Quantitative Data Analysis: A Comprehensive Guide

By: Ofem Eteng | Published: May 18, 2022

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data analysis in quantitative research article

A healthcare giant successfully introduces the most effective drug dosage through rigorous statistical modeling, saving countless lives. A marketing team predicts consumer trends with uncanny accuracy, tailoring campaigns for maximum impact.

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These trends and dosages are not just any numbers but are a result of meticulous quantitative data analysis. Quantitative data analysis offers a robust framework for understanding complex phenomena, evaluating hypotheses, and predicting future outcomes.

In this blog, we’ll walk through the concept of quantitative data analysis, the steps required, its advantages, and the methods and techniques that are used in this analysis. Read on!

What is Quantitative Data Analysis?

Quantitative data analysis is a systematic process of examining, interpreting, and drawing meaningful conclusions from numerical data. It involves the application of statistical methods, mathematical models, and computational techniques to understand patterns, relationships, and trends within datasets.

Quantitative data analysis methods typically work with algorithms, mathematical analysis tools, and software to gain insights from the data, answering questions such as how many, how often, and how much. Data for quantitative data analysis is usually collected from close-ended surveys, questionnaires, polls, etc. The data can also be obtained from sales figures, email click-through rates, number of website visitors, and percentage revenue increase. 

Quantitative Data Analysis vs Qualitative Data Analysis

When we talk about data, we directly think about the pattern, the relationship, and the connection between the datasets – analyzing the data in short. Therefore when it comes to data analysis, there are broadly two types – Quantitative Data Analysis and Qualitative Data Analysis.

Quantitative data analysis revolves around numerical data and statistics, which are suitable for functions that can be counted or measured. In contrast, qualitative data analysis includes description and subjective information – for things that can be observed but not measured.

Let us differentiate between Quantitative Data Analysis and Quantitative Data Analysis for a better understanding.

Numerical data – statistics, counts, metrics measurementsText data – customer feedback, opinions, documents, notes, audio/video recordings
Close-ended surveys, polls and experiments.Open-ended questions, descriptive interviews
What? How much? Why (to a certain extent)?How? Why? What are individual experiences and motivations?
Statistical programming software like R, Python, SAS and Data visualization like Tableau, Power BINVivo, Atlas.ti for qualitative coding.
Word processors and highlighters – Mindmaps and visual canvases
Best used for large sample sizes for quick answers.Best used for small to middle sample sizes for descriptive insights

Data Preparation Steps for Quantitative Data Analysis

Quantitative data has to be gathered and cleaned before proceeding to the stage of analyzing it. Below are the steps to prepare a data before quantitative research analysis:

  • Step 1: Data Collection

Before beginning the analysis process, you need data. Data can be collected through rigorous quantitative research, which includes methods such as interviews, focus groups, surveys, and questionnaires.

  • Step 2: Data Cleaning

Once the data is collected, begin the data cleaning process by scanning through the entire data for duplicates, errors, and omissions. Keep a close eye for outliers (data points that are significantly different from the majority of the dataset) because they can skew your analysis results if they are not removed.

This data-cleaning process ensures data accuracy, consistency and relevancy before analysis.

  • Step 3: Data Analysis and Interpretation

Now that you have collected and cleaned your data, it is now time to carry out the quantitative analysis. There are two methods of quantitative data analysis, which we will discuss in the next section.

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Now that you are familiar with what quantitative data analysis is and how to prepare your data for analysis, the focus will shift to the purpose of this article, which is to describe the methods and techniques of quantitative data analysis.

Methods and Techniques of Quantitative Data Analysis

Quantitative data analysis employs two techniques to extract meaningful insights from datasets, broadly. The first method is descriptive statistics, which summarizes and portrays essential features of a dataset, such as mean, median, and standard deviation.

Inferential statistics, the second method, extrapolates insights and predictions from a sample dataset to make broader inferences about an entire population, such as hypothesis testing and regression analysis.

An in-depth explanation of both the methods is provided below:

  • Descriptive Statistics
  • Inferential Statistics

1) Descriptive Statistics

Descriptive statistics as the name implies is used to describe a dataset. It helps understand the details of your data by summarizing it and finding patterns from the specific data sample. They provide absolute numbers obtained from a sample but do not necessarily explain the rationale behind the numbers and are mostly used for analyzing single variables. The methods used in descriptive statistics include: 

  • Mean:   This calculates the numerical average of a set of values.
  • Median: This is used to get the midpoint of a set of values when the numbers are arranged in numerical order.
  • Mode: This is used to find the most commonly occurring value in a dataset.
  • Percentage: This is used to express how a value or group of respondents within the data relates to a larger group of respondents.
  • Frequency: This indicates the number of times a value is found.
  • Range: This shows the highest and lowest values in a dataset.
  • Standard Deviation: This is used to indicate how dispersed a range of numbers is, meaning, it shows how close all the numbers are to the mean.
  • Skewness: It indicates how symmetrical a range of numbers is, showing if they cluster into a smooth bell curve shape in the middle of the graph or if they skew towards the left or right.

2) Inferential Statistics

In quantitative analysis, the expectation is to turn raw numbers into meaningful insight using numerical values, and descriptive statistics is all about explaining details of a specific dataset using numbers, but it does not explain the motives behind the numbers; hence, a need for further analysis using inferential statistics.

Inferential statistics aim to make predictions or highlight possible outcomes from the analyzed data obtained from descriptive statistics. They are used to generalize results and make predictions between groups, show relationships that exist between multiple variables, and are used for hypothesis testing that predicts changes or differences.

There are various statistical analysis methods used within inferential statistics; a few are discussed below.

  • Cross Tabulations: Cross tabulation or crosstab is used to show the relationship that exists between two variables and is often used to compare results by demographic groups. It uses a basic tabular form to draw inferences between different data sets and contains data that is mutually exclusive or has some connection with each other. Crosstabs help understand the nuances of a dataset and factors that may influence a data point.
  • Regression Analysis: Regression analysis estimates the relationship between a set of variables. It shows the correlation between a dependent variable (the variable or outcome you want to measure or predict) and any number of independent variables (factors that may impact the dependent variable). Therefore, the purpose of the regression analysis is to estimate how one or more variables might affect a dependent variable to identify trends and patterns to make predictions and forecast possible future trends. There are many types of regression analysis, and the model you choose will be determined by the type of data you have for the dependent variable. The types of regression analysis include linear regression, non-linear regression, binary logistic regression, etc.
  • Monte Carlo Simulation: Monte Carlo simulation, also known as the Monte Carlo method, is a computerized technique of generating models of possible outcomes and showing their probability distributions. It considers a range of possible outcomes and then tries to calculate how likely each outcome will occur. Data analysts use it to perform advanced risk analyses to help forecast future events and make decisions accordingly.
  • Analysis of Variance (ANOVA): This is used to test the extent to which two or more groups differ from each other. It compares the mean of various groups and allows the analysis of multiple groups.
  • Factor Analysis:   A large number of variables can be reduced into a smaller number of factors using the factor analysis technique. It works on the principle that multiple separate observable variables correlate with each other because they are all associated with an underlying construct. It helps in reducing large datasets into smaller, more manageable samples.
  • Cohort Analysis: Cohort analysis can be defined as a subset of behavioral analytics that operates from data taken from a given dataset. Rather than looking at all users as one unit, cohort analysis breaks down data into related groups for analysis, where these groups or cohorts usually have common characteristics or similarities within a defined period.
  • MaxDiff Analysis: This is a quantitative data analysis method that is used to gauge customers’ preferences for purchase and what parameters rank higher than the others in the process. 
  • Cluster Analysis: Cluster analysis is a technique used to identify structures within a dataset. Cluster analysis aims to be able to sort different data points into groups that are internally similar and externally different; that is, data points within a cluster will look like each other and different from data points in other clusters.
  • Time Series Analysis: This is a statistical analytic technique used to identify trends and cycles over time. It is simply the measurement of the same variables at different times, like weekly and monthly email sign-ups, to uncover trends, seasonality, and cyclic patterns. By doing this, the data analyst can forecast how variables of interest may fluctuate in the future. 
  • SWOT analysis: This is a quantitative data analysis method that assigns numerical values to indicate strengths, weaknesses, opportunities, and threats of an organization, product, or service to show a clearer picture of competition to foster better business strategies

How to Choose the Right Method for your Analysis?

Choosing between Descriptive Statistics or Inferential Statistics can be often confusing. You should consider the following factors before choosing the right method for your quantitative data analysis:

1. Type of Data

The first consideration in data analysis is understanding the type of data you have. Different statistical methods have specific requirements based on these data types, and using the wrong method can render results meaningless. The choice of statistical method should align with the nature and distribution of your data to ensure meaningful and accurate analysis.

2. Your Research Questions

When deciding on statistical methods, it’s crucial to align them with your specific research questions and hypotheses. The nature of your questions will influence whether descriptive statistics alone, which reveal sample attributes, are sufficient or if you need both descriptive and inferential statistics to understand group differences or relationships between variables and make population inferences.

Pros and Cons of Quantitative Data Analysis

1. Objectivity and Generalizability:

  • Quantitative data analysis offers objective, numerical measurements, minimizing bias and personal interpretation.
  • Results can often be generalized to larger populations, making them applicable to broader contexts.

Example: A study using quantitative data analysis to measure student test scores can objectively compare performance across different schools and demographics, leading to generalizable insights about educational strategies.

2. Precision and Efficiency:

  • Statistical methods provide precise numerical results, allowing for accurate comparisons and prediction.
  • Large datasets can be analyzed efficiently with the help of computer software, saving time and resources.

Example: A marketing team can use quantitative data analysis to precisely track click-through rates and conversion rates on different ad campaigns, quickly identifying the most effective strategies for maximizing customer engagement.

