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Published by Alvin Nicolas at August 16th, 2021 , Revised On October 26, 2023
A researcher must test the collected data before making any conclusion. Every research design needs to be concerned with reliability and validity to measure the quality of the research.
Reliability refers to the consistency of the measurement. Reliability shows how trustworthy is the score of the test. If the collected data shows the same results after being tested using various methods and sample groups, the information is reliable. If your method has reliability, the results will be valid.
Example: If you weigh yourself on a weighing scale throughout the day, you’ll get the same results. These are considered reliable results obtained through repeated measures.
Example: If a teacher conducts the same math test of students and repeats it next week with the same questions. If she gets the same score, then the reliability of the test is high.
Validity refers to the accuracy of the measurement. Validity shows how a specific test is suitable for a particular situation. If the results are accurate according to the researcher’s situation, explanation, and prediction, then the research is valid.
If the method of measuring is accurate, then it’ll produce accurate results. If a method is reliable, then it’s valid. In contrast, if a method is not reliable, it’s not valid.
Example: Your weighing scale shows different results each time you weigh yourself within a day even after handling it carefully, and weighing before and after meals. Your weighing machine might be malfunctioning. It means your method had low reliability. Hence you are getting inaccurate or inconsistent results that are not valid.
Example: Suppose a questionnaire is distributed among a group of people to check the quality of a skincare product and repeated the same questionnaire with many groups. If you get the same response from various participants, it means the validity of the questionnaire and product is high as it has high reliability.
Most of the time, validity is difficult to measure even though the process of measurement is reliable. It isn’t easy to interpret the real situation.
Example: If the weighing scale shows the same result, let’s say 70 kg each time, even if your actual weight is 55 kg, then it means the weighing scale is malfunctioning. However, it was showing consistent results, but it cannot be considered as reliable. It means the method has low reliability.
One of the key features of randomised designs is that they have significantly high internal and external validity.
Internal validity is the ability to draw a causal link between your treatment and the dependent variable of interest. It means the observed changes should be due to the experiment conducted, and any external factor should not influence the variables .
Example: age, level, height, and grade.
External validity is the ability to identify and generalise your study outcomes to the population at large. The relationship between the study’s situation and the situations outside the study is considered external validity.
Also, read about Inductive vs Deductive reasoning in this article.
We hear you.
Threat | Definition | Example |
---|---|---|
Confounding factors | Unexpected events during the experiment that are not a part of treatment. | If you feel the increased weight of your experiment participants is due to lack of physical activity, but it was actually due to the consumption of coffee with sugar. |
Maturation | The influence on the independent variable due to passage of time. | During a long-term experiment, subjects may feel tired, bored, and hungry. |
Testing | The results of one test affect the results of another test. | Participants of the first experiment may react differently during the second experiment. |
Instrumentation | Changes in the instrument’s collaboration | Change in the may give different results instead of the expected results. |
Statistical regression | Groups selected depending on the extreme scores are not as extreme on subsequent testing. | Students who failed in the pre-final exam are likely to get passed in the final exams; they might be more confident and conscious than earlier. |
Selection bias | Choosing comparison groups without randomisation. | A group of trained and efficient teachers is selected to teach children communication skills instead of randomly selecting them. |
Experimental mortality | Due to the extension of the time of the experiment, participants may leave the experiment. | Due to multi-tasking and various competition levels, the participants may leave the competition because they are dissatisfied with the time-extension even if they were doing well. |
Threat | Definition | Example |
---|---|---|
Reactive/interactive effects of testing | The participants of the pre-test may get awareness about the next experiment. The treatment may not be effective without the pre-test. | Students who got failed in the pre-final exam are likely to get passed in the final exams; they might be more confident and conscious than earlier. |
Selection of participants | A group of participants selected with specific characteristics and the treatment of the experiment may work only on the participants possessing those characteristics | If an experiment is conducted specifically on the health issues of pregnant women, the same treatment cannot be given to male participants. |
Reliability can be measured by comparing the consistency of the procedure and its results. There are various methods to measure validity and reliability. Reliability can be measured through various statistical methods depending on the types of validity, as explained below:
Type of reliability | What does it measure? | Example |
---|---|---|
Test-Retests | It measures the consistency of the results at different points of time. It identifies whether the results are the same after repeated measures. | Suppose a questionnaire is distributed among a group of people to check the quality of a skincare product and repeated the same questionnaire with many groups. If you get the same response from a various group of participants, it means the validity of the questionnaire and product is high as it has high test-retest reliability. |
Inter-Rater | It measures the consistency of the results at the same time by different raters (researchers) | Suppose five researchers measure the academic performance of the same student by incorporating various questions from all the academic subjects and submit various results. It shows that the questionnaire has low inter-rater reliability. |
Parallel Forms | It measures Equivalence. It includes different forms of the same test performed on the same participants. | Suppose the same researcher conducts the two different forms of tests on the same topic and the same students. The tests could be written and oral tests on the same topic. If results are the same, then the parallel-forms reliability of the test is high; otherwise, it’ll be low if the results are different. |
Inter-Term | It measures the consistency of the measurement. | The results of the same tests are split into two halves and compared with each other. If there is a lot of difference in results, then the inter-term reliability of the test is low. |
As we discussed above, the reliability of the measurement alone cannot determine its validity. Validity is difficult to be measured even if the method is reliable. The following type of tests is conducted for measuring validity.
Type of reliability | What does it measure? | Example |
---|---|---|
Content validity | It shows whether all the aspects of the test/measurement are covered. | A language test is designed to measure the writing and reading skills, listening, and speaking skills. It indicates that a test has high content validity. |
Face validity | It is about the validity of the appearance of a test or procedure of the test. | The type of included in the question paper, time, and marks allotted. The number of questions and their categories. Is it a good question paper to measure the academic performance of students? |
Construct validity | It shows whether the test is measuring the correct construct (ability/attribute, trait, skill) | Is the test conducted to measure communication skills is actually measuring communication skills? |
Criterion validity | It shows whether the test scores obtained are similar to other measures of the same concept. | The results obtained from a prefinal exam of graduate accurately predict the results of the later final exam. It shows that the test has high criterion validity. |
If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.
