Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

  • Knowledge Base

Methodology

  • Internal Validity in Research | Definition, Threats, & Examples

Internal Validity in Research | Definition, Threats & Examples

Published on May 1, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

Table of contents

Why internal validity matters, how to check whether your study has internal validity, trade-off between internal and external validity, threats to internal validity and how to counter them, other interesting articles, frequently asked questions about internal validity.

Internal validity makes the conclusions of a causal relationship credible and trustworthy. Without high internal validity, an experiment cannot demonstrate a causal link between two variables.

Once they arrive at the laboratory, the treatment group participants are given a cup of coffee to drink, while control group participants are given water. You also give both groups memory tests. After analyzing the results, you find that the treatment group performed better than the control group on the memory test.

For your conclusion to be valid, you need to be able to rule out other explanations (including control , extraneous , and confounding variables) for the results.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

  • Academic style
  • Vague sentences
  • Style consistency

See an example

in qualitative research internal validity

There are three necessary conditions for internal validity. All three conditions must occur to experimentally establish causality between an independent variable A (your treatment variable) and dependent variable B (your response variable).

  • Your treatment and response variables change together.
  • Your treatment precedes changes in your response variables
  • No confounding or extraneous factors can explain the results of your study.

In the research example above, only two out of the three conditions have been met.

  • Drinking coffee and memory performance increased together.
  • Drinking coffee happened before the memory test.
  • The time of day of the sessions is an extraneous factor that can equally explain the results of the study.

Because you assigned participants to groups based on the schedule, the groups were different at the start of the study. Any differences in memory performance may be due to a difference in the time of day. Therefore, you cannot say for certain whether the time of day or drinking a cup of coffee improved memory performance.

That means your study has low internal validity, and you cannot deduce a causal relationship between drinking coffee and memory performance.

External validity is the extent to which you can generalize the findings of a study to other measures, settings or groups. In other words, can you apply the findings of your study to a broader context?

There is an inherent trade-off between internal and external validity ; the more you control extraneous factors in your study, the less you can generalize your findings to a broader context.

Threats to internal validity are important to recognize and counter in a research design for a robust study. Different threats can apply to single-group and multi-group studies.

Single-group studies

How to counter threats in single-group studies.

Altering the experimental design can counter several threats to internal validity in single-group studies.

  • Adding a comparable control group counters threats to single-group studies. If comparable control and treatment groups each face the same threats, the outcomes of the study won’t be affected by them.
  • A large sample size counters testing, because results would be more sensitive to any variability in the outcomes and less likely to suffer from sampling bias .
  • Using filler-tasks or questionnaires to hide the purpose of study also counters testing threats and demand characteristics .

Multi-group studies

How to counter threats in multi-group studies.

Altering the experimental design can counter several threats to internal validity in multi-group studies.

  • Random assignment of participants to groups counters selection bias and regression to the mean by making groups comparable at the start of the study.
  • Blinding participants to the aim of the study counters the effects of social interaction.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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

  • Normal distribution
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Ecological validity

Research bias

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

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Attrition bias is a threat to internal validity . In experiments, differential rates of attrition between treatment and control groups can skew results.

This bias can affect the relationship between your independent and dependent variables . It can make variables appear to be correlated when they are not, or vice versa .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 22). Internal Validity in Research | Definition, Threats & Examples. Scribbr. Retrieved February 20, 2024, from https://www.scribbr.com/methodology/internal-validity/

Is this article helpful?

Pritha Bhandari

Pritha Bhandari

Other students also liked, external validity | definition, types, threats & examples, guide to experimental design | overview, steps, & examples, correlation vs. causation | difference, designs & examples, what is your plagiarism score.

  • Bipolar Disorder
  • Therapy Center
  • When To See a Therapist
  • Types of Therapy
  • Best Online Therapy
  • Best Couples Therapy
  • Best Family Therapy
  • Managing Stress
  • Sleep and Dreaming
  • Understanding Emotions
  • Self-Improvement
  • Healthy Relationships
  • Student Resources
  • Personality Types
  • Verywell Mind Insights
  • 2023 Verywell Mind 25
  • Mental Health in the Classroom
  • Editorial Process
  • Meet Our Review Board
  • Crisis Support

Internal Validity vs. External Validity in Research

Both help determine how meaningful the results of the study are

Arlin Cuncic, MA, is the author of "Therapy in Focus: What to Expect from CBT for Social Anxiety Disorder" and "7 Weeks to Reduce Anxiety." She has a Master's degree in psychology.

in qualitative research internal validity

Rachel Goldman, PhD FTOS, is a licensed psychologist, clinical assistant professor, speaker, wellness expert specializing in eating behaviors, stress management, and health behavior change.

in qualitative research internal validity

Verywell / Bailey Mariner

  • Internal Validity
  • External Validity

Internal validity is a measure of how well a study is conducted (its structure) and how accurately its results reflect the studied group.

External validity relates to how applicable the findings are in the real world. These two concepts help researchers gauge if the results of a research study are trustworthy and meaningful.

Conclusions are warranted

Controls extraneous variables

Eliminates alternative explanations

Focus on accuracy and strong research methods

Findings can be generalized

Outcomes apply to practical situations

Results apply to the world at large

Results can be translated into another context

What Is Internal Validity in Research?

Internal validity is the extent to which a research study establishes a trustworthy cause-and-effect relationship. This type of validity depends largely on the study's procedures and how rigorously it is performed.

Internal validity is important because once established, it makes it possible to eliminate alternative explanations for a finding. If you implement a smoking cessation program, for instance, internal validity ensures that any improvement in the subjects is due to the treatment administered and not something else.

Internal validity is not a "yes or no" concept. Instead, we consider how confident we can be with study findings based on whether the research avoids traps that may make those findings questionable. The less chance there is for "confounding," the higher the internal validity and the more confident we can be.

Confounding refers to uncontrollable variables that come into play and can confuse the outcome of a study, making us unsure of whether we can trust that we have identified the cause-and-effect relationship.

In short, you can only be confident that a study is internally valid if you can rule out alternative explanations for the findings. Three criteria are required to assume cause and effect in a research study:

  • The cause preceded the effect in terms of time.
  • The cause and effect vary together.
  • There are no other likely explanations for the relationship observed.

Factors That Improve Internal Validity

To ensure the internal validity of a study, you want to consider aspects of the research design that will increase the likelihood that you can reject alternative hypotheses. Many factors can improve internal validity in research, including:

  • Blinding : Participants—and sometimes researchers—are unaware of what intervention they are receiving (such as using a placebo on some subjects in a medication study) to avoid having this knowledge bias their perceptions and behaviors, thus impacting the study's outcome
  • Experimental manipulation : Manipulating an independent variable in a study (for instance, giving smokers a cessation program) instead of just observing an association without conducting any intervention (examining the relationship between exercise and smoking behavior)
  • Random selection : Choosing participants at random or in a manner in which they are representative of the population that you wish to study
  • Randomization or random assignment : Randomly assigning participants to treatment and control groups, ensuring that there is no systematic bias between the research groups
  • Strict study protocol : Following specific procedures during the study so as not to introduce any unintended effects; for example, doing things differently with one group of study participants than you do with another group

Internal Validity Threats

Just as there are many ways to ensure internal validity, there is also a list of potential threats that should be considered when planning a study.

  • Attrition : Participants dropping out or leaving a study, which means that the results are based on a biased sample of only the people who did not choose to leave (and possibly who all have something in common, such as higher motivation)
  • Confounding : A situation in which changes in an outcome variable can be thought to have resulted from some type of outside variable not measured or manipulated in the study
  • Diffusion : This refers to the results of one group transferring to another through the groups interacting and talking with or observing one another; this can also lead to another issue called resentful demoralization, in which a control group tries less hard because they feel resentful over the group that they are in
  • Experimenter bias : An experimenter behaving in a different way with different groups in a study, which can impact the results (and is eliminated through blinding)
  • Historical events : May influence the outcome of studies that occur over a period of time, such as a change in the political leader or a natural disaster that occurs, influencing how study participants feel and act
  • Instrumentation : This involves "priming" participants in a study in certain ways with the measures used, causing them to react in a way that is different than they would have otherwise reacted
  • Maturation : The impact of time as a variable in a study; for example, if a study takes place over a period of time in which it is possible that participants naturally change in some way (i.e., they grew older or became tired), it may be impossible to rule out whether effects seen in the study were simply due to the impact of time
  • Statistical regression : The natural effect of participants at extreme ends of a measure falling in a certain direction due to the passage of time rather than being a direct effect of an intervention
  • Testing : Repeatedly testing participants using the same measures influences outcomes; for example, if you give someone the same test three times, it is likely that they will do better as they learn the test or become used to the testing process, causing them to answer differently

What Is External Validity in Research?

External validity refers to how well the outcome of a research study can be expected to apply to other settings. This is important because, if external validity is established, it means that the findings can be generalizable to similar individuals or populations.

External validity affirmatively answers the question: Do the findings apply to similar people, settings, situations, and time periods?

Population validity and ecological validity are two types of external validity. Population validity refers to whether you can generalize the research outcomes to other populations or groups. Ecological validity refers to whether a study's findings can be generalized to additional situations or settings.

Another term called transferability refers to whether results transfer to situations with similar characteristics. Transferability relates to external validity and refers to a qualitative research design.

Factors That Improve External Validity

If you want to improve the external validity of your study, there are many ways to achieve this goal. Factors that can enhance external validity include:

  • Field experiments : Conducting a study outside the laboratory, in a natural setting
  • Inclusion and exclusion criteria : Setting criteria as to who can be involved in the research, ensuring that the population being studied is clearly defined
  • Psychological realism : Making sure participants experience the events of the study as being real by telling them a "cover story," or a different story about the aim of the study so they don't behave differently than they would in real life based on knowing what to expect or knowing the study's goal
  • Replication : Conducting the study again with different samples or in different settings to see if you get the same results; when many studies have been conducted on the same topic, a meta-analysis can also be used to determine if the effect of an independent variable can be replicated, therefore making it more reliable
  • Reprocessing or calibration : Using statistical methods to adjust for external validity issues, such as reweighting groups if a study had uneven groups for a particular characteristic (such as age)

External Validity Threats

External validity is threatened when a study does not take into account the interaction of variables in the real world. Threats to external validity include:

  • Pre- and post-test effects : When the pre- or post-test is in some way related to the effect seen in the study, such that the cause-and-effect relationship disappears without these added tests
  • Sample features : When some feature of the sample used was responsible for the effect (or partially responsible), leading to limited generalizability of the findings
  • Selection bias : Also considered a threat to internal validity, selection bias describes differences between groups in a study that may relate to the independent variable—like motivation or willingness to take part in the study, or specific demographics of individuals being more likely to take part in an online survey
  • Situational factors : Factors such as the time of day of the study, its location, noise, researcher characteristics, and the number of measures used may affect the generalizability of findings

While rigorous research methods can ensure internal validity, external validity may be limited by these methods.

Internal Validity vs. External Validity

Internal validity and external validity are two research concepts that share a few similarities while also having several differences.

Similarities

One of the similarities between internal validity and external validity is that both factors should be considered when designing a study. This is because both have implications in terms of whether the results of a study have meaning.

Both internal validity and external validity are not "either/or" concepts. Therefore, you always need to decide to what degree a study performs in terms of each type of validity.

Each of these concepts is also typically reported in research articles published in scholarly journals . This is so that other researchers can evaluate the study and make decisions about whether the results are useful and valid.

Differences

The essential difference between internal validity and external validity is that internal validity refers to the structure of a study (and its variables) while external validity refers to the universality of the results. But there are further differences between the two as well.

For instance, internal validity focuses on showing a difference that is due to the independent variable alone. Conversely, external validity results can be translated to the world at large.

Internal validity and external validity aren't mutually exclusive. You can have a study with good internal validity but be overall irrelevant to the real world. You could also conduct a field study that is highly relevant to the real world but doesn't have trustworthy results in terms of knowing what variables caused the outcomes.

Examples of Validity

Perhaps the best way to understand internal validity and external validity is with examples.

Internal Validity Example

An example of a study with good internal validity would be if a researcher hypothesizes that using a particular mindfulness app will reduce negative mood. To test this hypothesis, the researcher randomly assigns a sample of participants to one of two groups: those who will use the app over a defined period and those who engage in a control task.

The researcher ensures that there is no systematic bias in how participants are assigned to the groups. They do this by blinding the research assistants so they don't know which groups the subjects are in during the experiment.