3. Identification of Patterns and Relationships:

  • Statistical techniques reveal hidden patterns and relationships between variables that might not be apparent through observation alone.
  • This can lead to new insights and understanding of complex phenomena.

Example: A medical researcher can use quantitative analysis to pinpoint correlations between lifestyle factors and disease risk, aiding in the development of prevention strategies.

1. Limited Scope:

  • Quantitative analysis focuses on quantifiable aspects of a phenomenon ,  potentially overlooking important qualitative nuances, such as emotions, motivations, or cultural contexts.

Example: A survey measuring customer satisfaction with numerical ratings might miss key insights about the underlying reasons for their satisfaction or dissatisfaction, which could be better captured through open-ended feedback.

2. Oversimplification:

  • Reducing complex phenomena to numerical data can lead to oversimplification and a loss of richness in understanding.

Example: Analyzing employee productivity solely through quantitative metrics like hours worked or tasks completed might not account for factors like creativity, collaboration, or problem-solving skills, which are crucial for overall performance.

3. Potential for Misinterpretation:

  • Statistical results can be misinterpreted if not analyzed carefully and with appropriate expertise.
  • The choice of statistical methods and assumptions can significantly influence results.

This blog discusses the steps, methods, and techniques of quantitative data analysis. It also gives insights into the methods of data collection, the type of data one should work with, and the pros and cons of such analysis.

Gain a better understanding of data analysis with these essential reads:

  • Data Analysis and Modeling: 4 Critical Differences
  • Exploratory Data Analysis Simplified 101
  • 25 Best Data Analysis Tools in 2024

Carrying out successful data analysis requires prepping the data and making it analysis-ready. That is where Hevo steps in.

Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing Hevo price , which will assist you in selecting the best plan for your requirements.

Share your experience of understanding Quantitative Data Analysis in the comment section below! We would love to hear your thoughts.

Ofem Eteng is a seasoned technical content writer with over 12 years of experience. He has held pivotal roles such as System Analyst (DevOps) at Dagbs Nigeria Limited and Full-Stack Developer at Pedoquasphere International Limited. He specializes in data science, data analytics and cutting-edge technologies, making him an expert in the data industry.

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An Overview of the Fundamentals of Data Management, Analysis, and Interpretation in Quantitative Research

Affiliations.

  • 1 Reader, School of Medicine, Dentistry & Nursing, University of Glasgow, Glasgow, Scotland, UK. Electronic address: [email protected].
  • 2 Clinical Nurse Specialist, Department of Head and Neck and ENT Cancer Surgery of the Portuguese Institute of Oncology of Francisco Gentil, Lisbon, Portugal.
  • 3 Senior Lecturer, School of Nursing and Midwifery, University of Galway, Galway, Ireland.
  • 4 Associate Professor, Catalan Institute of Oncology and Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain.
  • 5 Senior Nurse Scientist, Institute of Higher Education and Research in Healthcare (IUFRS), Faculty of Biology and Medicine, University of Lausanne, and Lausanne University Hospital, Lausanne, Switzerland.
  • 6 Associate Professor, School of Nursing, Koc University, Istanbul, Turkey.
  • 7 Clinical Nurse Specialist, Department of Gastrointestinal Surgery, Cancer Center, Ghent University Hospital, Ghent, Belgium.
  • 8 Associate Professor, School of Nursing, Psychotherapy and Community Health, Dublin City University, Dublin, Ireland.
  • 9 Reader, School of Nursing, Institute of Nursing and Health Research, Ulster University, Belfast, UK.
  • 10 Professor, Department of Clinical Research, University of Southern Denmark, Department of Oncology, Odense University Hospital, Odense, Denmark.
  • 11 Reader, School of Health and Life Sciences, University of the West of Scotland, South Lanarkshire, Scotland, UK.
  • PMID: 36868925
  • DOI: 10.1016/j.soncn.2023.151398

Objectives: To provide an overview of three consecutive stages involved in the processing of quantitative research data (ie, data management, analysis, and interpretation) with the aid of practical examples to foster enhanced understanding.

Data sources: Published scientific articles, research textbooks, and expert advice were used.

Conclusion: Typically, a considerable amount of numerical research data is collected that require analysis. On entry into a data set, data must be carefully checked for errors and missing values, and then variables must be defined and coded as part of data management. Quantitative data analysis involves the use of statistics. Descriptive statistics help summarize the variables in a data set to show what is typical for a sample. Measures of central tendency (ie, mean, median, mode), measures of spread (standard deviation), and parameter estimation measures (confidence intervals) may be calculated. Inferential statistics aid in testing hypotheses about whether or not a hypothesized effect, relationship, or difference is likely true. Inferential statistical tests produce a value for probability, the P value. The P value informs about whether an effect, relationship, or difference might exist in reality. Crucially, it must be accompanied by a measure of magnitude (effect size) to help interpret how small or large this effect, relationship, or difference is. Effect sizes provide key information for clinical decision-making in health care.

Implications for nursing practice: Developing capacity in the management, analysis, and interpretation of quantitative research data can have a multifaceted impact in enhancing nurses' confidence in understanding, evaluating, and applying quantitative evidence in cancer nursing practice.

Keywords: Data analysis; Data management; Empirical research; Interpretation; Quantitative studies; Statistics.

Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.

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Research Method

Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

What is Quantitative Research

Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

  • Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
  • Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
  • Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
  • Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
  • Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

  • Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
  • Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
  • Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
  • Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
  • Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
  • Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
  • Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

  • Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
  • Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
  • Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
  • Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
  • Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
  • Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
  • Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

  • Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
  • Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
  • Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
  • Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
  • Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
  • Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

  • To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
  • To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
  • To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
  • To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
  • To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

  • Description : To provide a detailed and accurate description of a particular phenomenon or population.
  • Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
  • Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
  • Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

  • Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
  • Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
  • Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
  • Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
  • Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
  • Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

  • Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
  • Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
  • Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
  • Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
  • Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
  • Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

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Muhammad Hassan

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  • Indian J Anaesth
  • v.60(9); 2016 Sep

Basic statistical tools in research and data analysis

Zulfiqar ali.

Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India

S Bala Bhaskar

1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

INTRODUCTION

Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ]

Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ].

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Classification of variables

Quantitative variables

Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.

A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].

Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables.

Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.

Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.

Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.

STATISTICS: DESCRIPTIVE AND INFERENTIAL STATISTICS

Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 .

Example of descriptive and inferential statistics

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Descriptive statistics

The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.

Measures of central tendency

The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is

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where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:

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where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula:

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where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula:

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where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:

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where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 .

Example of mean, variance, standard deviation

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Normal distribution or Gaussian distribution

Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ].

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Normal distribution curve

Skewed distribution

It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.

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Curves showing negatively skewed and positively skewed distribution

Inferential statistics

In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects.

Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).

In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ]

Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ]

The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ].

P values with interpretation

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If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ]

Illustration for null hypothesis

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PARAMETRIC AND NON-PARAMETRIC TESTS

Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ]

Two most basic prerequisites for parametric statistical analysis are:

  • The assumption of normality which specifies that the means of the sample group are normally distributed
  • The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.

However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data.

Parametric tests

The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA.

Student's t -test

Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:

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where X = sample mean, u = population mean and SE = standard error of mean

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where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference.

  • To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment.

The formula for paired t -test is:

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where d is the mean difference and SE denotes the standard error of this difference.

The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly.

Analysis of variance

The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups.

In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples.

However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test.

A simplified formula for the F statistic is:

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where MS b is the mean squares between the groups and MS w is the mean squares within groups.

Repeated measures analysis of variance

As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time.

As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used.

Non-parametric tests

When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power.

As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 .

Analogue of parametric and non-parametric tests

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Median test for one sample: The sign test and Wilcoxon's signed rank test

The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value.

This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample.

If the null hypothesis is true, there will be an equal number of + signs and − signs.

The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values.

Wilcoxon's signed rank test

There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample.

Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.

Mann-Whitney test

It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other.

Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2.

Kolmogorov-Smirnov test

The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.

Kruskal-Wallis test

The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.

Jonckheere test

In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ]

Friedman test

The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ]

Tests to analyse the categorical data

Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula:

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A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used.

SOFTWARES AVAILABLE FOR STATISTICS, SAMPLE SIZE CALCULATION AND POWER ANALYSIS

Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft).

There are a number of web resources which are related to statistical power analyses. A few are:

  • StatPages.net – provides links to a number of online power calculators
  • G-Power – provides a downloadable power analysis program that runs under DOS
  • Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
  • SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.

It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

RESEARCH THEMATICANALYSISMETHOD

Key Features

  • Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
  • Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
  • The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
  • The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
  • The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

  • Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
  • The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
  • Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
  • The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

  • Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
  • Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
  • Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
  • Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

  • Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
  • The research aims for objectivity (i.e., without bias) and is separated from the data.
  • The design of the study is determined before it begins.
  • For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
  • Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

  • Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
  • Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
  • Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
  • Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

  • Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
  • Useful for testing and validating already constructed theories.
  • Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
  • Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
  • Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

  • Mixed methods research
  • Designing qualitative research
  • Methods of data collection and analysis
  • Introduction to quantitative and qualitative research
  • Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
  • Qualitative research in health care: Analysing qualitative data
  • Qualitative data analysis: the framework approach
  • Using the framework method for the analysis of
  • Qualitative data in multi-disciplinary health research
  • Content Analysis
  • Grounded Theory
  • Thematic Analysis

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  • Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Qualitative vs. quantitative research

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

  • Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
  • Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
  • Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

  • Interviews : Asking open-ended questions verbally to respondents.
  • Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
  • Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
  • Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

  • Use quantitative research if you want to confirm or test something (a theory or hypothesis )
  • Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

  • Average scores ( means )
  • The number of times a particular answer was given
  • The correlation or causation between two or more variables
  • The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

  • Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
  • Thematic analysis : Closely examining the data to identify the main themes and patterns
  • Discourse analysis : Studying how communication works in social contexts

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.