Ensuring Validity is also not an easy job. A proper functioning method to ensure validity is given below:
According to the experts, it is helpful if to implement the concept of reliability and Validity. Especially, in the thesis and the dissertation, these concepts are adopted much. The method for implementation given below:
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Frequently Asked QuestionsWhat is reliability and validity in research. Reliability in research refers to the consistency and stability of measurements or findings. Validity relates to the accuracy and truthfulness of results, measuring what the study intends to. Both are crucial for trustworthy and credible research outcomes. What is validity?Validity in research refers to the extent to which a study accurately measures what it intends to measure. It ensures that the results are truly representative of the phenomena under investigation. Without validity, research findings may be irrelevant, misleading, or incorrect, limiting their applicability and credibility. What is reliability?Reliability in research refers to the consistency and stability of measurements over time. If a study is reliable, repeating the experiment or test under the same conditions should produce similar results. Without reliability, findings become unpredictable and lack dependability, potentially undermining the study’s credibility and generalisability. What is reliability in psychology?In psychology, reliability refers to the consistency of a measurement tool or test. A reliable psychological assessment produces stable and consistent results across different times, situations, or raters. It ensures that an instrument’s scores are not due to random error, making the findings dependable and reproducible in similar conditions. What is test retest reliability?Test-retest reliability assesses the consistency of measurements taken by a test over time. It involves administering the same test to the same participants at two different points in time and comparing the results. A high correlation between the scores indicates that the test produces stable and consistent results over time. How to improve reliability of an experiment?
What is the difference between reliability and validity?Reliability refers to the consistency and repeatability of measurements, ensuring results are stable over time. Validity indicates how well an instrument measures what it’s intended to measure, ensuring accuracy and relevance. While a test can be reliable without being valid, a valid test must inherently be reliable. Both are essential for credible research. Are interviews reliable and valid?Interviews can be both reliable and valid, but they are susceptible to biases. The reliability and validity depend on the design, structure, and execution of the interview. Structured interviews with standardised questions improve reliability. Validity is enhanced when questions accurately capture the intended construct and when interviewer biases are minimised. Are IQ tests valid and reliable?IQ tests are generally considered reliable, producing consistent scores over time. Their validity, however, is a subject of debate. While they effectively measure certain cognitive skills, whether they capture the entirety of “intelligence” or predict success in all life areas is contested. Cultural bias and over-reliance on tests are also concerns. Are questionnaires reliable and valid?Questionnaires can be both reliable and valid if well-designed. Reliability is achieved when they produce consistent results over time or across similar populations. Validity is ensured when questions accurately measure the intended construct. However, factors like poorly phrased questions, respondent bias, and lack of standardisation can compromise their reliability and validity. You May Also LikeThis post provides the key disadvantages of secondary research so you know the limitations of secondary research before making a decision. In correlational research, a researcher measures the relationship between two or more variables or sets of scores without having control over the variables. In historical research, a researcher collects and analyse the data, and explain the events that occurred in the past to test the truthfulness of observations. USEFUL LINKS LEARNING RESOURCES COMPANY DETAILS
Know the Differences & Comparisons Difference Between Validity and ReliabilityFor the purpose of checking the accuracy and applicability, a multi-item measurement scale needs to be evaluated, in terms of reliability, validity, and generalizability. These are certain preferred qualities which gauge the goodness in measuring the characteristics under consideration. Validity is all about the genuineness of the research, whereas reliability is nothing but the repeatability of the outcomes. This article will break down the fundamental differences between validity and reliability. Content: Validity Vs ReliabilityComparison chart.
Definition of ValidityIn statistics, the term validity implies utility. It is the most important yardstick that signals the degree to which research instrument gauges, what it is supposed to measure. Simply, it measures the point to which differences discovered with the scale reflect true differences, among objects on the characteristics under study, instead of a systematic and random error. To be considered as perfectly valid, it should not possess any measurement error. There are three types of validity, which are:
Definition of ReliabilityReliability is used to mean the extent to which the measurement tool provides consistent outcomes if the measurement is repeatedly performed. To assess reliability approaches used are test-retest, internal consistency methods, and alternative forms. There are two key aspects, which requires being indicated separately are:
Systematic errors do not affect reliability, but random errors lead to inconsistency of the results, thus lower reliability. When the research instrument conforms to reliability, then one can be sure that the temporary and situational factors are not interfering. Reliability can be improved by way of:
Key Differences Between Validity and ReliabilityThe points presented below, explains the fundamental differences between validity and reliability:
To sum up, validity and reliability are two vital test of sound measurement. Reliability of the instrument can be evaluated by identifying the proportion of systematic variation in the instrument. On the other hand, the validity of the instrument is assessed by determining the degree to which variation in observed scale score indicates actual variation among those being tested. You Might Also Like:April 11, 2018 at 12:04 am Quite informative and user-friendly. It is written in simple language to understand. Bravo! Md. Zahirul Islam says June 12, 2019 at 5:58 pm Thanks for details. #Respect Quickbooks Enterprise Support says June 22, 2019 at 5:20 pm that was really greatly explained. thanks for sharing this. Pierrette Nyiramahirwe says October 24, 2019 at 10:32 pm provides good and valid information! thank you so much 👏 Dipak RaI says December 20, 2019 at 11:11 am Thanks a lot for your clear explanation. I am overwhelmed. Avi Deshmukh says November 17, 2020 at 7:24 pm Indeed, good clarity on two interrelated components of statistical and research tools. Kipsang Gideon says November 27, 2020 at 1:12 pm good information on research details and detailed information on matters of research. Leave a Reply Cancel replyYour email address will not be published. Required fields are marked * Save my name, email, and website in this browser for the next time I comment. Have a language expert improve your writingRun a free plagiarism check in 10 minutes, generate accurate citations for free.