A strict study protocol is also used to outline the procedures of the study. Potential confounding variables are measured along with mood , such as the participants' socioeconomic status, gender, age, and other factors. If participants drop out of the study, their characteristics are examined to make sure there is no systematic bias in terms of who stays in.

External Validity Example

An example of a study with good external validity would be if, in the above example, the participants used the mindfulness app at home rather than in the laboratory. This shows that results appear in a real-world setting.

To further ensure external validity, the researcher clearly defines the population of interest and chooses a representative sample . They might also replicate the study's results using different technological devices.

A Word From Verywell

Setting up an experiment so that it has both sound internal validity and external validity involves being mindful from the start about factors that can influence each aspect of your research.

It's best to spend extra time designing a structurally sound study that has far-reaching implications rather than to quickly rush through the design phase only to discover problems later on. Only when both internal validity and external validity are high can strong conclusions be made about your results.

San Jose State University. Internal and external validity .

Michael RS. Threats to internal & external validity: Y520 strategies for educational inquiry .

Pahus L, Burgel PR, Roche N, Paillasseur JL, Chanez P. Randomized controlled trials of pharmacological treatments to prevent COPD exacerbations: applicability to real-life patients . BMC Pulm Med . 2019;19(1):127. doi:10.1186/s12890-019-0882-y

By Arlin Cuncic, MA Arlin Cuncic, MA, is the author of "Therapy in Focus: What to Expect from CBT for Social Anxiety Disorder" and "7 Weeks to Reduce Anxiety." She has a Master's degree in psychology.

Log in using your username and password

  • Search More Search for this keyword Advanced search
  • Latest content
  • Current issue
  • Write for Us
  • BMJ Journals More You are viewing from: Google Indexer

You are here

  • Volume 18, Issue 2
  • Issues of validity and reliability in qualitative research
  • Article Text
  • Article info
  • Citation Tools
  • Rapid Responses
  • Article metrics

Download PDF

  • Helen Noble 1 ,
  • Joanna Smith 2
  • 1 School of Nursing and Midwifery, Queens's University Belfast , Belfast , UK
  • 2 School of Human and Health Sciences, University of Huddersfield , Huddersfield , UK
  • Correspondence to Dr Helen Noble School of Nursing and Midwifery, Queens's University Belfast, Medical Biology Centre, 97 Lisburn Rd, Belfast BT9 7BL, UK; helen.noble{at}qub.ac.uk

https://doi.org/10.1136/eb-2015-102054

Statistics from Altmetric.com

Request permissions.

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Evaluating the quality of research is essential if findings are to be utilised in practice and incorporated into care delivery. In a previous article we explored ‘bias’ across research designs and outlined strategies to minimise bias. 1 The aim of this article is to further outline rigour, or the integrity in which a study is conducted, and ensure the credibility of findings in relation to qualitative research. Concepts such as reliability, validity and generalisability typically associated with quantitative research and alternative terminology will be compared in relation to their application to qualitative research. In addition, some of the strategies adopted by qualitative researchers to enhance the credibility of their research are outlined.

Are the terms reliability and validity relevant to ensuring credibility in qualitative research?

Although the tests and measures used to establish the validity and reliability of quantitative research cannot be applied to qualitative research, there are ongoing debates about whether terms such as validity, reliability and generalisability are appropriate to evaluate qualitative research. 2–4 In the broadest context these terms are applicable, with validity referring to the integrity and application of the methods undertaken and the precision in which the findings accurately reflect the data, while reliability describes consistency within the employed analytical procedures. 4 However, if qualitative methods are inherently different from quantitative methods in terms of philosophical positions and purpose, then alterative frameworks for establishing rigour are appropriate. 3 Lincoln and Guba 5 offer alternative criteria for demonstrating rigour within qualitative research namely truth value, consistency and neutrality and applicability. Table 1 outlines the differences in terminology and criteria used to evaluate qualitative research.

  • View inline

Terminology and criteria used to evaluate the credibility of research findings

What strategies can qualitative researchers adopt to ensure the credibility of the study findings?

Unlike quantitative researchers, who apply statistical methods for establishing validity and reliability of research findings, qualitative researchers aim to design and incorporate methodological strategies to ensure the ‘trustworthiness’ of the findings. Such strategies include:

Accounting for personal biases which may have influenced findings; 6

Acknowledging biases in sampling and ongoing critical reflection of methods to ensure sufficient depth and relevance of data collection and analysis; 3

Meticulous record keeping, demonstrating a clear decision trail and ensuring interpretations of data are consistent and transparent; 3 , 4

Establishing a comparison case/seeking out similarities and differences across accounts to ensure different perspectives are represented; 6 , 7

Including rich and thick verbatim descriptions of participants’ accounts to support findings; 7

Demonstrating clarity in terms of thought processes during data analysis and subsequent interpretations 3 ;

Engaging with other researchers to reduce research bias; 3

Respondent validation: includes inviting participants to comment on the interview transcript and whether the final themes and concepts created adequately reflect the phenomena being investigated; 4

Data triangulation, 3 , 4 whereby different methods and perspectives help produce a more comprehensive set of findings. 8 , 9

Table 2 provides some specific examples of how some of these strategies were utilised to ensure rigour in a study that explored the impact of being a family carer to patients with stage 5 chronic kidney disease managed without dialysis. 10

Strategies for enhancing the credibility of qualitative research

In summary, it is imperative that all qualitative researchers incorporate strategies to enhance the credibility of a study during research design and implementation. Although there is no universally accepted terminology and criteria used to evaluate qualitative research, we have briefly outlined some of the strategies that can enhance the credibility of study findings.

  • Sandelowski M
  • Lincoln YS ,
  • Barrett M ,
  • Mayan M , et al
  • Greenhalgh T
  • Lingard L ,

Twitter Follow Joanna Smith at @josmith175 and Helen Noble at @helnoble

Competing interests None.

Read the full text or download the PDF:

  • Privacy Policy
  • SignUp/Login

Research Method

Home » Internal Validity – Threats, Examples and Guide

Internal Validity – Threats, Examples and Guide

Table of Contents

Internal Validity

Internal Validity

Definition:

Internal validity refers to the extent to which a research study accurately establishes a cause-and-effect relationship between the independent variable (s) and the dependent variable (s) being investigated. It assesses whether the observed changes in the dependent variable(s) are actually caused by the manipulation of the independent variable(s) rather than other extraneous factors.

How to Increase Internal Validity

To enhance internal validity, researchers need to carefully design and conduct their studies. Here are some considerations for improving internal validity:

  • Random Assignment: Use random assignment to allocate participants to different groups in experimental studies. Random assignment helps ensure that the groups are comparable, minimizing the influence of individual differences on the results.
  • Control Group: Include a control group in experimental studies. This group should be similar to the experimental group but not exposed to the treatment or intervention being tested. The control group helps establish a baseline against which the effects of the treatment can be compared.
  • Control Extraneous Variables: Identify and control for extraneous variables that could potentially influence the relationship being studied. This can be achieved through techniques like matching participants, using homogeneous samples, or statistically controlling for the variables.
  • Standardized Procedures: Use standardized procedures and protocols across all participants and conditions. This helps ensure consistency in the administration of the study, reducing the potential for systematic biases.
  • Counterbalancing: In studies with multiple conditions or treatment sequences, employ counterbalancing techniques. This involves systematically varying the order of conditions or treatments across participants to eliminate any potential order effects.
  • Minimize Experimenter Bias: Take steps to minimize experimenter bias or expectancy effects. These biases can inadvertently influence the behavior of participants or the interpretation of results. Using blind or double-blind procedures, where the experimenter is unaware of the conditions or group assignments, can help mitigate these biases.
  • Use Reliable and Valid Measures: Ensure that the measures used in the study are reliable and valid. Reliable measures yield consistent results, while valid measures accurately assess the construct being measured.
  • Pilot Testing: Conduct pilot testing before the main study to refine the study design and procedures. Pilot testing helps identify potential issues, such as unclear instructions or unforeseen confounds, and allows for necessary adjustments to enhance internal validity.
  • Sample Size: Increase the sample size to improve statistical power and reduce the likelihood of random variation influencing the results. Adequate sample sizes increase the generalizability and reliability of the findings.
  • Researcher Bias: Researchers need to be aware of their own biases and take steps to minimize their impact on the study. This can be done through careful experimental design, blind data collection and analysis, and the use of standardized protocols.

Threats To Internal Validity

Several threats can undermine internal validity and compromise the validity of research findings. Here are some common threats to internal validity:

Events or circumstances that occur during the course of a study and affect the outcome, making it difficult to attribute the results solely to the treatment or intervention being studied.

Changes that naturally occur in participants over time, such as physical or psychological development, which can influence the results independently of the treatment or intervention.

Testing Effects

The act of being tested or measured on a particular variable in an initial assessment may influence participants’ subsequent responses. This effect can arise due to familiarity with the test or increased sensitization to the topic being studied.

Instrumentation

Changes or inconsistencies in the measurement tools or procedures used across different stages or conditions of the study. If the measurement methods are not standardized or if there are variations in the administration of tests, it can lead to measurement errors and threaten internal validity.

Selection Bias

When there are systematic differences between the characteristics of individuals selected for different groups or conditions in a study. If participants are not randomly assigned to groups or conditions, the results may be influenced by pre-existing differences rather than the treatment itself.

Attrition or Dropout

The loss of participants from a study over time can introduce bias if those who drop out differ systematically from those who remain. The characteristics of participants who drop out may affect the outcomes and compromise internal validity.

Regression to The Mean

The tendency for extreme scores on a variable to move closer to the average on subsequent measurements. If participants are selected based on extreme scores, their scores are likely to regress toward the mean in subsequent measurements, leading to erroneous conclusions about the effectiveness of a treatment.

Diffusion of Treatment

When participants in one group of a study receive knowledge or benefits from participants in another group, it can dilute the treatment effect and compromise internal validity. This can occur through communication or sharing of information among participants.

Demand Characteristics

Cues or expectations within a study that may influence participants to respond in a certain way or guess the purpose of the research. Participants may modify their behavior to align with perceived expectations, leading to biased results.

Experimenter Bias

Biases or expectations on the part of the researchers that may unintentionally influence the study’s outcomes. Researchers’ behavior, interactions, or inadvertent cues can impact participants’ responses, introducing bias and threatening internal validity.

Types of Internal Validity

There are several types of internal validity that researchers consider when designing and conducting studies. Here are some common types of internal validity:

Construct validity

Refers to the extent to which the operational definitions of the variables used in the study accurately represent the theoretical concepts they are intended to measure. It ensures that the measurements or manipulations used in the study accurately reflect the intended constructs.

Statistical Conclusion Validity

Relates to the degree to which the statistical analysis accurately reflects the relationships between variables. It involves ensuring that the appropriate statistical tests are used, the data is analyzed correctly, and the reported findings are reliable.

Internal Validity of Causal Inferences

Focuses on establishing a cause-and-effect relationship between the independent variable (treatment or intervention) and the dependent variable (outcome or response variable). It involves eliminating alternative explanations or confounding factors that could account for the observed relationship.

Temporal Precedence

Ensures that the cause (independent variable) precedes the effect (dependent variable) in time. It establishes the temporal sequence necessary for making causal claims.

Covariation

Refers to the presence of a relationship or association between the independent variable and the dependent variable. It ensures that changes in the independent variable are accompanied by corresponding changes in the dependent variable.

Elimination of Confounding Variables

Involves controlling for and minimizing the influence of extraneous variables that could affect the relationship between the independent and dependent variables. It helps isolate the true effect of the independent variable on the dependent variable.

Selection bias Control

Ensures that the process of assigning participants to different groups or conditions (randomization) is unbiased. Random assignment helps create equivalent groups, reducing the influence of participant characteristics on the dependent variable.

Controlling for Testing Effects

Involves minimizing the impact of repeated testing or measurement on participants’ responses. Counterbalancing, using control groups, or employing appropriate time intervals between assessments can help control for testing effects.

Controlling for Experimenter Effects

Aims to minimize the influence of the experimenter on participants’ responses. Blinding, using standardized protocols, or automating data collection processes can reduce the potential for experimenter bias.

Replication

Conducting the study multiple times with different samples or settings to verify the consistency and generalizability of the findings. Replication enhances internal validity by ensuring that the observed effects are not due to chance or specific characteristics of the study sample.

