dissertation example survey

Dissertation surveys: Questions, examples, and best practices

Collect data for your dissertation with little effort and great results.

Dissertation surveys are one of the most powerful tools to get valuable insights and data for the culmination of your research. However, it’s one of the most stressful and time-consuming tasks you need to do. You want useful data from a representative sample that you can analyze and present as part of your dissertation. At SurveyPlanet, we’re committed to making it as easy and stress-free as possible to get the most out of your study.

With an intuitive and user-friendly design, our templates and premade questions can be your allies while creating a survey for your dissertation. Explore all the options we offer by simply signing up for an account—and leave the stress behind.

How to write dissertation survey questions

The first thing to do is to figure out which group of people is relevant for your study. When you know that, you’ll also be able to adjust the survey and write questions that will get the best results.

The next step is to write down the goal of your research and define it properly. Online surveys are one of the best and most inexpensive ways to reach respondents and achieve your goal.

Before writing any questions, think about how you’ll analyze the results. You don’t want to write and distribute a survey without keeping how to report your findings in mind. When your thesis questionnaire is out in the real world, it’s too late to conclude that the data you’re collecting might not be any good for assessment. Because of that, you need to create questions with analysis in mind.

You may find our five survey analysis tips for better insights helpful. We recommend reading it before analyzing your results.

Once you understand the parameters of your representative sample, goals, and analysis methodology, then it’s time to think about distribution. Survey distribution may feel like a headache, but you’ll find that many people will gladly participate.

Find communities where your targeted group hangs out and share the link to your survey with them. If you’re not sure how large your research sample should be, gauge it easily with the survey sample size calculator.

Need help with writing survey questions? Read our guide on well-written examples of good survey questions .

Dissertation survey examples

Whatever field you’re studying, we’re sure the following questions will prove useful when crafting your own.

At the beginning of every questionnaire, inform respondents of your topic and provide a consent form. After that, start with questions like:

• Please select your gender:
• What is the highest educational level you’ve completed?
• High school
• Bachelor degree
• Master’s degree
• On a scale of 1-7, how satisfied are you with your current job?
• Please rate the following statements:
• I always wait for people to text me first.
• Strongly Disagree
• Neither agree nor disagree
• Strongly agree
• My friends always complain that I never invite them anywhere.
• I prefer spending time alone.
• Rank which personality traits are most important when choosing a partner. Rank 1 - 7, where 1 is the most and 7 is the least important.
• Flexibility
• Independence
• How openly do you share feelings with your partner?
• Almost never
• Almost always
• In the last two weeks, how often did you experience headaches?

Dissertation survey best practices

There are a lot of DOs and DON’Ts you should keep in mind when conducting any survey, especially for your dissertation. To get valuable data from your targeted sample, follow these best practices:

Use the consent form.

The consent form is a must when distributing a research questionnaire. A respondent has to know how you’ll use their answers and that the survey is anonymous.

Avoid leading and double-barreled questions

Leading and double-barreled questions will produce inconclusive results—and you don’t want that. A question such as: “Do you like to watch TV and play video games?” is double-barreled because it has two variables.

On the other hand, leading questions such as “On a scale from 1-10 how would you rate the amazing experience with our customer support?” influence respondents to answer in a certain way, which produces biased results.

Use easy and straightforward language and questions

Don’t use terms and professional jargon that respondents won’t understand. Take into consideration their educational level and demographic traits and use easy-to-understand language when writing questions.

Mix close-ended and open-ended questions

Too many open-ended questions will annoy respondents. Also, analyzing the responses is harder. Use more close-ended questions for the best results and only a few open-ended ones.

Strategically use different types of responses

Likert scale, multiple-choice, and ranking are all types of responses you can use to collect data. But some response types suit some questions better. Make sure to strategically fit questions with response types.

Ensure that data privacy is a priority

Make sure to use an online survey tool that has SSL encryption and secure data processing. You don’t want to risk all your hard work going to waste because of poorly managed data security. Ensure that you only collect data that’s relevant to your dissertation survey and leave out any questions (such as name) that can identify the respondents.

Create dissertation questionnaires with SurveyPlanet

Overall, survey methodology is a great way to find research participants for your research study. You have all the tools required for creating a survey for a dissertation with SurveyPlanet—you only need to sign up . With powerful features like question branching, custom formatting, multiple languages, image choice questions, and easy export you will find everything needed to create, distribute, and analyze a dissertation survey.

Happy data gathering!

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• Knowledge Base
• Methodology
• Doing Survey Research | A Step-by-Step Guide & Examples

Doing Survey Research | A Step-by-Step Guide & Examples

Published on 6 May 2022 by Shona McCombes . Revised on 10 October 2022.

Survey research means collecting information about a group of people by asking them questions and analysing the results. To conduct an effective survey, follow these six steps:

• Determine who will participate in the survey
• Decide the type of survey (mail, online, or in-person)
• Design the survey questions and layout
• Distribute the survey
• Analyse the responses
• Write up the results

Surveys are a flexible method of data collection that can be used in many different types of research .

Table of contents

What are surveys used for, step 1: define the population and sample, step 2: decide on the type of survey, step 3: design the survey questions, step 4: distribute the survey and collect responses, step 5: analyse the survey results, step 6: write up the survey results, frequently asked questions about surveys.

Surveys are used as a method of gathering data in many different fields. They are a good choice when you want to find out about the characteristics, preferences, opinions, or beliefs of a group of people.

Common uses of survey research include:

• Social research: Investigating the experiences and characteristics of different social groups
• Market research: Finding out what customers think about products, services, and companies
• Health research: Collecting data from patients about symptoms and treatments
• Politics: Measuring public opinion about parties and policies
• Psychology: Researching personality traits, preferences, and behaviours

Surveys can be used in both cross-sectional studies , where you collect data just once, and longitudinal studies , where you survey the same sample several times over an extended period.

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Before you start conducting survey research, you should already have a clear research question that defines what you want to find out. Based on this question, you need to determine exactly who you will target to participate in the survey.

Populations

The target population is the specific group of people that you want to find out about. This group can be very broad or relatively narrow. For example:

• The population of Brazil
• University students in the UK
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18 to 24
• British transgender women over the age of 50

Your survey should aim to produce results that can be generalised to the whole population. That means you need to carefully define exactly who you want to draw conclusions about.

It’s rarely possible to survey the entire population of your research – it would be very difficult to get a response from every person in Brazil or every university student in the UK. Instead, you will usually survey a sample from the population.

The sample size depends on how big the population is. You can use an online sample calculator to work out how many responses you need.

There are many sampling methods that allow you to generalise to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions.

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by post, online, or in person, and respondents fill it out themselves
• An interview , where the researcher asks a set of questions by phone or in person and records the responses

Which type you choose depends on the sample size and location, as well as the focus of the research.

Questionnaires

Sending out a paper survey by post is a common method of gathering demographic information (for example, in a government census of the population).

• You can easily access a large sample.
• You have some control over who is included in the sample (e.g., residents of a specific region).
• The response rate is often low.

Online surveys are a popular choice for students doing dissertation research , due to the low cost and flexibility of this method. There are many online tools available for constructing surveys, such as SurveyMonkey and Google Forms .

• You can quickly access a large sample without constraints on time or location.
• The data is easy to process and analyse.
• The anonymity and accessibility of online surveys mean you have less control over who responds.

If your research focuses on a specific location, you can distribute a written questionnaire to be completed by respondents on the spot. For example, you could approach the customers of a shopping centre or ask all students to complete a questionnaire at the end of a class.

• You can screen respondents to make sure only people in the target population are included in the sample.
• You can collect time- and location-specific data (e.g., the opinions of a shop’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations.

Oral interviews are a useful method for smaller sample sizes. They allow you to gather more in-depth information on people’s opinions and preferences. You can conduct interviews by phone or in person.

• You have personal contact with respondents, so you know exactly who will be included in the sample in advance.
• You can clarify questions and ask for follow-up information when necessary.
• The lack of anonymity may cause respondents to answer less honestly, and there is more risk of researcher bias.

Like questionnaires, interviews can be used to collect quantitative data : the researcher records each response as a category or rating and statistically analyses the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analysed individually to gain a richer understanding of their opinions and feelings.

Next, you need to decide which questions you will ask and how you will ask them. It’s important to consider:

• The type of questions
• The content of the questions
• The phrasing of the questions
• The ordering and layout of the survey

Open-ended vs closed-ended questions

There are two main forms of survey questions: open-ended and closed-ended. Many surveys use a combination of both.

Closed-ended questions give the respondent a predetermined set of answers to choose from. A closed-ended question can include:

• A binary answer (e.g., yes/no or agree/disagree )
• A scale (e.g., a Likert scale with five points ranging from strongly agree to strongly disagree )
• A list of options with a single answer possible (e.g., age categories)
• A list of options with multiple answers possible (e.g., leisure interests)

Closed-ended questions are best for quantitative research . They provide you with numerical data that can be statistically analysed to find patterns, trends, and correlations .

Open-ended questions are best for qualitative research. This type of question has no predetermined answers to choose from. Instead, the respondent answers in their own words.

Open questions are most common in interviews, but you can also use them in questionnaires. They are often useful as follow-up questions to ask for more detailed explanations of responses to the closed questions.

The content of the survey questions

To ensure the validity and reliability of your results, you need to carefully consider each question in the survey. All questions should be narrowly focused with enough context for the respondent to answer accurately. Avoid questions that are not directly relevant to the survey’s purpose.

When constructing closed-ended questions, ensure that the options cover all possibilities. If you include a list of options that isn’t exhaustive, you can add an ‘other’ field.

Phrasing the survey questions

In terms of language, the survey questions should be as clear and precise as possible. Tailor the questions to your target population, keeping in mind their level of knowledge of the topic.

Use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no bias towards one answer or another.

Ordering the survey questions

The questions should be arranged in a logical order. Start with easy, non-sensitive, closed-ended questions that will encourage the respondent to continue.