Methodology
The 4 Types of Reliability in Research | Definitions & ExamplesPublished on August 8, 2019 by Fiona Middleton . Revised on June 22, 2023. Reliability tells you how consistently a method measures something. When you apply the same method to the same sample under the same conditions, you should get the same results. If not, the method of measurement may be unreliable or bias may have crept into your research. There are four main types of reliability. Each can be estimated by comparing different sets of results produced by the same method.
Table of contentsTest-retest reliability, interrater reliability, parallel forms reliability, internal consistency, which type of reliability applies to my research, other interesting articles, frequently asked questions about types of reliability. Test-retest reliability measures the consistency of results when you repeat the same test on the same sample at a different point in time. You use it when you are measuring something that you expect to stay constant in your sample. Why it’s importantMany factors can influence your results at different points in time: for example, respondents might experience different moods, or external conditions might affect their ability to respond accurately. Test-retest reliability can be used to assess how well a method resists these factors over time. The smaller the difference between the two sets of results, the higher the test-retest reliability. How to measure itTo measure test-retest reliability, you conduct the same test on the same group of people at two different points in time. Then you calculate the correlation between the two sets of results. Test-retest reliability exampleYou devise a questionnaire to measure the IQ of a group of participants (a property that is unlikely to change significantly over time).You administer the test two months apart to the same group of people, but the results are significantly different, so the test-retest reliability of the IQ questionnaire is low. Improving test-retest reliability
Prevent plagiarism. Run a free check.Interrater reliability (also called interobserver reliability) measures the degree of agreement between different people observing or assessing the same thing. You use it when data is collected by researchers assigning ratings, scores or categories to one or more variables , and it can help mitigate observer bias . People are subjective, so different observers’ perceptions of situations and phenomena naturally differ. Reliable research aims to minimize subjectivity as much as possible so that a different researcher could replicate the same results. When designing the scale and criteria for data collection, it’s important to make sure that different people will rate the same variable consistently with minimal bias . This is especially important when there are multiple researchers involved in data collection or analysis. To measure interrater reliability, different researchers conduct the same measurement or observation on the same sample. Then you calculate the correlation between their different sets of results. If all the researchers give similar ratings, the test has high interrater reliability. Interrater reliability exampleA team of researchers observe the progress of wound healing in patients. To record the stages of healing, rating scales are used, with a set of criteria to assess various aspects of wounds. The results of different researchers assessing the same set of patients are compared, and there is a strong correlation between all sets of results, so the test has high interrater reliability. Improving interrater reliability
Parallel forms reliability measures the correlation between two equivalent versions of a test. You use it when you have two different assessment tools or sets of questions designed to measure the same thing. If you want to use multiple different versions of a test (for example, to avoid respondents repeating the same answers from memory), you first need to make sure that all the sets of questions or measurements give reliable results. The most common way to measure parallel forms reliability is to produce a large set of questions to evaluate the same thing, then divide these randomly into two question sets. The same group of respondents answers both sets, and you calculate the correlation between the results. High correlation between the two indicates high parallel forms reliability. Parallel forms reliability exampleA set of questions is formulated to measure financial risk aversion in a group of respondents. The questions are randomly divided into two sets, and the respondents are randomly divided into two groups. Both groups take both tests: group A takes test A first, and group B takes test B first. The results of the two tests are compared, and the results are almost identical, indicating high parallel forms reliability. Improving parallel forms reliability
Internal consistency assesses the correlation between multiple items in a test that are intended to measure the same construct. You can calculate internal consistency without repeating the test or involving other researchers, so it’s a good way of assessing reliability when you only have one data set. When you devise a set of questions or ratings that will be combined into an overall score, you have to make sure that all of the items really do reflect the same thing. If responses to different items contradict one another, the test might be unreliable. Two common methods are used to measure internal consistency.
Internal consistency exampleA group of respondents are presented with a set of statements designed to measure optimistic and pessimistic mindsets. They must rate their agreement with each statement on a scale from 1 to 5. If the test is internally consistent, an optimistic respondent should generally give high ratings to optimism indicators and low ratings to pessimism indicators. The correlation is calculated between all the responses to the “optimistic” statements, but the correlation is very weak. This suggests that the test has low internal consistency. Improving internal consistency
It’s important to consider reliability when planning your research design , collecting and analyzing your data, and writing up your research. The type of reliability you should calculate depends on the type of research and your methodology .
If possible and relevant, you should statistically calculate reliability and state this alongside your results . 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.
Research bias
Reliability and validity are both about how well a method measures something:
If you are doing experimental research, you also have to consider the internal and external validity of your experiment. You can use several tactics to minimize observer bias .
Reproducibility and replicability are related terms.
Research bias affects the validity and reliability of your research findings , leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where, for example, a new form of treatment may be evaluated. Cite this Scribbr articleIf 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. Middleton, F. (2023, June 22). The 4 Types of Reliability in Research | Definitions & Examples. Scribbr. Retrieved September 9, 2024, from https://www.scribbr.com/methodology/types-of-reliability/ Is this article helpful?Fiona MiddletonOther students also liked, reliability vs. validity in research | difference, types and examples, what is quantitative research | definition, uses & methods, data collection | definition, methods & examples, what is your plagiarism score. 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.