Internal Validity Examples

Here are some real-time examples that illustrate internal validity:

Drug Trial: A pharmaceutical company conducts a clinical trial to test the effectiveness of a new medication for treating a specific disease. The study uses a randomized controlled design, where participants are randomly assigned to receive either the medication or a placebo. The internal validity is high because the random assignment helps ensure that any observed differences between the groups can be attributed to the medication rather than other factors.

Education Intervention: A researcher investigates the impact of a new teaching method on student performance in mathematics. The researcher selects two comparable groups of students from the same school and randomly assigns one group to receive the new teaching method while the other group continues with the traditional method. By controlling for factors such as the school environment and student characteristics, the study enhances internal validity by isolating the effects of the teaching method.

Psychological Experiment: A psychologist conducts an experiment to examine the relationship between sleep deprivation and cognitive performance. Participants are randomly assigned to either a sleep-deprived group or a control group. The internal validity is strengthened by manipulating the independent variable (amount of sleep) and controlling for other variables that could influence cognitive performance, such as age, gender, and prior sleep habits.

Quasi-Experimental Study: A researcher investigates the impact of a new traffic law on accident rates in a specific city. Since random assignment is not feasible, the researcher selects two similar neighborhoods: one where the law is implemented and another where it is not. By comparing accident rates before and after the law’s implementation in both areas, the study attempts to establish a causal relationship while acknowledging potential confounding variables, such as driver behavior or road conditions.

Workplace Training Program: An organization introduces a new training program aimed at improving employee productivity. To assess the effectiveness of the program, the company implements a pre-post design where performance metrics are measured before and after the training. By tracking changes in productivity within the same group of employees, the study attempts to attribute any improvements to the training program while controlling for individual differences.

Applications of Internal Validity

Internal validity is a crucial concept in research design and is applicable across various fields of study. Here are some applications of internal validity:

Experimental Research

Internal validity is particularly important in experimental research, where researchers manipulate independent variables to determine their effects on dependent variables. By ensuring strong internal validity, researchers can confidently attribute any observed changes in the dependent variable to the manipulation of the independent variable, establishing a cause-and-effect relationship.

Quasi-experimental Research

Quasi-experimental studies aim to establish causal relationships but lack random assignment to groups. Internal validity becomes crucial in such designs to minimize alternative explanations for the observed effects. Careful selection and control of potential confounding variables help strengthen internal validity in quasi-experimental research.

Observational Studies

While observational studies may not involve experimental manipulation, internal validity is still relevant. Researchers need to identify and control for confounding variables to establish a relationship between variables of interest and rule out alternative explanations for observed associations.

Program Evaluation

Internal validity is essential in evaluating the effectiveness of interventions, programs, or policies. By designing rigorous evaluation studies with strong internal validity, researchers can determine whether the observed outcomes can be attributed to the specific intervention or program being evaluated.

Clinical Trials

Internal validity is critical in clinical trials to determine the effectiveness of new treatments or therapies. Well-designed randomized controlled trials (RCTs) with strong internal validity can provide reliable evidence on the efficacy of interventions and guide clinical decision-making.

Longitudinal Studies

Longitudinal studies track participants over an extended period to examine changes and establish causal relationships. Maintaining internal validity throughout the study helps ensure that observed changes in the dependent variable(s) are indeed caused by the independent variable(s) under investigation and not other factors.

Psychology and Social Sciences

Internal validity is pertinent in psychological and social science research. Researchers aim to understand human behavior and social phenomena, and establishing strong internal validity allows them to draw accurate conclusions about the causal relationships between variables.

Advantages of Internal Validity

Internal validity is essential in research for several reasons. Here are some of the advantages of having high internal validity in a study:

  • Causal Inference: Internal validity allows researchers to make valid causal inferences. When a study has high internal validity, it establishes a cause-and-effect relationship between the independent variable (treatment or intervention) and the dependent variable (outcome). This provides confidence that changes in the dependent variable are genuinely due to the manipulation of the independent variable.
  • Elimination of Confounding Factors: High internal validity helps eliminate or control confounding factors that could influence the relationship being studied. By systematically accounting for potential confounds, researchers can attribute the observed effects to the intended independent variable rather than extraneous variables.
  • Accuracy of Measurements: Internal validity ensures accurate and reliable measurements. Researchers employ rigorous methods to measure variables, reducing measurement errors and increasing the validity and precision of the data collected.
  • Replicability and Generalizability: Studies with high internal validity are more likely to yield consistent results when replicated by other researchers. This is important for the advancement of scientific knowledge, as replication strengthens the validity of findings and allows for the generalizability of results across different populations and settings.
  • Intervention Effectiveness: High internal validity helps determine the effectiveness of interventions or treatments. By controlling for confounding factors and utilizing robust research designs, researchers can accurately assess whether an intervention produces the desired outcomes or effects.
  • Enhanced Decision-making: Studies with high internal validity provide a solid basis for decision-making. Policymakers, practitioners, and professionals can rely on research with high internal validity to make informed decisions about the implementation of interventions or treatments in real-world settings.
  • Validity of Theory Development: Internal validity contributes to the development and refinement of theories. By establishing strong cause-and-effect relationships, researchers can build and test theories, enhancing our understanding of underlying mechanisms and contributing to theoretical advancements.
  • Scientific Credibility: Research with high internal validity enhances the overall credibility of the scientific field. Studies that prioritize internal validity uphold the rigorous standards of scientific inquiry and contribute to the accumulation of reliable knowledge.

Limitations of Internal Validity

While internal validity is crucial for research, it is important to recognize its limitations. Here are some limitations or considerations associated with internal validity:

  • Artificial Experimental Settings: Research studies with high internal validity often take place in controlled laboratory settings. While this allows for rigorous control over variables, it may limit the generalizability of the findings to real-world settings. The controlled environment may not fully capture the complexity and variability of natural settings, potentially affecting the external validity of the study.
  • Demand Characteristics and Experimenter Effects: Participants in a study may behave differently due to demand characteristics or their awareness of being in a research setting. They might alter their behavior to align with their perceptions of the expected or desired responses, which can introduce bias and compromise internal validity. Similarly, experimenter effects, such as unintentional cues or biases conveyed by the researcher, can influence participant responses and affect internal validity.
  • Selection Bias: The process of selecting participants for a study may introduce biases and limit the generalizability of the findings. For example, if participants are not randomly selected or if they self-select into the study, the sample may not represent the larger population, impacting both internal and external validity.
  • Reactive or Interactive Effects: Participants’ awareness of being observed or their exposure to the experimental manipulation may elicit reactive or interactive effects. These effects can influence their behavior, leading to artificial responses that may not be representative of their natural behavior in real-world situations.
  • Limited Sample Characteristics: The characteristics of the sample used in a study can affect internal validity. If the sample is not diverse or representative of the population of interest, it can limit the generalizability of the findings. Additionally, small sample sizes may reduce statistical power and increase the likelihood of chance findings.
  • Time-related Factors: Internal validity can be influenced by factors related to the timing of the study. For example, the immediate effects observed in a short-term study may not reflect the long-term effects of an intervention. Additionally, history or maturation effects occurring during the course of the study may confound the relationship being studied.
  • Exclusion of Complex Variables: To establish internal validity, researchers often simplify the research design by focusing on a limited number of variables. While this allows for controlled experimentation, it may neglect the complex interactions and multiple factors that exist in real-world situations. This limitation can impact the ecological validity and external validity of the findings.
  • Publication Bias: Publication bias occurs when studies with significant or positive results are more likely to be published, while studies with null or negative results remain unpublished or overlooked. This bias can distort the body of evidence and compromise the overall internal validity of the research field.

Alo see Validity

About the author

' src=

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Internal_Consistency_Reliability

Internal Consistency Reliability – Methods...

Split-Half Reliability

Split-Half Reliability – Methods, Examples and...

Alternate Forms Reliability

Alternate Forms Reliability – Methods, Examples...

Reliability

Reliability – Types, Examples and Guide

Test-Retest Reliability

Test-Retest Reliability – Methods, Formula and...

Parallel Forms Reliability

Parallel Forms Reliability – Methods, Example...

Popular searches

  • How to Get Participants For Your Study
  • How to Do Segmentation?
  • Conjoint Preference Share Simulator
  • MaxDiff Analysis
  • Likert Scales
  • Reliability & Validity

Request consultation

Do you need support in running a pricing or product study? We can help you with agile consumer research and conjoint analysis.

Looking for an online survey platform?

Conjointly offers a great survey tool with multiple question types, randomisation blocks, and multilingual support. The Basic tier is always free.

Research Methods Knowledge Base

  • Navigating the Knowledge Base
  • Foundations
  • Construct Validity
  • Reliability
  • Levels of Measurement
  • Survey Research
  • Scaling in Measurement
  • The Qualitative Debate
  • Qualitative Data
  • Qualitative Approaches
  • Qualitative Methods

Qualitative Validity

  • Unobtrusive Measures
  • Research Design
  • Table of Contents

Fully-functional online survey tool with various question types, logic, randomisation, and reporting for unlimited number of surveys.

Completely free for academics and students .

Depending on their philosophical perspectives , some qualitative researchers reject the framework of validity that is commonly accepted in more quantitative research in the social sciences. They reject the basic realist assumption that there is a reality external to our perception of it. Consequently, it doesn’t make sense to be concerned with the “truth” or “falsity” of an observation with respect to an external reality (which is a primary concern of validity). These qualitative researchers argue for different standards for judging the quality of research.

For instance, Guba and Lincoln proposed four criteria for judging the soundness of qualitative research and explicitly offered these as an alternative to more traditional quantitatively-oriented criteria. They felt that their four criteria better reflected the underlying assumptions involved in much qualitative research. Their proposed criteria and the “analogous” quantitative criteria are listed in the table.

Credibility

The credibility criteria involves establishing that the results of qualitative research are credible or believable from the perspective of the participant in the research. Since from this perspective, the purpose of qualitative research is to describe or understand the phenomena of interest from the participant’s eyes, the participants are the only ones who can legitimately judge the credibility of the results.

Transferability

Transferability refers to the degree to which the results of qualitative research can be generalized or transferred to other contexts or settings. From a qualitative perspective transferability is primarily the responsibility of the one doing the generalizing. The qualitative researcher can enhance transferability by doing a thorough job of describing the research context and the assumptions that were central to the research. The person who wishes to “transfer” the results to a different context is then responsible for making the judgment of how sensible the transfer is.

Dependability

The traditional quantitative view of reliability is based on the assumption of replicability or repeatability. Essentially it is concerned with whether we would obtain the same results if we could observe the same thing twice. But we can’t actually measure the same thing twice – by definition if we are measuring twice, we are measuring two different things. In order to estimate reliability, quantitative researchers construct various hypothetical notions (e.g., true score theory ) to try to get around this fact.

The idea of dependability, on the other hand, emphasizes the need for the researcher to account for the ever-changing context within which research occurs. The research is responsible for describing the changes that occur in the setting and how these changes affected the way the research approached the study.

Confirmability

Qualitative research tends to assume that each researcher brings a unique perspective to the study. Confirmability refers to the degree to which the results could be confirmed or corroborated by others. There are a number of strategies for enhancing confirmability. The researcher can document the procedures for checking and rechecking the data throughout the study. Another researcher can take a “devil’s advocate” role with respect to the results, and this process can be documented. The researcher can actively search for and describe and negative instances that contradict prior observations. And, after he study, one can conduct a data audit that examines the data collection and analysis procedures and makes judgements about the potential for bias or distortion.

There has been considerable debate among methodologists about the value and legitimacy of this alternative set of standards for judging qualitative research. On the one hand, many quantitative researchers see the alternative criteria as just a relabeling of the very successful quantitative criteria in order to accrue greater legitimacy for qualitative research. They suggest that a correct reading of the quantitative criteria would show that they are not limited to quantitative research alone and can be applied equally well to qualitative data. They argue that the alternative criteria represent a different philosophical perspective that is subjectivist rather than realist in nature. They claim that research inherently assumes that there is some reality that is being observed and can be observed with greater or less accuracy or validity. if you don’t make this assumption, they would contend, you simply are not engaged in research (although that doesn’t mean that what you are doing is not valuable or useful).