If the survey covers several different topics or themes, group together related questions. You can divide a questionnaire into sections to help respondents understand what is being asked in each part.

If a question refers back to or depends on the answer to a previous question, they should be placed directly next to one another.

Before you start, create a clear plan for where, when, how, and with whom you will conduct the survey. Determine in advance how many responses you require and how you will gain access to the sample.

When you are satisfied that you have created a strong research design suitable for answering your research questions, you can conduct the survey through your method of choice – by post, online, or in person.

There are many methods of analysing the results of your survey. First you have to process the data, usually with the help of a computer program to sort all the responses. You should also cleanse the data by removing incomplete or incorrectly completed responses.

If you asked open-ended questions, you will have to code the responses by assigning labels to each response and organising them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analysing interviews.

Statistical analysis is usually conducted using programs like SPSS or Stata. The same set of survey data can be subject to many analyses.

Finally, when you have collected and analysed all the necessary data, you will write it up as part of your thesis, dissertation , or research paper .

In the methodology section, you describe exactly how you conducted the survey. You should explain the types of questions you used, the sampling method, when and where the survey took place, and the response rate. You can include the full questionnaire as an appendix and refer to it in the text if relevant.

Then introduce the analysis by describing how you prepared the data and the statistical methods you used to analyse it. In the results section, you summarise the key results from your analysis.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

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Dissertation Questionnaire

Questionnaire generator.

A dissertation is a document usually a requirement for a doctoral degree especially in the field of philosophy. This long essay discusses a particular subject matter uses questionnaires   and other sources of data and is used to validate its content. The  questionnaire’s importance is evident in the processes of data gathering as it can make the dissertation factual, effective and usable.

Having a well-curated and formatted document to follow when making a dissertation can be very beneficial to an individual who is currently immersed in the data gathering stage of the specific research study. We have gathered downloadable samples and templates of questionnaires so it will be easier for you to curate your own.

Dissertation Timeline Gantt Chart Template

Size: 55 KB

Dissertation Research Gantt Chart Template

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Dissertation Project Gantt Chart Template

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Dissertation Plan Gantt Chart Template

Size: 51 KB

Dissertation Research Questionnaire

Size: 18 KB

Dissertation Proposal Questionnaire

Size: 131 KB

Sample Dissertation Questionnaire

Size: 10 KB

What Is a Dissertation Questionnaire?

A dissertation questionnaire can be defined as follows:

• It is a document used in the processes of data gathering.
• Questionnaires in PDF used for a dissertation contain questions that can help assess the current condition of the community which is the subject of study within the dissertation.
• It specifies the questions that are needed to be answered to assure that there is a basis in terms of the results that will be presented in a dissertation.

How to Write a Dissertation Questionnaire

Writing an efficient and comprehensive dissertation questionnaire can greatly affect the entire dissertation. You can make one by following these steps:

• Be specific with the kind of dissertation that you are creating and align the purposes of the dissertation questionnaire that you need to make to your study.
• List down the information needed from the community who will provide the answers to your questions.
• Open a software where you can create a questionnaire template. You may also download  survey questionnaire examples   and templates to have a faster time in formatting the document.
• The purpose of the dissertation questionnaire.
• The guidelines and instructions in answering the dissertation questions.
• The name of the person to who will use the questionnaire results to his/her dissertation.
• The institution to whom the dissertation will be passed.
• List down the questions based on your needs.

Undergraduate Dissertation Questionnaire

Size: 12 KB

Project Management Dissertation

Size: 54 KB

Guidelines for Writing a Dissertation Questionnaire

There are no strict rules in writing a dissertation questionnaire. However, there are some tips that can help you to create a dissertation questionnaire that is relevant to the study that you are currently doing. Some guidelines:

• Make sure that you are well aware of the data that is needed in your dissertation so you can properly curate questions that can supply your information needs.
• It will be best to use a dissertation questionnaire format that is organized, easy to understand, and properly structured. This will help the people who will answer the dissertation questionnaire quickly know how they can provide the items that you would like to know.
• Always make sure that your instructions in answering the questions are precise and directly stated.
• You may look at  questionnaires in Word   for comparisons. Doing this will help you assess whether there are still areas of improvement that you may tap with the content and format of the dissertation questionnaire that you have created.

Keeping this guidelines in mind and implementing them accordingly will allow you to create a dissertation questionnaire that is beneficial to the processes that you need to have an outstanding dissertation.

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Dissertation examples

Listed below are some of the best examples of research projects and dissertations from undergraduate and taught postgraduate students at the University of Leeds We have not been able to gather examples from all schools. The module requirements for research projects may have changed since these examples were written. Refer to your module guidelines to make sure that you address all of the current assessment criteria. Some of the examples below are only available to access on campus.

• Undergraduate examples
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Find out how to use a dissertation questionnaire for your masters.

Prof Martyn Denscombe, author of " The Good Research Guide, 6th edition ", gives expert advice on using a questionnaire survey for your postgraduate dissertation.

Questionnaire surveys are a well-established way of collecting data. They work with relatively small-scale research projects so design and deliver research questionnaires quickly and cheaply. When it comes to conducting research for a master’s dissertation, questionnaire surveys feature prominently as the method of choice.

Using the post for bulky and lengthy surveys is normal. Sometimes questionnaires go by hand. The popularity of questionnaire surveys is principally due to the benefits of using online web-based questionnaires. There are two main aspects to this.

Designing questionnaires

First, the software for producing and delivering web questionnaires. Simple to use features such as drop-down menus and tick-box answers, is user-friendly and inexpensive.

Second, online surveys make it possible to contact people across the globe without travelling anywhere. Given the time and resource constraints faced when producing a dissertation, makes online surveys all the more enticing. Social media such as Facebook, Instagram and WhatsApp is great for contacting people to participate in the survey.

In the context of a master’s dissertation, however, the quality of the survey data is a vital issue. The grade for the dissertation will depend on being able to defend the use of the data from the survey. This is the basis for advanced, master’s level academic enquiry.

Pro's and con's

It is not good enough to simply rely on getting 100 or so people to complete your questionnaire. Be aware of the pros and cons of questionnaire surveys. You need to justify the value of the data you have collected in the face of probing questions, such as:

• Who are the respondents and how they were selected?
• How representative are the respondents of the whole group being studied?
• What response rate was achieved by the survey?
• Are the questions suitable in relation to the topic and the particular respondents?
• What likelihood is there that respondents gave honest answers to the questions?

This is where The Good Research Guide, 6th edition becomes so valuable.

It identifies the key points that need to be addressed in order to conduct a competent questionnaire survey. It gets right to the heart of the matter, with plenty of practical guidance on how to deal with issues.

In a straightforward style, using plain language, this bestselling book covers a range of alternative strategies and methods for conducting small-scale social research projects and outlines some of the main ways in which the data can be analysed.

Read Prof Martyn Denscombe's advice on using a Case Study for your postgraduate dissertation.

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• Knowledge Base

Methodology

• Questionnaire Design | Methods, Question Types & Examples

Questionnaire Design | Methods, Question Types & Examples

Published on July 15, 2021 by Pritha Bhandari . Revised on June 22, 2023.

A questionnaire is a list of questions or items used to gather data from respondents about their attitudes, experiences, or opinions. Questionnaires can be used to collect quantitative and/or qualitative information.

Questionnaires are commonly used in market research as well as in the social and health sciences. For example, a company may ask for feedback about a recent customer service experience, or psychology researchers may investigate health risk perceptions using questionnaires.

Table of contents

Questionnaires vs. surveys, questionnaire methods, open-ended vs. closed-ended questions, question wording, question order, step-by-step guide to design, other interesting articles, frequently asked questions about questionnaire design.

A survey is a research method where you collect and analyze data from a group of people. A questionnaire is a specific tool or instrument for collecting the data.

Designing a questionnaire means creating valid and reliable questions that address your research objectives , placing them in a useful order, and selecting an appropriate method for administration.

But designing a questionnaire is only one component of survey research. Survey research also involves defining the population you’re interested in, choosing an appropriate sampling method , administering questionnaires, data cleansing and analysis, and interpretation.

Sampling is important in survey research because you’ll often aim to generalize your results to the population. Gather data from a sample that represents the range of views in the population for externally valid results. There will always be some differences between the population and the sample, but minimizing these will help you avoid several types of research bias , including sampling bias , ascertainment bias , and undercoverage bias .

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Questionnaires can be self-administered or researcher-administered . Self-administered questionnaires are more common because they are easy to implement and inexpensive, but researcher-administered questionnaires allow deeper insights.

Self-administered questionnaires

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Self-administered questionnaires can be:

• cost-effective
• easy to administer for small and large groups
• anonymous and suitable for sensitive topics

But they may also be:

• unsuitable for people with limited literacy or verbal skills
• susceptible to a nonresponse bias (most people invited may not complete the questionnaire)
• biased towards people who volunteer because impersonal survey requests often go ignored.

Researcher-administered questionnaires

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents.

Researcher-administered questionnaires can:

• help you ensure the respondents are representative of your target audience
• allow clarifications of ambiguous or unclear questions and answers
• have high response rates because it’s harder to refuse an interview when personal attention is given to respondents

But researcher-administered questionnaires can be limiting in terms of resources. They are:

• costly and time-consuming to perform
• more difficult to analyze if you have qualitative responses
• likely to contain experimenter bias or demand characteristics
• likely to encourage social desirability bias in responses because of a lack of anonymity

Your questionnaire can include open-ended or closed-ended questions or a combination of both.

Using closed-ended questions limits your responses, while open-ended questions enable a broad range of answers. You’ll need to balance these considerations with your available time and resources.

Closed-ended questions

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. Closed-ended questions are best for collecting data on categorical or quantitative variables.

Categorical variables can be nominal or ordinal. Quantitative variables can be interval or ratio. Understanding the type of variable and level of measurement means you can perform appropriate statistical analyses for generalizable results.

Examples of closed-ended questions for different variables

Nominal variables include categories that can’t be ranked, such as race or ethnicity. This includes binary or dichotomous categories.