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Validity, reliability, and generalizability in qualitative researchLawrence leung. 1 Department of Family Medicine, Queen's University, Kingston, Ontario, Canada 2 Centre of Studies in Primary Care, Queen's University, Kingston, Ontario, Canada In general practice, qualitative research contributes as significantly as quantitative research, in particular regarding psycho-social aspects of patient-care, health services provision, policy setting, and health administrations. In contrast to quantitative research, qualitative research as a whole has been constantly critiqued, if not disparaged, by the lack of consensus for assessing its quality and robustness. This article illustrates with five published studies how qualitative research can impact and reshape the discipline of primary care, spiraling out from clinic-based health screening to community-based disease monitoring, evaluation of out-of-hours triage services to provincial psychiatric care pathways model and finally, national legislation of core measures for children's healthcare insurance. Fundamental concepts of validity, reliability, and generalizability as applicable to qualitative research are then addressed with an update on the current views and controversies. Nature of Qualitative Research versus Quantitative ResearchThe essence of qualitative research is to make sense of and recognize patterns among words in order to build up a meaningful picture without compromising its richness and dimensionality. Like quantitative research, the qualitative research aims to seek answers for questions of “how, where, when who and why” with a perspective to build a theory or refute an existing theory. Unlike quantitative research which deals primarily with numerical data and their statistical interpretations under a reductionist, logical and strictly objective paradigm, qualitative research handles nonnumerical information and their phenomenological interpretation, which inextricably tie in with human senses and subjectivity. While human emotions and perspectives from both subjects and researchers are considered undesirable biases confounding results in quantitative research, the same elements are considered essential and inevitable, if not treasurable, in qualitative research as they invariable add extra dimensions and colors to enrich the corpus of findings. However, the issue of subjectivity and contextual ramifications has fueled incessant controversies regarding yardsticks for quality and trustworthiness of qualitative research results for healthcare. Impact of Qualitative Research upon Primary CareIn many ways, qualitative research contributes significantly, if not more so than quantitative research, to the field of primary care at various levels. 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 ] evaluating care pathways for community psychiatry[ 4 ] and finally prioritization of healthcare initiatives for legislation purposes at national levels.[ 5 ] With the recent advances of information technology and mobile connecting device, self-monitoring and management of chronic diseases via tele-health technology may seem beneficial to both the patient and healthcare provider. Recruiting COPD patients who were given tele-health devices that monitored lung functions, Williams et al. [ 1 ] conducted phone interviews and analyzed their transcripts via a grounded theory approach, identified themes which enabled them to conclude that such mobile-health setup and application helped to engage patients with better adherence to treatment and overall improvement in mood. Such positive findings were in contrast to previous studies, which opined that elderly patients were often challenged by operating computer tablets,[ 6 ] or, conversing with the tele-health software.[ 7 ] To explore the content of recommendations for colorectal cancer screening given out by family physicians, Wackerbarth, et al. [ 2 ] conducted semi-structure interviews with subsequent content analysis and found that most physicians delivered information to enrich patient knowledge with little regard to patients’ true understanding, ideas, and preferences in the matter. These findings suggested room for improvement for family physicians to better engage their patients in recommending preventative care. Faced with various models of out-of-hours triage services for GP consultations, Egbunike et al. [ 3 ] conducted thematic analysis on semi-structured telephone interviews with patients and doctors in various urban, rural and mixed settings. They found that the efficiency of triage services remained a prime concern from both users and providers, among issues of access to doctors and unfulfilled/mismatched expectations from users, which could arouse dissatisfaction and legal implications. In UK, a care pathways model for community psychiatry had been introduced but its benefits were unclear. Khandaker et al. [ 4 ] hence conducted a qualitative study using semi-structure interviews with medical staff and other stakeholders; adopting a grounded-theory approach, major themes emerged which included improved equality of access, more focused logistics, increased work throughput and better accountability for community psychiatry provided under the care pathway model. Finally, at the US national level, Mangione-Smith et al. [ 5 ] employed a modified Delphi method to gather consensus from a panel of nominators which were recognized experts and stakeholders in their disciplines, and identified a core set of quality measures for children's healthcare under the Medicaid and Children's Health Insurance Program. These core measures were made transparent for public opinion and later passed on for full legislation, hence illustrating the impact of qualitative research upon social welfare and policy improvement. Overall Criteria for Quality in Qualitative ResearchGiven the diverse genera and forms of qualitative research, there is no consensus for assessing any piece of qualitative research work. Various approaches have been suggested, the two leading schools of thoughts being the school of Dixon-Woods et al. [ 8 ] which emphasizes on methodology, and that of Lincoln et al. [ 9 ] which stresses the rigor of interpretation of results. By identifying commonalities of qualitative research, Dixon-Woods produced a checklist of questions for assessing clarity and appropriateness of the research question; the description and appropriateness for sampling, data collection and data analysis; levels of support and evidence for claims; coherence between data, interpretation and conclusions, and finally level of contribution of the paper. These criteria foster the 10 questions for the Critical Appraisal Skills Program checklist for qualitative studies.[ 10 ] However, these methodology-weighted criteria may not do justice to qualitative studies that differ in epistemological and philosophical paradigms,[ 11 , 12 ] one classic example will be positivistic versus interpretivistic.[ 13 ] Equally, without a robust methodological layout, rigorous interpretation of results advocated by Lincoln et al. [ 9 ] will not be good either. Meyrick[ 14 ] argued from a different angle and proposed fulfillment of the dual core criteria of “transparency” and “systematicity” for good quality qualitative research. In brief, every step of the research logistics (from theory formation, design of study, sampling, data acquisition and analysis to results and conclusions) has to be validated if it is transparent or systematic enough. In this manner, both the research process and results can be assured of high rigor and robustness.