Perhaps there is some legitimacy to this counter argument. Certainly a broad reading of the traditional quantitative criteria might make them appropriate to the qualitative realm as well. But historically the traditional quantitative criteria have been described almost exclusively in terms of quantitative research. No one has yet done a thorough job of translating how the same criteria might apply in qualitative research contexts. For instance, the discussions of external validity have been dominated by the idea of statistical sampling as the basis for generalizing. And, considerations of reliability have traditionally been inextricably linked to the notion of true score theory.

But qualitative researchers do have a point about the irrelevance of traditional quantitative criteria. How could we judge the external validity of a qualitative study that does not use formalized sampling methods? And, how can we judge the reliability of qualitative data when there is no mechanism for estimating the true score? No one has adequately explained how the operational procedures used to assess validity and reliability in quantitative research can be translated into legitimate corresponding operations for qualitative research.

While alternative criteria may not in the end be necessary (and I personally hope that more work is done on broadening the “traditional” criteria so that they legitimately apply across the entire spectrum of research approaches), and they certainly can be confusing for students and newcomers to this discussion, these alternatives do serve to remind us that qualitative research cannot easily be considered only an extension of the quantitative paradigm into the realm of nonnumeric data.

Cookie Consent

Conjointly uses essential cookies to make our site work. We also use additional cookies in order to understand the usage of the site, gather audience analytics, and for remarketing purposes.

For more information on Conjointly's use of cookies, please read our Cookie Policy .

Which one are you?

I am new to conjointly, i am already using conjointly.

Criteria for Good Qualitative Research: A Comprehensive Review

  • Regular Article
  • Open access
  • Published: 18 September 2021
  • Volume 31 , pages 679–689, ( 2022 )

Cite this article

You have full access to this open access article

  • Drishti Yadav   ORCID: orcid.org/0000-0002-2974-0323 1  

64k Accesses

19 Citations

72 Altmetric

Explore all metrics

This review aims to synthesize a published set of evaluative criteria for good qualitative research. The aim is to shed light on existing standards for assessing the rigor of qualitative research encompassing a range of epistemological and ontological standpoints. Using a systematic search strategy, published journal articles that deliberate criteria for rigorous research were identified. Then, references of relevant articles were surveyed to find noteworthy, distinct, and well-defined pointers to good qualitative research. This review presents an investigative assessment of the pivotal features in qualitative research that can permit the readers to pass judgment on its quality and to condemn it as good research when objectively and adequately utilized. Overall, this review underlines the crux of qualitative research and accentuates the necessity to evaluate such research by the very tenets of its being. It also offers some prospects and recommendations to improve the quality of qualitative research. Based on the findings of this review, it is concluded that quality criteria are the aftereffect of socio-institutional procedures and existing paradigmatic conducts. Owing to the paradigmatic diversity of qualitative research, a single and specific set of quality criteria is neither feasible nor anticipated. Since qualitative research is not a cohesive discipline, researchers need to educate and familiarize themselves with applicable norms and decisive factors to evaluate qualitative research from within its theoretical and methodological framework of origin.

Similar content being viewed by others

in qualitative research internal validity

What is Qualitative in Qualitative Research

Patrik Aspers & Ugo Corte

in qualitative research internal validity

The multi-criteria evaluation of research efforts based on ETL software: from business intelligence approach to big data and semantic approaches

Chaimae Boulahia, Hicham Behja, … Zoubair Boulahia

Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach

Zachary Munn, Micah D. J. Peters, … Edoardo Aromataris

Avoid common mistakes on your manuscript.

Introduction

“… It is important to regularly dialogue about what makes for good qualitative research” (Tracy, 2010 , p. 837)

To decide what represents good qualitative research is highly debatable. There are numerous methods that are contained within qualitative research and that are established on diverse philosophical perspectives. Bryman et al., ( 2008 , p. 262) suggest that “It is widely assumed that whereas quality criteria for quantitative research are well‐known and widely agreed, this is not the case for qualitative research.” Hence, the question “how to evaluate the quality of qualitative research” has been continuously debated. There are many areas of science and technology wherein these debates on the assessment of qualitative research have taken place. Examples include various areas of psychology: general psychology (Madill et al., 2000 ); counseling psychology (Morrow, 2005 ); and clinical psychology (Barker & Pistrang, 2005 ), and other disciplines of social sciences: social policy (Bryman et al., 2008 ); health research (Sparkes, 2001 ); business and management research (Johnson et al., 2006 ); information systems (Klein & Myers, 1999 ); and environmental studies (Reid & Gough, 2000 ). In the literature, these debates are enthused by the impression that the blanket application of criteria for good qualitative research developed around the positivist paradigm is improper. Such debates are based on the wide range of philosophical backgrounds within which qualitative research is conducted (e.g., Sandberg, 2000 ; Schwandt, 1996 ). The existence of methodological diversity led to the formulation of different sets of criteria applicable to qualitative research.

Among qualitative researchers, the dilemma of governing the measures to assess the quality of research is not a new phenomenon, especially when the virtuous triad of objectivity, reliability, and validity (Spencer et al., 2004 ) are not adequate. Occasionally, the criteria of quantitative research are used to evaluate qualitative research (Cohen & Crabtree, 2008 ; Lather, 2004 ). Indeed, Howe ( 2004 ) claims that the prevailing paradigm in educational research is scientifically based experimental research. Hypotheses and conjectures about the preeminence of quantitative research can weaken the worth and usefulness of qualitative research by neglecting the prominence of harmonizing match for purpose on research paradigm, the epistemological stance of the researcher, and the choice of methodology. Researchers have been reprimanded concerning this in “paradigmatic controversies, contradictions, and emerging confluences” (Lincoln & Guba, 2000 ).

In general, qualitative research tends to come from a very different paradigmatic stance and intrinsically demands distinctive and out-of-the-ordinary criteria for evaluating good research and varieties of research contributions that can be made. This review attempts to present a series of evaluative criteria for qualitative researchers, arguing that their choice of criteria needs to be compatible with the unique nature of the research in question (its methodology, aims, and assumptions). This review aims to assist researchers in identifying some of the indispensable features or markers of high-quality qualitative research. In a nutshell, the purpose of this systematic literature review is to analyze the existing knowledge on high-quality qualitative research and to verify the existence of research studies dealing with the critical assessment of qualitative research based on the concept of diverse paradigmatic stances. Contrary to the existing reviews, this review also suggests some critical directions to follow to improve the quality of qualitative research in different epistemological and ontological perspectives. This review is also intended to provide guidelines for the acceleration of future developments and dialogues among qualitative researchers in the context of assessing the qualitative research.

The rest of this review article is structured in the following fashion: Sect.  Methods describes the method followed for performing this review. Section Criteria for Evaluating Qualitative Studies provides a comprehensive description of the criteria for evaluating qualitative studies. This section is followed by a summary of the strategies to improve the quality of qualitative research in Sect.  Improving Quality: Strategies . Section  How to Assess the Quality of the Research Findings? provides details on how to assess the quality of the research findings. After that, some of the quality checklists (as tools to evaluate quality) are discussed in Sect.  Quality Checklists: Tools for Assessing the Quality . At last, the review ends with the concluding remarks presented in Sect.  Conclusions, Future Directions and Outlook . Some prospects in qualitative research for enhancing its quality and usefulness in the social and techno-scientific research community are also presented in Sect.  Conclusions, Future Directions and Outlook .

For this review, a comprehensive literature search was performed from many databases using generic search terms such as Qualitative Research , Criteria , etc . The following databases were chosen for the literature search based on the high number of results: IEEE Explore, ScienceDirect, PubMed, Google Scholar, and Web of Science. The following keywords (and their combinations using Boolean connectives OR/AND) were adopted for the literature search: qualitative research, criteria, quality, assessment, and validity. The synonyms for these keywords were collected and arranged in a logical structure (see Table 1 ). All publications in journals and conference proceedings later than 1950 till 2021 were considered for the search. Other articles extracted from the references of the papers identified in the electronic search were also included. A large number of publications on qualitative research were retrieved during the initial screening. Hence, to include the searches with the main focus on criteria for good qualitative research, an inclusion criterion was utilized in the search string.

From the selected databases, the search retrieved a total of 765 publications. Then, the duplicate records were removed. After that, based on the title and abstract, the remaining 426 publications were screened for their relevance by using the following inclusion and exclusion criteria (see Table 2 ). Publications focusing on evaluation criteria for good qualitative research were included, whereas those works which delivered theoretical concepts on qualitative research were excluded. Based on the screening and eligibility, 45 research articles were identified that offered explicit criteria for evaluating the quality of qualitative research and were found to be relevant to this review.

Figure  1 illustrates the complete review process in the form of PRISMA flow diagram. PRISMA, i.e., “preferred reporting items for systematic reviews and meta-analyses” is employed in systematic reviews to refine the quality of reporting.

figure 1

PRISMA flow diagram illustrating the search and inclusion process. N represents the number of records

Criteria for Evaluating Qualitative Studies

Fundamental criteria: general research quality.

Various researchers have put forward criteria for evaluating qualitative research, which have been summarized in Table 3 . Also, the criteria outlined in Table 4 effectively deliver the various approaches to evaluate and assess the quality of qualitative work. The entries in Table 4 are based on Tracy’s “Eight big‐tent criteria for excellent qualitative research” (Tracy, 2010 ). Tracy argues that high-quality qualitative work should formulate criteria focusing on the worthiness, relevance, timeliness, significance, morality, and practicality of the research topic, and the ethical stance of the research itself. Researchers have also suggested a series of questions as guiding principles to assess the quality of a qualitative study (Mays & Pope, 2020 ). Nassaji ( 2020 ) argues that good qualitative research should be robust, well informed, and thoroughly documented.

Qualitative Research: Interpretive Paradigms

All qualitative researchers follow highly abstract principles which bring together beliefs about ontology, epistemology, and methodology. These beliefs govern how the researcher perceives and acts. The net, which encompasses the researcher’s epistemological, ontological, and methodological premises, is referred to as a paradigm, or an interpretive structure, a “Basic set of beliefs that guides action” (Guba, 1990 ). Four major interpretive paradigms structure the qualitative research: positivist and postpositivist, constructivist interpretive, critical (Marxist, emancipatory), and feminist poststructural. The complexity of these four abstract paradigms increases at the level of concrete, specific interpretive communities. Table 5 presents these paradigms and their assumptions, including their criteria for evaluating research, and the typical form that an interpretive or theoretical statement assumes in each paradigm. Moreover, for evaluating qualitative research, quantitative conceptualizations of reliability and validity are proven to be incompatible (Horsburgh, 2003 ). In addition, a series of questions have been put forward in the literature to assist a reviewer (who is proficient in qualitative methods) for meticulous assessment and endorsement of qualitative research (Morse, 2003 ). Hammersley ( 2007 ) also suggests that guiding principles for qualitative research are advantageous, but methodological pluralism should not be simply acknowledged for all qualitative approaches. Seale ( 1999 ) also points out the significance of methodological cognizance in research studies.

Table 5 reflects that criteria for assessing the quality of qualitative research are the aftermath of socio-institutional practices and existing paradigmatic standpoints. Owing to the paradigmatic diversity of qualitative research, a single set of quality criteria is neither possible nor desirable. Hence, the researchers must be reflexive about the criteria they use in the various roles they play within their research community.

Improving Quality: Strategies

Another critical question is “How can the qualitative researchers ensure that the abovementioned quality criteria can be met?” Lincoln and Guba ( 1986 ) delineated several strategies to intensify each criteria of trustworthiness. Other researchers (Merriam & Tisdell, 2016 ; Shenton, 2004 ) also presented such strategies. A brief description of these strategies is shown in Table 6 .

It is worth mentioning that generalizability is also an integral part of qualitative research (Hays & McKibben, 2021 ). In general, the guiding principle pertaining to generalizability speaks about inducing and comprehending knowledge to synthesize interpretive components of an underlying context. Table 7 summarizes the main metasynthesis steps required to ascertain generalizability in qualitative research.

Figure  2 reflects the crucial components of a conceptual framework and their contribution to decisions regarding research design, implementation, and applications of results to future thinking, study, and practice (Johnson et al., 2020 ). The synergy and interrelationship of these components signifies their role to different stances of a qualitative research study.

figure 2

Essential elements of a conceptual framework

In a nutshell, to assess the rationale of a study, its conceptual framework and research question(s), quality criteria must take account of the following: lucid context for the problem statement in the introduction; well-articulated research problems and questions; precise conceptual framework; distinct research purpose; and clear presentation and investigation of the paradigms. These criteria would expedite the quality of qualitative research.