It’s best to include categories that cover all possible answers and are mutually exclusive. There should be no overlap between response items.

In binary or dichotomous questions, you’ll give respondents only two options to choose from.

White Black or African American American Indian or Alaska Native Asian Native Hawaiian or Other Pacific Islander

Ordinal variables include categories that can be ranked. Consider how wide or narrow a range you’ll include in your response items, and their relevance to your respondents.

Likert scale questions collect ordinal data using rating scales with 5 or 7 points.

When you have four or more Likert-type questions, you can treat the composite data as quantitative data on an interval scale . Intelligence tests, psychological scales, and personality inventories use multiple Likert-type questions to collect interval data.

With interval or ratio scales , you can apply strong statistical hypothesis tests to address your research aims.

Pros and cons of closed-ended questions

Well-designed closed-ended questions are easy to understand and can be answered quickly. However, you might still miss important answers that are relevant to respondents. An incomplete set of response items may force some respondents to pick the closest alternative to their true answer. These types of questions may also miss out on valuable detail.

To solve these problems, you can make questions partially closed-ended, and include an open-ended option where respondents can fill in their own answer.

Open-ended questions

Open-ended, or long-form, questions allow respondents to give answers in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered. For example, respondents may want to answer “multiracial” for the question on race rather than selecting from a restricted list.

• How do you feel about open science?
• How would you describe your personality?
• In your opinion, what is the biggest obstacle for productivity in remote work?

Open-ended questions have a few downsides.

They require more time and effort from respondents, which may deter them from completing the questionnaire.

For researchers, understanding and summarizing responses to these questions can take a lot of time and resources. You’ll need to develop a systematic coding scheme to categorize answers, and you may also need to involve other researchers in data analysis for high reliability .

Question wording can influence your respondents’ answers, especially if the language is unclear, ambiguous, or biased. Good questions need to be understood by all respondents in the same way ( reliable ) and measure exactly what you’re interested in ( valid ).

Use clear language

You should design questions with your target audience in mind. Consider their familiarity with your questionnaire topics and language and tailor your questions to them.

For readability and clarity, avoid jargon or overly complex language. Don’t use double negatives because they can be harder to understand.

Use balanced framing

Respondents often answer in different ways depending on the question framing. Positive frames are interpreted as more neutral than negative frames and may encourage more socially desirable answers.

Use a mix of both positive and negative frames to avoid research bias , and ensure that your question wording is balanced wherever possible.

Unbalanced questions focus on only one side of an argument. Respondents may be less likely to oppose the question if it is framed in a particular direction. It’s best practice to provide a counter argument within the question as well.

Avoid leading questions

Leading questions guide respondents towards answering in specific ways, even if that’s not how they truly feel, by explicitly or implicitly providing them with extra information.

It’s best to keep your questions short and specific to your topic of interest.

• The average daily work commute in the US takes 54.2 minutes and costs $29 per day. Since 2020, working from home has saved many employees time and money. Do you favor flexible work-from-home policies even after it’s safe to return to offices?
• Experts agree that a well-balanced diet provides sufficient vitamins and minerals, and multivitamins and supplements are not necessary or effective. Do you agree or disagree that multivitamins are helpful for balanced nutrition?

Keep your questions focused

Ask about only one idea at a time and avoid double-barreled questions. Double-barreled questions ask about more than one item at a time, which can confuse respondents.

This question could be difficult to answer for respondents who feel strongly about the right to clean drinking water but not high-speed internet. They might only answer about the topic they feel passionate about or provide a neutral answer instead – but neither of these options capture their true answers.

Instead, you should ask two separate questions to gauge respondents’ opinions.

Strongly Agree Agree Undecided Disagree Strongly Disagree

Do you agree or disagree that the government should be responsible for providing high-speed internet to everyone?

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You can organize the questions logically, with a clear progression from simple to complex. Alternatively, you can randomize the question order between respondents.

Logical flow

Using a logical flow to your question order means starting with simple questions, such as behavioral or opinion questions, and ending with more complex, sensitive, or controversial questions.

The question order that you use can significantly affect the responses by priming them in specific directions. Question order effects, or context effects, occur when earlier questions influence the responses to later questions, reducing the validity of your questionnaire.

While demographic questions are usually unaffected by order effects, questions about opinions and attitudes are more susceptible to them.

• How knowledgeable are you about Joe Biden’s executive orders in his first 100 days?
• Are you satisfied or dissatisfied with the way Joe Biden is managing the economy?
• Do you approve or disapprove of the way Joe Biden is handling his job as president?

It’s important to minimize order effects because they can be a source of systematic error or bias in your study.

Randomization

Randomization involves presenting individual respondents with the same questionnaire but with different question orders.

When you use randomization, order effects will be minimized in your dataset. But a randomized order may also make it harder for respondents to process your questionnaire. Some questions may need more cognitive effort, while others are easier to answer, so a random order could require more time or mental capacity for respondents to switch between questions.

Step 1: Define your goals and objectives

The first step of designing a questionnaire is determining your aims.

• What topics or experiences are you studying?
• What specifically do you want to find out?
• Is a self-report questionnaire an appropriate tool for investigating this topic?

Once you’ve specified your research aims, you can operationalize your variables of interest into questionnaire items. Operationalizing concepts means turning them from abstract ideas into concrete measurements. Every question needs to address a defined need and have a clear purpose.

Step 2: Use questions that are suitable for your sample

Create appropriate questions by taking the perspective of your respondents. Consider their language proficiency and available time and energy when designing your questionnaire.

• Are the respondents familiar with the language and terms used in your questions?
• Would any of the questions insult, confuse, or embarrass them?
• Do the response items for any closed-ended questions capture all possible answers?
• Are the response items mutually exclusive?
• Do the respondents have time to respond to open-ended questions?

Consider all possible options for responses to closed-ended questions. From a respondent’s perspective, a lack of response options reflecting their point of view or true answer may make them feel alienated or excluded. In turn, they’ll become disengaged or inattentive to the rest of the questionnaire.

Step 3: Decide on your questionnaire length and question order

Once you have your questions, make sure that the length and order of your questions are appropriate for your sample.

If respondents are not being incentivized or compensated, keep your questionnaire short and easy to answer. Otherwise, your sample may be biased with only highly motivated respondents completing the questionnaire.

Decide on your question order based on your aims and resources. Use a logical flow if your respondents have limited time or if you cannot randomize questions. Randomizing questions helps you avoid bias, but it can take more complex statistical analysis to interpret your data.

Step 4: Pretest your questionnaire

When you have a complete list of questions, you’ll need to pretest it to make sure what you’re asking is always clear and unambiguous. Pretesting helps you catch any errors or points of confusion before performing your study.

Ask friends, classmates, or members of your target audience to complete your questionnaire using the same method you’ll use for your research. Find out if any questions were particularly difficult to answer or if the directions were unclear or inconsistent, and make changes as necessary.

If you have the resources, running a pilot study will help you test the validity and reliability of your questionnaire. A pilot study is a practice run of the full study, and it includes sampling, data collection , and analysis. You can find out whether your procedures are unfeasible or susceptible to bias and make changes in time, but you can’t test a hypothesis with this type of study because it’s usually statistically underpowered .

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

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Questionnaires can be self-administered or researcher-administered.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

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Type of Academic Paper – Dissertation Chapter

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Word Count – 3017 words

Introduction

The current chapter presents developing the research methods needed to complete the experimentation portion of the current study. The chapter will discuss in detail the various stages of developing the methodology of the current study. This includes a detailed discussion of the philosophical background of the research method chosen. In addition to this, the chapter describes the data collection strategy, including the selection of research instrumentation and sampling. The chapter closes with a discussion on the analysis tools used to analyse the data collected.

Selecting an Appropriate Research Approach

Creswall (2013) stated that research approaches are plans and procedures that range from steps, including making broad assumptions to detailed methods of data collection, analysis, and interpretation. The several decisions involved in the process are used to decide which approach should be used in a specific study that is informed using philosophical assumptions brought to the study (Creswall 2013). Included in this are procedures of inquiry or research designs and specific research methods used for data collection, its analysis, and finally, its interpretation. However, Guetterman (2015); Lewis (2015); and Creswall (2013) argue that the selection of the specific research approach is based on the nature of the research problem, or the issue that is being addressed by any study, personal experiences of the researchers’, and even the audience for which the study is being developed for.

There are many ways to customise research approaches to develop an approach most suited for a particular study. However, the main three categories with which research approaches are organised include; qualitative, quantitative, and mixed research methods. Creswall (2013) comments that all three approaches are not considered so discrete or distinct from one another. Creswall (2013) states, “qualitative and quantitative approaches should not be viewed as rigid, distinct categories, polar opposite, or dichotomies” (p.32). Newmand and Benz (1998) pointed out that quantitative and qualitative approaches instead represent different ends on a continuum since a study “tends” to be more quantitative than qualitative or vice versa. Lastly, mixed methods research resides in the middle of the continuum as it can incorporate elements and characteristics of both quantitative and qualitative approaches. Lewis (2015) points out that the main distinction that is often cited between quantitative and qualitative research is that it is framed in terms of using numbers rather than words; or using closed-ended questions for quantitative hypotheses over open-ended questions for qualitative interview questions. Guetterman (2015) points out that a clearer way of viewing gradations of differences between the approaches is to examine the basic philosophical assumptions brought to the study, the kinds of research strategies used, and the particular methods implemented in conducting the strategies.

Underlying Philosophical Assumptions

An important component of defining the research approach involves philosophical assumptions that contribute to the broad research approach of planning or proposing to conduct research. It involves the intersection of philosophy, research designs and specific methods as illustrated in Fig. 1 below.

Figure 3.2-1- Research Onion (Source; Saunders and Tosey 2013)

Slife and Williams (1995) have argued that philosophical ideas have remained hidden within the research. However, they still play an influential role in the research practice, and it is for this reason that it is most identified. Various philosophical assumptions are used to construct or develop a study. Saunders et al. (2009) define research philosophy as a belief about how data about a phenomenon should be gathered, analysed and used. Saunders et al. (2009) identify common research philosophies such as positivism, realism, interpretivism, subjectivism, and pragmatism. Dumke (2002) believes that two views, positivism and phenomenology, mainly characterise research philosophy.

