Quantitative study designs: Introduction

Quantitative study designs, introduction.

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Throughout our lives, we are exposed to many factors that can affect our health and wellbeing. But what kinds of factors influence specific health outcomes? And what do we do when we become ill? Health researchers dedicate their working lives to answering a huge range of different clinical research questions and they do so by carrying out very specific studies. The findings from these studies eventually lead to the development of interventions that can help save lives.

study design used in quantitative research

Flowchart adapted from: Grimes, D. A., & Schulz, K. F. (2002). “ An overview of clinical research: The lay of the land ”. The Lancet, 359(9300), 57-61

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So many study designs – what’s the difference?

In clinical research, a study design is a plan for selecting study subjects and for obtaining data. These study designs fall into two different categories:

  • Experimental
  • Observational

These categories are based on whether or not the investigators assign a particular exposure to a cohort.

Experimental trials

Investigators assign exposures (for example, a trial to test the effectiveness of a new medication) and these are categorised into randomized (studies with a control, or comparison, group) and non-randomized trials (those without a control group).

Observational studies

These studies focus on exposures that are already present in a population and assess the effects of the exposure on that cohort. These studies are further categorised into analytical and descriptive.

Analytical studies

  • Include a control (or comparison group)
  • In Cohort studies people are tracked forward in time from exposure to outcome.
  • Case-control studies, by contrast, trace back from outcome to exposure.
  • Cross-sectional studies are like a snapshot in time, measuring both exposure and outcome at a particular time point.

Descriptive studies

  • Include case reports, case-series and case studies. Cross-sectional studies can also be descriptive.
  • Do not have a control or comparison group
  • Cannot examine associations between an exposure and an outcome.

Which study type will answer my clinical question?

Not all study types will be appropriate for answering a particular clinical question. For example, if you wanted to investigate the impact of maternal smoking on foetal development, then a randomized-controlled trial would not be appropriate as it is not ethical to assign a disease or potentially harmful exposure to an individual. In this case, an observational study would be more appropriate. This table illustrates the most appropriate study designs for answering specific types of clinical questions.  

In this learning series, we will examine each study design type in more detail, the types of clinical questions they investigate and the methodologies applied in each study design.

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3.2 Quantitative Research Designs

Quantitive research study designs can be broadly classified into two main groups (observational and experimental) depending on if an intervention is assigned. If an intervention is assigned, then an experimental study design will be considered; however, if no intervention is planned or assigned, then an observational study will be conducted. 3 These broad classes are further subdivided into specific study designs, as shown in Figure 3.1. In practice, quantitative studies usually begin simply as descriptive studies, which could subsequently be progressed to more complex analytic studies and then to experimental studies where appropriate.

study design used in quantitative research

Observational studies

Observational studies are research designs that involve observing and measuring the characteristics of a sample or population without intervening, altering or manipulating any variables (Figure 3.1). 3 Observational studies can be further subdivided into descriptive and analytic studies. 3

Descriptive observational studies

Descriptive studies are research designs that describe or measure the characteristics of a specific population or phenomenon. These characteristics include descriptions related to the phenomenon under investigation, the people involved, the place, and the time. 4 These study designs are typically non-experimental and do not involve manipulating variables; rather, they rely on the collection and analysis of numerical data to draw conclusions. Examples of descriptive studies include case reports, case series, ecological studies and cross-sectional (prevalence studies). 2 These are discussed below

  • Case Reports and Case series

Case reports and case series are both types of descriptive studies in research. A case report is a detailed account of the medical history, diagnosis, treatment, and outcome of a single patient. 5 On the other hand, case series is a collection of cases with similar clinical features. 5 Case series are frequently used to explain the natural history of a disease, the clinical characteristics, and the health outcomes for a group of patients who underwent a certain treatment. Case series typically involve a larger number of patients than case reports. 5 Both case reports and case series are used to illustrate unusual or atypical features found in patients in practice. 5 In a typical, real-world clinical situation, they are both used to describe the clinical characteristics and outcomes of individual patients or a group of patients with a particular condition. These studies have the potential to generate new research questions and ideas. 5 However, there are drawbacks to both case reports and case series, such as the absence of control groups and the potential for bias. Yet, they can be useful sources of clinical data, particularly when researching uncommon or recently discovered illnesses. 5 An example of a case report is the study by van Tulleken, Tipton and Haper, 2018 which showed that open-water swimming was used as a treatment for major depressive disorder for a 24-year-old female patient. 6 Weekly open (cold) water swimming was trialled, leading to an immediate improvement in mood following each swim. A sustained and gradual reduction in symptoms of depression, and consequently a reduction in, and cessation of, medication was observed. 6 An example of a case series is the article by Chen et al , 2020  which described the epidemiology and clinical characteristics of COVID-19 infection among 12 confirmed cases in Jilin Province, China. 7

  • Ecological studies

Ecological studies examine the relationship between exposure and outcome at the population level. Unlike other epidemiological studies focusing on individual-level data, ecological studies use aggregate data to investigate the relationship between exposure and outcome of interest. 8 In ecological studies, data on prevalence and the degree of exposure to a given risk factor within a population are typically collected and analysed to see if exposure and results are related. 8 Ecological studies shed light on the total burden of disease or health-related events within a population and assist in the identification of potential risk factors that might increase the incidence of disease/event. However,  these studies cannot prove causation or take into account characteristics at the individual level that can influence the connection between exposure and result. This implies that ecological findings cannot be interpreted and extrapolated to individuals. 9 For example, the association between urbanisation and Type 2 Diabetes was investigated at the country level, and the role of intermediate variables (physical inactivity, sugar consumption and obesity) was examined. One of the key findings of the study showed that in high-income countries (HIC), physical inactivity and obesity were the main determinants of T2D prevalence. 10 However, it will be wrong to infer that people who are physically inactive and obese in HIC have a higher risk of T2D.

  • Cross-sectional Descriptive (Prevalence) studies

A cross-sectional study is an observational study in which the researcher collects data on a group of participants at a single point in time. 11 The goal is to describe the characteristics of the group or to explore relationships between variables. Cross-sectional studies can be either descriptive or analytical (Figure 3.2). 11 Descriptive cross-sectional studies are also known as prevalence studies measuring the proportions of health events or conditions in a given population. 11 Although analytical cross-sectional studies also measure prevalence, however, the relationship between the outcomes and other variables, such as risk factors, is also assessed. 12 The main strength of cross-sectional studies is that they are quick and cost-effective. However, they cannot establish causality and may be vulnerable to bias and confounding ( these concepts will be discussed further later in this chapter under “avoiding error in quantitative research) .  An example of a cross-sectional study is the study by Kim et al., 2020 which examined burnout and job stress among physical and occupational therapists in various Korean hospital settings. 13 Findings of the study showed that burnout and work-related stress differed significantly based on several factors, with hospital size, gender, and age as the main contributory factors. The more vulnerable group consisted of female therapists in their 20s at small- or medium-sized hospitals with lower scores for quality of life. 13

study design used in quantitative research

Analytical Observational studies

Analytical observational studies aim to establish an association between exposure and outcome and identify causes of disease (causal relationship). 14 Analytical observational studies include analytical cross-sectional ( discussed above ), case-control and cohort studies. 14 This research method could be prospective(cohort study) or retrospective (case-control study), depending on the direction of the enquiry. 14