  • Chi square goodness of fit test
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Inclusion and exclusion criteria

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

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

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

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

  • If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
  • If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
  • If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

  • Prepare and organize your data.
  • Review and explore your data.
  • Develop a data coding system.
  • Assign codes to the data.
  • Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Quantitative vs. Qualitative Research Design: Understanding the Differences

data analysis in quantitative research article

As a future professional in the social and education landscape, research design is one of the most critical strategies that you will master to identify challenges, ask questions and form data-driven solutions to address problems specific to your industry. 

Many approaches to research design exist, and not all work in every circumstance. While all data-focused research methods are valid in their own right, certain research design methods are more appropriate for specific study objectives.

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We will discuss the differences between quantitative (numerical and statistics-focused) and qualitative (non-numerical and human-focused) research design methods so that you can determine which approach is most strategic given your specific area of graduate-level study. 

Understanding Social Phenomena: Qualitative Research Design

Qualitative research focuses on understanding a phenomenon based on human experience and individual perception. It is a non-numerical methodology relying on interpreting a process or result. Qualitative research also paves the way for uncovering other hypotheses related to social phenomena. 

In its most basic form, qualitative research is exploratory in nature and seeks to understand the subjective experience of individuals based on social reality.

Qualitative data is…

  • often used in fields related to education, sociology and anthropology; 
  • designed to arrive at conclusions regarding social phenomena; 
  • focused on data-gathering techniques like interviews, focus groups or case studies; 
  • dedicated to perpetuating a flexible, adaptive approach to data gathering;
  • known to lead professionals to deeper insights within the overall research study.

You want to use qualitative data research design if:

  • you work in a field concerned with enhancing humankind through the lens of social change;
  • your research focuses on understanding complex social trends and individual perceptions of those trends;
  • you have interests related to human development and interpersonal relationships.

Examples of Qualitative Research Design in Education

Here are just a few examples of how qualitative research design methods can impact education:

Example 1: Former educators participate in in-depth interviews to help determine why a specific school is experiencing a higher-than-average turnover rate compared to other schools in the region. These interviews help determine the types of resources that will make a difference in teacher retention. 

Example 2: Focus group discussions occur to understand the challenges that neurodivergent students experience in the classroom daily. These discussions prepare administrators, staff, teachers and parents to understand the kinds of support that will augment and improve student outcomes.

Example 3: Case studies examine the impacts of a new education policy that limits the number of teacher aids required in a special needs classroom. These findings help policymakers determine whether the new policy affects the learning outcomes of a particular class of students.

Interpreting the Numbers: Quantitative Research Design

Quantitative research tests hypotheses and measures connections between variables. It relies on insights derived from numbers — countable, measurable and statistically sound data. Quantitative research is a strategic research design used when basing critical decisions on statistical conclusions and quantifiable data.

Quantitative research provides numerical-backed quantifiable data that may approve or discount a theory or hypothesis.

Quantitative data is…

  • often used in fields related to education, data analysis and healthcare; 
  • designed to arrive at numerical, statistical conclusions based on objective facts;
  • focused on data-gathering techniques like experiments, surveys or observations;
  • dedicated to using mathematical principles to arrive at conclusions;
  • known to lead professionals to indisputable observations within the overall research study.

You want to use quantitative data research design if:

  • you work in a field concerned with analyzing data to inform decisions;
  • your research focuses on studying relationships between variables to form data-driven conclusions;
  • you have interests related to mathematics, statistical analysis and data science.

Examples of Quantitative Research Design in Education

Here are just a few examples of how quantitative research design methods may impact education:

Example 1: Researchers compile data to understand the connection between class sizes and standardized test scores. Researchers can determine if and what the relationship is between smaller, intimate class sizes and higher test scores for grade-school children using statistical and data analysis.

Example 2: Professionals conduct an experiment in which a group of high school students must complete a certain number of community service hours before graduation. Researchers compare those students to another group of students who did not complete service hours — using statistical analysis to determine if the requirement increased college acceptance rates.

Example 3: Teachers take a survey to examine an education policy that restricts the number of extracurricular activities offered at a particular academic institution. The findings help better understand the far-reaching impacts of extracurricular opportunities on academic performance.

Making the Most of Research Design Methods for Good: Vanderbilt University’s Peabody College

Vanderbilt University's Peabody College of Education and Human Development offers a variety of respected, nationally-recognized graduate programs designed with future agents of social change in mind. We foster a culture of excellence and compassion and guide you to become the best you can be — both in the classroom and beyond.

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  • Cognitive Psychology in Context M.S. — an impactful Master of Science program that emphasizes research design and statistical analysis to understand cognitive processes and real-world applications best, making it perfect for those interested in pursuing doctoral studies in cognitive science.
  • Education Policy M.P.P — an analysis-focused Master of Public Policy program designed for future leaders in education policy and practice, allowing students to specialize in either K-12 Education Policy, Higher Education Policy or Quantitative Methods in Education Policy. 
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  • Published: 09 July 2024

Outcome evaluation of technical strategies on reduction of patient waiting time in the outpatient department at Kilimanjaro Christian Medical Centre—Northern Tanzania

  • Manasseh J. Mwanswila   ORCID: orcid.org/0000-0003-3378-2865 1 , 2 ,
  • Henry A. Mollel 2 &
  • Lawrencia D. Mushi 2  

BMC Health Services Research volume  24 , Article number:  785 ( 2024 ) Cite this article

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Metrics details

The Tanzania healthcare system is beset by prolonged waiting time in its hospitals particularly in the outpatient departments (OPD). Previous studies conducted at Kilimanjaro Christian Medical Centre (KCMC) revealed that patients typically waited an average of six hours before receiving the services at the OPD making KCMC have the longest waiting time of all the Zonal and National Referral Hospitals. KCMC implemented various interventions from 2016 to 2021 to reduce the waiting time. This study evaluates the outcome of the interventions on waiting time at the OPD.

This is an analytical cross-sectional mixed method using an explanatory sequential design. The study enrolled 412 patients who completed a structured questionnaire and in-depth interviews (IDI) were conducted among 24 participants (i.e., 12 healthcare providers and 12 patients) from 3rd to 14th July, 2023. Also, a documentary review was conducted to review benchmarks with regards to waiting time. Quantitative data analysis included descriptive statistics, bivariable and multivariable. All statistical tests were conducted at 5% significance level. Thematic analysis was used to analyse qualitative data.

The findings suggest that post-intervention of technical strategies, the overall median OPD waiting time significantly decreased to 3 h 30 min IQR (2.51–4.08), marking a 45% reduction from the previous six-hour wait. Substantial improvements were observed in the waiting time for registration (9 min), payment (10 min), triage (14 min for insured patients), and pharmacy (4 min). Among the implemented strategies, electronic medical records emerged as a significant predictor to reduced waiting time (AOR = 2.08, 95% CI, 1.10–3.94, p -value = 0.025). IDI findings suggested a positive shift in patients' perceptions of OPD waiting time. Problems identified that still need addressing include, ineffective implementation of block appointment and extension of clinic days was linked to issues of ownership, organizational culture, insufficient training, and ineffective follow-up. The shared use of central modern diagnostic equipment between inpatient and outpatient services at the radiology department resulted in delays.

The established technical strategies have been effective in reducing waiting time, although further action is needed to attain the global standard of 30 min to 2 h OPD waiting time.

Peer Review reports

The Tanzanian healthcare system is beset by prolonged waiting times in its hospitals, particularly in the outpatient departments. The reported contributing factors include the increased need for healthcare due to uncontrolled population growth, an inadequate number of medical experts, underdeveloped healthcare systems, and ineffective referral systems [ 1 ]. The audit report from the Ministry of Health on the management of referral and emergency healthcare services at zonal and regional referral hospitals showed a high OPD waiting time. Previous studies suggest that the average waiting time at, Muhimbili National Hospital OPD was 4 – 6 h; Muloganzila Zonal Referral Hospital was 3 – 4 h; Bugando Medical Centre was 2.5 h, Mbeya Zonal Hospital was 3 – 4 h and Kilimanjaro Christian Medical Centre (KCMC) was 6 h [ 1 , 2 ]. According to these data, KCMC has the longest waiting time of any zonal and National referral hospital in Tanzania. In response to the long waiting time, KCMC implemented a series of interventions that were incorporated into the strategic plan from 2016 to 2021. The interventions included the use of a block appointment system, the transition from paper to electronic medical records (EMRs), the extension of clinic days and the acquisition of modern diagnostic equipment.

Effective scheduling is crucial to minimize patient waiting times. Appointment systems should include rules for setting appointments and sequencing patients' arrivals, aligning them with doctors' schedules. Studies have shown that optimizing block appointment scheduling can significantly reduce patient waiting times without increasing physician idle time [ 3 , 4 , 5 ]. Effective appointment scheduling has been shown to significantly reduce patient waiting time in outpatient facilities. A study conducted in the USA demonstrated that planning appointment slots can decrease waiting time by as much as 56%.This evidence suggests that optimizing block appointment scheduling is a viable strategy to enhance outpatient efficiency [ 6 ]. Another study in Sri Lanka, demonstrated that implementing a well-structured appointment scheduling system could reduce total patient waiting time by over 60%. Therefore, adopting a block appointment system allows for more efficient allocation of resources and scheduling, ultimately enhancing the overall patient experience and optimizing healthcare delivery [ 7 , 8 ]. In Mozambique they introduced a block appointment scheduling system to evaluate its impact on waiting time. The findings revealed a reduction in waiting time by 1 h and 40 min (100 min) The study concluded that by introducing block appointment scheduling, patient arrivals were distributed more evenly throughout the day, resulting in reduced waiting times [ 9 ].