Positivism reflects acceptance in adopting the philosophical stance of natural scientists (Saunders, 2003). According to Remenyi et al. (1998), there is a greater preference in working with an “observable social reality” and that the outcome of such research can be “law-like” generalisations that are the same as those which are produced by physical and natural scientists. Gill and Johnson (1997) add that it will also emphasise a high structure methodology to allow for replication for other studies. Dumke (2002) agrees and explains

that a positivist philosophical assumption produces highly structured methodologies and allows for generalisation and quantification of objectives that can be evaluated by statistical methods. For this philosophical approach, the researcher is considered an objective observer who should not be impacted by or impact the subject of research.

On the other hand, more phenomenological approaches agree that the social world of business and management is too complex to develop theories and laws similar to natural sciences. Saunders et al. (2000) argue that this is the reason why reducing observations in the real world to simple laws and generalisations produces a sense of reality which is a bit superficial and doesn’t present the complexity of it.

The current study chooses positivistic assumptions due to the literature review’s discussion of the importance of Big Data in industrial domains and the need for measuring its success in the operations of the business. The current study aims to examine the impact that Big Data has on automobile companies’ operations. To identify a positive relationship between Big Data usage and beneficial business outcomes, the theory needs to be used to generate hypotheses that can later be tested of the relationship which would allow for explanations of laws that can later be assessed (Bryman and Bell, 2015).

Selecting Interpretive Research Approach

Interpretive research approaches are derived from the research philosophy that is adopted. According to Dumke (2002), the two main research approaches are deductive and inductive. The inductive approach is commonly referred to when theory is derived from observations. Thus, the research begins with specific observations and measures. It is then from detecting some pattern that a hypothesis is developed. Dumke (2002) argues that researchers who use an inductive approach usually work with qualitative data and apply various methods to gather specific information that places different views. From the philosophical assumptions discussed in the previous section, it is reasonable to use the deductive approach for the current study. It is also considered the most commonly used theory to establish a relationship between theory and research. The figure below illustrates the steps used for the process of deduction.

Data Collection

• confirmed or rejected
• Revision of theory

Based on what is known about a specific domain, the theoretical considerations encompassing it a hypothesis or hypotheses are deduced that will later be subjected to empirical enquiry (Daum, 2013). Through these hypotheses, concepts of the subject of interest will be translated into entities that are rational for a study. Researchers are then able to deduce their hypotheses and convert them into operational terms.

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Justifying the Use of Quantitative Research Method

Saunders (2003) notes that almost all research will involve some numerical data or even contain data quantified to help a researcher answer their research questions and meet the study’s objectives. However, quantitative data refers to all data that can be a product of all research strategies (Bryman and Bell, 2015; Guetterman, 2015; Lewis, 2015; Saunders, 2003). Based on the philosophical assumptions and interpretive research approach, a quantitative research method is the best suited for the current study. Haq (2014) explains that quantitative research is about collecting numerical data and then analysing it through statistical methods to explain a specific phenomenon. Mujis (2010) defends the use of quantitative research because, unlike qualitative research, which argues that there is no pre-existing reality, quantitative assumes that there is only a single reality about social conditions that researchers cannot influence in any way. Also, qualitative research is commonly used when there is little to no knowledge of a phenomenon, whereas quantitative research is used to find the cause and effect relationship between variables to either verify or nullify some theory or hypothesis (Creswall 2002; Feilzer 2010; Teddlie and Tashakkori 2012).

Selecting an Appropriate Research Strategy

There are many strategies available to implement in a study, as evidenced from Fig. 1. There are many mono-quantitative methods, such as telephone interviews, web-based surveys, postal surveys, and structured questionnaires (Haq 2014). Each instrument has its own pros and cons in terms of quality, time, and data cost. Brymand (2006); Driscoll et al. (2007); Edwards et al. (2002); and Newby et al. (2003) note that most researchers use structured questionnaires for data collection they are unable to control or influence respondents, which leads to low response rates but more accurate data obtained. Saunders and Tosey (2015) have argued that quantitative data is simpler to obtain and more concise to present. Therefore, the current study uses a survey-based questionnaire (See Appendix A).

Justifying the use of Survey Based Questionnaire

Surveys are considered the most traditional forms of research and are used in non-experimental descriptive designs that describe some reality. Survey-based questionnaires are often restricted to a representative sample of a potential group of the study’s interest. In this case, it is the executives currently working for automobile companies in the UK. The survey instrument is then chosen for its effectiveness at being practical and inexpensive (Kelley et al., 2003). Due to the philosophical assumptions, interpretive approach, and methodological approach, the survey design for the current study is considered the best instrument in line with these premises, besides being cost-effective.

Empirical Research Methodology

Research design.

This section describes how research is designed to use the techniques used for data collection, sampling strategy, and data analysis for a quantitative method. Before going into the strategies of data collection and analysis, a set of hypotheses were developed.

Hypotheses Development

The current study uses a quantitative research approach, making it essential to develop a set of hypotheses that will be used as a test standard for the mono-method quantitative design. The following are a set of hypotheses that have been developed from the examination of the literature review.

H1- The greater the company’s budget for Big Data initiatives (More than 1 million GBP), the greater its ability to monetise and generate new revenues.

H2- The greater the company’s budget for Big Data initiatives (More than 1 million GBP) the more decrease in expenses in found.

H3- The greatest impact of Big Data on a company is changing the way business is done.

H4- Big Data integrating with a company has resulted in competitive significance.

H5- The analytical abilities of a company allows for achieved measurable results.

H6- Investing in Big Data will lead to highly successful business results.

H7- A business’s operations function is fuelling Big Data initiatives and effecting change in operations.

H8- The implementation of Big Data in the company has positive impacts on business.

This section includes the sampling method used to collect the number of respondents needed to provide information, then analysed after collection.

Sampling Method

Collis (2009) explains that there are many kinds of sampling methods that can be used for creating a specific target sample from a population. This current study uses simple random sampling to acquire respondents with which the survey will be conducted. Simple random sampling is considered the most basic form of probability sampling. Under the method, elements are taken from the population at random, with all elements having an equal chance of being selected. According to () as of 2014, there are about thirty-five active British car manufacturers in the UK, each having an employee population of 150 or more. This is why the total population of employees in car manufacturers is estimated to be 5,250 employees. The sample, therefore, developed used the following equation;

2  ×   (1 −   )

+(   2 × (1−  ) )  2

Where; N is the population size,  e  is the margin of error (as a decimal),  z  is confidence level (as a z-score), and  p  is percentage value (as a decimal). Thus, the sample size is with a normal distribution of 50%. With the above equation, a population of 5,250; with a 95% confidence level and 5% margin of error, the total sample size needed for the current equals 300. Therefore, N=300, which is the sample size of the current study.

The survey develops (see Appendix A) has a total of three sections, A, B, and C, with a total of 39 questions. Each section has its own set of questions to accomplish. The survey is a mix of closed-end questions that look to comprehend the respondents’ demographic makeup, the Big Data initiatives of the company, and the impact that Big Data was having on their company. The survey is designed to take no longer than twenty minutes. The survey was constructed on Survey Monkey.com, and an online survey provided website. The survey was left on the website for a duration of 40 days to ensure that the maximum number of respondents were able to answer the survey. The only way that the survey was allowed for a respondent is if they passed a security question as if they were working for an automobile company in the UK when taking the survey. Gupta et al. (2004) believe that web surveys are visual stimuli, and the respondent has complete control about whether or how each question is read and understood. That is why Dillman (2000) argued that web questionnaires are expected to resemble those taken through the mail/postal services closely.

Data Analysis

The collected data is then analysed through the Statistical Package for Social Science (SPSS) version 24 for descriptive analysis. The demographic section of the survey will be analysed using descriptive statistics. Further analysis of the data includes regression analysis. Simple regression analysis includes only one independent variable and one dependent variable. Farrar and Glauber (1967) assert that the purpose of regression analysis is to estimate the parameters of dependency, and it should not be used to determine the interdependency of a relationship.

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Conclusions.

The chapter provides a descriptive and in-depth discussion of the methods involved in the current study’s research. The current study is looking towards a quantitative approach that considers positivism as its philosophical undertaking, using deductive reasoning for its interpretive approach, is a mono-quantitative method that involves the use of a survey instrument for data collection. The methodology chapter also provided the data analysis technique, which is descriptive statistics through frequency analysis and regression analysis.

Examples of results;

Question 8- Of these staff, are mostly working in or for your consumer-facing (B2C) businesses, your commercial or wholesale (B2B) businesses, or both?

Based on the illustration, nineteen (19) respondents indicated that 501-1000 employees are dedicated to analytics for both B2B and B2C. The category of using Big Data analytics for both B2B and B2C comprises the most agreement of respondents with 72 of 132 indicated.

The figure above represents the respondents’ answers to their automobile company’s plan for measuring Big Data’s success. Of the 132 participants, 44.70 per cent responded that the company is planning on using quantitative metrics associated with business performance to analyse if Big Data is actually successful. Another, 30.30 per cent indicated that their company was planning on using qualitative metrics tied to business performance. Using business performance to analyse the success of Big Data is coherent to the results of the literature review that indicated previous studies of doing such. As an automobile company, they need to know the results of using Big Data analytics, and that is only by using business performance indicators regardless of being qualitative or quantitative.

Fig. 4.3-6 portrays the response of participants in regards to actually achieving measurable results from Big Data. According to 68.18 per cent of respondents, the company that they worked for did indeed show measurable results from their investments in Big Data. However, 31.82 per cent indicated that there was indeed no measurable result in investing in Big Data.

Bryman, A., Bell, E., 2015. Business Research Methods. Oxford University Press.

Daum, P., 2013. International Synergy Management: A Strategic Approach for Raising Efficiencies in the Cross-border Interaction Process. Anchor Academic Publishing (aap_verlag).