  • Case-control studies

A case-control study is a retrospective study in which the researcher compares a group of individuals with a specific outcome (cases) to a group of individuals without that outcome (controls) to identify factors associated with the outcome. 15 As shown in Figure 3.3 below, the cases and controls are recruited and asked questions retrospectively (going back in time) about possible risk factors for the outcome under investigation.  A case-control study is relatively efficient in terms of time, money and effort, suited for rare diseases or outcomes with a long latent period, and can examine multiple risk factors. 15 For example, before the cause of lung cancer, was established, a case-control study was conducted by British researchers Richard Doll and Bradford Hill in 1950. 16 Subjects with lung cancer were compared with those who did not have lung cancer, and details about their smoking habits were obtained. 16 The findings from this initial study showed that cancer patients were more frequent and heavy smokers. 16 Over the years, more evidence has been generated implicating tobacco as a significant cause of lung cancer. 17, 18 Case-control studies are, therefore, useful for examining rare outcomes and can be conducted more quickly and with fewer resources than other study designs. Nonetheless, it should be noted that case-control studies are susceptible to bias in selecting cases and controls and may not be representative of the overall population. 15

study design used in quantitative research

  • Cohort Study

Cohort studies are longitudinal studies in which the researcher follows a group of individuals who share a common characteristic (e.g., age, occupation) over time to monitor the occurrence of a particular health outcome. 19 The study begins with the selection of a group of individuals who are initially free of the disease or health outcome of interest (the “cohort”). The cohort is then divided into two or more groups based on their level of exposure (for example, those who have been exposed to a certain risk factor and those who have not). 19 Participants are then followed up, and their health outcomes are tracked over time. The incidence of the health outcome is compared between exposed and non-exposed groups, and the relationship between exposure and the outcome is quantified using statistical methods. 19 Cohort studies can be prospective or retrospective (Figure 3.4). 20 In a prospective cohort study, the researchers plan the study so that participants are enrolled at the start of the study and followed over time. 20, 21 In a retrospective cohort study, data on exposure and outcome are collected from existing records or databases. The researchers go back in time (via available records) to find a cohort that was initially healthy and “at risk” and assess each participant’s exposure status at the start of the observation period. 20, 21 Cohort studies provide an understanding of disease risk factors based on findings in thousands of individuals over many years and are the foundation of epidemiological research. 19 They are useful for investigating the natural history of a disease, identifying risk factors for a disease, providing strong evidence for causality and estimating the incidence of a disease or health outcome in a population. However, they can be expensive and time-consuming to conduct. 15 An example of a cohort study is the study by Watts et al, 2015 which investigated whether the communication and language skills of children who have a history of stuttering are different from children who do not have a history of stuttering at ages 2–5 years. 22 The findings revealed that children with a history of stuttering, as a group, demonstrated higher scores on early communication and language measures compared to their fluent peers. According to the authors, clinicians can be reassured by the finding that, on average, children who stutter have early communication and language skills that meet developmental expectations. 22

study design used in quantitative research

Experimental Study Designs (Interventional studies)

Experimental studies involve manipulating one or more variables in order to measure their effects on one or more outcomes. 23 In this type of study, the researcher assigns individuals to two or more groups that receive or do not receive the intervention. Well-designed and conducted interventional studies are used to establish cause-and-effect relationships between variables. 23  Experimental studies can be broadly classified into two – randomised controlled trials and non-randomised controlled trials. 23 These study designs are discussed below:

  • Randomised Controlled Trial

Randomised controlled trials (RCTs) are experimental studies in which participants are randomly assigned to the intervention or control arm of the study. 23 The experimental group receives the intervention, while the control group does not (Figure 3.5). RCTs involve random allocation (not by choice of the participants or investigators) of participants to a control or intervention group (Figure 3.5). 24   Randomization or random allocation minimises bias and offers a rigorous method to analyse cause-and-effect links between an intervention and outcome. 24 Randomization balances participant characteristics (both observed and unobserved) between the groups. 24 This is so that any differences in results can be attributed to the research intervention. 24 The most basic form of randomisation is allocating treatment by tossing a coin. Other methods include using statistical software to generate random number tables and assigning participants by simple randomisation or allocating them sequentially using numbered opaque envelopes containing treatment information. 25 This is why RCTs are often considered the gold standard in research methodology. 24 While RCTs are effective in establishing causality, they are not without limitations. RCTs are expensive to conduct and time-consuming. In addition, ethical considerations may limit the types of interventions that can be tested in RCTs. They may also not be appropriate for rare events or diseases and may not always reflect real-world situations, limiting their application in clinical practice. 24   An example of a randomised controlled trial is the study by Shebib et al., 2019 which investigated the effect of a 12-week digital care program (DCP) on improving lower-back pain. The treatment group (DCP) received the 12-week DCP, consisting of sensor-guided exercise therapy, education, cognitive behavioural therapy, team and individual behavioural coaching, activity tracking, and symptom tracking – all administered remotely via an app. 26 While the control group received three digital education articles only. The findings of the study showed that the DCP resulted in improved health outcomes compared to treatment-as-usual and has the potential to scale personalised evidence-based non-invasive treatment for patients with lower-back pain. 26

study design used in quantitative research

  • Non-randomised controlled design (Quasi-experimental)

Non-randomised controlled trial (non-RCT) designs are used where randomisation is impossible or difficult to achieve. This type of study design requires allocation of the exposure/intervention by the researcher. 23 In some clinical settings, it is impossible to randomise or blind participants. In such cases, non-randomised designs are employed. 27 Examples include pre-posttest design (with or without controls) and interrupted time series. 27, 28 For the pre-posttest design that involves a control group, participants (subjects) are allocated to intervention or control groups (without randomisation) by the researcher. 28 On the other hand, it could be a single pre-posttest design study where all subjects are assessed at baseline, the intervention is given, and the subjects are re-assessed post-intervention. 28 An example of this type of study was reported by Lamont and Brunero (2018 ), who examined the effect of a workplace violence training program for generalist nurses in the acute hospital setting. The authors found a statistically significant increase in behaviour intention scores and overall confidence in coping with patient aggression post-test. 29 Another type of non-RCT study is the interrupted time series (ITS) in which data are gathered before and after intervention at various evenly spaced time points (such as weekly, monthly, or yearly). 30 Thus, it is crucial to take note of the precise moment an intervention occurred. The primary goal of an interrupted time series is to determine whether the data pattern observed post-intervention differs from that noted prior. 30 Several ITS were conducted to investigate the effectiveness of the different prevention strategies (such as lockdown and border closure) used during the COVID pandemic. 31, 32 Although non-RCT may be more feasible to RCTs, they are more prone to bias than RCTs due to the lack of randomisation and may not be able to control for all the variables that might affect the outcome. 23

Hierarchy of Evidence

While each study design has its unique characteristics and strengths, they are not without weaknesses (as already discussed) that impact the accuracy of the results and research evidence they provide. The hierarchy of evidence is a framework used to rank the evidence provided by different study designs in research evaluating healthcare interventions with respect to the strength of the presented results (i.e., validity and reliability of the findings). 33 Study designs can be ranked in terms of their ability to provide valid evidence on the effectiveness (intervention achieves the intended outcomes), appropriateness (impact of the intervention from the perspective of its recipient) and feasibility (intervention is implementable) of the research results they provide. 33 As shown in Figure 3.6, meta-analyses, systematic reviews, and RCTs provide stronger best-practice evidence and scientific base for clinical practice than descriptive studies as well as case reports and case series. Nonetheless, it is important to note that the research question/ hypothesis determines the study design, and not all questions can be answered using an interventional design. In addition, there are other factors that need to be considered when choosing a study design, such as funding, time constraints, and ethical considerations, and these factors are discussed in detail in chapter 6.

study design used in quantitative research

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

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  • What Is Quantitative Research? | Definition & Methods

What Is Quantitative Research? | Definition & Methods

Published on 4 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

Quantitative research is the process of collecting and analysing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalise results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analysing non-numerical data (e.g. text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

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Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

  • In descriptive research , you simply seek an overall summary of your study variables.
  • In correlational research , you investigate relationships between your study variables.
  • In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalised to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

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Once data is collected, you may need to process it before it can be analysed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualise your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalisations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardise data collection and generalise findings . Strengths of this approach include:

  • Replication

Repeating the study is possible because of standardised data collection protocols and tangible definitions of abstract concepts.

  • Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

  • Large samples

Data from large samples can be processed and analysed using reliable and consistent procedures through quantitative data analysis.

  • Hypothesis testing

Using formalised and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

  • Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

  • Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

  • Structural bias

Despite standardised procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

  • Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

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

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 organisations.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Designing Quantitative Research Studies

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There are variety of quantitative research designs that are amenable to use in educational scholarship. The design complexity will depend on available resources and the question(s) being investigated. A simulation-based medical education (SBME) quantitative study can range from an observational study to a complex, multiple group effort with or without randomization. Ensuring adequate number of participants are enrolled to have sufficient power to detect important differences is key; most educational research is underpowered. Certain designs (e.g., mastery learning) have grown in use.

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Mangold, K., Adler, M. (2019). Designing Quantitative Research Studies. In: Nestel, D., Hui, J., Kunkler, K., Scerbo, M., Calhoun, A. (eds) Healthcare Simulation Research. Springer, Cham. https://doi.org/10.1007/978-3-030-26837-4_23

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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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Your ultimate guide to quantitative research.

12 min read You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

What is quantitative research?

Quantitative is the research method of collecting quantitative data – this is data that can be converted into numbers or numerical data, which can be easily quantified, compared, and analyzed.

Quantitative research deals with primary and secondary sources where data is represented in numerical form. This can include closed-question poll results, statistics, and census information or demographic data .

Quantitative data tends to be used when researchers are interested in understanding a particular moment in time and examining data sets over time to find trends and patterns.

To collect numerical data, surveys are often employed as one of the main research methods to source first-hand information in primary research . Quantitative research can also come from third-party research studies .

Quantitative research is widely used in the realms of social sciences, such as biology, chemistry, psychology, economics, sociology, and marketing .

Research teams collect data that is significant to proving or disproving a hypothesis research question – known as the research objective. When they collect quantitative data, researchers will aim to use a sample size that is representative of the total population of the target market they’re interested in.

Then the data collected will be manually or automatically stored and compared for insights.

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Quantitative vs qualitative research

While the quantitative research definition focuses on numerical data, qualitative research is defined as data that supplies non-numerical information.

Quantitative research focuses on the thoughts, feelings, and values of a participant , to understand why people act in the way they do . They result in data types like quotes, symbols, images, and written testimonials.

These data types tell researchers subjective information, which can help us assign people into categories, such as a participant’s religion, gender , social class, political alignment, likely favored products to buy, or their preferred training learning style.

For this reason, qualitative research is often used in social research, as this gives a window into the behavior and actions of people.

study design used in quantitative research

In general, if you’re interested in measuring something or testing a hypothesis, use quantitative methods. If you want to explore ideas, thoughts, and meanings, use qualitative methods.

However, quantitative and qualitative research methods are both recommended when you’re looking to understand a point in time, while also finding out the reason behind the facts.

Quantitative research data collection methods

Quantitative research methods can use structured research instruments like:

  • Surveys : A survey is a simple-to-create and easy-to-distribute research method , which helps gather information from large groups of participants quickly. Traditionally, paper-based surveys can now be made online, so costs can stay quite low.

Quantitative questions tend to be closed questions that ask for a numerical result, based on a range of options, or a yes/no answer that can be tallied quickly.

  • Face-to-face or phone interviews: Interviews are a great way to connect with participants , though they require time from the research team to set up and conduct.

Researchers may also have issues connecting with participants in different geographical regions . The researcher uses a set of predefined close-ended questions, which ask for yes/no or numerical values.

  • Polls: Polls can be a shorter version of surveys , used to get a ‘flavor’ of what the current situation is with participants. Online polls can be shared easily, though polls are best used with simple questions that request a range or a yes/no answer.

Quantitative data is the opposite of qualitative research, another dominant framework for research in the social sciences, explored further below.

Quantitative data types

Quantitative research methods often deliver the following data types:

  • Test Scores
  • Percent of training course completed
  • Performance score out of 100
  • Number of support calls active
  • Customer Net Promoter Score (NPS)

When gathering numerical data, the emphasis is on how specific the data is, and whether they can provide an indication of what ‘is’ at the time of collection. Pre-existing statistical data can tell us what ‘was’ for the date and time range that it represented

Quantitative research design methods (with examples)

Quantitative research has a number of quantitative research designs you can choose from:


This design type describes the state of a data type is telling researchers, in its native environment. There won’t normally be a clearly defined research question to start with. Instead, data analysis will suggest a conclusion , which can become the hypothesis to investigate further.

Examples of descriptive quantitative design include:

  • A description of child’s Christmas gifts they received that year
  • A description of what businesses sell the most of during Black Friday
  • A description of a product issue being experienced by a customer


This design type looks at two or more data types, the relationship between them, and the extent that they differ or align. This does not look at the causal links deeper – instead statistical analysis looks at the variables in a natural environment.

Examples of correlational quantitative design include:

  • The relationship between a child’s Christmas gifts and their perceived happiness level
  • The relationship between a business’ sales during Black Friday and the total revenue generated over the year
  • The relationship between a customer’s product issue and the reputation of the product


This design type looks at two or more data types and tries to explain any relationship and differences between them, using a cause-effect analysis. The research is carried out in a near-natural environment, where information is gathered from two groups – a naturally occurring group that matches the original natural environment, and one that is not naturally present.

This allows for causal links to be made, though they might not be correct, as other variables may have an impact on results.

Examples of causal-comparative/quasi-experimental quantitative design include:

  • The effect of children’s Christmas gifts on happiness
  • The effect of Black Friday sales figures on the productivity of company yearly sales
  • The effect of product issues on the public perception of a product

Experimental Research

This design type looks to make a controlled environment in which two or more variables are observed to understand the exact cause and effect they have. This becomes a quantitative research study, where data types are manipulated to assess the effect they have. The participants are not naturally occurring groups, as the setting is no longer natural. A quantitative research study can help pinpoint the exact conditions in which variables impact one another.

Examples of experimental quantitative design include:

  • The effect of children’s Christmas gifts on a child’s dopamine (happiness) levels
  • The effect of Black Friday sales on the success of the company
  • The effect of product issues on the perceived reliability of the product

Quantitative research methods need to be carefully considered, as your data collection of a data type can be used to different effects. For example, statistics can be descriptive or correlational (or inferential). Descriptive statistics help us to summarize our data, while inferential statistics help infer conclusions about significant differences.

Advantages of quantitative research

  • Easy to do : Doing quantitative research is more straightforward, as the results come in numerical format, which can be more easily interpreted.
  • Less interpretation : Due to the factual nature of the results, you will be able to accept or reject your hypothesis based on the numerical data collected.
  • Less bias : There are higher levels of control that can be applied to the research, so bias can be reduced , making your data more reliable and precise.