How to Assess the Quality of the Research Findings?

The inclusion of quotes or similar research data enhances the confirmability in the write-up of the findings. The use of expressions (for instance, “80% of all respondents agreed that” or “only one of the interviewees mentioned that”) may also quantify qualitative findings (Stenfors et al., 2020 ). On the other hand, the persuasive reason for “why this may not help in intensifying the research” has also been provided (Monrouxe & Rees, 2020 ). Further, the Discussion and Conclusion sections of an article also prove robust markers of high-quality qualitative research, as elucidated in Table 8 .

Quality Checklists: Tools for Assessing the Quality

Numerous checklists are available to speed up the assessment of the quality of qualitative research. However, if used uncritically and recklessly concerning the research context, these checklists may be counterproductive. I recommend that such lists and guiding principles may assist in pinpointing the markers of high-quality qualitative research. However, considering enormous variations in the authors’ theoretical and philosophical contexts, I would emphasize that high dependability on such checklists may say little about whether the findings can be applied in your setting. A combination of such checklists might be appropriate for novice researchers. Some of these checklists are listed below:

The most commonly used framework is Consolidated Criteria for Reporting Qualitative Research (COREQ) (Tong et al., 2007 ). This framework is recommended by some journals to be followed by the authors during article submission.

Standards for Reporting Qualitative Research (SRQR) is another checklist that has been created particularly for medical education (O’Brien et al., 2014 ).

Also, Tracy ( 2010 ) and Critical Appraisal Skills Programme (CASP, 2021 ) offer criteria for qualitative research relevant across methods and approaches.

Further, researchers have also outlined different criteria as hallmarks of high-quality qualitative research. For instance, the “Road Trip Checklist” (Epp & Otnes, 2021 ) provides a quick reference to specific questions to address different elements of high-quality qualitative research.

Conclusions, Future Directions, and Outlook

This work presents a broad review of the criteria for good qualitative research. In addition, this article presents an exploratory analysis of the essential elements in qualitative research that can enable the readers of qualitative work to judge it as good research when objectively and adequately utilized. In this review, some of the essential markers that indicate high-quality qualitative research have been highlighted. I scope them narrowly to achieve rigor in qualitative research and note that they do not completely cover the broader considerations necessary for high-quality research. This review points out that a universal and versatile one-size-fits-all guideline for evaluating the quality of qualitative research does not exist. In other words, this review also emphasizes the non-existence of a set of common guidelines among qualitative researchers. In unison, this review reinforces that each qualitative approach should be treated uniquely on account of its own distinctive features for different epistemological and disciplinary positions. Owing to the sensitivity of the worth of qualitative research towards the specific context and the type of paradigmatic stance, researchers should themselves analyze what approaches can be and must be tailored to ensemble the distinct characteristics of the phenomenon under investigation. Although this article does not assert to put forward a magic bullet and to provide a one-stop solution for dealing with dilemmas about how, why, or whether to evaluate the “goodness” of qualitative research, it offers a platform to assist the researchers in improving their qualitative studies. This work provides an assembly of concerns to reflect on, a series of questions to ask, and multiple sets of criteria to look at, when attempting to determine the quality of qualitative research. Overall, this review underlines the crux of qualitative research and accentuates the need to evaluate such research by the very tenets of its being. Bringing together the vital arguments and delineating the requirements that good qualitative research should satisfy, this review strives to equip the researchers as well as reviewers to make well-versed judgment about the worth and significance of the qualitative research under scrutiny. In a nutshell, a comprehensive portrayal of the research process (from the context of research to the research objectives, research questions and design, speculative foundations, and from approaches of collecting data to analyzing the results, to deriving inferences) frequently proliferates the quality of a qualitative research.

Prospects : A Road Ahead for Qualitative Research

Irrefutably, qualitative research is a vivacious and evolving discipline wherein different epistemological and disciplinary positions have their own characteristics and importance. In addition, not surprisingly, owing to the sprouting and varied features of qualitative research, no consensus has been pulled off till date. Researchers have reflected various concerns and proposed several recommendations for editors and reviewers on conducting reviews of critical qualitative research (Levitt et al., 2021 ; McGinley et al., 2021 ). Following are some prospects and a few recommendations put forward towards the maturation of qualitative research and its quality evaluation:

In general, most of the manuscript and grant reviewers are not qualitative experts. Hence, it is more likely that they would prefer to adopt a broad set of criteria. However, researchers and reviewers need to keep in mind that it is inappropriate to utilize the same approaches and conducts among all qualitative research. Therefore, future work needs to focus on educating researchers and reviewers about the criteria to evaluate qualitative research from within the suitable theoretical and methodological context.

There is an urgent need to refurbish and augment critical assessment of some well-known and widely accepted tools (including checklists such as COREQ, SRQR) to interrogate their applicability on different aspects (along with their epistemological ramifications).

Efforts should be made towards creating more space for creativity, experimentation, and a dialogue between the diverse traditions of qualitative research. This would potentially help to avoid the enforcement of one's own set of quality criteria on the work carried out by others.

Moreover, journal reviewers need to be aware of various methodological practices and philosophical debates.

It is pivotal to highlight the expressions and considerations of qualitative researchers and bring them into a more open and transparent dialogue about assessing qualitative research in techno-scientific, academic, sociocultural, and political rooms.

Frequent debates on the use of evaluative criteria are required to solve some potentially resolved issues (including the applicability of a single set of criteria in multi-disciplinary aspects). Such debates would not only benefit the group of qualitative researchers themselves, but primarily assist in augmenting the well-being and vivacity of the entire discipline.

To conclude, I speculate that the criteria, and my perspective, may transfer to other methods, approaches, and contexts. I hope that they spark dialog and debate – about criteria for excellent qualitative research and the underpinnings of the discipline more broadly – and, therefore, help improve the quality of a qualitative study. Further, I anticipate that this review will assist the researchers to contemplate on the quality of their own research, to substantiate research design and help the reviewers to review qualitative research for journals. On a final note, I pinpoint the need to formulate a framework (encompassing the prerequisites of a qualitative study) by the cohesive efforts of qualitative researchers of different disciplines with different theoretic-paradigmatic origins. I believe that tailoring such a framework (of guiding principles) paves the way for qualitative researchers to consolidate the status of qualitative research in the wide-ranging open science debate. Dialogue on this issue across different approaches is crucial for the impending prospects of socio-techno-educational research.

Amin, M. E. K., Nørgaard, L. S., Cavaco, A. M., Witry, M. J., Hillman, L., Cernasev, A., & Desselle, S. P. (2020). Establishing trustworthiness and authenticity in qualitative pharmacy research. Research in Social and Administrative Pharmacy, 16 (10), 1472–1482.

Article   Google Scholar  

Barker, C., & Pistrang, N. (2005). Quality criteria under methodological pluralism: Implications for conducting and evaluating research. American Journal of Community Psychology, 35 (3–4), 201–212.

Bryman, A., Becker, S., & Sempik, J. (2008). Quality criteria for quantitative, qualitative and mixed methods research: A view from social policy. International Journal of Social Research Methodology, 11 (4), 261–276.

Caelli, K., Ray, L., & Mill, J. (2003). ‘Clear as mud’: Toward greater clarity in generic qualitative research. International Journal of Qualitative Methods, 2 (2), 1–13.

CASP (2021). CASP checklists. Retrieved May 2021 from https://casp-uk.net/casp-tools-checklists/

Cohen, D. J., & Crabtree, B. F. (2008). Evaluative criteria for qualitative research in health care: Controversies and recommendations. The Annals of Family Medicine, 6 (4), 331–339.

Denzin, N. K., & Lincoln, Y. S. (2005). Introduction: The discipline and practice of qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), The sage handbook of qualitative research (pp. 1–32). Sage Publications Ltd.

Google Scholar  

Elliott, R., Fischer, C. T., & Rennie, D. L. (1999). Evolving guidelines for publication of qualitative research studies in psychology and related fields. British Journal of Clinical Psychology, 38 (3), 215–229.

Epp, A. M., & Otnes, C. C. (2021). High-quality qualitative research: Getting into gear. Journal of Service Research . https://doi.org/10.1177/1094670520961445

Guba, E. G. (1990). The paradigm dialog. In Alternative paradigms conference, mar, 1989, Indiana u, school of education, San Francisco, ca, us . Sage Publications, Inc.

Hammersley, M. (2007). The issue of quality in qualitative research. International Journal of Research and Method in Education, 30 (3), 287–305.

Haven, T. L., Errington, T. M., Gleditsch, K. S., van Grootel, L., Jacobs, A. M., Kern, F. G., & Mokkink, L. B. (2020). Preregistering qualitative research: A Delphi study. International Journal of Qualitative Methods, 19 , 1609406920976417.

Hays, D. G., & McKibben, W. B. (2021). Promoting rigorous research: Generalizability and qualitative research. Journal of Counseling and Development, 99 (2), 178–188.

Horsburgh, D. (2003). Evaluation of qualitative research. Journal of Clinical Nursing, 12 (2), 307–312.

Howe, K. R. (2004). A critique of experimentalism. Qualitative Inquiry, 10 (1), 42–46.

Johnson, J. L., Adkins, D., & Chauvin, S. (2020). A review of the quality indicators of rigor in qualitative research. American Journal of Pharmaceutical Education, 84 (1), 7120.

Johnson, P., Buehring, A., Cassell, C., & Symon, G. (2006). Evaluating qualitative management research: Towards a contingent criteriology. International Journal of Management Reviews, 8 (3), 131–156.

Klein, H. K., & Myers, M. D. (1999). A set of principles for conducting and evaluating interpretive field studies in information systems. MIS Quarterly, 23 (1), 67–93.

Lather, P. (2004). This is your father’s paradigm: Government intrusion and the case of qualitative research in education. Qualitative Inquiry, 10 (1), 15–34.

Levitt, H. M., Morrill, Z., Collins, K. M., & Rizo, J. L. (2021). The methodological integrity of critical qualitative research: Principles to support design and research review. Journal of Counseling Psychology, 68 (3), 357.

Lincoln, Y. S., & Guba, E. G. (1986). But is it rigorous? Trustworthiness and authenticity in naturalistic evaluation. New Directions for Program Evaluation, 1986 (30), 73–84.

Lincoln, Y. S., & Guba, E. G. (2000). Paradigmatic controversies, contradictions and emerging confluences. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (2nd ed., pp. 163–188). Sage Publications.

Madill, A., Jordan, A., & Shirley, C. (2000). Objectivity and reliability in qualitative analysis: Realist, contextualist and radical constructionist epistemologies. British Journal of Psychology, 91 (1), 1–20.

Mays, N., & Pope, C. (2020). Quality in qualitative research. Qualitative Research in Health Care . https://doi.org/10.1002/9781119410867.ch15

McGinley, S., Wei, W., Zhang, L., & Zheng, Y. (2021). The state of qualitative research in hospitality: A 5-year review 2014 to 2019. Cornell Hospitality Quarterly, 62 (1), 8–20.

Merriam, S., & Tisdell, E. (2016). Qualitative research: A guide to design and implementation. San Francisco, US.

Meyer, M., & Dykes, J. (2019). Criteria for rigor in visualization design study. IEEE Transactions on Visualization and Computer Graphics, 26 (1), 87–97.

Monrouxe, L. V., & Rees, C. E. (2020). When I say… quantification in qualitative research. Medical Education, 54 (3), 186–187.

Morrow, S. L. (2005). Quality and trustworthiness in qualitative research in counseling psychology. Journal of Counseling Psychology, 52 (2), 250.

Morse, J. M. (2003). A review committee’s guide for evaluating qualitative proposals. Qualitative Health Research, 13 (6), 833–851.

Nassaji, H. (2020). Good qualitative research. Language Teaching Research, 24 (4), 427–431.

O’Brien, B. C., Harris, I. B., Beckman, T. J., Reed, D. A., & Cook, D. A. (2014). Standards for reporting qualitative research: A synthesis of recommendations. Academic Medicine, 89 (9), 1245–1251.

O’Connor, C., & Joffe, H. (2020). Intercoder reliability in qualitative research: Debates and practical guidelines. International Journal of Qualitative Methods, 19 , 1609406919899220.

Reid, A., & Gough, S. (2000). Guidelines for reporting and evaluating qualitative research: What are the alternatives? Environmental Education Research, 6 (1), 59–91.