Dümke, R., 2002. Corporate Reputation and its Importance for Business Success: A European

Perspective and its Implication for Public Relations Consultancies. diplom.de.

Guetterman, T.C., 2015. Descriptions of Sampling Practices Within Five Approaches to Qualitative Research in Education and the Health Sciences. Forum Qualitative Sozialforschung /

Forum: Qualitative Social Research 16.

Haq, M., 2014. A Comparative Analysis of Qualitative and Quantitative Research Methods and a Justification for Adopting Mixed Methods in Social Research (PDF Download Available).

ResearchGate 1–22. doi:http://dx.doi.org/10.13140/RG.2.1.1945.8640

Kelley, K., Clark, B., Brown, V., Sitzia, J., 2003. Good practice in the conduct and reporting of survey research. Int J Qual Health Care 15, 261–266. doi:10.1093/intqhc/mzg031

Lewis, S., 2015. Qualitative Inquiry and Research Design: Choosing Among Five Approaches.

Health Promotion Practice 16, 473–475. doi:10.1177/1524839915580941

Saunders, M., 2003. Research Methods for Business Students. Pearson Education India.

Saunders, M.N.K., Tosey, P., 2015. Handbook of Research Methods on Human Resource

Development. Edward Elgar Publishing.

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Home » Dissertation – Format, Example and Template

Dissertation – Format, Example and Template

Table of Contents

Dissertation

Definition:

Dissertation is a lengthy and detailed academic document that presents the results of original research on a specific topic or question. It is usually required as a final project for a doctoral degree or a master’s degree.

Dissertation Meaning in Research

In Research , a dissertation refers to a substantial research project that students undertake in order to obtain an advanced degree such as a Ph.D. or a Master’s degree.

Dissertation typically involves the exploration of a particular research question or topic in-depth, and it requires students to conduct original research, analyze data, and present their findings in a scholarly manner. It is often the culmination of years of study and represents a significant contribution to the academic field.

Types of Dissertation

Types of Dissertation are as follows:

Empirical Dissertation

An empirical dissertation is a research study that uses primary data collected through surveys, experiments, or observations. It typically follows a quantitative research approach and uses statistical methods to analyze the data.

Non-Empirical Dissertation

A non-empirical dissertation is based on secondary sources, such as books, articles, and online resources. It typically follows a qualitative research approach and uses methods such as content analysis or discourse analysis.

Narrative Dissertation

A narrative dissertation is a personal account of the researcher’s experience or journey. It typically follows a qualitative research approach and uses methods such as interviews, focus groups, or ethnography.

Systematic Literature Review

A systematic literature review is a comprehensive analysis of existing research on a specific topic. It typically follows a qualitative research approach and uses methods such as meta-analysis or thematic analysis.

Case Study Dissertation

A case study dissertation is an in-depth analysis of a specific individual, group, or organization. It typically follows a qualitative research approach and uses methods such as interviews, observations, or document analysis.

Mixed-Methods Dissertation

A mixed-methods dissertation combines both quantitative and qualitative research approaches to gather and analyze data. It typically uses methods such as surveys, interviews, and focus groups, as well as statistical analysis.

How to Write a Dissertation

Here are some general steps to help guide you through the process of writing a dissertation:

• Choose a topic : Select a topic that you are passionate about and that is relevant to your field of study. It should be specific enough to allow for in-depth research but broad enough to be interesting and engaging.
• Conduct research : Conduct thorough research on your chosen topic, utilizing a variety of sources, including books, academic journals, and online databases. Take detailed notes and organize your information in a way that makes sense to you.
• Create an outline : Develop an outline that will serve as a roadmap for your dissertation. The outline should include the introduction, literature review, methodology, results, discussion, and conclusion.
• Write the introduction: The introduction should provide a brief overview of your topic, the research questions, and the significance of the study. It should also include a clear thesis statement that states your main argument.
• Write the literature review: The literature review should provide a comprehensive analysis of existing research on your topic. It should identify gaps in the research and explain how your study will fill those gaps.
• Write the methodology: The methodology section should explain the research methods you used to collect and analyze data. It should also include a discussion of any limitations or weaknesses in your approach.
• Write the results: The results section should present the findings of your research in a clear and organized manner. Use charts, graphs, and tables to help illustrate your data.
• Write the discussion: The discussion section should interpret your results and explain their significance. It should also address any limitations of the study and suggest areas for future research.
• Write the conclusion: The conclusion should summarize your main findings and restate your thesis statement. It should also provide recommendations for future research.
• Edit and revise: Once you have completed a draft of your dissertation, review it carefully to ensure that it is well-organized, clear, and free of errors. Make any necessary revisions and edits before submitting it to your advisor for review.

Dissertation Format

The format of a dissertation may vary depending on the institution and field of study, but generally, it follows a similar structure:

• Title Page: This includes the title of the dissertation, the author’s name, and the date of submission.
• Abstract : A brief summary of the dissertation’s purpose, methods, and findings.
• Table of Contents: A list of the main sections and subsections of the dissertation, along with their page numbers.
• Introduction : A statement of the problem or research question, a brief overview of the literature, and an explanation of the significance of the study.
• Literature Review : A comprehensive review of the literature relevant to the research question or problem.
• Methodology : A description of the methods used to conduct the research, including data collection and analysis procedures.
• Results : A presentation of the findings of the research, including tables, charts, and graphs.
• Discussion : A discussion of the implications of the findings, their significance in the context of the literature, and limitations of the study.
• Conclusion : A summary of the main points of the study and their implications for future research.
• References : A list of all sources cited in the dissertation.
• Appendices : Additional materials that support the research, such as data tables, charts, or transcripts.

Dissertation Outline

Dissertation Outline is as follows:

Title Page:

• Title of dissertation
• Author name
• Institutional affiliation
• Date of submission
• Brief summary of the dissertation’s research problem, objectives, methods, findings, and implications
• Usually around 250-300 words

Table of Contents:

• List of chapters and sections in the dissertation, with page numbers for each

I. Introduction

• Background and context of the research
• Research problem and objectives
• Significance of the research

II. Literature Review

• Overview of existing literature on the research topic
• Identification of gaps in the literature
• Theoretical framework and concepts

III. Methodology

• Research design and methods used
• Data collection and analysis techniques
• Ethical considerations

IV. Results

• Presentation and analysis of data collected
• Findings and outcomes of the research
• Interpretation of the results

V. Discussion

• Discussion of the results in relation to the research problem and objectives
• Evaluation of the research outcomes and implications
• Suggestions for future research

VI. Conclusion

• Summary of the research findings and outcomes
• Implications for the research topic and field
• Limitations and recommendations for future research

VII. References

• List of sources cited in the dissertation

VIII. Appendices

• Additional materials that support the research, such as tables, figures, or questionnaires.

Example of Dissertation

Here is an example Dissertation for students:

Title : Exploring the Effects of Mindfulness Meditation on Academic Achievement and Well-being among College Students

This dissertation aims to investigate the impact of mindfulness meditation on the academic achievement and well-being of college students. Mindfulness meditation has gained popularity as a technique for reducing stress and enhancing mental health, but its effects on academic performance have not been extensively studied. Using a randomized controlled trial design, the study will compare the academic performance and well-being of college students who practice mindfulness meditation with those who do not. The study will also examine the moderating role of personality traits and demographic factors on the effects of mindfulness meditation.

Chapter Outline:

Chapter 1: Introduction

• Background and rationale for the study
• Research questions and objectives
• Significance of the study
• Overview of the dissertation structure

Chapter 2: Literature Review

• Definition and conceptualization of mindfulness meditation
• Theoretical framework of mindfulness meditation
• Empirical research on mindfulness meditation and academic achievement
• Empirical research on mindfulness meditation and well-being
• The role of personality and demographic factors in the effects of mindfulness meditation

Chapter 3: Methodology

• Research design and hypothesis
• Participants and sampling method
• Intervention and procedure
• Measures and instruments
• Data analysis method

Chapter 4: Results

• Descriptive statistics and data screening
• Analysis of main effects
• Analysis of moderating effects
• Post-hoc analyses and sensitivity tests

Chapter 5: Discussion

• Summary of findings
• Implications for theory and practice
• Limitations and directions for future research
• Conclusion and contribution to the literature

Chapter 6: Conclusion

• Recap of the research questions and objectives
• Summary of the key findings
• Contribution to the literature and practice
• Implications for policy and practice
• Final thoughts and recommendations.

References :

List of all the sources cited in the dissertation

Appendices :

Additional materials such as the survey questionnaire, interview guide, and consent forms.

Note : This is just an example and the structure of a dissertation may vary depending on the specific requirements and guidelines provided by the institution or the supervisor.

How Long is a Dissertation

The length of a dissertation can vary depending on the field of study, the level of degree being pursued, and the specific requirements of the institution. Generally, a dissertation for a doctoral degree can range from 80,000 to 100,000 words, while a dissertation for a master’s degree may be shorter, typically ranging from 20,000 to 50,000 words. However, it is important to note that these are general guidelines and the actual length of a dissertation can vary widely depending on the specific requirements of the program and the research topic being studied. It is always best to consult with your academic advisor or the guidelines provided by your institution for more specific information on dissertation length.

Applications of Dissertation

Here are some applications of a dissertation:

• Advancing the Field: Dissertations often include new research or a new perspective on existing research, which can help to advance the field. The results of a dissertation can be used by other researchers to build upon or challenge existing knowledge, leading to further advancements in the field.
• Career Advancement: Completing a dissertation demonstrates a high level of expertise in a particular field, which can lead to career advancement opportunities. For example, having a PhD can open doors to higher-paying jobs in academia, research institutions, or the private sector.
• Publishing Opportunities: Dissertations can be published as books or journal articles, which can help to increase the visibility and credibility of the author’s research.
• Personal Growth: The process of writing a dissertation involves a significant amount of research, analysis, and critical thinking. This can help students to develop important skills, such as time management, problem-solving, and communication, which can be valuable in both their personal and professional lives.
• Policy Implications: The findings of a dissertation can have policy implications, particularly in fields such as public health, education, and social sciences. Policymakers can use the research to inform decision-making and improve outcomes for the population.