The implementation of electronic medical records (EMRs) has been shown to offer significant advantages in healthcare delivery, particularly in less developed nations. Evidence indicates that EMRs can decrease patient waiting time, lower hospital operating costs and communication between departments; enable doctors to share best practices. Unlike paper-based records, EMRs provide greater flexibility and leverage, enhancing overall healthcare efficiency [ 10 ]. Long waiting times in the OPDs are often exacerbated by inefficiencies in managing patient records. A tertiary medical college hospital in Mangalore, Karnataka, evaluated patient waiting and identified disorganized manual files as a primary cause of delays. These findings underscore the disadvantages of paper-based records and suggest that implementing electronic medical records (EMRs) can greatly enhance efficiency [ 11 ]. Reducing outpatient waiting times is a critical challenge for healthcare systems. Evidence from a study in Korea demonstrated that implementing EMRs can significantly reduce waiting time by nearly 60% and enhance operational efficiency. [ 12 ]. Addressing long waiting time in the OPD is essential for enhancing patient satisfaction and healthcare efficiency. A systematic survey study aimed at utilizing various models to shorten OPD waiting time found that healthcare providers significantly favored electronic medical records (EMRs) over manual records. The primary reasons cited were significant time savings and a consequent reduction in long waiting time.

[ 13 ]. The issue of long waiting time in outpatient departments (OPDs) is a prevalent problem faced by healthcare facilities worldwide. A study conducted in Brazil applied Lean thinking and an action research strategy to address patient flow issues and identify the causes of prolonged waiting time at the OPD. The study's findings highlighted that many hospitals globally are tackling this issue by investing in electronic medical records (EMRs) to transition away from manual medical records. This evidence suggests that implementing technical strategies, such as EMRs, can significantly improve patient flow and reduce waiting times [ 14 ].

Extending clinic days throughout the week has been found to be more effective in reducing waiting times than extending clinic hours. Studies have demonstrated substantial reductions in patient waiting times and increased patient satisfaction following the extension of clinic days. In Canada the study found that extending clinic day was more effective in reducing waiting time than extending clinic hours. Extending clinic days resulted in a 26% reduction in average waiting time, whereas extending clinic hours led to a 16% reduction. This research provides valuable insights for healthcare administrators seeking to optimize clinic operations and enhance patient experience [ 15 ]. At a tertiary care hospital in Oman the findings revealed a substantial 56% reduction in patient waiting time following the extension of clinic days. Additionally, patient feedback indicated a high level of satisfaction with the extended clinic days, with 97% of patients reporting satisfaction with the service [ 16 ]. Extending clinic days throughout the week has demonstrated promising results in a study conducted at a tertiary care hospital in India. The findings revealed a noteworthy 46% reduction in average patient waiting time following the extension of clinic days. This substantial decrease underscores the effectiveness of extending clinic hours in streamlining patient flow and improving efficiency. Consequently, these results provide compelling evidence supporting the rationale for extending clinic days throughout the week as a viable intervention to alleviate patient waiting times and enhance overall healthcare service delivery [ 17 ].

Utilizing modern equipment in healthcare settings has shown significant potential in reducing patient waiting times. A study conducted at a tertiary care hospital in Italy evaluated the effectiveness of modern equipment on patient. The findings indicated a notable reduction in patient waiting time, with an average decrease of 14 min per patient following the introduction of modern equipment. These results suggest that integrating modern equipment into can be a highly effective intervention for improving operational efficiency and reducing patient waiting time [ 18 ]. Modern equipment can be instrumental in reducing patient waiting times. A tertiary care hospital in Pakistan revealed that one of the primary causes of prolonged waiting time was the lack of adequate examination equipment. By addressing the equipment deficiencies highlighted in the study, healthcare providers can significantly reduce waiting times, thereby improving patient satisfaction and overall efficiency. Therefore, investing in modern equipment is justified as a strategic intervention to enhance patient flow and optimize healthcare service delivery [ 19 , 20 , 21 , 22 ]. Modern equipment is essential for reducing patient waiting times in healthcare facilities. An audit assessment conducted in zonal hospitals in Tanzania by the Ministry of Health revealed that outdated equipment, such as x-ray machines, significantly contributed to long waiting time. The limited capacity of these machines meant that only a certain number of patients could be attended to each day, and the equipment required rest periods to avoid overheating. These findings underscore the necessity of updating and maintaining modern medical equipment to improve patient throughput and reduce waiting times [ 2 ].

In Tanzania the Ministry of Health has not established the gold standard waiting time for patients to wait for services at the OPD [ 2 ]. However the United States Institute of Medicine (IOM) has established their gold standard patient waiting time at the OPD which suggests that medical care should be provided to at least 90% of patients no later than 30 min after their scheduled appointment time [ 23 , 24 ]. The Patient's Charter of UK, has recommended the same standard as the IOM [ 25 ]. The absence of a gold standard waiting time carries several significant implications. It results in inconsistent patient experiences with unpredictable waiting time across facilities, leading to frustration and dissatisfaction. Prolonged and varied waiting time can compromise the quality of care, affecting patient outcomes. Inefficient resource allocation becomes a challenge, hampering the ability to determine staffing and infrastructure needs [ 26 ]. This lack of a benchmark reduces accountability, and healthcare providers may not be incentivized to improve waiting time. It adversely affects patient satisfaction, the reputation of healthcare providers, and can exacerbate healthcare disparities. [ 19 ]. Hence, the findings from this research will provide valuable insights to the hospital management, enabling them to reinforce substantial improvements in patient waiting time and target areas where progress has been limited within the OPD at KCMC.

The objective of this study is to assess the patient waiting time at KCMC after intervention. Thus, the specific objectives were to determine the OPD patient waiting time since the inception of implementation of the interventions and to assess the effect of technical strategies on patient waiting time.

Design and methods

The study was conducted at Kilimanjaro Christian Medical Centre (KCMC) Outpatient department. KCMC is located in the foothills of the snow-capped Mount Kilimanjaro. It is one among the six zonal consultant hospitals in Tanzania. It was established in 1971 as a Zonal Referral Consultant hospital owned by the Evangelical Lutheran Church of Tanzania (ELCT) under the Good Samaritan Foundation (GSF). The referral hospital was established in order to serve the northern, eastern and central zone of Tanzania. Its record in medical services, research, and education has significant influence in Tanzania, East Africa and beyond. It serves a potential catchment population of 15 million people with 630 official bed capacity. The hospital has a number of clinical departments namely, General Surgery, Orthopaedic and Trauma, Dental, Dermatology, Paediatric, Eye, Otorhinolaryngology, Obstetric and Gynaecological and Internal Medicine. There are 1300 staff seeing about 1200 outpatients and 800 inpatients. The hospital has 100 specialists, 52 medical doctors, 465 nurses and the remaining 643 are paramedical and supporting staff. This area was chosen because the outpatient department at KCMC sees a high volume of patients on a regular basis from diverse backgrounds, including rural and urban populations of Tanzania as well as neighbouring countries. For instance in the year 2022, a total of 301,091 patients attended KCMC hospital, of which 92% ( n  = 277,013) attended the OPD. This high patient volume made it a suitable location for studying patient waiting time.

Study design

This was an outcome evaluation whereby an analytical cross-sectional design was used to examine the subject matter. This study employed a mixed method explanatory sequential evaluation approach.

Population and sampling

The study surveyed 412 patients quantitatively and conducted qualitative interviews with 12 patients and 12 healthcare providers. In addition patients who were involved in quantitative were not involved in the qualitative sample. The quantitative sample size was obtained using the following formula [ 27 ]:

n  = sample size.

Z = is the standard normal deviation which is 1.96 for a 95% confidence interval.

P = is the percentage of patients attending the OPD at KCMC is estimated to be 0.5, attributed to the absence of prior research data.

d = is the margin of error, which is 5% (0.05).

Therefore, the minimum sample size for this study was 384 patients approximated to be 422 after adjustment for a 10 percent non response rate.

Quantitative sampling

The systematic sampling process was designed to select 412 patients for interviews for working 10 days, with a daily minimum patient arrival of 500 patients. The daily interview target was calculated by dividing the total number of patients (412) by the number of days (10), resulting in an average of 41.2 interviews per day.

The systematic sampling process began with setting up a consent desk and queue number system. Patients were informed about the survey, and consent was obtained. Each patient was assigned a unique queue number upon arrival.

To determine the sampling interval, the total daily patients (500) were divided by the daily interview targets (41 or 42 patients). This resulted in a sampling interval of approximately 12. A random starting point between 1 and 12 was selected, and from this point, every 12th patient was chosen for the interview.

For the daily interview allocation, 42 patients were interviewed on the first 5 days, and 41 patients were interviewed on the remaining 5 days. This method ensured an even distribution of interviews and a representative sample for the survey.

Qualitative sample size

This study adopted a sample size of 12 respondents for the qualitative data collection, because it has been suggested that in practical research data saturation in a relatively homogeneous population can be achieved with this sample size [ 28 ]. Therefore, twelve (12) healthcare providers at the OPD and twelve (12) patients were selected making a total sample size of 24 for qualitative study.