[ 14 ] Finally, Kitto et al. [ 15 ] epitomized six criteria for assessing overall quality of qualitative research: (i) Clarification and justification, (ii) procedural rigor, (iii) sample representativeness, (iv) interpretative rigor, (v) reflexive and evaluative rigor and (vi) transferability/generalizability, which also double as evaluative landmarks for manuscript review to the Medical Journal of Australia. Same for quantitative research, quality for qualitative research can be assessed in terms of validity, reliability, and generalizability. Validity in qualitative research means “appropriateness” of the tools, processes, and data. Whether the research question is valid for the desired outcome, the choice of methodology is appropriate for answering the research question, the design is valid for the methodology, the sampling and data analysis is appropriate, and finally the results and conclusions are valid for the sample and context. In assessing validity of qualitative research, the challenge can start from the ontology and epistemology of the issue being studied, e.g. the concept of “individual” is seen differently between humanistic and positive psychologists due to differing philosophical perspectives:[ 16 ] Where humanistic psychologists believe “individual” is a product of existential awareness and social interaction, positive psychologists think the “individual” exists side-by-side with formation of any human being. Set off in different pathways, qualitative research regarding the individual's wellbeing will be concluded with varying validity. Choice of methodology must enable detection of findings/phenomena in the appropriate context for it to be valid, with due regard to culturally and contextually variable. For sampling, procedures and methods must be appropriate for the research paradigm and be distinctive between systematic,[ 17 ] purposeful[ 18 ] or theoretical (adaptive) sampling[ 19 , 20 ] where the systematic sampling has no a priori theory, purposeful sampling often has a certain aim or framework and theoretical sampling is molded by the ongoing process of data collection and theory in evolution. For data extraction and analysis, several methods were adopted to enhance validity, including 1 st tier triangulation (of researchers) and 2 nd tier triangulation (of resources and theories),[ 17 , 21 ] well-documented audit trail of materials and processes,[ 22 , 23 , 24 ] multidimensional analysis as concept- or case-orientated[ 25 , 26 ] and respondent verification.[ 21 , 27 ] ReliabilityIn quantitative research, reliability refers to exact replicability of the processes and the results. In qualitative research with diverse paradigms, such definition of reliability is challenging and epistemologically counter-intuitive. Hence, the essence of reliability for qualitative research lies with consistency.[ 24 , 28 ] A margin of variability for results is tolerated in qualitative research provided the methodology and epistemological logistics consistently yield data that are ontologically similar but may differ in richness and ambience within similar dimensions. Silverman[ 29 ] proposed five approaches in enhancing the reliability of process and results: Refutational analysis, constant data comparison, comprehensive data use, inclusive of the deviant case and use of tables. As data were extracted from the original sources, researchers must verify their accuracy in terms of form and context with constant comparison,[ 27 ] either alone or with peers (a form of triangulation).[ 30 ] The scope and analysis of data included should be as comprehensive and inclusive with reference to quantitative aspects if possible.[ 30 ] Adopting the Popperian dictum of falsifiability as essence of truth and science, attempted to refute the qualitative data and analytes should be performed to assess reliability.[ 31 ] GeneralizabilityMost qualitative research studies, if not all, are meant to study a specific issue or phenomenon in a certain population or ethnic group, of a focused locality in a particular context, hence generalizability of qualitative research findings is usually not an expected attribute. However, with rising trend of knowledge synthesis from qualitative research via meta-synthesis, meta-narrative or meta-ethnography, evaluation of generalizability becomes pertinent. A pragmatic approach to assessing generalizability for qualitative studies is to adopt same criteria for validity: That is, use of systematic sampling, triangulation and constant comparison, proper audit and documentation, and multi-dimensional theory.[ 17 ] However, some researchers espouse the approach of analytical generalization[ 32 ] where one judges the extent to which the findings in one study can be generalized to another under similar theoretical, and the proximal similarity model, where generalizability of one study to another is judged by similarities between the time, place, people and other social contexts.[ 33 ] Thus said, Zimmer[ 34 ] questioned the suitability of meta-synthesis in view of the basic tenets of grounded theory,[ 35 ] phenomenology[ 36 ] and ethnography.[ 37 ] He concluded that any valid meta-synthesis must retain the other two goals of theory development and higher-level abstraction while in search of generalizability, and must be executed as a third level interpretation using Gadamer's concepts of the hermeneutic circle,[ 38 , 39 ] dialogic process[ 38 ] and fusion of horizons.[ 39 ] Finally, Toye et al. [ 40 ] reported the practicality of using “conceptual clarity” and “interpretative rigor” as intuitive criteria for assessing quality in meta-ethnography, which somehow echoed Rolfe's controversial aesthetic theory of research reports.[ 41 ] Food for ThoughtDespite various measures to enhance or ensure quality of qualitative studies, some researchers opined from a purist ontological and epistemological angle that qualitative research is not a unified, but ipso facto diverse field,[ 8 ] hence any attempt to synthesize or appraise different studies under one system is impossible and conceptually wrong. Barbour argued from a philosophical angle that these special measures or “technical fixes” (like purposive sampling, multiple-coding, triangulation, and respondent validation) can never confer the rigor as conceived.[ 11 ] In extremis, Rolfe et al. opined from the field of nursing research, that any set of formal criteria used to judge the quality of qualitative research are futile and without validity, and suggested that any qualitative report should be judged by the form it is written (aesthetic) and not by the contents (epistemic).[ 41 ] Rolfe's novel view is rebutted by Porter,[ 42 ] who argued via logical premises that two of Rolfe's fundamental statements were flawed: (i) “The content of research report is determined by their forms” may not be a fact, and (ii) that research appraisal being “subject to individual judgment based on insight and experience” will mean those without sufficient experience of performing research will be unable to judge adequately – hence an elitist's principle. From a realism standpoint, Porter then proposes multiple and open approaches for validity in qualitative research that incorporate parallel perspectives[ 43 , 44 ] and diversification of meanings.[ 44 ] Any work of qualitative research, when read by the readers, is always a two-way interactive process, such that validity and quality has to be judged by the receiving end too and not by the researcher end alone. In summary, the three gold criteria of validity, reliability and generalizability apply in principle to assess quality for both quantitative and qualitative research, what differs will be the nature and type of processes that ontologically and epistemologically distinguish between the two. Source of Support: Nil. Conflict of Interest: None declared. 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. 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Reliability and validity in a nutshellAffiliation.