When to Write a Dissertation

Here are some situations where writing a dissertation may be necessary:

• Pursuing a Doctoral Degree: Writing a dissertation is usually a requirement for earning a doctoral degree, so if you are interested in pursuing a doctorate, you will likely need to write a dissertation.
• Conducting Original Research : Dissertations require students to conduct original research on a specific topic. If you are interested in conducting original research on a topic, writing a dissertation may be the best way to do so.
• Advancing Your Career: Some professions, such as academia and research, may require individuals to have a doctoral degree. Writing a dissertation can help you advance your career by demonstrating your expertise in a particular area.
• Contributing to Knowledge: Dissertations are often based on original research that can contribute to the knowledge base of a field. If you are passionate about advancing knowledge in a particular area, writing a dissertation can help you achieve that goal.
• Meeting Academic Requirements : If you are a graduate student, writing a dissertation may be a requirement for completing your program. Be sure to check with your academic advisor to determine if this is the case for you.

Purpose of Dissertation

some common purposes of a dissertation include:

• To contribute to the knowledge in a particular field : A dissertation is often the culmination of years of research and study, and it should make a significant contribution to the existing body of knowledge in a particular field.
• To demonstrate mastery of a subject: A dissertation requires extensive research, analysis, and writing, and completing one demonstrates a student’s mastery of their subject area.
• To develop critical thinking and research skills : A dissertation requires students to think critically about their research question, analyze data, and draw conclusions based on evidence. These skills are valuable not only in academia but also in many professional fields.
• To demonstrate academic integrity: A dissertation must be conducted and written in accordance with rigorous academic standards, including ethical considerations such as obtaining informed consent, protecting the privacy of participants, and avoiding plagiarism.
• To prepare for an academic career: Completing a dissertation is often a requirement for obtaining a PhD and pursuing a career in academia. It can demonstrate to potential employers that the student has the necessary skills and experience to conduct original research and make meaningful contributions to their field.
• To develop writing and communication skills: A dissertation requires a significant amount of writing and communication skills to convey complex ideas and research findings in a clear and concise manner. This skill set can be valuable in various professional fields.
• To demonstrate independence and initiative: A dissertation requires students to work independently and take initiative in developing their research question, designing their study, collecting and analyzing data, and drawing conclusions. This demonstrates to potential employers or academic institutions that the student is capable of independent research and taking initiative in their work.
• To contribute to policy or practice: Some dissertations may have a practical application, such as informing policy decisions or improving practices in a particular field. These dissertations can have a significant impact on society, and their findings may be used to improve the lives of individuals or communities.
• To pursue personal interests: Some students may choose to pursue a dissertation topic that aligns with their personal interests or passions, providing them with the opportunity to delve deeper into a topic that they find personally meaningful.

Advantage of Dissertation

Some advantages of writing a dissertation include:

• Developing research and analytical skills: The process of writing a dissertation involves conducting extensive research, analyzing data, and presenting findings in a clear and coherent manner. This process can help students develop important research and analytical skills that can be useful in their future careers.
• Demonstrating expertise in a subject: Writing a dissertation allows students to demonstrate their expertise in a particular subject area. It can help establish their credibility as a knowledgeable and competent professional in their field.
• Contributing to the academic community: A well-written dissertation can contribute new knowledge to the academic community and potentially inform future research in the field.
• Improving writing and communication skills : Writing a dissertation requires students to write and present their research in a clear and concise manner. This can help improve their writing and communication skills, which are essential for success in many professions.
• Increasing job opportunities: Completing a dissertation can increase job opportunities in certain fields, particularly in academia and research-based positions.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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This PSR Tip Sheet provides some basic tips about how to write good survey questions and design a good survey questionnaire.

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Surveys for dissertation

We are starting a series of articles created for students who are looking for an online survey and questionnaire tool for their dissertations.

While planning your survey you can select from multiple research techniques. Each one can be the subject of a separate article. In this article, we will focus on CAWI (Computer-Assisted Web Interview).

Thanks to Internet and survey tools development (like SurveyLab) CAWI is gaining popularity not only among students but also among big organizations and corporations.

The most important advantages of CAWI are low cost of the research, a clear process that is easy to control, and short time needed to create a survey and collect responses.

How to start – research goal

Before you start work on your questionnaire design, define the goal you want to accomplish. It may occur that survey research is not the best way to do it.

It is always good to write your goal on a blank sheet of paper. It will help you to clarify your thoughts.

Target group

Define your target group. It means estimate how many responses you need to be able to start data analysis and what is your respondent profile (e.g. people living in big cities – over 200k citizens, that are active tourists – spend at least one weekend outside the city).

Questionnaire

Now you can start work on your questionnaire. This phase is very important as questionnaire quality will impact survey results and report quality. There are a few rules you should follow :

• Remember that your respondents won’t be able to ask you additional questions and you won’t be able to provide additional explanations. Therefore your questionnaire should be clear and easy to understand.
• Your questionnaire and questions quality will impact report and survey results.
• Use easy-to-understand language, and avoid technical jargon, slang, or abbreviations.
• Don’t try to add every question to your survey. Ask yourself if you need each question that has been added.
• Your questionnaire should be logical, which means questions should be grouped by subjects.
• Don’t forget about statistical and demographic questions. It will help you to analyze your database using such criteria as gender, age, city, occupation, …
• Test your questionnaire, send it to your friends, and test if the survey is clear and easy to complete.

Project start

Select the best date to start data collection. For example, December, 24 won’t be the best idea to start data collection. Make sure that selected date is best for your target group.

Response analysis

SurveyLab was designed to make hard work for you. The system will automatically collect responses, aggregate them, and present them in a single report.

When selecting a survey tool make sure it will be able to export data in the format you need it. SurveyLab allows you to download survey results in CSV, Excel, or SPSS format.

Try SurveyLab for free Best survey tool with great features

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Quantitative Research

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7+ Reasons to Use Surveys in Your Dissertation

Writing a dissertation is a serious milestone. Your degree depends on it, so it takes a lot of effort and time to figure out what direction to choose. Everything starts with the topic: you read background literature, consult with your supervisor and seek approval before you start writing the first draft. After that, you need to decide how you will collect the data that is supposed to contribute to the research field.

This is where it gets complicated. If you have never tried conducting primary research (i.e. working with human subjects), it can seem quite scary. Analyzing articles may sound like the safest and the coolest option. Yet, there might not be enough information for you to claim that your research is somehow novel.

To make sure it is, you might need to conduct primary research, and the survey method is the most widespread tool to do that. The number of advantages surveys present is huge. However, there are various perks depending on what approach you pursue. So, let’s go through all of them before you decide to pay for essay and order a dissertation that will go on and on about analyzing literature and nothing else except it.

In the quantitative primary research, students have to calculate the data received from typical a, b, c, d questionnaires. The latter provides precise answers and helps prove or reject the formulated hypothesis. For the research to be legit, there are several stages to go through like:

• Discarding irrelevant or subjective questions/answers included in questionnaires.
• Setting criteria for credible answers.
• Composing an explanation of how you will manage ethical concerns (for participants and university committee).

However, all this is done to prevent issues in the future. Provided you have taken care of all the points above, you will get to enjoy the following benefits.

Data Collection Is Less Tedious

There are numerous services, like Survey Monkey, that the best write my essay services use. It can help you distribute your questionnaire among potential participants. These platforms simplify the data collection process. You don’t have to arrange calls or convince someone that they can safely share the information. Just upload the consent letter each participant has to sign and let the platform guide them further.

Data Analysis Is Fast

In quantitative analysis, all you have to take care of is mainly data entry. It requires focus and accuracy, but the rest can be done with the help of software. Whether it’s ordinary Excel or something like SPSS, you don’t have to reread loads of text. Just make sure you download the collected data from the platform correctly, remove irrelevant fields, and feed the rest to your computer.

Numbers Rule

Numbers don’t lie (unless you miscalculated them, of course). They give a clear answer: it’s either ‘yes’ or ‘no’. Moreover, they leave more room for creating good visuals and making your paper less boring. Just make sure you explain the numbers properly and compare the results between various graphs and charts.

No Room For Subjectivity

A quantitative dissertation is mostly a technical paper. It’s not about creativity and your ability to impress like in admission essays students usually delegate to admission essay writing services to avoid babbling about things they deem senseless. It’s about following particular procedures. And there is also a less abstract analysis.

Qualitative-oriented surveys are about conducting full-fledged personal interviews, working with focus groups, or distributing open-ended questionnaires requiring short but unique answers. Let’s talk about what makes this approach worth trying!

First-Hand Experience

The ability to gain a unique perspective is what distinguishes interviews from other surveys. Close-ended questions may be too rigid and make participants omit a lot of information that might help the research. In an interview, you may also correct some of your questions, and add more details to them, thus improving the outcomes.

More Diverse and Honest Answers

When participants are limited by only several options, they might choose something they cannot fully relate to. So, there is no guarantee that the results will be authentic. Meanwhile, with open-ended questions, participants share a lot of details.

Sure, some of them may be less relevant to your topic, but the researcher gains a deeper understanding of the issues lying beneath the topic. Of course, all of it is guaranteed only if the researcher provides anonymity and a safe space for the interviewees to share their thoughts freely.

No Need For Complex Software

In contrast to quantitative analysis, here, you won’t have to use formulae and learn how to perform complex tests. You might not even need Excel, except for storing some data about your participants. However, no calculations will be needed, which is also a relief for those who are not used to working with such kind of data.

Both types of research have also other advantages:

• With surveys, you have more chances to fill the literature gap you’ve discovered.
• Primary research may not be quite easy, but it’s highly valued at the doctoral level of education.
• You receive a lot of new information and stay away from retelling literature that has been published before.
• Primary research is less boring.