Qualitative sampling

To select 12 healthcare providers purposive sampling was employed. We targeted specific roles to ensure a comprehensive representation of the outpatient department: doctors, nurses, management, cashiers, and medical records personnel. The selection included 3 doctors, 3 nurses, 2 management personnel, 2 cashiers, and 2 medical records personnel. Doctors were chosen based on their direct patient interaction and diverse specializations within outpatient care. Nurses were selected to represent varying levels of experience, from junior to senior roles. Management personnel were chosen for their administrative and operational oversight responsibilities. Cashiers who handle patient transactions and medical records personnel involved in managing patient records were also included. This purposive sampling strategy aimed to capture a holistic view of the outpatient department's operations and challenges, providing valuable insights for the study. Also to select 12 patients we used convenience sampling. We chose individuals based on their accessibility and willingness to participate at the outpatient department. This approach involved approaching patients who were readily available and consented to participate in the study. The sampling process took place over several days, with researchers stationed in the waiting area to identify potential participants. Patients were approached in a systematic manner, ensuring a mix of different ages, genders, and medical conditions to achieve a varied sample. Each patient was briefly informed about the study's purpose and asked for their consent to participate. Those who agreed were included in the sample until the target of 12 patients was reached. This method was chosen for its practicality and ease of implementation, allowing researchers to quickly gather insights from a diverse group of patients without the need for complex selection criteria.

Inclusion criteria

The study focused on patients aged 18 and older who attended the OPD during the data collection period.

Exclusion criteria

Patients below 18 years or who were severely ill or had scheduled admission appointments were excluded, as well as first time attendees (new patients) because they lacked prior experience with the implemented interventions.

Data collection tools and procedures

The researcher developed a structured questionnaire as a data collection tool. The tool had socio-demographic characteristics which included age, gender, marital status, education level, occupation, place of address, mode of payment and year of attendance at KCMC. The measurement scale for technical strategy was typically ordinal, based on fourteen (14) Likert scale questions with response options of 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree and 5 = strongly agree. Allowing patients to indicate their level of agreement or disagreement with statements related to technical strategies. Additionally, strongly disagree and disagree were consolidated as disagree and neutral, agree and strongly agree were consolidated as agree following the approach used in a previous study [ 29 ]. The internal reliability of the fourteen items used to assess effectiveness of technical strategies on reducing patient waiting time was measured using Cronbach’s alpha which was found to be 0.940. The survey included questions on arrival time, time the queue number was issued, registration waiting time, payment waiting time, triage waiting time, waiting time to see the doctor, pharmacy waiting time, laboratory waiting time, radiology waiting time and exit time. This data was collected from patients who attended clinics such as the general OPD clinic, orthopedic clinic, Medical clinic, surgical clinic, Urology clinic, Ear, Nose & Throat, Diabetic, cardiac clinic, Neurology and Neurosurgery. Waiting time was measured with a stopwatch.

Semi-structured guides for conducting in-depth interviews with patients and healthcare providers were developed. The interview guide had questions on socio-demographic and technical strategies such as the new block appointment system, use of EMR, extension of clinic days and availability of modern diagnostic equipment.

Also, the researcher conducted a documentary review, analyzing written records detailing time allocation before the studied event. This approach offered insights into past practices, aiding pattern and trend analysis. It involved reviewing benchmarks like a six-hour average waiting time, median waiting time for specific clinics, and total treatment duration for patients in various clinics. The six-hour benchmark was derived from the Ministry of Health's assessment report on OPD waiting time at KCMC and patients' information was not matched or linked to this report. Therefore, we considered the six-hour mark as our reference point." The data collection was conducted for two consecutive weeks from 3rd July to 14th July 2023.

Data analysis

Quantitative data.

The data collected were imported to the STATA programme (version 18.0) for further analysis. Descriptive Statistics: The analysis began with the presentation of data using various methods, including figures, graphs, and frequency distributions. The effect each response was rated on a scale of 1 to 5. Subsequently, cut-off points were utilized for each area to categorize the effectiveness of each intervention strategies as follows: 1–1.8 (very low), 1.8–2.6 (low), 2.6–3.4 (medium), 3.4–4.2 (high), and 4.2–5 (very high) [ 30 ]. Also, in this study, efficacy was determined by calculating the percentage reduction in OPD waiting time achieved through the implementation of intervention strategies. The current overall OPD waiting time (as shown in Table  4 ) was used as the numerator and the 6-h benchmark as the denominator [ 2 ].

The study defined the dependent variable as follows: overall patient waiting time, which was captured using a stopwatch, was categorized as a binary dummy variable. A value of 1 represented OPD waiting time less than 3 h, while a value of 0 indicated OPD waiting time exceeding 3 h. Comparison with Standards: The analysis involved evaluating OPD waiting time against established benchmarks. This included comparing the waiting time with the standards outlined in the Patients Charter of the United Kingdom (UK) and the recommendations from the United States Institute of Medicine (IOM), which advocate that at least 90% of patients should receive medical care within 30 min of their scheduled appointment time. Additionally, the study compared the observed 6-h waiting time, set as outpatient waiting time at KCMC Zonal Hospital, to assess whether there was any reduction post-intervention. Statistical Tests: To explore potential associations between dependent and independent variables, statistical tests were employed. Logistic regression analysis, encompassing both bivariate and multivariate analyses, was conducted. The multivariable analysis included all variables with p  < 0.200 as identified during the bivariable analysis. It was further adjusted for sex, level of education, and mode of payment. All statistical analyses were conducted at a significance level of 0.05. These analytical steps were taken to provide a comprehensive assessment of the effect of the intervention on patient waiting time.

Qualitative data

All interview transcripts were transcribed verbatim and translated into English. In order to maintain the original meaning back translation was employed. The analysis was done using the English transcript. Thematic data analysis was employed using both deductive and inductive reasoning. Consequently, a preliminary codebook for data analysis was developed, aligning with the study objectives, after which the final codebook was imported into Atlas.ti 7.0 qualitative data analysis computer software. Inductive coding was assigned to text segments which built on emerged new themes that were not pre-determined. The codes were sorted into categories then were clustered into sub-themes which were aligned into themes. The entire process of analysis was iterative. In ensuring rigor, validity, and the mitigation of bias in the qualitative component, it was considered important to ensure the credibility, transferability, dependability, and confirmability of qualitative component to enhance its trustworthiness [ 31 , 32 ]. In this study, credibility ensured that the data accurately reflects the real experiences and perceptions of those involved in the waiting process, allowing for subsequent decision-making. Transferability sought to make the findings relevant and to be applied to various healthcare settings beyond the specific study setting, ensuring that solutions can be adapted and implemented effectively in different contexts. Dependability ensured that the methods used to reduce waiting time were consistent and reliable over time, thus enabling the replication of the study's results. Confirmability ensures that the strategies for reducing waiting time are grounded in the data collected, rather than being influenced by the researchers' biases, thus enhancing the trustworthiness and effectiveness of the research findings in addressing waiting time issues in healthcare settings, thereby increasing the objectivity and validity of the research.

Ethical clearance

The Clearance Committee from Mzumbe University from the Directorates of Research, Publication and Postgraduate Studies provided ethical clearance with reference number MU/DPGS/INT/38/Vol. IV/236. Subsequently, the proposal was submitted for evaluation to the College Research Ethics and Review Committee (CRERC) at Kilimanjaro Christian Medical University College – Moshi. The CRERC granted approval, as indicated by certificate number 2639. Additionally, the data collection procedure received endorsement from the directors of KCMC Hospital reference number KCMC/P.1/Vol. XII. Prior to data collection, participants provided written informed consent. To ensure respondents’ autonomy, patients were fully informed about the purpose and nature of the study and provided with the option to withdraw at any time without any impact on their medical care. Patients were then questioned after completing their medical care. Also interviews were conducted in a private office within the OPD premises.

In this study, the initial calculated sample size was 422 patients. However, out of this group, only 412 patients consented to participate and completed the questionnaire. This resulted in a response rate of 97.6%. The median age was 52 (IQR, 38–65), with the majority aged over sixty. Over half were female (53.6%, n  = 221), and the majority were married (76%, n  = 313). Most had a basic education, including primary (44.7%, n  = 184) and secondary education (26.7%, n  = 110). More than half were peasant farmers (52.4%, n  = 218), and the vast majority (94.7%, n  = 338) resided within the KCMC catchment area. The majority were insurance patients (82.0%, n  = 338), and more than two-thirds (66.5%, n  = 274) had attended KCMC before the intervention's inception (Table  1 ).

Demographic characteristics in the qualitative sample for healthcare providers

A total of 12 healthcare providers were enrolled of whom half were male (50%, n  = 6) and half (50%, n  = 6) were female (Table  2 ).

Demographic characteristics in the qualitative sample for patients

A total of 12 patients were enrolled of whom half were male (50%, n  = 6) and half (50%, n  = 6) were female (Table  3 ).

Sub-themes from the in-depth interviews

During IDIs sub-themes that emerged were; ownership, training, organization culture, ineffective follow up, effective follow up and enhanced process simplification (Table  4 ).

OPD waiting time since the inception of implementation of the interventions

Following the intervention, the overall median waiting time in the OPD was 3.30 h IQR (2.51–4.08) a reduction of 2.30 h after the intervention.

The median waiting time for registration was 9 min IOR (0.03–0.15). For payment, the median waiting time was 10 min IOR (0.07–0.15). For triage patients using out-of-pocket payments experienced median waiting time of 17 min IQR (0.05–0.19) while those with insurance had median waiting time of 14 min IQR (0.06–0.19) and the median waiting time to see a doctor was 1.36 h IQR (0.51–2.01). The time from arrival to actually seeing a doctor was measured at 3.08 h IQR (2.13–3.30). Furthermore, the median consultation time was 19 min IQR (0.15–0.24), waiting time at the pharmacy was 4 min IQR (0.02–0.06), at the laboratory it was 31 min IQR (0.20–0.37) and waiting time at Radiology varied based on the specific service. X-ray services in different rooms had average waiting time ranging from 35 min to 1.15 h with varying IQR (0.23–2.19). Ultrasound services had median waiting time of 32 min (Table  5 ).