Rocco, T. S. (2010). Criteria for evaluating qualitative studies. Human Resource Development International . https://doi.org/10.1080/13678868.2010.501959

Sandberg, J. (2000). Understanding human competence at work: An interpretative approach. Academy of Management Journal, 43 (1), 9–25.

Schwandt, T. A. (1996). Farewell to criteriology. Qualitative Inquiry, 2 (1), 58–72.

Seale, C. (1999). Quality in qualitative research. Qualitative Inquiry, 5 (4), 465–478.

Shenton, A. K. (2004). Strategies for ensuring trustworthiness in qualitative research projects. Education for Information, 22 (2), 63–75.

Sparkes, A. C. (2001). Myth 94: Qualitative health researchers will agree about validity. Qualitative Health Research, 11 (4), 538–552.

Spencer, L., Ritchie, J., Lewis, J., & Dillon, L. (2004). Quality in qualitative evaluation: A framework for assessing research evidence.

Stenfors, T., Kajamaa, A., & Bennett, D. (2020). How to assess the quality of qualitative research. The Clinical Teacher, 17 (6), 596–599.

Taylor, E. W., Beck, J., & Ainsworth, E. (2001). Publishing qualitative adult education research: A peer review perspective. Studies in the Education of Adults, 33 (2), 163–179.

Tong, A., Sainsbury, P., & Craig, J. (2007). Consolidated criteria for reporting qualitative research (COREQ): A 32-item checklist for interviews and focus groups. International Journal for Quality in Health Care, 19 (6), 349–357.

Tracy, S. J. (2010). Qualitative quality: Eight “big-tent” criteria for excellent qualitative research. Qualitative Inquiry, 16 (10), 837–851.

Download references

Open access funding provided by TU Wien (TUW).

Author information

Authors and affiliations.

Faculty of Informatics, Technische Universität Wien, 1040, Vienna, Austria

Drishti Yadav

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Drishti Yadav .

Ethics declarations

Conflict of interest.

The author declares no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Yadav, D. Criteria for Good Qualitative Research: A Comprehensive Review. Asia-Pacific Edu Res 31 , 679–689 (2022). https://doi.org/10.1007/s40299-021-00619-0

Download citation

Accepted : 28 August 2021

Published : 18 September 2021

Issue Date : December 2022

DOI : https://doi.org/10.1007/s40299-021-00619-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Qualitative research
  • Evaluative criteria
  • Find a journal
  • Publish with us
  • Track your research
  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer
  • QuestionPro

survey software icon

  • Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
  • Resources Blog eBooks Survey Templates Case Studies Training Help center

in qualitative research internal validity

Home Market Research Research Tools and Apps

Internal Validity in Research: What it is & Examples

Internal validity in research is the method of verifying cause-and-effect relationships between your test situation and your research outcome.

Internal validity refers to how confident you are in your research findings. It is one of the most essential aspects of scientific research and a key idea in understanding facts in general. You can only determine the accuracy of your research as a researcher if no factors contradict your findings. It is the extent of confidence in the outcome. This post will examine internal validity, why it’s important, its examples, and more.

LEARN ABOUT: Causal Research

Content Index

What is internal validity?

The importance of internal validity, internal validity threats, internal validity examples, what is internal validity vs. external validity.

Internal validity in research verifies cause-and-effect relationships between your test situation and your research outcome. It also refers to the ability of research to rule out other causes for a result.

Confounding is a condition in which various factors interfere with the outcome of a research . The lower the chance of confounding in a study, the higher its internal validity. And from this, we can be more confident in the outcome.

In other words, if you can rule out other possible causes for your findings, you can be sure that your research is internally valid. If your study meets the following three requirements, then you can only presume cause and effect:

  • The cause and effect change simultaneously.
  • In terms of time, the cause comes before the effect.
  • There are no other plausible reasons for the correlation you’ve found.

Internal validity further reveals that having the standard data helps a researcher to exclude irrelevant outcomes from the study. If the sample groups are correctly selected and measured, the relationship between the data will be acceptable.

Researchers frequently attempt to establish clear relationships between the variables when they conduct experiments. Internal validity permits them to trust and believe the conclusions of a study’s causal relationship. 

When an experiment’s external validity is low, it cannot confidently demonstrate a causal relationship between the variables under consideration. That’s why it matters in research. This can be one of the most powerful research tools if applied correctly.

Establishing causal relationships

It lets researchers come to correct conclusions about how one variable leads to another. Researchers can be sure that changes in the independent variable cause changes in the dependent variable if they design studies with good internal validity.

Reducing confounding factors

It helps control for and reduce the effect of confusing variables, which are outside factors that could affect the results. By carefully planning experiments, using control groups, and assigning things at random, researchers can isolate the effects of the independent variable and be more sure of the link between the two.

Ensuring measurement accuracy

This makes sure that the tools used to measure things in a study are accurate and reliable. By using valid and reliable measures, researchers can correctly measure the variables of interest and reduce measurement error, increasing the study’s internal validity.

Enhancing replicability

Replication is an important part of scientific study because it makes it more likely that the results can be used in other situations. Researchers are more likely to repeat studies that have a lot of internal validity. When it is a top priority, other researchers can use the research design and methods to get similar results, which adds to the scientific knowledge base.

Generalizability to real-world settings

It is a part of external validity, which is the ability of study results to be used in real-world settings and with real-world populations. By addressing concerns about it, researchers can make it more likely that their findings properly reflect what would happen in real life. This makes it easier for research results to be used in real life.

Influencing decision-making and policy development

Studies with high internal validity have a greater impact on decision-making processes and policy development. Policymakers rely on rigorous and internally valid research to make informed decisions and implement effective policies. Strong internal validity increases the credibility and trustworthiness of research findings, leading to greater influence in decision-making processes.

Internal validity threats need to be identified in a research project. This will help researchers in creating appropriate controls in research. There are numerous techniques to ensure that a study is internally valid. Still, there is also a list of potential risks to it that need to be considered while designing a survey .

Consider the following typical threats:

internal validity threats disadvantages

Historical events

Historical events have an impact on the outcomes of research conducted during a given time period. This is because many events might influence how people feel or respond to a particular subject. For example, changes in political leadership or natural disasters can have an impact on how survey respondents think and behave.

Experiments that are performed over a lengthy moment are most vulnerable to maturation. The effect of time as a variable in research is explained in this way. It may be challenging to prove that the results of your study were not affected by time if your subjects grew older or went through a biological change.

Experimenter bias

This happens when the experimenter behaves differently in one group than in another. This can be for or against the group. The researcher’s bias may have an impact on the study’s findings. If an experimenter behaves differently in different study groups, it may have an effect on the results and reduce the study’s internal validity.

This happens when experiment participants interact and observe one another, compromising the study’s findings’ reliability. Resentful demoralization is an issue that might arise as a result of this. Control group members work less complicated because they feel resentful of their group.

Experiments can require testing the same subjects multiple times in order to collect more accurate information. Testing participants with the same measurements on a regular basis has an impact on their results. Participants are likely to do better as they learn the test or become more familiar with the testing process; therefore, repeated testing could significantly impact outcomes.

Internal validity can be seen in the following examples:

It is lower in an inquiry that examines the link between income level and the risk of smoking. According to a study, there is a correlation between smoking and being a low-income person.

Occupation, culture, education, social standing, and other variables are examples of different sorts of factors. Such factors cannot be eliminated from the research. It is a concept that aids in establishing that you have evidence that your findings significantly impact the outcomes.

An investigator performs research to examine the effect of specially designed computer applications for teaching on traditional classroom techniques. According to the study’s findings, children who are taught using computer software learn faster.

Another research finding is that computerized teaching has significantly improved children’s grade levels. Other researchers’ research suggests that youngsters taught using computer software believe they are not being paid attention to.

Because research data manipulation has an impact, an experiment still has excellent internal validity. The construct validity of the study report studies is low because the cause is not defined clearly. The researcher prioritizes attention over the benefits of computer programs.

It requires correlation, which occurs when the two events coincide. For example, the egg must directly result from the chicken’s biological abilities. The study must also be non-spurious, which indicates that no other believable possibility exists, such as an angel continuously impregnating all hens on the globe.

Another example of it is time priority or proving that the cause occurred before the consequence. One could argue that smoking cigarettes cause lung cancer by demonstrating that most of those treated had a smoking history.

Both internal and external validity have different meanings. While internal validity means the extent to which the results are reliable and external validity means the extent to which they are applicable to a different situation. It’s also crucial to consider their importance in the building and analyzing of your own research. 

It counts as it ensures that the variable you change actually affects your measurement outcome. External validation is important because the changes measured are more universal.

An experiment cannot demonstrate a causal link between two variables without strong internal validity. It ensures that the experiment design selected by the researcher complies with the concept of cause and effect. It lends credibility and trustworthiness to the conclusions of a causal link.

It is a very important part of research design. It refers to how well a study shows a cause-and-effect link between variables. It makes sure that the effects seen can be linked to the independent variable and not to other factors that could be confusing. 

Maintaining a high level of it makes research results more credible and reliable. This lets researchers come to accurate conclusions and make important contributions to the field. Researchers can use rigorous methods and control measures to improve it and reduce threats, such as selection bias , confounding factors, and measurement errors.

It’s important to remember that internal validity alone doesn’t guarantee external validity or that the results can be used in the real world. Other measures of validity should be used along with this to judge the total quality of the research.

At QuestionPro, we provide researchers with data-gathering tools such as our survey software and an insights library for long-term study of all types. If you’d like to see a demo or learn more about it, please go to the Insight Hub.

FREE TRIAL         LEARN MORE

Frequently asked questions(FAQ)

Internal validity is a way to measure the quality of the research.

Internal validity is very important because it ensures that the effects seen in the research are caused by the independent variable and not by something else. It lets experts come to accurate conclusions about the links between causes and effects.

Blinding, which is also called “masking,” is a way to keep subjects or researchers from knowing who is in which group or what the conditions are. It helps reduce biases that can happen because of assumptions or ideas you already have, which improves internal validity.

Internal validity is often threatened by things like participant dropout or attrition, history effects, maturation, testing effects, instrumentation effects, return to the mean, and selection bias.

MORE LIKE THIS

5-point vs 7-point Likert scale

5-point vs 7-point Likert scale: Choosing the Best

Feb 19, 2024

Zero-party data

Zero-Party Data: What it is, Why it Matters, & How to Get It?

Feb 16, 2024

360 review questions

360 Review Questions: Best Practices & Tips

survey analysis software

Exploring 8 Best Survey Analysis Software for Your Research

Feb 15, 2024

Other categories

  • Academic Research
  • Artificial Intelligence
  • Assessments
  • Brand Awareness
  • Case Studies
  • Communities
  • Consumer Insights
  • Customer effort score
  • Customer Engagement
  • Customer Experience
  • Customer Loyalty
  • Customer Research
  • Customer Satisfaction
  • Employee Benefits
  • Employee Engagement
  • Employee Retention
  • Friday Five
  • General Data Protection Regulation
  • Insights Hub
  • Life@QuestionPro
  • Market Research
  • Mobile diaries
  • Mobile Surveys
  • New Features
  • Online Communities
  • Question Types
  • Questionnaire
  • QuestionPro Products
  • Release Notes
  • Research Tools and Apps
  • Revenue at Risk
  • Survey Templates
  • Training Tips
  • Uncategorized
  • Video Learning Series
  • What’s Coming Up
  • Workforce Intelligence

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings
  • Advanced Search
  • Journal List
  • Indian J Psychol Med
  • v.40(5); Sep-Oct 2018

Internal, External, and Ecological Validity in Research Design, Conduct, and Evaluation

Chittaranjan andrade.

Department of Psychopharmacology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India

Reliability and validity describe desirable psychometric characteristics of research instruments. The concept of validity is also applied to research studies and their findings. Internal validity examines whether the study design, conduct, and analysis answer the research questions without bias. External validity examines whether the study findings can be generalized to other contexts. Ecological validity examines, specifically, whether the study findings can be generalized to real-life settings; thus ecological validity is a subtype of external validity. These concepts are explained using examples so that readers may understand why the consideration of internal, external, and ecological validity is important for designing and conducting studies, and for understanding the merits of published research.

DID CATIE HAVE EXTERNAL VALIDITY?