However, there is a must-remember thing: not every supervisor or university committee approves of surveys and primary research in general. It depends on numerous aspects like topic and subject, the conditions of research, your approach to handling human subjects, etc.

It means that the methodology you are going to use should be approved by your professor first. Otherwise, you may have to discard some parts of your draft and lose time gathering data you won’t be able to use. So, take care and good luck!

7+ Reasons to Use Surveys in Your Dissertation FAQ

What are the benefits of using surveys in a dissertation, surveys can provide a large amount of data in a short amount of time, they are cost-effective and can allow for anonymity, they can reach a wide audience, and they can be used to obtain feedback from the participants., how can i ensure that my survey results are accurate, make sure to ask questions that are clear and concise and that there are no bias in the questions. make sure to have a good sample size and to have a response rate that is high enough to provide accurate results., how can i analyze the survey results, depending on the type of survey, there are various analysis techniques that can be used. these include descriptive statistics, inferential statistics, correlation analysis, and regression analysis., what are the limitations of surveys, surveys can be subject to sampling errors, response bias, and interviewer effects. they may also not be able to capture the full range of opinions and attitudes of the population., like what you see share with a friend..

Sarath Shyamson

Sarath Shyamson is the customer success person at BlockSurvey and also heads the outreach. He enjoys volunteering for the church choir.

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Example application of MetaIPM to the Illinois River v2.0

This repository contains an example application of the Meta-IPM Python package ( https://doi.org/10.5066/P9427H6M ). The specific application focuses on the Illinois River using existing public data. The code for this project assumes the reader is familiar with Jupyter Notebooks, enough Conda and Python to install the Meta-IPM package, and population ecology. The example also assumes the user is familiar with the Meta-IPM package from the package's documentation.

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Richard erickson, phd, research ecologist, hannah m thompson, phd, mathematical statistician, michael n fienen, research hydrologist.

If we’re all so busy, why isn’t anything getting done?

Have you ever asked why it’s so difficult to get things done in business today—despite seemingly endless meetings and emails? Why it takes so long to make decisions—and even then not necessarily the right ones? You’re not the first to think there must be a better way. Many organizations address these problems by redesigning boxes and lines: who does what and who reports to whom. This exercise tends to focus almost obsessively on vertical command relationships and rarely solves for what, in our experience, is the underlying disease: the poor design and execution of collaborative interactions.

About the authors

This article is a collaborative effort by Aaron De Smet , Caitlin Hewes, Mengwei Luo, J.R. Maxwell , and Patrick Simon , representing views from McKinsey’s People & Organizational Performance Practice.

In our efforts to connect across our organizations, we’re drowning in real-time virtual interaction technology, from Zoom to Slack to Teams, plus group texting, WeChat, WhatsApp, and everything in between. There’s seemingly no excuse to not collaborate. The problem? Interacting is easier than ever, but true, productive, value-creating collaboration is not. And what’s more, where engagement is occurring, its quality is deteriorating. This wastes valuable resources, because every minute spent on a low-value interaction eats into time that could be used for important, creative, and powerful activities.

It’s no wonder a recent McKinsey survey  found 80 percent of executives were considering or already implementing changes in meeting structure and cadence in response to the evolution in how people work due to the COVID-19 pandemic. Indeed, most executives say they frequently find themselves spending way too much time on pointless interactions that drain their energy and produce information overload.

Most executives say they frequently find themselves spending way too much time on pointless interactions.

Three critical collaborative interactions

What can be done? We’ve found it’s possible to quickly improve collaborative interactions by categorizing them by type and making a few shifts accordingly. We’ve observed three broad categories of collaborative interactions (exhibit):

• Decision making, including complex or uncertain decisions (for example, investment decisions) and cross-cutting routine decisions (such as quarterly business reviews)
• Creative solutions and coordination, including innovation sessions (for example, developing new products) and routine working sessions (such as daily check-ins)
• Information sharing, including one-way communication (video, for instance) and two-way communication (such as town halls with Q&As)

Below we describe the key shifts required to improve each category of collaborative interaction, as well as tools you can use to pinpoint problems in the moment and take corrective action.

Decision making: Determining decision rights

When you’re told you’re “responsible” for a decision, does that mean you get to decide? What if you’re told you’re “accountable”? Do you cast the deciding vote, or does the person responsible? What about those who must be “consulted”? Sometimes they are told their input will be reflected in the final answer—can they veto a decision if they feel their input was not fully considered?

It’s no wonder one of the key factors for fast, high-quality decisions is to clarify exactly who makes them. Consider a success story at a renewable-energy company. To foster accountability and transparency, the company developed a 30-minute “role card” conversation for managers to have with their direct reports. As part of this conversation, managers explicitly laid out the decision rights and accountability metrics for each direct report. The result? Role clarity enabled easier navigation for employees, sped up decision making, and resulted in decisions that were much more customer focused.

How to define decision rights

We recommend a simple yet comprehensive approach for defining decision rights. We call it DARE, which stands for deciders, advisers, recommenders, and executors:

Deciders are the only ones with a vote (unlike the RACI model, which helps determine who is responsible, accountable, consulted, and informed). If the deciders get stuck, they should jointly agree on how to escalate the decision or figure out a way to move the process along, even if it means agreeing to “disagree and commit.”

Advisers have input and help shape the decision. They have an outsize voice in setting the context of the decision and have a big stake in its outcome—for example, it may affect their profit-and-loss statements—but they don’t get a vote.

Recommenders conduct the analyses, explore the alternatives, illuminate the pros and cons, and ultimately recommend a course of action to advisers and deciders. They see the day-to-day implications of the decision but also have no vote. Best-in-class recommenders offer multiple options and sometimes invite others to suggest more if doing so may lead to better outcomes. A common mistake of recommenders, though, is coming in with only one recommendation (often the status quo) and trying to convince everyone it’s the best path forward. In general, the more recommenders, the better the process—but not in the decision meeting itself.

Executers don’t give input but are deeply involved in implementing the decision. For speed, clarity, and alignment, executers need to be in the room when the decision is made so they can ask clarifying questions and spot flaws that might hinder implementation. Notably, the number of executers doesn’t necessarily depend on the importance of the decision. An M&A decision, for example, might have just two executors: the CFO and a business-unit head.

To make this shift, ensure everyone is crystal clear about who has a voice but no vote or veto. Our research indicates while it is often helpful to involve more people in decision making, not all of them should be deciders—in many cases, just one individual should be the decider (see sidebar “How to define decision rights”). Don’t underestimate the difficulty of implementing this. It often goes against our risk-averse instinct to ensure everyone is “happy” with a decision, particularly our superiors and major stakeholders. Executing and sustaining this change takes real courage and leadership.

Creative solutions and coordination: Open innovation

Routine working sessions are fairly straightforward. What many organizations struggle with is finding innovative ways to identify and drive toward solutions. How often do you tell your teams what to do versus empowering them to come up with solutions? While they may solve the immediate need to “get stuff done,” bureaucracies and micromanagement are a recipe for disaster. They slow down the organizational response to the market and customers, prevent leaders from focusing on strategic priorities, and harm employee engagement. Our research suggests  key success factors in winning organizations are empowering employees  and spending more time on high-quality coaching interactions.

How microenterprises empower employees to drive innovative solutions

Haier, a Chinese appliance maker, created more than 4,000 microenterprises (MEs) that share common approaches but operate independently. Haier has three types of microenterprises:

• Market-facing MEs have roots in Haier’s legacy appliance business, reinvented for today’s customer-centric, web-enabled world. They are expected to grow revenue and profit ten times faster than the industry average.
• Incubating MEs focus on emerging markets such as e-gaming or wrapping new business models around familiar products. They currently account for more than 10 percent of Haier’s market capitalization.
• “Node” MEs sell market-facing ME products and services such as design, manufacturing, and human-resources support.

Take Haier. The Chinese appliance maker divided itself into more than 4,000 microenterprises with ten to 15 employees each, organized in an open ecosystem of users, inventors, and partners (see sidebar “How microenterprises empower employees to drive innovative solutions”). This shift turned employees into energetic entrepreneurs who were directly accountable for customers. Haier’s microenterprises are free to form and evolve with little central direction, but they share the same approach to target setting, internal contracting, and cross-unit coordination. Empowering employees to drive innovative solutions has taken the company from innovation-phobic to entrepreneurial at scale. Since 2015, revenue from Haier Smart Home, the company’s listed home-appliance business, has grown by more than 18 percent a year, topping 209 billion renminbi ($32 billion) in 2020. The company has also made a string of acquisitions, including the 2016 purchase of GE Appliances, with new ventures creating more than $2 billion in market value.

Empowering others doesn’t mean leaving them alone. Successful empowerment, counterintuitively, doesn’t mean leaving employees alone. Empowerment requires leaders to give employees both the tools and the right level of guidance and involvement. Leaders should play what we call the coach role: coaches don’t tell people what to do but instead provide guidance and guardrails and ensure accountability, while stepping back and allowing others to come up with solutions.

Haier was able to use a variety of tools—including objectives and key results (OKRs) and common problem statements—to foster an agile way of working across the enterprise that focuses innovative organizational energy on the most important topics. Not all companies can do this, and some will never be ready for enterprise agility. But every organization can take steps to improve the speed and quality of decisions made by empowered individuals.

Managers who are great coaches, for example, have typically benefited from years of investment by mentors, sponsors, and organizations. We think all organizations should do more to improve the coaching skills of managers and help them to create the space and time to coach teams, as opposed to filling out reports, presenting in meetings, and other activities that take time away from driving impact through the work of their teams.

But while great coaches take time to develop, something as simple as a daily stand-up or check-in can drive horizontal connectivity, creating the space for teams to understand what others are doing and where they need help to drive work forward without having to specifically task anyone in a hierarchical way. You may also consider how you are driving a focus on outcomes over activities on a near-term and long-term basis. Whether it’s OKRs or something else, how is your organization proactively communicating a focus on impact and results over tasks and activities? What do you measure? How is it tracked? How is the performance of your people and your teams managed against it? Over what time horizons?