Aims: To explore and explain the different concepts of reliability and validity as they are related to measurement instruments in social science and health care. Background: There are different concepts contained in the terms reliability and validity and these are often explained poorly and there is often confusion between them. Design: To develop some clarity about reliability and validity a conceptual framework was built based on the existing literature. Results: The concepts of reliability, validity and utility are explored and explained. Conclusions: Reliability contains the concepts of internal consistency and stability and equivalence. Validity contains the concepts of content, face, criterion, concurrent, predictive, construct, convergent (and divergent), factorial and discriminant. In addition, for clinical practice and research, it is essential to establish the utility of a measurement instrument. Relevance to clinical practice: To use measurement instruments appropriately in clinical practice, the extent to which they are reliable, valid and usable must be established. PubMed Disclaimer
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Validity Vs. Reliability: What’s The Difference?
Find a Job You Really Want In The difference between validity and reliability is important in research, testing, and statistical analysis. Both are used to determine how well a test measures something, but the two of them tell you different things about your test. Validity is all about accuracy in your measurements, while reliability determines consistency. Ideally, you want your equipment to be both reliable and valid – or consistent and accurate – be it a thermometer, questionnaire, or scale. Key Takeaways: Validity Reliability If a measurement is accurate, then it’s valid. If a measurement is consistent, then it’s reliable. Validity is essential in all types of testing. If your results are skewed, then your conclusion is likely to be as well. Reliability is also important. If your instruments for collecting data don’t produce reliable results, you can’t draw any conclusions. Test results can’t be valid if they aren’t reliable. If you keep getting different results from measurements under the same conditions, then it’s neither reliable nor correct. A tool can have reliable measurements that aren’t valid. If a radar gun isn’t properly calibrated, it may register 50 mph for every car that goes by at 35 mph. It’s reliable, but it isn’t valid. There are three major types of determinations of validity: criterion, content, and construct. There are four major types of determinations of reliability: test-retest, inter-rater , parallel forms, and internal consistency. What is Validity?Validity is the measure of whether or not your test is accurate. If you have a ten-pound weight and your scale reads it as ten pounds, then it’s valid. Valid test results need not be consistent as long as they’re accurate. If the conditions change – even if you’re unaware of them – then you should get a different measurement. Hard measurements – such as weight, temperature, and pH – aren’t the only type of measurements that require determining validity. It’s also used in medicine and psychology to determine how useful their surveys and questionnaires are. For instance, a questionnaire created to determine if a person has a type of illness is valid if the answers predict whether or not the patient suffers from that disease. And if it’s valid, it can be a useful tool for diagnosis. Of course, validity isn’t quite as simple as that. There are three major types of validity that are referenced in tests. Criterion Validity. This determines whether or not the test fits the criteria. To put it plainly, it’s whether or not it stacks up to other valid measurements of the same thing. Construct Validity. Does this test measure what it’s meant to measure? If you want to measure someone’s reading comprehension and instead design a test that is a great indicator of short-term memory, it’s not valid. Content Validity. Sometimes also called face validity, this measures whether or not the test adequately covers what you’re attempting to measure. For instance, if it’s a test to determine comprehension of a subject in a course, it should cover all the key knowledge learned in the course. As with most things in studies, validity isn’t a hard measure. Most studies have a sliding scale of validity, and they try to get it as close to the top as reasonably possible, but it’s essentially impossible to have something that’s truly, completely valid. What is Reliability?Reliability is the measure of the consistency of your instruments. If a weight put on a scale consistently comes up as ten pounds, then your scale is reliable. It should be noted that the weight in question doesn’t need to weigh ten pounds. If it’s a five-pound weight and the scale is off by five pounds, but it comes up with the same answer every time, it’s still reliable. As with validity, there are different types of ways to determine reliability. Test-retest reliability. This determination is exactly what it sounds like. Tests are conducted multiple different times in order to determine the reliability of the results. This is best for something like temperature under similar conditions – something that isn’t going to change. Parallel forms reliability. With this one, they use different tests that are designed to be equivalent to one another. Sometimes this is also done with split-half reliability , where the test is split into two pieces, and those are compared. Internal consistency reliability. This is often used in personality tests, where the questions are related to what you’re trying to determine. In personality tests , they will even ask multiple similar questions in order to help determine reliability. Inter-rater reliability. For this type of reliability, different people run the same study or test, and the results are compared. This is the basis of many serious studies, as someone will run a study, then another person will run a similar or identical study in order to make sure the results can be replicated. Like validity, reliability isn’t binary in most studies. The goal is to try to get as high a level of reliability as possible. The idea of limited reliability is seen most often in polling – there’s always a listed margin of error . If the margin of error is large enough, it also calls into question the validity. Validity vs. Reliability FAQWhat is the relationship between validity and reliability? The relationship between validity and reliability is that they’re both used to determine the efficacy of a test or a study. Validity determines whether or not it’s accurate, while reliability determines whether or not the results are consistent. What are examples of reliability and validity? An example of validity is a poll accurately predicting whether or not a candidate will win reelection. An example of reliability is that a poll gets similar results from similar parts of the electorate. Can something be valid but not reliable? No, something can’t be valid but not reliable. If your results aren’t reliable, they’re inherently not valid. Validity is accuracy, so if your results aren’t consistent in similar conditions, they can’t possibly be accurate. However, something can be reliable but not valid. How do you measure reliability and validity? There are several different ways to measure reliability and validity. For reliability, the best way to do it is to repeat the test multiple times in order to make sure you get the same results. For validity, it’s best to try to compare to other similar results that you know are valid. How useful was this post? Click on a star to rate it! Average rating / 5. Vote count: No votes so far! Be the first to rate this post. Di has been a writer for more than half her life. Most of her writing so far has been fiction, and she’s gotten short stories published in online magazines Kzine and Silver Blade, as well as a flash fiction piece in the Bookends review. Di graduated from Mary Baldwin College (now University) with a degree in Psychology and Sociology. Related posts Inbound vs. Outbound: What’s The Difference? Interpolation Vs. Extrapolation: What’s The Difference? Transactional Leadership Vs. Transformational Leadership: What’s The Difference? Weighted GPA Vs. Unweighted GPA: What’s The Difference?
