisfied with the position and if they are likely to stay in the role for the long term. Additionally, this question can give the interviewer a sense of the research analyst's priorities and how they might approach their work.

Example: “ The most rewarding aspect of a career in quantitative research is the ability to make a real difference in the world. With the help of data and analysis, quantitative researchers are able to provide insights that can lead to positive change. They can help decision-makers understand complex problems and identify potential solutions. In addition, quantitative researchers often have the opportunity to work on cutting-edge projects that can have a real impact on people’s lives. ”

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