Disadvantages of quantitative research

  • Can’t understand reasons: Quantitative research doesn’t always tell you the full story, meaning you won’t understand the context – or the why, of the data you see, why do you see the results you have uncovered?
  • Useful for simpler situations: Quantitative research on its own is not great when dealing with complex issues. In these cases, quantitative research may not be enough.

How to use quantitative research to your business’s advantage

Quantitative research methods may help in areas such as:

  • Identifying which advert or landing page performs better
  • Identifying how satisfied your customers are
  • How many customers are likely to recommend you
  • Tracking how your brand ranks in awareness and customer purchase intent
  • Learn what consumers are likely to buy from your brand.

6 steps to conducting good quantitative research

Businesses can benefit from quantitative research by using it to evaluate the impact of data types. There are several steps to this:

  • Define your problem or interest area : What do you observe is happening and is it frequent? Identify the data type/s you’re observing.
  • Create a hypothesis : Ask yourself what could be the causes for the situation with those data types.
  • Plan your quantitative research : Use structured research instruments like surveys or polls to ask questions that test your hypothesis.
  • Data Collection : Collect quantitative data and understand what your data types are telling you. Using data collected on different types over long time periods can give you information on patterns.
  • Data analysis : Does your information support your hypothesis? (You may need to redo the research with other variables to see if the results improve)
  • Effectively present data : Communicate the results in a clear and concise way to help other people understand the findings.

How Qualtrics products can enhance & simplify the quantitative research process

The Qualtrics XM system gives you an all-in-one, integrated solution to help you all the way through conducting quantitative research. From survey creation and data collection to statistical analysis and data reporting, it can help all your internal teams gain insights from your numerical data.

Quantitative methods are catered to your business through templates or advanced survey designs. While you can manually collect data and conduct data analysis in a spreadsheet program, this solution helps you automate the process of quantitative research, saving you time and administration work.

Using computational techniques helps you to avoid human errors, and participant results come in are already incorporated into the analysis in real-time.

Our key tools, Stats IQ™ and Driver IQ™ make analyzing numerical data easy and simple. Choose to highlight key findings based on variables or highlight statistically insignificant findings. The choice is yours.

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Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Quantitative Research

What is Quantitative Research?

Quantitative research is the methodology which researchers use to test theories about people’s attitudes and behaviors based on numerical and statistical evidence. Researchers sample a large number of users (e.g., through surveys) to indirectly obtain measurable, bias-free data about users in relevant situations.

“Quantification clarifies issues which qualitative analysis leaves fuzzy. It is more readily contestable and likely to be contested. It sharpens scholarly discussion, sparks off rival hypotheses, and contributes to the dynamics of the research process.” — Angus Maddison, Notable scholar of quantitative macro-economic history
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See how quantitative research helps reveal cold, hard facts about users which you can interpret and use to improve your designs.

Use Quantitative Research to Find Mathematical Facts about Users

Quantitative research is a subset of user experience (UX) research . Unlike its softer, more individual-oriented “counterpart”, qualitative research , quantitative research means you collect statistical/numerical data to draw generalized conclusions about users’ attitudes and behaviors . Compare and contrast quantitative with qualitative research, below:

Qualitative Research

You Aim to Determine

The “what”, “where” & “when” of the users’ needs & problems – to help keep your project’s focus on track during development

The “why” – to get behind how users approach their problems in their world

Highly structured (e.g., surveys) – to gather data about what users do & find patterns in large user groups

Loosely structured (e.g., contextual inquiries) – to learn why users behave how they do & explore their opinions

Number of Representative Users

Ideally 30+

Often around 5

Level of Contact with Users

Less direct & more remote (e.g., analytics)

More direct & less remote (e.g., usability testing to examine users’ stress levels when they use your design)


Reliable – if you have enough test users

Less reliable, with need for great care with handling non-numerical data (e.g., opinions), as your own opinions might influence findings

Quantitative research is often best done from early on in projects since it helps teams to optimally direct product development and avoid costly design mistakes later. As you typically get user data from a distance—i.e., without close physical contact with users—also applying qualitative research will help you investigate why users think and feel the ways they do. Indeed, in an iterative design process quantitative research helps you test the assumptions you and your design team develop from your qualitative research. Regardless of the method you use, with proper care you can gather objective and unbiased data – information which you can complement with qualitative approaches to build a fuller understanding of your target users. From there, you can work towards firmer conclusions and drive your design process towards a more realistic picture of how target users will ultimately receive your product.

study design used in quantitative research

Quantitative analysis helps you test your assumptions and establish clearer views of your users in their various contexts.

Quantitative Research Methods You Can Use to Guide Optimal Designs

There are many quantitative research methods, and they help uncover different types of information on users. Some methods, such as A/B testing, are typically done on finished products, while others such as surveys could be done throughout a project’s design process. Here are some of the most helpful methods:

A/B testing – You test two or more versions of your design on users to find the most effective. Each variation differs by just one feature and may or may not affect how users respond. A/B testing is especially valuable for testing assumptions you’ve drawn from qualitative research. The only potential concerns here are scale—in that you’ll typically need to conduct it on thousands of users—and arguably more complexity in terms of considering the statistical significance involved.

Analytics – With tools such as Google Analytics, you measure metrics (e.g., page views, click-through rates) to build a picture (e.g., “How many users take how long to complete a task?”).

Desirability Studies – You measure an aspect of your product (e.g., aesthetic appeal) by typically showing it to participants and asking them to select from a menu of descriptive words. Their responses can reveal powerful insights (e.g., 78% associate the product/brand with “fashionable”).

Surveys and Questionnaires – When you ask for many users’ opinions, you will gain massive amounts of information. Keep in mind that you’ll have data about what users say they do, as opposed to insights into what they do . You can get more reliable results if you incentivize your participants well and use the right format.

Tree Testing – You remove the user interface so users must navigate the site and complete tasks using links alone. This helps you see if an issue is related to the user interface or information architecture.

Another powerful benefit of conducting quantitative research is that you can keep your stakeholders’ support with hard facts and statistics about your design’s performance—which can show what works well and what needs improvement—and prove a good return on investment. You can also produce reports to check statistics against different versions of your product and your competitors’ products.

Most quantitative research methods are relatively cheap. Since no single research method can help you answer all your questions, it’s vital to judge which method suits your project at the time/stage. Remember, it’s best to spend appropriately on a combination of quantitative and qualitative research from early on in development. Design improvements can be costly, and so you can estimate the value of implementing changes when you get the statistics to suggest that these changes will improve usability. Overall, you want to gather measurements objectively, where your personality, presence and theories won’t create bias.

Learn More about Quantitative Research

Take our User Research course to see how to get the most from quantitative research.

See how quantitative research methods fit into your design research landscape .

This insightful piece shows the value of pairing quantitative with qualitative research .

Find helpful tips on combining quantitative research methods in mixed methods research .

Questions related to Quantitative Research

Qualitative and quantitative research differ primarily in the data they produce. Quantitative research yields numerical data to test hypotheses and quantify patterns. It's precise and generalizable. Qualitative research, on the other hand, generates non-numerical data and explores meanings, interpretations, and deeper insights. Watch our video featuring Professor Alan Dix on different types of research methods.

This video elucidates the nuances and applications of both research types in the design field.