Reliability vs Validity in Research: Main Differences
Table of contents Use our free Readability checker Reliability and validity are two important concepts in research design that are used to assess the quality of research results.
Why is it important to know the difference between reliability vs validity? Conducting complex research typically requires some preparation, particularly to evaluate your data collection and analysis methods. Do they produce correct results? Are they applicable for this subject? Both validity and reliability values make it possible to quickly evaluate how well your research approach works in a particular case. Specific techniques like test-retest help to calculate the correlation between the results of subsequent measurements and thus show whether these results are reliable. Checking how well these results correspond to common sense may help to learn whether they are valid. If you want to learn more about these two major parameters and get help in writing a research paper , let’s get into this together! Reliability vs Validity: DefinitionTo better explain validity vs reliability, we need to start with the basics. In fact, there is a strong relation between both these parameters as they all are elements of quality. However, it can happen that your assessment method provides valid results at first, but its reliability turns out to be low because you cannot achieve consistency after using it again. So, let’s dive into details with our coursework writing service . What Does Valid Mean: DefinitionLet's start with validity meaning . It is a quality parameter that shows how accurately a measurement is performed. In case the test results match the expected values or correspond to other properties of the subject or the surrounding environment, they are most probably valid. The meaning of this parameter is that it indicates whether it is safe to make assumptions based on results of a measurement . Main types of validity are: What Is Reliability: DefinitionAs for a reliability definition , it is a parameter that indicates consistency of a tool or a method. In case it repeatedly produces the same or similar results we can call it reliable, meaning that it does not degrade as time passes. The goal of a researcher who measures some values again and again is to understand whether the tool in question can be safely reused. Main types of reliability are:
Reliable vs Valid: What Is the DifferenceTo understand the concept of reliability vs validity just keep in mind that they represent different aspects of quality and evaluate measurement results from different angles. The first indicates whether an assessment tool works properly under different conditions and after being used repeatedly. And the validity level of this tool shows it is able to measure properly at all. Both these parameters are crucial for ensuring the internal quality level of a research and the mark it scores, regardless of an academic field it belongs to. Let’s see how to use them and how exactly they can help with dissertation or other research. Reliability and Validity: How to Use in Your ResearchValidity and reliability of your results indicate the quality level of your research. Therefore, they show whether its results can be trusted, whether they are useful, or whether they support your statements as intended. So, you should use these parameters in order to create a strong design research , ensuring all your methods, samples, and other parts of content are appropriate. Results of these parameters are equally crucial for in-depth scientific research and for student-level works. So, let’s dig deeper and find out how to use both of them in research. Validity in ResearchIn general, validity and reliability in research are to be used together to ensure you can reach your research goals. When it comes to ensuring validity, it is often recommended to do that at earlier stages of your research. When you work on your research design and particularly decide how you will collect your data, you can verify available methods to see whether they are helpful in your particular case. Once you ensure they are valid, you can proceed evaluating their reliability. Otherwise it would hardly be useful to have reliable methods that consistently provide incorrect results. Reliability in ResearchSpeaking about validity vs reliability in research, it is important to understand that it doesn’t always help to check whether your methods are valid after a first run. Depending on specific conditions, their efficiency may change at further steps. So it is highly recommended to verify their consistency. You need to consider the reliability of your tools and methods throughout the entire data collection process . The more you invest into this verification, the more confidence about the quality of your overall work you will have. Reliability vs Validity: ExamplesFinally, let’s review some reliability vs validity examples. This will help to illustrate the meaning and usage of both these concepts in case you still have any questions after reading the explanations above. Let’s suppose that a group of a local mall’s consumers is monitored by a research team for several years. Their shopping habits and preferences are examined by conducting surveys. If their responses do not change significantly over time, this indicates high reliability of this approach. Alternatively, if different researchers conducting the survey on this group’s subsections also get correlated results, it is safe to assume that these tests are reliable. Now let’s suppose that at some point it becomes clear that some questions in the survey contain mistakes and aren’t actually collecting the data which is needed. In this case this approach is invalid despite the tests being consistent. It is necessary to ensure the validity at the start of research to avoid such outcomes. Validity vs Reliability: Key TakeawaysSo, we have learned about the concept of reliable vs valid approach in research. This article covers the most important elements of this construct: the meaning of both these quality parameters, their main differences and their usage in research projects. Or just don’t know where to start from? Feel free to check out our academic writing service and buy research paper online . We are a team of skilled writers with vast experience in different academic fields, ready to help any student with their paper. All our articles are well-written, proofread and always delivered on time! Validity and Reliability: Frequently Asked Questions1. where do you write about reliability and validity in a thesis. You may write about reliability and validity in various sections of your thesis or dissertation, as it depends on your work’s structure. However, it would be best to include these evaluations to the part where you describe your research design. You need to explain how you will assess the quality of your approach and your results before you conduct actual research steps and make conclusions about your topic. 2. Can something be valid but not reliable?A measure can be valid but not reliable if it returns correct results at first but then fails to do so for some reasons, particularly because of changing circumstances. It is also possible that a measure is reliable if it is measuring something very consistently, but not valid however, in case a wrong construct is measured all the time. Therefore both these parameters aren’t alway correlated despite being closely connected. 3. Is reliability necessary for validity?What makes reliability necessary for validity? In most cases we cannot say a test is valid if it isn’t reliable. Test score reliability is actually a component of validity. However a researcher must remember that additional verifications are needed to ensure validity of a group of tests in addition to verifying their reliability. These two parameters cannot replace one another. 4. What does it mean that reliability is necessary but not sufficient for validity?In most cases, reliability is a component of validity. We cannot say a test is valid, if it produces errors or gets inappropriate data at some point. At the same time it is important to remember that overall reliability of tests is not sufficient for assuming their validity, since they might provide wrong results consistently. It would make them reliable but at the end they would just repeat the same error again and again. Joe Eckel is an expert on Dissertations writing. He makes sure that each student gets precious insights on composing A-grade academic writing. You may also like |
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Reliability is about the consistency of a measure, and validity is about the accuracy of a measure.opt. It's important to consider reliability and validity when you are creating your research design, planning your methods, and writing up your results, especially in quantitative research. Failing to do so can lead to several types of research ...