Qualitative findings

Registration (medical records department).

The adoption of electronic medical records (EMRs) appears to have enhanced the overall efficiency of the KCMC OPD registration process, benefiting both patients and staff.

"I have been receiving treatment here at KCMC for over 20 years. In the past, in the medical records department, it was necessary to have someone, a staff member, whom you would contact in advance, preferably three days before your clinic day, so that they could start looking for your file. This way, you could save time waiting. However, nowadays, this process is no longer in place. When I arrive, I simply present my card, and in no time, I'm on my way to the next area. There's no longer any time wasted at the reception." (IDI – Male Patient, aged 67 years)

Another interviewee added that:

"Nowadays, with the system in place, the process is streamlined, allowing me to efficiently register as many patients as possible in a short amount of time. I no longer have to leave the reception area to search for files, which has significantly improved the efficiency of the registration process." (IDI – Male healthcare provider (HCP), aged 45 years)

Waiting time to see the doctor

The issue of waiting time for patients to see the doctor has emerged as a significant concern within the healthcare facility. This concern is consistently echoed in both the quantitative data and qualitative interviews.

For example a female HCP aged 40 years reported:

" […] commencing clinics promptly can be challenging for doctors, as it is crucial for them to first participate in the morning report, which provides essential updates on the status of hospitalized patients." (IDI – male HCP, aged 40 years).

After probing as to why the medical staff cannot split into two teams of doctors so that one team could attend to outpatients the response was as follows:

"We have a limited number of doctors, making it challenging to divide them into two groups. Moreover, admitted patients demand our additional attention, as some rely on oxygen for breathing, while others are too ill to walk. Unlike outpatients, the majority of whom can independently come for treatment, we kindly request their understanding as we prioritize the care of our admitted patients." (IDI – male HCP, aged 40 years).

A female patient aged 53 years gave some observations.

“[….] Mmh! I want to highlight that delay in seeing the doctor can have serious consequences. It can lead to a worsening of symptoms or conditions, increase stress levels, and ultimately result in reduced satisfaction with the healthcare service. It's imperative that we address these extended waiting times. This is crucial not just for the comfort of the patient, but also to ensure that medical care is administered in a timely and effective manner.” (IDI – female patient, aged 53 years).

In the pharmacy department, there has been a notable improvement in waiting time. Patients now experience a comfortable and efficient process, with minimal time spent before receiving their prescribed medications.

"With the use of a computerized system, things have been greatly simplified. The waiting time to collect medicine has become short. When I come here, I wait for just a little while and quickly get my medicine." (IDI – Male patient, aged 45 years).
“Apart from using the computerized system in place, which has simplified things, the hospital administration has managed to establish three additional pharmacies apart from this one, thus reducing congestion in a single pharmacy, as it used to be in the past. That's why now a patient can be served quickly.” (IDI – male HCP, aged 50).

Laboratory department

In the laboratory department, the waiting time has been a subject of varying experiences among patients. Some patients have reported relatively short waiting periods, while others have encountered longer waits.

“I have been patiently waiting for a long time to be called for my tests, I’ve not yet been called up to now.” (IDI – female patient, aged 43 years).

Another interviewee shared that:

"I've noticed that one of the main reasons for long waiting time at the laboratory here is the limited space. The laboratory rooms at the Outpatient Department (OPD) have remained the same since the hospital was established, which means they can only accommodate a small number of patients at a time. This often leads to a backlog of patients waiting to get their tests done. It's clear that expanding the laboratory facilities is crucial to reduce these extended waiting time and ensure more efficient service delivery for everyone” (IDI – male HCP, aged 55 years).

Radiology department

Despite having modern diagnostic equipment, which appears to have significantly contributed to reducing patient waiting time, there are still instances where patients experience long waiting time in the radiology department.

"For me, even though waiting for an X-ray may take some time, I don't mind the wait. I've noticed a significant improvement in waiting time compared to before. In addition nowadays, when I have an X-ray, I can also consult with my doctor on the same day, which wasn't possible in the past” (IDI – male patient, aged 40 years).

One interviewee highlighted a crucial factor contributing to the extended waiting time at the radiology department and pointed out that:

“The same rooms at the radiology department are utilized for both outpatient and inpatient cases. As a result, priority is often given to the admitted patients, leading to longer waiting time for those seeking outpatient radiology services. This dual-use of facilities poses a challenge in managing patient flow and significantly contributes to the observed delays in the radiology department”. (IDI – female HCP, aged 49 years)

Patient OPD waiting time with Six (6) and Three (3) Hours Threshold

Not a single patient managed to complete the treatment within the recommended 30-min window following their scheduled appointment. When assessed based on the KCMC benchmark of a 6-h timeframe, the vast majority of patients (98.3%, n  = 407, 95% CI, 97.0%-99.5%) indicated that they received the OPD services within a period of less than six hours. However, when the time threshold was further reduced to three hours, 31% ( n  = 128, 95% CI, 26.6%-35.6%) of all surveyed patients reported that they received OPD services within a duration of fewer than three hours (Fig.  1 ).

figure 1

Patient OPD waiting time with six (6) and three (3) hours threshold ( n  = 412)

Furthermore, during the in-depth interviews (IDIs), patients emphasized receiving OPD services within a timeframe of below three hours.

For instance, a 58-year-old female patient remarked:

“ Certainly, drawing from my extensive experience of over 15 years attending KCMC hospital, I can attest to the positive changes in the waiting time for OPD services. Patients, including myself, are genuinely appreciative of this effective reduction in waiting time. I personally find it remarkable that I can now complete all the necessary OPD services in just about three hours, which is a stark contrast to the longer waiting periods we used to endure. This improvement has undoubtedly enhanced the overall patient experience and contributes positively to our healthcare journey”. (IDI – female patient, aged 58 years)

Effect of technical strategies on patient waiting time

Descriptive statistics of the technical strategies.

The study assessed the effectiveness of various technical strategies on reducing patient waiting time, categorized into four domains: block appointment, implementation of electronic medical records (EMR), extension of clinic days throughout the week, and utilization of modern diagnostic tools. The self-reported data were analyzed using mean scores and standard deviations, measured on a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The effectiveness of strategies in reducing patient waiting time was categorized as follows: very low (1–1.8), low (1.8–2.6), medium (2.6–3.4), high (3.4–4.2), and very high (4.2–5).

Overall, the average effectiveness of technical strategies in reducing patient waiting time was found to be very high with a mean score of 4.27 (SD = 0.904) with a descriptive equivalent of “very high”. Specifically, the new block appointment system obtained a mean score of 4.36 (SD = 0.856) with a descriptive equivalent of “high”. Additionally, the introduction of hourly appointments demonstrated positive effects with a mean score of 4.18 (SD = 1.024) with a descriptive equivalent of “high”. The transition from paper based to electronic medical records was also effective and obtained a mean score of 4.09 (SD = 1.033) with a descriptive equivalent of “high”. Moreover, the extension of clinic days obtained a mean score of 4.31 (SD = 0.832) with a descriptive equivalent of “very high”. Finally, the availability of modern diagnostic services, achieving a mean score of 4.30 (SD = 0.861) with a descriptive equivalent of “very high” (Table  6 ).

Bivariable analysis of technical strategies and patient waiting time

Bivariable regression analysis established a significant association between new block appointment system (OR 3.34; CI 1.28–8.77: p  = 0.014), hourly appointment system (OR 2.49; CI 1.01–6.13; p  = 0.047) and patient waiting time (Table  7 ).

Multivariable analysis between technical strategies and patient waiting time

Multivariable logistic analysis was employed to determine which technical strategy played a significant role in reducing patient waiting time. The findings in the adjusted odds ratio indicate that there was an association between reduction of patient waiting time and migrating from paper based to electronic medical records, thus electronic medical records remained a significant factor for patient waiting (AOR = 2.08, 95% CI, 1.10–3.94, p -value = 0.025). However, the introduction of the new block appointment system demonstrated a higher likelihood for a positive effect on reducing waiting time, although the findings were not statistically significant (AOR = 2.49; 95% CI, 0.68–9.10, p -value = 0.168) (Table  8 ).

Qualitative findings with regards to technical strategies

  • Block appointment

Based on the findings from in-depth interviews, both patients and healthcare providers expressed varying opinions on the block appointment system.

One female patient aged 62 years said that:

"Over the years, I have become accustomed to coming in the morning. I can't come at other time besides the morning; it would disrupt my plans." (IDI, female patient, aged 62 years).

A male healthcare provider aged 39 years shared the experience:

“ The truth is, we haven't been very successful in using block appointments. We tried it on the first and second days, but things went back to how they were before. The problem is, patients arrive very early in the morning, and you find them all crowded, waiting for service. Once a patient arrives, they must be attended to. We've realized that this block appointment system requires the whole team to be involved, from medical records (reception) to doctors, nurses, and the patients themselves” (IDI – male HCP, aged 51 years).

However, it's important to note that amidst these negative perspectives, several interviewees also acknowledged the positive effect of the system.

“ Since the introduction of the appointment system in 2020, we've observed a significant reduction in patient waiting time, which has led to quicker and more efficient service delivery for patients. When a patient arrives, the waiting area is usually less crowded. Furthermore, doctors now have more spaced-out appointments, allowing them to devote ample time to each patient.” (IDI – female HCP, aged 47 years).
  • Electronic medical records

The quantitative finding regarding migrating from paper based to electronic medical records aligns with our qualitative findings.

"From my experience, dealing with physical files presented its own set of challenges. There was a lengthy process, and files were prone to being misplaced, including important test results. Sometimes, files would be delayed in reaching the clinic. This was particularly problematic for patients who arrived early; if their files couldn't be located promptly, it would cause a delay. However, with the new system in place, everything operates swiftly and efficiently. The system has truly revolutionized the process” (IDI – female HCP, aged 55 years)

One interviewee shared the experience.