In quantitative research, determining a good sample size is crucial for the reliability of the results. William Hudson, CEO of Syntagm, emphasizes the importance of statistical significance with an example in our video. 

He illustrates that even with varying results between design choices, we need to discern whether the differences are statistically significant or products of chance. This ensures the validity of the results, allowing for more accurate interpretations. Statistical tools like chi-square tests can aid in analyzing the results effectively. To delve deeper into these concepts, take William Hudson’s Data-Driven Design: Quantitative UX Research Course . 

Quantitative research is crucial as it provides precise, numerical data that allows for high levels of statistical inference. Our video from William Hudson, CEO of Syntagm, highlights the importance of analytics in examining existing solutions. 

Quantitative methods, like analytics and A/B testing, are pivotal for identifying areas for improvement, understanding user behaviors, and optimizing user experiences based on solid, empirical evidence. This empirical nature ensures that the insights derived are reliable, allowing for practical improvements and innovations. Perhaps most importantly, numerical data is useful to secure stakeholder buy-in and defend design decisions and proposals. Explore this approach in our Data-Driven Design: Quantitative Research for UX Research course and learn from William Hudson’s detailed explanations of when and why to use analytics in the research process.

After establishing initial requirements, statistical data is crucial for informed decisions through quantitative research. William Hudson, CEO of Syntagm, sheds light on the role of quantitative research throughout a typical project lifecycle in this video:

 During the analysis and design phases, quantitative research helps validate user requirements and understand user behaviors. Surveys and analytics are standard tools, offering insights into user preferences and design efficacy. Quantitative research can also be used in early design testing, allowing for optimal design modifications based on user interactions and feedback, and it’s fundamental for A/B and multivariate testing once live solutions are available.

To write a compelling quantitative research question:

Create clear, concise, and unambiguous questions that address one aspect at a time.

Use common, short terms and provide explanations for unusual words.

Avoid leading, compound, and overlapping queries and ensure that questions are not vague or broad.

According to our video by William Hudson, CEO of Syntagm, quality and respondent understanding are vital in forming good questions. 

He emphasizes the importance of addressing specific aspects and avoiding intimidating and confusing elements, such as extensive question grids or ranking questions, to ensure participant engagement and accurate responses. For more insights, see the article Writing Good Questions for Surveys .

Survey research is typically quantitative, collecting numerical data and statistical analysis to make generalizable conclusions. However, it can also have qualitative elements, mainly when it includes open-ended questions, allowing for expressive responses. Our video featuring the CEO of Syntagm, William Hudson, provides in-depth insights into when and how to effectively utilize surveys in the product or service lifecycle, focusing on user satisfaction and potential improvements.

He emphasizes the importance of surveys in triangulating data to back up qualitative research findings, ensuring we have a complete understanding of the user's requirements and preferences.

Descriptive research focuses on describing the subject being studied and getting answers to questions like what, where, when, and who of the research question. However, it doesn’t include the answers to the underlying reasons, or the “why” behind the answers obtained from the research. We can use both f qualitative and quantitative methods to conduct descriptive research. Descriptive research does not describe the methods, but rather the data gathered through the research (regardless of the methods used).

When we use quantitative research and gather numerical data, we can use statistical analysis to understand relationships between different variables. Here’s William Hudson, CEO of Syntagm with more on correlation and how we can apply tests such as Pearson’s r and Spearman Rank Coefficient to our data.

This helps interpret phenomena such as user experience by analyzing session lengths and conversion values, revealing whether variables like time spent on a page affect checkout values, for example.

Random Sampling: Each individual in the population has an equitable opportunity to be chosen, which minimizes biases and simplifies analysis.

Systematic Sampling: Selecting every k-th item from a list after a random start. It's simpler and faster than random sampling when dealing with large populations.

Stratified Sampling: Segregate the population into subgroups or strata according to comparable characteristics. Then, samples are taken randomly from each stratum.

Cluster Sampling: Divide the population into clusters and choose a random sample.

Multistage Sampling: Various sampling techniques are used at different stages to collect detailed information from diverse populations.

Convenience Sampling: The researcher selects the sample based on availability and willingness to participate, which may only represent part of the population.

Quota Sampling: Segment the population into subgroups, and samples are non-randomly selected to fulfill a predetermined quota from each subset.

These are just a few techniques, and choosing the right one depends on your research question, discipline, resource availability, and the level of accuracy required. In quantitative research, there isn't a one-size-fits-all sampling technique; choosing a method that aligns with your research goals and population is critical. However, a well-planned strategy is essential to avoid wasting resources and time, as highlighted in our video featuring William Hudson, CEO of Syntagm.

He emphasizes the importance of recruiting participants meticulously, ensuring their engagement and the quality of their responses. Accurate and thoughtful participant responses are crucial for obtaining reliable results. William also sheds light on dealing with failing participants and scrutinizing response quality to refine the outcomes.

The 4 types of quantitative research are Descriptive, Correlational, Causal-Comparative/Quasi-Experimental, and Experimental Research. Descriptive research aims to depict ‘what exists’ clearly and precisely. Correlational research examines relationships between variables. Causal-comparative research investigates the cause-effect relationship between variables. Experimental research explores causal relationships by manipulating independent variables. To gain deeper insights into quantitative research methods in UX, consider enrolling in our Data-Driven Design: Quantitative Research for UX course.

The strength of quantitative research is its ability to provide precise numerical data for analyzing target variables.This allows for generalized conclusions and predictions about future occurrences, proving invaluable in various fields, including user experience. William Hudson, CEO of Syntagm, discusses the role of surveys, analytics, and testing in providing objective insights in our video on quantitative research methods, highlighting the significance of structured methodologies in eliciting reliable results.

To master quantitative research methods, enroll in our comprehensive course, Data-Driven Design: Quantitative Research for UX . 

This course empowers you to leverage quantitative data to make informed design decisions, providing a deep dive into methods like surveys and analytics. Whether you’re a novice or a seasoned professional, this course at Interaction Design Foundation offers valuable insights and practical knowledge, ensuring you acquire the skills necessary to excel in user experience research. Explore our diverse topics to elevate your understanding of quantitative research methods.

Answer a Short Quiz to Earn a Gift

What is the primary goal of quantitative research in design?

  • To analyze numerical data and identify patterns
  • To explore abstract design concepts for implementation
  • To understand people's subjective experiences and opinions

Which of the following methods is an example of quantitative research?

  • Conduct a focus groups to collect detailed user feedback
  • Participate in open-ended interviews to explore user experiences
  • Run usability tests and measure task completion times

What is one key advantage of quantitative research?

  • It allows participants to express their opinions in a flexible manner.
  • It provides researchers with detailed narratives of user experiences and perspectives.
  • It produces standardized, comparable data that researchers can statistically analyze.

What is a significant challenge of quantitative research?

  • It lacks objectivity which makes its results difficult to reproduce.
  • It may oversimplify complex user behaviors into numbers and miss contextual insights.
  • It often results in biased or misleading conclusions.

How can designers effectively combine qualitative and quantitative research?

  • They can collect quantitative data first, followed by qualitative insights to explain the findings.
  • They can completely replace quantitative methods with qualitative approaches.
  • They can treat them as interchangeable methods to gather similar data.

Better luck next time!

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Literature on Quantitative Research

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Quantitative and Qualitative Research

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What is Quantitative Research?