Reliability and validity are criteria by which researchers assess measurement quality. Measuring a person or item involves assigning scores to represent an attribute. This process creates the data that we analyze. However, to provide meaningful research results, that data must be good. And not all data are good!
Revised on 10 October 2022. Reliability and validity are concepts used to evaluate the quality of research. They indicate how well a method, technique, or test measures something. Reliability is about the consistency of a measure, and validity is about the accuracy of a measure. It's important to consider reliability and validity when you are ...
Validity indicates the extent to which your research usefully and accurately measures what you are trying to measure and how that stacks up with other established concepts. Validity can be harder to determine than reliability, but a high level of reliability assists in proving that your research is valid.
What is the difference between reliability and validity in a study? In the domain of research, whether qualitative or quantitative, two concepts often arise when discussing the quality and rigor of a study: reliability and validity.These two terms, while interconnected, have distinct meanings that hold significant weight in the world of research.
However, in research and testing, reliability and validity are not the same things. When it comes to data analysis, reliability refers to how easily replicable an outcome is. For example, if you measure a cup of rice three times, and you get the same result each time, that result is reliable. The validity, on the other hand, refers to the ...
Example of Reliability and Validity in Research. In this section, we'll explore instances that highlight the differences between reliability and validity and how they play a crucial role in ensuring the credibility of research findings. Example of reliability; Imagine you are studying the reliability of a smartphone's battery life measurement.
As with validity, reliability is an attribute of a measurement instrument - for example, a survey, a weight scale or even a blood pressure monitor. But while validity is concerned with whether the instrument is measuring the "thing" it's supposed to be measuring, reliability is concerned with consistency and stability.
Test-retest reliability, inter-rater reliability, internal consistency reliability: Content validity, criterion validity, construct validity: Measure: Degree of agreement or correlation between repeated measures or observers: Degree of association between a measure and an external criterion, or degree to which a measure assesses the intended ...
See why leading organizations rely on MasterClass for learning & development. In the fields of science and technology, the terms reliability and validity are used to describe the robustness of qualitative and quantitative research methods. While these criteria are related, the terms aren't interchangeable.
Reliability and validity are both about how well a method measures something: Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions). Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure). If you are doing ...
Reliability refers to the consistency of the measurement. Reliability shows how trustworthy is the score of the test. If the collected data shows the same results after being tested using various methods and sample groups, the information is reliable. If your method has reliability, the results will be valid. Example: If you weigh yourself on a ...
The difference is that face validity is subjective, and assesses content at surface level. ... Reliability vs. Validity in Research | Difference, Types and Examples Reliability is about a method's consistency, and validity is about its accuracy. You can assess both using various types of evidence.
Date updated: May 1, 2020. Reliability and validity are concepts used to evaluate the quality of research. They indicate how well a method, technique or test measures something. Reliability is about the consistency of a measure, and validity is about the accuracy of a measure.
Validity implies the extent to which the research instrument measures, what it is intended to measure. Reliability refers to the degree to which scale produces consistent results, when repeated measurements are made. Instrument. A valid instrument is always reliable. A reliable instrument need not be a valid instrument.
There are four main types of reliability. Each can be estimated by comparing different sets of results produced by the same method. Type of reliability. Measures the consistency of…. Test-retest. The same test over time. Interrater. The same test conducted by different people. Parallel forms.
Reliability and validity are among the most important and fundamental domains in the assessment of any measuring methodology for data-collection in a good research. Validity is about what an instrument measures and how well it does so, whereas reliability concerns the truthfulness in the data obtained and the degree to which any measuring tool ...
Fundamental concepts of validity, reliability, and generalizability as applicable to qualitative research are then addressed with an update on the current views and controversies. Keywords: Controversies, generalizability, primary care research, qualitative research, reliability, validity. Source of Support: Nil.
Validity and reliability are both used in the evaluation of research quality. They are equally important in creating the research design, selecting research methods, analyzing, and interpreting ...
Conclusions: Reliability contains the concepts of internal consistency and stability and equivalence. Validity contains the concepts of content, face, criterion, concurrent, predictive, construct, convergent (and divergent), factorial and discriminant. In addition, for clinical practice and research, it is essential to establish the utility of ...
Find Jobs. The difference between validity and reliability is important in research, testing, and statistical analysis. Both are used to determine how well a test measures something, but the two of them tell you different things about your test. Validity is all about accuracy in your measurements, while reliability determines consistency.
Reliability refers to the consistency of research findings over time or across different studies. Research is considered reliable if it produces identical outcomes when repeated under similar conditions. Validity means the accuracy or truthfulness of research findings. A valid study measures what it is supposed to measure and its results can be ...