“When I come for treatment nowadays, I no longer experience the frustration of my test results going missing or my file being unavailable." (IDI – male patient, aged 50 years)

Extension of clinic days

The implementation of the daily clinic schedule has yielded mixed results.

One interviewee stated that:

"In our department, the limited number of staff has posed a challenge. Conducting daily clinics becomes demanding, as the same doctor is tasked with conducting ward rounds, making decisions for admitted patients, and performing surgery. However, once we have an adequate staff complement, we can begin seeing patients on a daily basis." (IDI – male HCP, aged 42 years)

On the contrary, extension of clinic days has proven to be a highly beneficial strategy in our facility serving as one of the key strategies to address patient waiting time.

“It has significantly reduced the patient waiting time. In the past, clinics used to run until 6 pm in the evening. Since they implemented the daily clinic schedule, patients are now seen earlier, and the clinics end earlier. This is because patients have been scheduled throughout the week”. (IDI – female HCP, aged 48 years)

Another interviewee supported that.

“It has helped in limiting the number of patients flocking to a single clinic, but it doesn't necessarily reduce patient waiting time." (IDI – male HCP, aged 51 years)

One interviewee shared the experience:

"These days, I finish my treatments earlier than I used to.” (IDI – male patient, aged 49 years)

Availability of modern diagnostic equipment

The integration of modern diagnostic equipment stands as a substantial contributor to the reduction of patient waiting time. This positive trend is supported by both our quantitative and qualitative findings, affirming the significance of having advanced diagnostic tools readily accessible within our healthcare facility.

" Nowadays, the procedure has become significantly more simplified. You just need to consult the system to retrieve the patient's results. When you open it, you can readily peruse the information, making the process more efficient. If I require additional specifics about the condition, it's easy to locate them in the patient's file. I simply access it in the system, and their image is readily available, leading to a substantial time-saving." (IDI – male HCP, aged 40 years)

More experience is shared from a male patient aged 45 years.

"I now do my investigations on the same day and return to the doctor for my results. This contrasts with the past when I needed to be scheduled for a different day to pick up the results. This has resulted in a considerable time-saving." (IDI – male patient, aged 45 years)

OPD waiting time

Following the intervention, it was observed that the overall median waiting time in the OPD was reduced to 3.30 h in contrast to the previous six-hour (6) waiting time prior to the intervention, showing the effectiveness of the intervention achieving a reduction of waiting time by 45%. This improvement is significant and suggests that the interventions have had a positive effect.

These findings align with other research involved adding more human resources and changing business and management practices. The findings demonstrated a significant success in reducing wait time in the USA, China, Sri Lanka and Taiwan by 15%, 78%, 60%, and 50%, respectively [ 33 ].

The study at KCMC found low median waiting time of 9 min for registration. This is not congruent with findings in China and Saudi Arabia where registration time were notably higher [ 24 , 34 ]. In Ethiopia, waiting time varied, with some patients waiting over an hour [ 35 ], while another study reported a median wait of 18 min [ 36 ]. In Kenya, registration waiting time were even shorter 5.8 min [ 37 ]. These discrepancies could be explained by variations in patient flow management techniques or data collection techniques. Overall, the study shows that the waiting time for registration has significantly decreased at KCMC, clearly demonstrating the efficiency of the technical strategies that have been put in place to cut down on waiting time.

In terms of payment processing, the median waiting time was 10 min. Although it appears majority of patients were insured, the mode of payments had no significant association with waiting time. This suggests that insured patients were handled just as quickly as patients paying with cash. These results are relatively congruent with a study conducted in a Tertiary Care Hospital in Pune, India, where patients spent an average of 7 min at the cashier [ 26 ]. This shared emphasis on streamlined payment processes underscores their significance in enhancing the patient experience, reinforcing the importance of efficient payment processing in healthcare settings.

At the triage area, patients paying cash had a median waiting time of 17 min, while insured patients experienced slightly shorter median waiting time of 14 min. These results are congruent with a study by [ 38 ], who found that insured patients at a hospital in Northeast Thailand had an average triage waiting time of 13 min. The consistency in findings between these studies suggests that insurance status may play a role in patient waiting time, with insured patients benefiting from somewhat more efficient service and well streamlined patient flow. However, it's important to note that regional contexts may influence waiting time, and these results may vary in different healthcare settings and countries.

The median waiting time before seeing a doctor from arrival to consultation was 3.08 h. These results resonate with research from Nigeria, where 38% of respondents waited for over 2 h for a consultation [ 39 , 40 ] found an average waiting time of 137.02 ± 53.64 min before seeing a doctor. In contrast, some studies reported shorter waiting time, such as 40 min in India [ 26 ], over 90% of patients waiting for more than 20 min in Saudi Arabia [ 24 ] and more than half of patients waiting for over 60 min in Ethiopia [ 35 ]. Involvement of doctors in teaching students, long ward rounds, staff constraints and prioritizing inpatients over outpatients could all contribute to doctors coming late to the clinics, thus, causing increased stress, discomfort, and impatience among patients.

The study's findings emphasize a positive aspect of healthcare delivery at KCMC, specifically in pharmacy services, with remarkably short median waiting time of 4 min. This aligns with research in Iran by [ 41 ], which also reported the pharmacy as having the shortest average waiting time of 5 ± 3 min and in Kenya, where patients experienced a similar pattern with an average waiting time of 5.5 min [ 37 ]. However, these results contrast with a study in a Tertiary Care Hospital in Pune, India, revealing a 15-min average waiting time at the pharmacy [ 26 ]. In Ethiopia, [ 35 ] found that only 23.6% of patients received their prescribed drugs within ≤ 30 min, while a comparable number received them within 30–60 min or > 60 min. During interview, patients commended the computerized system's effectiveness in streamlining the medication collection process, which the study attributes to its implementation. In addition, three new pharmacies have been added to the existing one, reducing congestion and allowing patients to receive faster service. The aforementioned positive results serve as evidence of the efficiency with which KCMC's pharmacy services have integrated technology.

The median waiting time at the laboratory department was 31 min. This is congruent with studies done in Ethiopia which reported a similar median of waiting time of 31 min, reflecting consistency in laboratory waiting time within Ethiopian healthcare settings [ 36 ]. Similarly, another study noted that 58.1% of patients received laboratory services within 30 to 60 min, with only 12.0% within ≤ 30 min [ 35 ]. On the contrary, in Nigeria a study revealed a longer waiting time, with patients waiting over 50 min on average for laboratory services. This suggests that KCMC’s laboratory waiting time maybe more favourable when compared to other hospitals [ 42 ]. Nevertheless, another study reported an average waiting time which was significantly shorter, 12.75 min which suggest that there may be variations in waiting time between KCMC and Indian healthcare facility. The reason for the long waiting time at KCMC could be due to the limited space within the laboratory rooms resulting in the accommodation of fewer patients at any given time. This emphasized the necessity of expanding facilities to improve the effectiveness of service delivery [ 26 ].

Waiting time at the Radiology department showed significant differences depending on which investigation was ordered. Thus the median waiting time for X-ray services varied between rooms, from 35 min to 1.15 h, whereas the median waiting time for ultrasound services was 32 min. Important insights into patient experiences were obtained through in-depth interviews. Some patients expressed contentment with the waiting time for X-rays because they were able to get the results on the same day and continue with further treatment from their doctors on the very same day. Various studies revealed differing median radiology waiting time. Iran reported 27 min ± 11 [ 41 ] while India recorded 36.05 min [ 26 ], Ethiopia’s studies indicated 33 min [ 36 ] and 60 min [ 35 ], all indicating relatively shorter waiting time. Conversely, Nigeria showed the longest waiting time for radiological services at 77 min [ 42 ]. There were issues identified within KCMC's Radiology department, such as the dual use of rooms for outpatient and inpatient cases, which prioritized admitted patients and resulted in longer wait time for outpatients. This organizational practice complicates patient flow management and contributes considerably to perceived delays in the radiology department. The findings emphasize that waiting time in Radiology are influenced by resource availability, facility organization, and patient flow management.

Technical strategies on patient waiting time

The implemented block appointment system appears to have the potential to improve waiting time, even though the effect was not statistically significant. Early patient arrivals continue to be problematic, which emphasizes how crucial it is to provide efficient patient education and coordination in order to reap the full rewards of this system. Similar findings in Nigeria demonstrate that appointments with specific time are uncommon, resulting in early patient arrivals and possible delays in the start of services [ 8 ]. However, in other nations where it has been used, the block appointment system has proved to be successful. Research conducted in the United States [ 43 ] and the United Kingdom [ 44 ] have demonstrated its effectiveness in reducing patient wait time. In Thailand [ 5 ] and Sri Lanka [ 7 ] demonstrated the possible advantages of carefully planned scheduling by demonstrating how the use of appointment systems can dramatically reduce average waiting time. Block appointment scheduling also successfully spread out patient arrivals throughout the day, as shown by a pilot study conducted in Mozambique, which significantly decreased waiting time [ 9 ]. Hence, coordinated efforts involving medical records, physicians, nurses, and patients themselves are needed to operate the system.

The transition from paper to electronic medical records had a significant and positive impact on reducing long waiting time at the OPD. Various studies underlined the benefits of electronic medical records over paper-based systems, including how it can improve patient waiting time, increase efficiency, and improve the delivery of healthcare services [ 10 , 11 ] and [ 12 ]. Another study highlighted the preference for electronic health records among healthcare providers due to their efficiency and speed in patient care. By eliminating labour intensive procedures, space limitations, and document misplacement problems associated with manual filing systems, the switch to electronic records helped to create more efficient and productive operations [ 13 ]. The entire patient experience was greatly enhanced since patients were no longer frustrated by lost records or delayed test results. The implementation of electronic health records has proven to be beneficial in reducing extended wait time in outpatient clinics, as evidenced by a study carried out in Brazil [ 14 ].