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Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns . Quantitative research gathers a range of numeric data. Some of the numeric data is intrinsically quantitative (e.g. personal income), while in other cases the numeric structure is  imposed (e.g. ‘On a scale from 1 to 10, how depressed did you feel last week?’). The collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data (e.g. averages, percentages), show relationships among the data (e.g. ‘Students with lower grade point averages tend to score lower on a depression scale’) or compare across aggregated data (e.g. the USA has a higher gross domestic product than Spain). Quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. Qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies.

Coghlan, D., Brydon-Miller, M. (2014).  The SAGE encyclopedia of action research  (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406

What is the purpose of quantitative research?

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

Allen, M. (2017).  The SAGE encyclopedia of communication research methods  (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411

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Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?

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Breaking bad news: A mix methods study reporting the need for improving communication skills among doctors in Pakistan

  • Muhammad Ahmed Abdullah 1 ,
  • Babar Tasneem Shaikh 1 ,
  • Kashif Rehman Khan 2 &
  • Muhammad Asif Yasin 3  

BMC Health Services Research volume  24 , Article number:  588 ( 2024 ) Cite this article

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Metrics details

Effective skills and training for physicians are essential for communicating difficult or distressing information, also known as breaking bad news (BBN). This study aimed to assess both the capacity and the practices of clinicians in Pakistan regarding BBN.

A cross-sectional study was conducted involving 151 clinicians. Quantitative component used a structured questionnaire, while qualitative data were obtained through in-depth interviews with 13 medical educationists. The responses were analyzed using descriptive statistics and thematic analysis.

While most clinicians acknowledged their responsibility of delivering difficult news, only a small percentage had received formal training in BBN. Areas for improvement include time and interruption management, rapport building, and understanding the patients’ point of view. Prognosis and treatment options were not consistently discussed. Limited importance is given to BBN in medical education.

Training in BBN will lead to improved patient and attendants’ satisfaction, and empathetic support during difficult times.

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The duties of physicians extend beyond providing an effective treatment to patients; they also encompass the development of strong communication skills and the establishment of trust with their patients [ 1 ]. This emphasis on communication is crucial as it enables patients to cope with the seriousness and severity of their illnesses, to make informed decisions regarding treatment options, and to manage potential side effects [ 2 ]. In recent years, there has been a shift in medical practice from a doctor-centered approach to a patient-centered one, where patients play a significant role in the decision-making process, ultimately leading to increased patient satisfaction [ 3 ]. However, physicians may find themselves burdened when faced with the task of breaking bad news, fearing the potential reactions of their patients [ 4 , 5 ]. Neglecting to address this challenge can have negative consequences in terms of patient-centered healthcare, as physicians’ reluctance to disclose a bad news may compromise mental and physical well-being of the patients, and at times of the family members too [ 6 ]. On the other hand, physicians are being uncomfortable with their own emotions and do not have enough coping skills to manage their emotions in the moment [ 7 ].

Research studies have documented the lack of training and protocols among doctors for breaking bad news. For instance, a research from Brazil revealed that none of the clinicians at a university hospital were aware of any specific protocol or guidelines for this purpose [ 5 ]. Similarly, in Canada and South Korea, physician training in breaking bad news is reported to be insufficient, and in many underdeveloped countries, it is virtually non-existent despite curricular reforms [ 8 ]. In Northern Portugal, a significant number of family physicians expressed apprehension about breaking bad news and deemed training in this area necessary [ 9 ]. In Iran, inadequate training was identified as the main reason behind physicians’ difficulty and fear in delivering bad news to patients, emphasizing the need for formal training in this domain [ 1 ]. In India, one research documented diverse opinions among oncologists regarding breaking bad news and sharing information with patients, accenting the necessity for physician training in this aspect [ 10 ]. Additionally, a study conducted in Pakistan identified a common reason for increasing violence against healthcare providers as the failure to communicate bad news in a timely and appropriate manner, highlighting the need for better preparation and communication skills during this process [ 4 ]. Several protocols and guidelines have been developed for breaking bad news, with the SPIKES protocol being one of the most widely used due to its comprehensive coverage of essential aspects, particularly the emotional aspect of the process [ 11 ]. This Six-Step Protocol for Delivering Bad News is SPIKES: S for setting up the meeting, P is assessing the patient’s perception, I for achieving the patient’s invitation, K is providing knowledge and information to the patient, E is addressing the patient’s emotions with empathic responses and S for strategy and summary.

Despite the recommendations of the Pakistan Medical and Dental Council to incorporate communication skills into formal medical curricula, and the ongoing discussions regarding medical curricular reforms in Pakistan over the past two decades, little progress has been made in this regard. This lack of action is evident from a recent study conducted in Peshawar, Pakistan [ 12 ]. Thus, the aim of our study was to assess the training as well as the practices of clinicians in Pakistan regarding BBN and provide recommendations for improvement.

Study design

This mixed methods study utilized a cross-sectional design to assess the training and practices of doctors in BBN. The study was conducted at five tertiary care hospitals located in the twin cities of Islamabad and Rawalpindi, namely, Akbar Niazi Teaching Hospital, Benazir Bhutto Hospital, Holy Family Hospital, NESCOM Hospital, and Combined Military Hospital. The data collection period was eight weeks in the first quarter of 2023 to ensure an adequate sample size and data representation. The study participants selected through a simple random sampling included medical personnel directly involved in healthcare delivery within the selected hospitals with a minimum of six months of clinical experience. Medical students and Basic Health Sciences faculty were excluded from the study sample.

Data collection

To collect the necessary data, a 25-item self-administered questionnaire was developed. The questionnaire encompassed two main sections. The first section focused on recording participants’ demographic information, including age, gender, designation, specialty, and years of experience. This section aimed to establish a comprehensive profile of the participating doctors, providing a contextual background for the subsequent analysis of their responses. The second section of the questionnaire delved into the participants’ knowledge and practices related to breaking bad news, drawing from the established SPIKES protocol [ 11 ]. This section comprised a series of questions designed to assess the doctors’ familiarity with the protocol, their adherence to its guidelines, and their overall comfort level in delivering challenging news to patients and their families. The SPIKES protocol, which stands for Setting, Perception, Invitation, Knowledge, Emotions, and Strategy, is a widely recognized framework for effective communication during difficult conversations. Before administering the questionnaire, a pilot study was conducted with ten doctors working in general practice clinics, in Rawalpindi/Islamabad, to ensure its clarity, comprehensibility, and relevance to the research objectives. Feedback from the pilot study participants was incorporated into the final version of the questionnaire to enhance its validity and reliability.

Sample size calculation

The sample size for this study was determined based on a 95% confidence level, considering a hypothesized population proportion of 11% with a 5% margin of error. The anticipated frequency of this outcome factor was derived from a previous study [ 13 ]. The population size was estimated to be 200,000. Using the formula for sample size calculation for frequency in a population (n = [DEFF * N * p * (1-p)] / [(d^2 / Z^2) * (N-1) + p * (1-p)]), where DEFF represents the design effect, N is the population size, p is the hypothesized proportion, d is the margin of error, and Z is the critical value corresponding to the desired confidence level, the required sample size was determined to be approximately 151 participants.

Data analysis and synthesis

After data collection, the collected data were subjected to comprehensive analysis using SPSS version 22.0. Descriptive statistics, such as frequencies and percentages were computed to summarize the data and gain insights into the training and practices of doctors in breaking bad news.