The answer is both yes and no. CATIE[ 1 ] was designed as an effectiveness study; that is, a study with relevance to real-world settings. The CATIE findings are relevant to clinical practice in the USA but are of questionable relevance in India. One reason is that, in the USA, where CATIE was conducted, the primary outcome, time to all-cause treatment discontinuation, is substantially patient-influenced, whereas in India, where families supervise treatment, it is largely caregiver-determined. Another and more important reason is that the healthcare delivery system in clinical practice is strikingly different in the two countries. Thus CATIE has good external validity for clinical practice in the USA but not in India.

RELIABILITY AND VALIDITY

Reliability and validity are concepts that are applied to instruments such as rating scales and screening tools. Validity describes how well an instrument does what it is supposed to do. For example, does an instrument that screens for depression do so with high sensitivity and specificity? Reliability describes the consistency with which results are obtained. For example, if an instrument that rates the severity of depression is administered to the same patient twice within the span of an hour, are the scores obtained closely similar? Different types of reliability and validity describe desirable psychometric properties of research and clinical instruments.[ 2 , 3 ] Validity can also be applied to laboratory and clinical studies, and to their findings, as well, as the sections below show.

INTERNAL VALIDITY

Internal validity examines whether the manner in which a study was designed, conducted, and analyzed allows trustworthy answers to the research questions in the study. For example, improper randomization, inadvertent unblinding of patients or raters, excessive use of rescue medication, and missing data can all undermine the fidelity of the results and conclusions of a randomized controlled trial (RCT). That is, the internal validity of the RCT is compromised. Internal validity is based on judgment and is not a computed statistic.

Internal validity examines the extent to which systematic error (bias) is present. Such systematic error can arise through selection bias, performance bias, detection bias, and attrition bias.[ 4 ] If internal validity is compromised, it can occasionally be improved, for example, by a modified plan of analysis. However, biases can be often fatal as, for example, if double-blind ratings were not obtained in an RCT.

EXTERNAL VALIDITY

External validity examines whether the findings of a study can be generalized to other contexts.[ 4 ] Studies are conducted on samples, and if sampling was random, the sample is representative of the population, and so the results of a study can validly be generalized to the population from which the sample was drawn. But results may not be generalizable to other populations. Thus external validity is poor for studies with sociodemographic restrictions; studies that exclude severely ill and suicidal patients, or patients with personality disorders, substance use disorders, and medical comorbidities; studies that disallow concurrent treatments; and so on. External validity is also limited in short-term studies of patients who need to be treated for months to years. External validity, like internal validity, is based on judgment and is not a computed statistic.

ECOLOGICAL VALIDITY

Ecological validity examines whether the results of a study can be generalized to real-life settings.[ 5 ] How is this different from external validity? External validity asks whether the findings of a study can be generalized to patients with characteristics that are different from those in the study, or patients who are treated in a different way, or patients who are followed up for longer durations. In contrast, ecological validity specifically examines whether the findings of a study can be generalized to naturalistic situations, such as clinical practice in everyday life. Ecological validity is, therefore, a subtype of external validity. The ecological validity of an instrument can be computed as a correlation between ratings obtained with that instrument and an appropriate measure in naturalistic practice or in everyday life. The ecological validity of a study is a judgment and is not a computed statistic.

Ecological validity was originally invoked in the context of laboratory studies that required to be generalized to real-life situations.[ 5 ] Thus, laboratory studies of the neuropsychological and psychomotor impairments produced by psychotropic drugs have poor ecological validity because what is studied in relaxed, rested, and healthy subjects tested in a controlled environment is very different from demands that stressed patients face in everyday life. In fact, these cognitive and psychomotor tests, especially when based on computerized tasks, have no parallel in everyday life. How much less ecological validity, then, would research in animal models of different neuropsychiatric states have for patients in clinical practice? This explains why drugs that work in animal models often fail in humans.[ 6 ]

On a parting note, a good understanding of the concepts of internal, external, and ecological validity is necessary to properly design and conduct studies and to evaluate the merits and applications of published research.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

  • All Publications
  • Priorities Magazine Spring 2018
  • The Next Plague and How Science Will Stop It
  • Priorities Magazine Winter 2018
  • Priorities Magazine Fall 2017
  • Little Black Book of Junk Science
  • Priorities Magazine Winter 2017
  • Should You Worry About Artificial Flavors Or Colors?
  • Should You Worry About Artificial Sweeteners?
  • Summer Health and Safety Tips
  • How Toxic Terrorists Scare You With Science Terms
  • Adult Immunization: The Need for Enhanced Utilization
  • Should You Worry About Salt?
  • Priorities Magazine Spring 2016
  • IARC Diesel Exhaust & Lung Cancer: An Analysis
  • Teflon and Human Health: Do the Charges Stick?
  • Helping Smokers Quit: The Science Behind Tobacco Harm Reduction
  • Irradiated Foods
  • Foods Are Not Cigarettes: Why Tobacco Lawsuits Are Not a Model for Obesity Lawsuits
  • The Prevention and Treatment of Osteoporosis: A Review
  • Are "Low Dose" Health Effects of Chemicals Real?
  • The Effects of Nicotine on Human Health
  • Traditional Holiday Dinner Replete with Natural Carcinogens - Even Organic Thanksgiving Dinners
  • A Primer On Dental Care: Quality and Quackery
  • Nuclear Energy and Health And the Benefits of Low-Dose Radiation Hormesis
  • Priorities in Caring for Your Children: A Primer for Parents
  • Endocrine Disrupters: A Scientific Perspective
  • Good Stories, Bad Science: A Guide for Journalists to the Health Claims of "Consumer Activist" Groups
  • A Comparison of the Health Effects of Alcohol Consumption and Tobacco Use in America
  • Moderate Alcohol Consumption and Health
  • Irradiated Foods Fifth Edition
  • Media/Contact
  • Write For Us

Of Much of Published Scientific Research, the Validity is Questionable (Part 1)

Related articles.

in qualitative research internal validity

Much of published science and the "knowledge" resulting from it is likely wrong and sends researchers chasing false leads. It is incumbent on the scientific community to find solutions. Without research integrity, we don’t know what we know.

in qualitative research internal validity

The validity of much published scientific research is questionable – so how much trust should we place in it?

An aphorism called the “ Einstein Effect ” holds that “People find nonsense credible if they think a scientist said it.” We agree, and it’s a major concern as trust in science is near an all-time low . There is a lot of nonsense masquerading as science circulating these days. Unfortunately, as a PEW study released last November indicates, it’s getting worse. 

This is part one of a two-part series. Read part two on Tuesday, February 27.

image

The importance of independent confirmation

Almost five years ago, we wrote  about the unreliability of much of the peer-reviewed scientific literature, especially in biomedicine and agriculture. Since then, according to a recent  news article  in the journal  Nature   that quantified the problem, the problem is far more significant than even the pessimists had posited.

Corruption is rife. Just last week, the journal Science related that even publishers of prominent scientific journals feel they are “under siege.”: “A spokesperson for Elsevier said every week its editors are offered cash in return for accepting manuscripts. Sabina Alam, director of publishing ethics and integrity at Taylor & Francis, said bribery attempts have also been directed at journal editors there and are 'a very real area of concern.’”

In 2022, the co-chair of the editorial board of the Wiley publication Chemistry–A European Journal received an email from someone claiming to be working with “young scholars” in China and offering to pay him $3000 for each paper he helped publish in his journal. Such dealings have become big business.

This article, and a Part II to follow, address several ways unreliability can occur, purposefully or unintentionally. 

Replication challenges

Science is seldom a set of immutable facts; conclusions can and should change as the instruments of interrogation get more sophisticated. Rather, science should be thought of as a method of inquiry – a process — that adjusts the consensus as more and more information is gathered.

Critically, science depends on corroboration — that is, researchers verifying others’ results, often making incremental advances as they do so. The nature of science dictates that no research paper is ever considered the final word, but, increasingly, there are far too many examples of papers in which results are not reproducible. 

Why is that happening? Explanations include the increasing complexity of experimental systems, misunderstanding (and often, misuse) of statistics, and pressures on researchers to publish. Of greatest concern is the proliferation of shoddy pay-to-play “ predatory ” journals willing to publish flawed articles produced by “paper mills,” which perpetrate a kind of industrial fraud prevalent in the publishing sector. They are profit-oriented, shady organizations that produce and sell fabricated or manipulated manuscripts that resemble genuine research.

image

In 2011 and 2012,  two   articles  rocked the scientific world. One reported attempting to reproduce the results of 53 preclinical research papers considered “landmark” studies. The scientific findings were confirmed in only six (11%) of them. Astonishingly, even the researchers making the claims  could not replicate their own work .

The second article  found  that claims made using observational data could not be replicated in randomized clinical trials (which is why the latter are known as the “gold standard”). Overall, there were 52 claims tested, and none replicated in the expected direction, although most had solid statistical support in the original papers.

Why this divergence? Any number of factors could cause this: the discovery of an unknown effect, inherent variability, different controls, substandard research practices, chance —and increasingly and distressingly, fraud perpetrated in the original or follow-up research by self-interested parties.

More recently, in 2015, 270 co-investigators  published  the results of their systematic attempt to replicate work reported in 98 original papers from three psychology journals to see how their results would compare. According to the replicators’ qualitative assessments, only 39 of the 100 replication attempts were successful. 

That same year, a multinational group attempted to replicate 21 systematically selected experimental studies in the social sciences published in the journals Nature and Science between 2010 and 2015. They  found  “a significant effect in the same direction as the original study for 13 (62%) studies, and the effect size of the replications is on average about 50% of the original effect size.”

Although these results may seem shocking to the public, which until recently had placed a lot of trust in the findings of scientists, it’s no surprise to scientists themselves. In a 2016 survey  of approximately 1500 scientists, 90% said there were major or minor problems with the replication of experiments .

Overly-ambitious or biased science researchers can ‘cook the books’

Failure rates for reports in prominent journals are astonishing — and worrisome because false claims can become canonized and affect the course of future research by other investigators.

Of course, technical problems with laboratory experiments – contamination of cell lines or reagents, unreliable equipment, the difficulty of doing a complex, multi-step experiment the same way time after time, etc. – are one explanation. 

Another is statistical sleight-of-hand. One technique for that is p-hacking: Scientists try one statistical or data manipulation after another until they get a small p-value that qualifies as “statistical significance,” although the finding results from chance, not reality.

Australian researchers  examined the publicly available literature and found evidence that p-hacking was common in almost every scientific field. Peer review and editorial oversight are inadequate to ensure that articles in scientific publications represent reality instead of statistical chicanery.  

screenshot pm

Credit: Statistically-Funny.blogpsot.com

This problem in the hard sciences is bad enough, but it’s an epidemic in the social sciences, where everything can be interpreted through a post-modern lens. A variety of studies have explored analytical variability in different fields and found substantial variability among results despite analysts having the same data and research question. If statistical modeling were objective and genuinely predictive, all results should be the same. Notably, peer review did not significantly reduce the variability of results. 

Publishers’ bias toward ‘positive’ findings exaggerates the problem 

Another problem is that competing scientists often do not retest questions; if they do, they don’t make known their failure to replicate earlier experiments. That leaves significant lacunae, or gaps, in the published literature – which, of course, is produced mostly in universities and is funded by taxpayers.

Many claims appearing in the literature do replicate, but even those are often not reliable. For example, many claims in the psychology literature are only “indirectly” replicated. If X is true, then Y, a consequence, should also be true. Often, Y is accepted as correct, but it turns out that neither X nor Y replicates when tested de novo.

Understandably, editors and referees are biased against papers that report negative results; they greatly prefer positive, statistically significant results. Researchers know this and often do not even submit negative studies – the so-called “file drawer effect.” Once enough nominally positive confirmatory papers appear the claim becomes canonized, making it even more difficult to publish an article that reports a contrary result. This distressing tendency happens in the media as well, which amplifies the misinformation. 

A need for humility 

The media-academic complex thus undermines the public’s trust, perverts the scientific method, the value of accumulated data, and the canons of science. It makes us wonder whether scientists who practice statistical trickery don’t understand statistics or are so confident of the “correct” outcome that they take shortcuts to get to it. 

If the latter, it would bring to mind the memorable observation about science by Richard Feynman , the late, great physicist and science communicator: “The first principle is that you must not fool yourself – and you are the easiest person to fool.”

An unacceptable level of published science and its canonized claims are wrong, sending researchers chasing false leads. Without research integrity, we don’t know what we know. It is incumbent on the scientific community, including government overseers, journal editors, universities, and the funders that support them, to find solutions. Otherwise, the disruption and deception in scientific research will only grow.