The extension of clinic days yielded a mean score of 4.31 (SD = 0.832) signifying positive effect. Similarly, qualitative findings from healthcare providers and patients shed light on the effect of extending clinic days. The department's small staffing posed a significant challenge, as doctors had to manage multiple responsibilities, such as ward rounds, decision-making for admitted patients, and surgery. These findings are not congruent with those of other locations where clinic days have been extended. For instance, a study suggested that extending clinic days was more effective, resulting in a 26% reduction in average waiting time [ 15 ]. Additionally, another study found a significant 56% reduction in average waiting time after extending clinic days, coupled with high patient satisfaction rates [ 16 ]. Similarly in other study extending clinic days resulted in an astounding 46% decrease in average waiting time. The study also found that patient satisfaction was high and that the number of patients seen each day had increased [ 17 ].

The availability of modern diagnostic services had a mean score of 4.30 (SD = 0.861), signifying a positive effect. This demonstrates that advanced diagnostic equipment played a significant role in streamlining healthcare processes and enhancing efficiency. Qualitative findings from both healthcare providers and patients supported this, highlighting how digital systems and modern equipment simplified procedures and expedited healthcare services. Access to electronic patient information and test results contributed to time savings. These findings are congruent with studies conducted in Italy [ 18 ], Pakistan [ 19 , 20 ], and Iran [ 21 ], which all demonstrated reductions in waiting time following the acquisition of modern equipment. A study from India also supported the positive impact of modern equipment on patient waiting time [ 22 ]. Additionally, audit assessments in Tanzania by the Ministry of Health and equipment-related observations in zonal hospitals emphasized the critical role of modern equipment in healthcare settings. Outdated equipment can lead to extended patient waiting time, underscoring the importance of maintaining and upgrading diagnostic facilities to improve healthcare efficiency and patient care [ 2 ].

The implemented technical strategies resulted in a significant reduction in overall OPD waiting time to an average of 3.30 h, marking a 45% reduction from the previous six-hour wait. While there have been notable improvements in registration, payment, triage, and pharmacy services, issues remain in doctor consultations, laboratory, and radiology services, resulting in extended waiting time for some patients. The adoption of electronic medical records emerged as the most effective technical strategy, emphasizing its critical role in improving OPD efficiency. Despite these advancements, additional improvements are required to meet the global standard of waiting time ranging from 30 min to 2 h. Nevertheless, ineffective implementation of block appointment and extension of clinic days appears to stem from lack of ownership and proactive involvement by hospital managers in driving these strategies forward. Furthermore, the hospital's dominant organizational culture seemed to be resistant to change, which could hinder the effective implementation of these strategies. The results indicated a possible training shortfall, suggesting that personnel may not have had enough training to properly adopt and implement these new strategies. Moreover, there was a lack of effective follow-up and management strategies by hospital managers, potentially hindering the sustained implementation of these strategies. Moreover, the shared use of central modern diagnostic equipment between inpatient and outpatient services at the radiology department resulted in delays, impacting waiting time. Alongside, a comprehensive review of the diagnostic service structure might be necessary to alleviate delays and streamline services for both inpatient and outpatient care.

Limitations of the study

Since only one hospital was involved in the study, generalization to cover the rest of Tanzania remains uncertain. Additionally, there was a chance that selection bias might have impacted the findings.

Availability of data and materials

Data is available upon request from the corresponding author.

Abbreviations

College research ethics and review committee

Healthcare provider

In-depth interview

Institute of medicine

Interquartile range

Kilimanjaro Christian Medical Centre

Ministry of health, community development, gender, elderly and children

Mzumbe University

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Acknowledgements

We extend our gratitude to the patients who participated in this study and the research assistants who contributed to data collection namely, Geofrey A. Sikaluzwe, Mbayani J. Kivuyo, Richard Hezron Mwamahonje, Emmanuel M. Mabula, Abel E. Lucas, Amos Francis, Dr. (Mrs) Angela Savage for proof reading and Dr. Bernard Njau for his continuity guidance. Also Dr. Theresia Mkenda for availing us with research assistants.

This study had no funding.

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Kilimanjaro Christian Medical Centre, P. O. Box 3010, Moshi, Tanzania

Manasseh J. Mwanswila

Department of Health Systems Management, School of Public Administration and Management, Mzumbe, P.O. Box 2, Morogoro, Tanzania

Manasseh J. Mwanswila, Henry A. Mollel & Lawrencia D. Mushi

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M.J.M conceptualized and conducted the study, handling data collection, analysis, and initial manuscript drafting. H.A.M and L.D.M provided oversight and reviewed the process from proposal to final manuscript. All authors reviewed the manuscript.

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Correspondence to Manasseh J. Mwanswila .

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The Clearance Committee from Mzumbe University from the Directorates of Research, Publication and Postgraduate Studies provided ethical clearance with reference number MU/DPGS/INT/38/Vol. IV/236. Subsequently, the proposal was submitted for evaluation to the College Research Ethics and Review Committee (CRERC) at Kilimanjaro Christian Medical University College – Moshi. The CRERC granted approval, as indicated by certificate number 2639. Additionally, the data collection procedure received endorsement from the directors of KCMC Hospital reference number KCMC/P.1/Vol. XII. Prior to data collection, participants provided written informed consent. To ensure respondent autonomy, patients were fully informed about the purpose and nature of the study and provided with the option to withdraw at any time without any impact on their medical care. Patients were then questioned as completing their medical care.

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Mwanswila, M.J., Mollel, H.A. & Mushi, L.D. Outcome evaluation of technical strategies on reduction of patient waiting time in the outpatient department at Kilimanjaro Christian Medical Centre—Northern Tanzania. BMC Health Serv Res 24 , 785 (2024). https://doi.org/10.1186/s12913-024-11231-5

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DOI : https://doi.org/10.1186/s12913-024-11231-5

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data analysis in quantitative research article

Insights into mobile assisted language learning research in Iran: A decade review (2010–2023)

  • Published: 13 July 2024

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data analysis in quantitative research article

  • Mostafa Morady Moghaddam   ORCID: orcid.org/0000-0002-7939-7105 1 ,
  • Faeze Esmaeilpour 1 &
  • Fatemeh Ranjbaran 2  

Mobile technologies and the widespread application of mobile devices have become significant in educational settings. Mobile-assisted language learning (MALL) is a thriving field of study in language learning and teaching contexts in Iran. This study investigates the current research trends and characteristics of MALL studies in Iran, identifying less-explored areas of investigation, and informing future directions for integrating mobile technologies into language learning and teaching practices in the Iranian context. To contribute to a context-specific analysis of digital literacy in low-resource environments, this study systematically reviewed 70 articles that were relevant to different facets of MALL in the Iranian context. The findings indicate that the majority of the articles reviewed focused on vocabulary learning and attitudes toward MALL, respectively. Many of the reviewed articles utilized a quantitative research design to study the effect of MALL on different aspects of language learning. The reviewed research studies were primarily conducted in private language institutes, where it is easier to conduct experimental studies. However, there was a lack of research analyzing the effect of MALL on some areas of language proficiency, specifically with regard to pragmatic competence and pronunciation. Additionally, a diverse range of research methodologies was observed in the articles, with ‘questionnaires’ being the most commonly used instrument for data collection. Furthermore, most studies employed an experimental research design. The majority of the studies illustrated that MALL is an effective method to improve learners’ language proficiency in comparison to traditional teaching methodologies. This study can serve as a valuable reference for educators and researchers interested in MALL.

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Morady Moghaddam, M., Esmaeilpour, F. & Ranjbaran, F. Insights into mobile assisted language learning research in Iran: A decade review (2010–2023). Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12879-6

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Global trend and hotspot of resin materials for dental caries repair: a bibliometric analysis

ObjectiveThe objective of this study is to explore the current research status, key areas, and future development trends in the field of resin materials for dental caries repair through an objective and quantitative analysis of the literature.MethodsA search was conducted on the Web of Science Core Collection using "dental cavity" and "resin" as keywords, covering the period from 2000 to 2023. Data including author names, journals, countries, institutions, keywords, and citation rates were extracted. The collected data was subjected to statistical analysis using bibliometrics methodology, and visual knowledge maps were generated using software like CiteSpace 6.2.R4, Microsoft365, and R.ResultsA total of 4800 articles were retrieved, involving 13,423 authors, 2654 institutions, 76 countries, and 560 journals. The number of publications and cumulative publications in this field showed an increasing trend, reaching a peak in 2022. Dental Materials was the journal with the highest number of publications, cumulative publications, and citation rates. XU HHK was the most prolific author in terms of publications and citations. The University of Maryland was the institution with the highest number of publications. Brazil was the country with the highest number of publications. The USA had the highest level of collaboration with other countries. Collaboration between different authors, institutions, and countries in this field was relatively close, which contributed to the rapid development of resin materials for caries repair. The current research focus is mainly on the nature of dental caries, characteristics of resin materials, and bonding strength of adhesives. Enhancing the bioactivity and remineralization of resin materials, advanced antibacterial strategies, longevity and durability of resin restorations, nanotechnology, and material innovation, as well as digital dentistry, will receive increased attention as future research trends.ConclusionResin materials for dental caries repair have received significant attention. Future research should combine nanotechnology and big data analysis to investigate the mechanisms of dental caries occurrence and development, enhance the performance and longevity of resin materials, and conduct high-quality, large-scale empirical research.

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