The qualitative part of the study aimed to gain insights into the practices and challenges associated with breaking bad news in a healthcare setting. The qualitative data were gathered through in-depth interviews with 13 medical educationists from Pakistan. Each interview lasted between 30 and 45 min and took place in the office spaces of the participants to ensure privacy and confidentiality. The participants were individuals who had been involved in teaching medicine for at least 5 years, including 6 clinicians, 4 individuals from medical education, and 3 from basic sciences departments. The interviews were conducted by the principal investigator along with a medical student who accompanied as a note-taker. Rigorous note-taking was done during the interviews to capture detailed information, and where possible, the interviews were audio recorded and later transcribed for analysis. The Braun and Clarke’s thematic analysis method was used as an iterative process which consisted of six steps: (1) becoming familiar with the data, (2) generating codes, (3) generating themes, (4) reviewing themes, (5) defining and naming themes, and (6) locating exemplars [ 14 ]. The analysis was conducted by carefully reading and familiarizing with the interview transcripts. Codes were generated to label and categorize meaningful segments of data, which were refined and grouped into broader themes. The research team engaged in discussions to validate the emerging themes and ensure the reliability of the analysis.

Demographic data of the participants showed that out of the total 151 respondents males were greater in number than females (62.3%), mean age was 30.7(± 8.6 SD) years and the proportion of house officers was the highest, as shown in Table  1 . Response rate of the employees of private hospitals was higher than that of the public sector and there were graduates from several medical institutions all over Pakistan.

Table  2 illustrates the responses to various questions related to BBN. Out of the total respondents, 74% reported that BBN was included in their daily duties, indicating that a significant majority of doctors in Pakistan are involved in delivering difficult news to their patients. However, only 9% of the participants reported receiving training specifically focused on BBN, while the remaining 91% had not received such training.

When considering the tenure of the BBN training, a small percentage of doctors (2%) reported receiving training during their MBBS education, followed by 3% during their house job, and 3% during postgraduate training. Surprisingly, the majority of respondents (92%) relied on personal experience rather than formal training to navigate the challenges of BBN. Regarding the availability of formal guidelines for BBN, only 10% of the participants reported having access to such guidelines, while the majority (90%) did not have formal guidelines to follow.

Maintaining privacy during the process of BBN was reported by 14% of the participants, indicating that privacy considerations may not be adequately addressed in some healthcare settings. Similarly, patient attendants’ involvement during the BBN was reported by 78% of the respondents, suggesting that involving family members or caregivers in the process is common.

When it comes to communication techniques during BBN, 64% of doctors reported sitting while delivering the news, while 36% did not. Time and interruption management, rapport building, patient perception exploration, and adequate patient speaking time were areas where improvements were needed, as reported by the participants.

Furthermore, while 52% of the respondents reported avoiding excessive bluntness and handling emotions appropriately, a considerable portion (48%) did not prioritize these aspects. Identification of emotional state, empathic response, and providing time for personal expression were areas where improvements were necessary, as reported by the participants. Moreover, the participants acknowledged the importance of avoiding jargon and technical terms (44%) and breaking the information into small chunks (45%) to enhance patient understanding. However, further efforts were needed to ensure that hopelessness was avoided during the conversation (50%).

Regarding prognosis and treatment options, 20% of the doctors reported discussing these aspects during BBN conversations, indicating that there is room for improvement in ensuring comprehensive information delivery and empathetic counseling.

In summary, the results highlight several areas where training and guidelines for BBN in Pakistan can be improved. The majority of doctors rely on personal experience rather than formal training, indicating a need for structured educational programs and guidelines in this critical area of healthcare communication. Privacy considerations, effective communication techniques, and emotional support for patients were identified as areas that require further attention and development. The findings emphasize the importance of enhancing training and providing formal guidelines to equip doctors with the necessary skills and strategies for delivering difficult news effectively and compassionately.

The qualitative component of the study involved in-depth interviews with 13 medical educationists from Pakistan. These interviews aimed to explore the level and standard of training on BBN in the curriculum and training of doctors in Pakistan. The interviews revealed several key themes that shed light on the current state of training and education in this area.

Theme 1: ambiguity in subject domains and integration of communication skills

The medical educationists expressed concerns regarding the lack of clarity in subject domains and the integration of communication skills into the medical curriculum. They suggested that communication skills, including BBN, should be incorporated into the community medicine curriculum. Furthermore, they proposed the introduction of family medicine as a dedicated subject at the undergraduate level, which would provide comprehensive training in communication skills and prepare doctors to handle sensitive conversations effectively.

One interviewee highlighted, “There is a lack of clarity when it comes to subject domains and the inclusion of communication skills in our medical curriculum. We believe that communication skills, including breaking bad news, should be integrated into the community medicine curriculum. Additionally, introducing family medicine as a dedicated subject at the undergraduate level would ensure that doctors receive extensive training in effective communication, addressing the emotional needs of patients and their families.” [P6].

This theme emphasizes the need for clear subject domains and the integration of communication skills including BBN within medical education. The proposal to introduce family medicine as an undergraduate subject reflects a holistic approach to training future doctors in effectively delivering difficult news and addressing the diverse needs of patients and their families.

Theme 2: limited importance of breaking bad news in medical education

The medical educationists expressed that at present BBN does not hold a significant place in the teaching and training of doctors in Pakistan. The focus is primarily on technical clinical knowledge and skill development, often neglecting important soft skills such as communication skills, research skills, and logistics. This lack of emphasis on communication training implies that doctors may not be adequately prepared to handle the complexities of BBN and managing the subsequent situations effectively.

During the interviews, one medical educationist highlighted, “In our curriculum, there is a major gap when it comes to training doctors in breaking bad news. The focus is more on technical aspects, and soft skills like communication are often overlooked. This can lead to doctors struggling in delivering difficult news and navigating the emotional complexities that follow.“ [P1].

The participants also expressed concerns about the limited exposure and opportunities for doctors to stay up to date with constantly evolving medical knowledge. They emphasized the importance of continuous professional development to ensure doctors are equipped with the latest information and best practices in BBN effectively.

One interviewee shared, “It is crucial for doctors to have appropriate exposure to stay updated with the latest medical knowledge. Breaking bad news requires not only clinical expertise but also an understanding of the emotional and psychological aspects. Continuous professional development programs can help doctors refine their skills and keep abreast of the advancements in this field.” [P3].

Theme 3: learning by example and long-term impact of communication

The interviewees emphasized that BBN cannot be solely taught through theoretical instruction but should be demonstrated through practical examples and role modeling. They highlighted the significance of the communication process itself, as it can have long-term effects on the lives of patients and their families.

An interviewee emphasized, “It’s not just about teaching the process of breaking bad news; it’s about demonstrating empathy, active listening, and providing support throughout the entire journey. Learning by example and observing experienced doctors can be invaluable in developing the necessary communication skills. We must realize that the way we communicate with people during difficult times can have a profound impact on their well-being.” [P2].

Theme 4: lack of standardized training and guidelines

The medical educationists highlighted the absence of standardized training programs and guidelines specifically tailored to breaking bad news in Pakistan. They emphasized the need for a structured curriculum that includes comprehensive training modules and clear guidelines to ensure consistent and effective communication when delivering difficult news.

One interviewee stated, “There is a lack of standardized training and guidelines for breaking bad news in our medical education system. Without a structured curriculum and clear guidelines, doctors may face challenges in approaching these sensitive conversations. Establishing standardized training programs would provide doctors with the necessary tools and frameworks to navigate such situations effectively.” [P4].