The importance of psychological safety. As you start this journey, be sure to take a close look at psychological safety. If employees don’t feel psychologically safe, it will be nearly impossible for leaders and managers to break through disempowering behaviors like constant escalation, hiding problems or risks, and being afraid to ask questions—no matter how skilled they are as coaches.

Employers should be on the lookout for common problems indicating that significant challenges to psychological safety lurk underneath the surface. Consider asking yourself and your teams questions to test the degree of psychological safety you have cultivated: Do employees have space to bring up concerns or dissent? Do they feel that if they make a mistake it will be held against them? Do they feel they can take risks or ask for help? Do they feel others may undermine them? Do employees feel valued for their unique skills and talents? If the answer to any of these is not a clear-cut “yes,” the organization likely has room for improvement on psychological safety and relatedness as a foundation to high-quality interactions within and between teams.

Information sharing: Fit-for-purpose interactions

Do any of these scenarios sound familiar? You spend a significant amount of time in meetings every day but feel like nothing has been accomplished. You jump from one meeting to another and don’t get to think on your own until 7 p.m. You wonder why you need to attend a series of meetings where the same materials are presented over and over again. You’re exhausted.

An increasing number of organizations have begun to realize the urgency of driving ruthless meeting efficiency and of questioning whether meetings are truly required at all to share information. Live interactions can be useful for information sharing, particularly when there is an interpretive lens required to understand the information, when that information is particularly sensitive, or when leaders want to ensure there’s ample time to process it and ask questions. That said, most of us would say that most meetings are not particularly useful and often don’t accomplish their intended objective.

We have observed that many companies are moving to shorter meetings (15 to 30 minutes) rather than the standard default of one-hour meetings in an effort to drive focus and productivity. For example, Netflix launched a redesign effort to drastically improve meeting efficiency, resulting in a tightly controlled meeting protocol. Meetings cannot go beyond 30 minutes. Meetings for one-way information sharing must be canceled in favor of other mechanisms such as a memo, podcast, or vlog. Two-way information sharing during meetings is limited by having attendees review materials in advance, replacing presentations with Q&As. Early data show Netflix has been able to reduce the number of meetings by more than 65 percent, and more than 85 percent of employees favor the approach.

Making meeting time a scarce resource is another strategy organizations are using to improve the quality of information sharing and other types of interactions occurring in a meeting setting. Some companies have implemented no-meeting days. In Japan, Microsoft’s “Work Life Choice Challenge” adopted a four-day workweek, reduced the time employees spend in meetings—and boosted productivity by 40 percent. 1 Bill Chappell, “4-day workweek boosted workers’ productivity by 40%, Microsoft Japan says,” NPR, November 4, 2019, npr.org. Similarly, Shopify uses “No Meeting Wednesdays” to enable employees to devote time to projects they are passionate about and to promote creative thinking. 2 Amy Elisa Jackson, “Feedback & meeting-free Wednesdays: How Shopify beats the competition,” Glassdoor, December 5, 2018, glassdoor.com. And Moveline’s product team dedicates every Tuesday to “Maker Day,” an opportunity to create and solve complex problems without the distraction of meetings. 3 Rebecca Greenfield, “Why your office needs a maker day,” Fast Company , April 17, 2014, fastcompany.com.

Finally, no meeting could be considered well scoped without considering who should participate, as there are real financial and transaction costs to meeting participation. Leaders should treat time spent in meetings as seriously as companies treat financial capital. Every leader in every organization should ask the following questions before attending any meeting: What’s this meeting for? What’s my role? Can I shorten this meeting by limiting live information sharing and focusing on discussion and decision making? We encourage you to excuse yourself from meetings if you don’t have a role in influencing the outcome and to instead get a quick update over email. If you are not essential, the meeting will still be successful (possibly more so!) without your presence. Try it and see what happens.

High-quality, focused interactions can improve productivity, speed, and innovation within any organization—and drive better business performance. We hope the above insights have inspired you to try some new techniques to improve the effectiveness and efficiency of collaboration within your organization.

Aaron De Smet is a senior partner in McKinsey’s New Jersey office; Caitlin Hewes is a consultant in the Atlanta office; Mengwei Luo is an associate partner in the New York office; J.R. Maxwell is a partner in the Washington, DC, office; and Patrick Simon is a partner in the Munich office.

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Biden vs. Trump remains close, so next week's debate offers them an opportunity

Ashley Lopez

NPR June poll

As President Biden and former President Donald Trump gear up for their first televised debate, a new NPR/ PBS News /Marist poll finds them tied among registered voters nationally. Mandel Ngan/AFP via Getty Images; Bill Pugliano/Getty Images hide caption

The presidential election remains essentially tied as President Biden and former President Donald Trump prepare for their first televised debate next week.

According to the latest NPR/PBS News/Marist poll , out Tuesday, Biden and Trump both received 49% support among registered voters nationally. That includes undecided voters who are leaning toward one candidate. The survey had a margin of error of nearly 4 percentage points.

That’s relatively unchanged since last month’s poll .

Lee Miringoff — director of the Marist College Institute for Public Opinion, which conducted the survey — said most voters have decided who they will vote for in the presidential election.

However, 9% of voters polled said they haven’t yet made up their mind who they’ll vote for. And another 25% said they have a “good idea” who they would vote for, but “could still change [their] mind.”

“I think that this to some degree taps into the notion that there are still a lot of months to go,” Miringoff said.

The first of two planned presidential debates is on June 27 , and 6 in 10 survey respondents said they plan to watch it. Because the debates and party conventions have yet to happen, Miringoff said voters are more likely to tell pollsters that they are open to seeing “how things play out.”

“That’s even if they do end up right back where they were,” he said, “because they interpret these events in ways that reinforce what they already think.”

In particular, the poll found younger voters and nonwhite voters were more likely than other groups to say their vote could change. Among both groups, just about 55% said they know for sure who they’re voting for.

The poll also found that Biden is making some inroads among independent voters. Biden’s support among these voters went from 42% in last month's survey to 50%.

“This may be the group that is most influenced by the results of Donald Trump's legal problems ,” Miringoff said. “But independents are very much more persuadable in the whole sea of things that are going to affect this outcome of the election.”

David North, an independent voter in Connecticut who participated in the survey, said he supported Republican candidates until eight years ago, but is also most likely going to vote for Biden in November.

“I think we have to go with Biden just because he's a regular, old time, slick politician that knows how to get things done,” he said. “They just might not like what he gets done. But at least he's playing the game kind of by our traditional rules.”

The poll found that while Biden is gaining an edge among independent voters, Trump is doing better among voters who disapprove of both candidates .

President Biden and former President Trump face off in CNN debate next week

Miringoff also said some of the more unusual electorate patterns in this race “are now starting to normalize” a little bit.

For example, Biden had previously been overperforming expectations among white voters. But since last month's survey, Trump’s percentage point advantage among white voters doubled from a 6-point to a 12-point lead.

Conversely, Biden is starting to improve his support among nonwhite voters. According to this latest poll, Biden (58%) leads Trump (40%) by 18 percentage points. The president previously had an 11-percentage point advantage among this group of voters.

In a broader contest, with independent and third-party candidates, Trump leads Biden, 42%-41%, with independent Robert F. Kennedy Jr., who’s not yet qualified for CNN’s debate next week, coming in third, at 11% support.

"What's been interesting and continues to be interesting is that Robert Kennedy, who gets the most votes of a minor-party candidate, is drawing from both of them [Biden and Trump] roughly equally,” Miringoff said.

What about Trump’s conviction?

A slim majority of poll respondents — 51% — said Trump definitely or probably should serve prison time after his historic recent hush money conviction in New York. Forty-seven percent said Trump should definitely or probably not be incarcerated.

What voters say about the issues that matter to them

Out of a set list of six issues, a plurality of respondents (30%) said inflation is top of mind when they think about voting in the general election. That was followed by preserving democracy (29%) and immigration (18%).

Comparing Trump and Biden, most respondents said Trump would be better at handling the economy and immigration, while they said the same of Biden on preserving democracy and abortion.

The survey of 1,311 adults was conducted June 10 to 12 by the Marist Institute for Public Opinion. The margin of error for the overall sample is +/- 3.6 percentage points.

• presidential election

Literature Review Example/Sample

Detailed Walkthrough + Free Literature Review Template

If you’re working on a dissertation or thesis and are looking for an example of a strong literature review chapter , you’ve come to the right place.

In this video, we walk you through an A-grade literature review from a dissertation that earned full distinction . We start off by discussing the five core sections of a literature review chapter by unpacking our free literature review template . This includes:

• The literature review opening/ introduction section
• The theoretical framework (or foundation of theory)
• The empirical research
• The research gap
• The closing section

We then progress to the sample literature review (from an A-grade Master’s-level dissertation) to show how these concepts are applied in the literature review chapter. You can access the free resources mentioned in this video below.

PS – If you’re working on a dissertation, be sure to also check out our collection of dissertation and thesis examples here .

FAQ: Literature Review Example

Literature review example: frequently asked questions, is the sample literature review real.

Yes. The literature review example is an extract from a Master’s-level dissertation for an MBA program. It has not been edited in any way.

Can I replicate this literature review for my dissertation?

As we discuss in the video, every literature review will be slightly different, depending on the university’s unique requirements, as well as the nature of the research itself. Therefore, you’ll need to tailor your literature review to suit your specific context.

You can learn more about the basics of writing a literature review here .

Where can I find more examples of literature reviews?

The best place to find more examples of literature review chapters would be within dissertation/thesis databases. These databases include dissertations, theses and research projects that have successfully passed the assessment criteria for the respective university, meaning that you have at least some sort of quality assurance. 

The Open Access Thesis Database (OATD) is a good starting point. 

How do I get the literature review template?

You can access our free literature review chapter template here .

Is the template really free?

Yes. There is no cost for the template and you are free to use it as you wish. 

Psst… there’s more!

This post is an extract from our bestselling short course, Literature Review Bootcamp . If you want to work smart, you don't want to miss this .

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