Part 2 will address the problems of meta-analyses and scientists buying dubious articles from “paper mills.”

Henry I. Miller, a physician and molecular biologist, is the Glenn Swogger Distinguished Fellow at the American Council on Science and Health. He was the founding director of the FDA’s Office of Biotechnology. Find Henry on X @HenryIMiller

Dr. S. Stanley Young is a statistician who has worked at pharmaceutical companies and the National Institute of Statistical Sciences on questions of applied statistics. He is an adjunct professor at several universities and a member of the EPA’s Science Advisory Board.

in qualitative research internal validity

By Henry I. Miller, MS, MD

Henry I. Miller, MS, MD, is the Glenn Swogger Distinguished Fellow at the American Council on Science and Health. His research focuses on public policy toward science, technology, and medicine, encompassing a number of areas, including pharmaceutical development, genetic engineering, models for regulatory reform, precision medicine, and the emergence of new viral diseases. Dr. Miller served for fifteen years at the US Food and Drug Administration (FDA) in a number of posts, including as the founding director of the Office of Biotechnology.

Latest from Henry I. Miller, MS, MD :

shopify analytics tool

Content validity testing of the INTERMED Self-Assessment in a sample of adults with rheumatoid arthritis and rheumatology healthcare providers

Affiliations.

  • 1 Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • 2 Faculty of Nursing, University of Calgary, Calgary, Alberta, Canada.
  • 3 Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • 4 Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • 5 Arthritis Research Canada, Vancouver, British Columbia, Canada.
  • 6 Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada.
  • 7 Arthritis Patient Advisory Board, Arthritis Research Canada, Vancouver, British Columbia, Canada.
  • 8 Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
  • 9 McCaig Institute for Bone and Joint Health, Calgary, Alberta, Canada.
  • 10 Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
  • 11 Centre for Health Services and Policy Research, University of British Columbia, Vancouver, British Columbia, Canada.
  • PMID: 38366795
  • PMCID: PMC10873686
  • DOI: 10.1111/hex.13978

Background: Care complexity can occur when patients experience health challenges simultaneously with social barriers including food and/or housing insecurity, lack of transportation or other factors that impact care and patient outcomes. People with rheumatoid arthritis (RA) may experience care complexity due to the chronicity of their condition and other biopsychosocial factors. There are few standardised instruments that measure care complexity and none that measure care complexity specifically in people with RA.

Objectives: We assessed the content validity of the INTERMEDS Self-Assessment (IMSA) instrument that measures care complexity with a sample of adults with RA and rheumatology healthcare providers (HCPs). Cognitive debriefing interviews utilising a reparative framework were conducted.

Methods: Patient participants were recruited through two existing studies where participants agreed to be contacted about future studies. Study information was also shared through email blasts, posters and brochures at rheumatology clinic sites and trusted arthritis websites. Various rheumatology HCPs were recruited through email blasts, and divisional emails and announcements. Interviews were conducted with nine patients living with RA and five rheumatology HCPs.

Results: Three main reparative themes were identified: (1) Lack of item clarity and standardisation including problems with item phrasing, inconsistency of the items and/or answer sets and noninclusive language; (2) item barrelling, where items asked about more than one issue, but only allowed a single answer choice; and (3) timeframes presented in the item or answer choices were either too long or too short, and did not fit the lived experiences of patients. Items predicting future healthcare needs were difficult to answer due to the episodic and fluctuating nature of RA.

Conclusions: Despite international use of the IMSA to measure care complexity, patients with RA and rheumatology HCPs in our setting perceived that it did not have content validity for use in RA and that revision for use in this population under a reparative framework was unfeasible. Future instrument development requires an iterative cognitive debriefing and repair process with the population of interest in the early stages to ensure content validity and comprehension.

Patient or public contribution: Patient and public contributions included both patient partners on the study team and people with RA who participated in the study. Patient partners were involved in study design, analysis and interpretation of the findings and manuscript preparation. Data analysis was structured according to emergent themes of the data that were grounded in patient perspectives and experiences.

Keywords: care complexity; outcomes research; qualitative research; rheumatoid arthritis.

© 2024 The Authors. Health Expectations published by John Wiley & Sons Ltd.

  • Arthritis, Rheumatoid* / psychology
  • Health Personnel
  • Rheumatology*
  • Self-Assessment

Grants and funding

  • 480898/CAPMC/ CIHR/Canada

IMAGES

  1. How to establish the validity and reliability of qualitative research?

    in qualitative research internal validity

  2. PPT

    in qualitative research internal validity

  3. | The validity of the research process entails both the internal

    in qualitative research internal validity

  4. Research Review: 7

    in qualitative research internal validity

  5. Factors that affect validity and reliability in qualitative research

    in qualitative research internal validity

  6. Qualitative Research

    in qualitative research internal validity

VIDEO

  1. Threat to internal validity |Research Method of Psychology

  2. What is Internal Validity and External Validity?

  3. Validity of Experiment ( Marathi)

  4. Threats to Internal Validity in Experimental Research

  5. Threats to Internal Validity in Experimental Research

  6. Threats to Internal Validity in Experimental Research

COMMENTS

  1. Internal Validity in Research

    Internal validity makes the conclusions of a causal relationship credible and trustworthy. Without high internal validity, an experiment cannot demonstrate a causal link between two variables. Research example You want to test the hypothesis that drinking a cup of coffee improves memory.

  2. Validity, reliability, and generalizability in qualitative research

    Five qualitative studies are chosen to illustrate how various methodologies of qualitative research helped in advancing primary healthcare, from novel monitoring of chronic obstructive pulmonary disease (COPD) via mobile-health technology, [ 1] informed decision for colorectal cancer screening, [ 2] triaging out-of-hours GP services, [ 3] evalua...

  3. Validity in Qualitative Research: A Processual Approach

    Validity in Qualitative Research: A Processual Approach. The Qualitative Report, 24(1), 98-112. https://doi.org/10.46743/2160-3715/2019.3443 This How To Article is brought to you for free and open access by the The Qualitative Report at NSUWorks.

  4. Internal Validity vs. External Validity in Research

    Internal validity is the extent to which a research study establishes a trustworthy cause-and-effect relationship. This type of validity depends largely on the study's procedures and how rigorously it is performed. Internal validity is important because once established, it makes it possible to eliminate alternative explanations for a finding.

  5. Levels of Evidence, Quality Assessment, and Risk of Bias: Evaluating

    The level of evidence framework for assessing internal validity assumes that internal validity can be determined based on the study design alone, and thus makes the strongest assumptions. Risk of bias assessments involve an evaluation of the potential for bias in the context of a specific study, and thus involve the least assumptions about ...

  6. PDF Validity in qualitative research revisited

    actional validity in qualitative research varies to the extent the researcher believes it achieves a level of certainty. On the other hand, we define transformational validity in qualitative research as a progressive, emancipatory process leading toward social change Cho and Trent: Validity in qualitative research 321

  7. Issues of validity and reliability in qualitative research

    Unlike quantitative researchers, who apply statistical methods for establishing validity and reliability of research findings, qualitative researchers aim to design and incorporate methodological strategies to ensure the 'trustworthiness' of the findings. Such strategies include: Accounting for personal biases which may have influenced findings; 6

  8. Quantitative and Qualitative Strategies to Strengthen Internal Validity

    Volume 54, Issue 1 https://doi.org/10.1177/0844562120974197 PDF / ePub More Abstract Although the randomized controlled trial (RCT) is the most reliable design to infer causality, evidence suggests that it is vulnerable to biases that weaken internal validity.

  9. Validity in Qualitative Evaluation: Linking Purposes, Paradigms, and

    However, the increased importance given to qualitative information in the evidence-based paradigm in health care and social policy requires a more precise conceptualization of validity criteria that goes beyond just academic reflection. After all, one can argue that policy verdicts that are based on qualitative information must be legitimized by valid research, just as quantitative effect ...

  10. International Journal of Qualitative Methods Validity in Qualitative

    Elaborating on epistemological and theoretical conceptualizations by Guba and Lincoln and Creswell and Miller, the article explores aspects of validity of qualitative research with the explicit objective of connecting them with aspects of evaluation in social policy.

  11. Internal Validity

    by Muhammad Hassan Table of Contents Internal Validity Definition: Internal validity refers to the extent to which a research study accurately establishes a cause-and-effect relationship between the independent variable (s) and the dependent variable (s) being investigated.

  12. Internal and external validity: can you apply research study results to

    The validity of a research study includes two domains: internal and external validity. Internal validity is defined as the extent to which the observed results represent the truth in the population we are studying and, thus, are not due to methodological errors.

  13. PDF VALIDITY IN QUALITATIVE RESEARCH

    Description: Interpretation: Theory: Generalization: "The main threat to valid description, in the sense of describing what you saw and heard, is the inaccuracy or incompleteness of the data" (Maxwell, 1996, p. 89).

  14. Qualitative Validity

    Qualitative Validity Depending on their philosophical perspectives, some qualitative researchers reject the framework of validity that is commonly accepted in more quantitative research in the social sciences. They reject the basic realist assumption that there is a reality external to our perception of it.

  15. Criteria for Good Qualitative Research: A Comprehensive Review

    Fundamental Criteria: General Research Quality. Various researchers have put forward criteria for evaluating qualitative research, which have been summarized in Table 3.Also, the criteria outlined in Table 4 effectively deliver the various approaches to evaluate and assess the quality of qualitative work. The entries in Table 4 are based on Tracy's "Eight big‐tent criteria for excellent ...

  16. A Review of the Quality Indicators of Rigor in Qualitative Research

    Abstract. Attributes of rigor and quality and suggested best practices for qualitative research design as they relate to the steps of designing, conducting, and reporting qualitative research in health professions educational scholarship are presented. A research question must be clear and focused and supported by a strong conceptual framework ...

  17. (PDF) Validity and Reliability in Qualitative Research

    In this context, the concepts of internal validity and external validity, internal consistency reliability, and external reliability used in quantitative research that are equivalents to...

  18. Redefining Qualitative Methods: Believability in the Fifth Moment

    Writing about validity in qualitative inquiry is challenging on many levels. Multiple perspectives about it flood the pages of books (e.g., Lincoln & Guba, 1985; Maxwell, 1996; Merriam, 1998; Schwandt, 1997) and articles and chapters (e.g. Altheide & Johnson, 1994; Lather, 1993; Maxwell, 1992).

  19. Internal Validity in Research: What it is & Examples

    Internal validity refers to how confident you are in your research findings. It is one of the most essential aspects of scientific research and a key idea in understanding facts in general. You can only determine the accuracy of your research as a researcher if no factors contradict your findings.

  20. Validity in Qualitative Research

    One measure of validity in qualitative research is to ask questions such as: "Does it make sense?" and "Can I trust it?" This may seem like a fuzzy measure of validity to someone disciplined in quantitative research, for example, but in a science that deals in themes and context, these questions are important. Steps in Ensuring Validity

  21. Three Aspects of Validity in Qualitative Research

    As Patton (1999) observed, the credibility of qualitative inquiry rests on three distinct but intertwined factors: the use of rigorous techniques and methods for gathering and analyzing the data, with attention to its validity, reliability, and transferability; the credibility of the researcher, evidenced through training, experience, track ...

  22. Internal, External, and Ecological Validity in Research Design, Conduct

    Internal validity examines whether the study design, conduct, and analysis answer the research questions without bias. External validity examines whether the study findings can be generalized to other contexts.

  23. Processual Validity in Qualitative Research in Healthcare

    However, a major concern regarding qualitative research is the great variety of epistemic, philosophical, and ontological aspects involved. 13,14,18 Recent evidence suggests that some structured ways of dealing with the diverse quality dimensions of qualitative research, such as internal and external validity, reliability, objectivity ...

  24. Of Much of Published Scientific Research, the Validity is Questionable

    The validity of much published scientific research is questionable - so how much trust should we place in it? An aphorism called the "Einstein Effect" holds that "People find nonsense credible if they think a scientist said it." We agree, and it's a major concern as trust in science is near an all-time low.There is a lot of nonsense masquerading as science circulating these days.

  25. Content validity testing of the INTERMED Self-Assessment in a sample of

    Background: Care complexity can occur when patients experience health challenges simultaneously with social barriers including food and/or housing insecurity, lack of transportation or other factors that impact care and patient outcomes. People with rheumatoid arthritis (RA) may experience care complexity due to the chronicity of their condition and other biopsychosocial factors.