Theme 5: inter-professional collaboration and team-based approach

The interviewees emphasized the importance of inter-professional collaboration and a team-based approach in BBN. They highlighted the need for effective communication and coordination among healthcare professionals, including doctors, nurses, psychologists, and social workers, to provide comprehensive support to patients and their families.

One medical educationist shared, “Breaking bad news is a complex process that requires a team-based approach. It is crucial for doctors to collaborate with other healthcare professionals, such as nurses, psychologists, and social workers, to ensure holistic care and support for patients and their families. Promoting effective inter-professional communication is essential in delivering sensitive news with empathy and addressing the diverse needs of patients.” [P7].

The present study aimed to explore the practices and training of clinicians in BBN to patients and their care givers in Pakistan. The combination of quantitative and qualitative findings, along with comparisons drawn from other studies conducted in developing countries, provides a comprehensive understanding of the current state of BBN practices and training in Pakistan and its relation to similar contexts.

Breaking bad news is part of the daily duties of almost all the clinicians. A study conducted in Sudan found that 56% of physicians had received training in BBN, indicating a relatively lower percentage compared to our study [ 15 ]. Similarly, a study from Ethiopia reported that 82% of participant physicians were not even aware of the SPIKES protocol, and 84% had no formal or informal training in BBN [ 8 ]. These findings suggest that the level of training and awareness regarding BBN varies across different developing countries. In our study revealed that only 9% of the participants reported receiving formal training specifically focused on BBN. This finding is consistent with studies conducted in other developing countries. For instance, a study from Lahore, Pakistan, involving postgraduate trainees, found a lack of knowledge and low satisfaction regarding BBN skills [ 16 ]. Similarly, a study in Peshawar, Pakistan, reported that 95% of participants had no training in BBN, highlighting a common gap in training among healthcare professionals [ 12 ]. Despite the fact that there is no formal training on BBN, the self-reported data in our study is quite positive.

The qualitative component of the study added valuable insights to complement the quantitative findings. Through in-depth interviews, participants’ experiences, perspectives, and challenges regarding BBN were explored. This approach provided a deeper understanding of the participants’ thoughts, emotions, and contextual factors influencing their communication practices. Themes and patterns emerged, offering a nuanced understanding of the quantitative results. The qualitative component also captured participants’ perceptions of training effectiveness, suggestions for improvement, and barriers to implementing optimal communication practices. Nonetheless, respondents were of the view that either at undergraduate or as part of the continuing education, inclusion of BBN training must be considered and that there should be a structured curriculum. However, there is an incongruent viewpoint too where some respondents said that skills of BBN come with experiential learning and maturity, and that it is about exhibiting one’s empathetic attitude and care during difficult times. This mixed methods approach allowed for a comprehensive examination of the research questions, generating practical implications for improving physician practices in breaking bad news [ 16 , 17 ].

Comparisons drawn from other developing countries also highlight the need for standardized training programs and guidelines for BBN. For instance, according to one research, adherence to the SPIKES protocol varied among participants, with 35–79% claiming to follow the protocol in routine practice [ 15 ]. Similarly, a study in Ethiopia found that a significant percentage of physicians were not complying with the guidelines of BBN [ 17 ]. These findings indicate the need for structured curricula and clear guidelines to ensure consistent and effective communication skills amongst doctors [ 18 ]. The importance of paying enough attention to the emotions of the recipient and the need to provide support after breaking bad news cannot be undermined at all [ 19 ]. A cultural shift is required within the medical profession and healthcare more generally so that BBN is viewed not merely as a soft skill but a professional responsibility for the doctor and a right for the patients and families who wish to have it [ 20 ].


Our study has few limitations too. Very few participants were of the consultant cadre, most of the responded were junior doctors. Patients as well as the care givers are important stakeholders in this issue. Their views and perceptions were not explored in qualitative component of the study.

This study offers valuable insights into the practices and training of clinicians involved in BBN in Pakistan. Comparisons with other studies conducted in developing countries reveal both similarities and differences in BBN practices and training. The findings underscore the necessity of standardized training programs, formal guidelines, and improved communication skills education within medical curricula across developing nations. Recommendations arising from this study include integrating communication skills into the medical curriculum, developing standardized training programs, promoting continuous professional development, fostering inter-professional collaboration, and recognizing the importance of communication skills. By taking these steps, healthcare professionals will be equipped with the necessary tools to navigate the complexities of breaking bad news effectively and to provide compassionate care. Collaboration among medical institutions, policymakers, and regulatory bodies is essential to prioritize communication skills training, establish clear guidelines, and emphasize the value of empathetic and effective communication. Efforts should be directed towards increasing awareness, providing comprehensive training, and emphasizing the significance of effective communication when delivering difficult news, thus ensuring optimal patient care and support during challenging situations. Implementation of these recommendations will enhance the delivery of difficult news, increase patient satisfaction, and ensure comprehensive support during challenging times.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Authors acknowledge the time given by the participants to answer our study questions and also for validating the transcripts.

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MAA & BTS were involved in conception and design of the study; MAA, KRK and MAY did the data collection, analysis and interpretation of the literature; and later developed the first draft of the paper; BTS helped in triangulation and contributed in revising it critically for substantial intellectual content and for adding references. All authors read and approved the final manuscript.

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Abdullah, M.A., Shaikh, B.T., Khan, K.R. et al. Breaking bad news: A mix methods study reporting the need for improving communication skills among doctors in Pakistan. BMC Health Serv Res 24 , 588 (2024). https://doi.org/10.1186/s12913-024-11056-2

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Received : 31 January 2024

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DOI : https://doi.org/10.1186/s12913-024-11056-2

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study design used in quantitative research

study design used in quantitative research

  International Journal of Educational Research Journal / International Journal of Educational Research / Vol. 12 No. 2 (2023) / Articles (function() { function async_load(){ var s = document.createElement('script'); s.type = 'text/javascript'; s.async = true; var theUrl = 'https://www.journalquality.info/journalquality/ratings/2405-www-ajol-info-ijer'; s.src = theUrl + ( theUrl.indexOf("?") >= 0 ? "&" : "?") + 'ref=' + encodeURIComponent(window.location.href); var embedder = document.getElementById('jpps-embedder-ajol-ijer'); embedder.parentNode.insertBefore(s, embedder); } if (window.attachEvent) window.attachEvent('onload', async_load); else window.addEventListener('load', async_load, false); })();  

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Perceptions of tshwane learners in the 9 grade on home background as influencing their performance in mathematics in south africa, bamidele segun donald odeyemi.

The study probed into the perceptions of the home background factor by the 9th grade learners on their performances in mathematics in Tshwane municipality public high schools in the Republic of South Africa. The study applied a mixed method research approach, that is,  the use of both quantitative and qualitative data collection, with a descriptive survey design. The samples in the survey consists of 120  male learners and 280 female learners, totaling 400 public school learners. The data collection was done through a self-developed  questionnaire, which also included a few open-ended questions on how mathematics performance can be boosted. The learners' end of  the term result in mathematics was appraised for ascertaining learners' degree of academic performance. Data collected quantitatively  were analysed using version 24.0 of the Statistical Package for the Social Sciences (SPSS), while the data collected qualitatively were  analysed in a narrative form. A null hypothesis was tested in the survey, which was rejected. The study presented that the perceptions of  the 9 grade learners on home background was insignificant, therefore, it was not taken as a factor responsible for their poor    performances in mathematics.

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