Reliability, Validity and Ethics
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- Lindy Woodrow 2
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This chapter is about writing about the procedure of the research. This includes a discussion of reliability, validity and the ethics of research and writing. The level of detail about these issues varies across texts, but the reliability and validity of the study must feature in the text. Some-times these issues are evident from the research instruments and analysis and sometimes they are referred to explicitly. This chapter includes the following sections:
Technical information
Reliability of a measure
Internal validity
External validity
Research ethics
Reporting on reliability
Writing about validity
Reporting on ethics
Writing about research procedure
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Further reading
Dörnyei, Z. (2007). Research methods in applied linguistics: Quantitative, qualitative and mixed methodologies . Oxford: Oxford University Press.
Google Scholar
Paltridge, B. & Phakiti, A. (2010) (Eds.). Continuum companion to research methods in applied linguistics . London: Continuum.
Sources of examples
Lee, J.-A. (2009). Teachers’ sense of efficacy in teaching English, perceived English proficiency and attitudes toward English language: A case of Korean public elementary teachers . PhD, Ohio State University.
Levine, G. S. (2003). Student and instructor beliefs and attitudes about target language use, first language use and anxiety: Report of a questionnaire study. Modern Language Journal , 87(3), 343–364, doi: 10.1111/1540-4781.00194.
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Lin, H., Chen, T., & Dwyer, F. (2006). Effects of static visuals and computer-generated animations in facilitating Immediate and delayed achievement in the EFL classroom. Foreign Language Annals , 39(2), doi: 203-219.10.1111/j.1944-9720.2006.tb02262.x.
Mills, N. (2011). Teaching assistants’ self-efficacy in teaching literature: Sources, personal assessments, and consequences. Modern Language Journal , 95(1), 61–80. doi: 10.1111/j.1540-4781.2010.01145.x.
Rai, M. K., Loschky, L. C., Harris, R. J., Peck, N. R., & Cook, L. G. (2011). Effects of stress and working memory capacity on foreign language readers’ inferential processing during comprehension. Language Learning , 61(1), 187–218. doi: 10.1111/j.1467-9922.2010.00592.x.
Rose, H. (2010). Kanji learning of Japanese language learners on a year-long study exchange at a Japanese university: An investigation of strategy use, motivation control and self regulation . PhD, University of Sydney.
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Woodrow, L. (2014). Reliability, Validity and Ethics. In: Writing about Quantitative Research in Applied Linguistics. Palgrave Macmillan, London. https://doi.org/10.1057/9780230369955_3
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Methodology
Reliability vs. Validity in Research | Difference, Types and Examples
Published on July 3, 2019 by Fiona Middleton . Revised on June 22, 2023.
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.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 bias and seriously affect your work.
| Reliability | Validity | |
|---|---|---|
| What does it tell you? | The extent to which the results can be reproduced when the research is repeated under the same conditions. | The extent to which the results really measure what they are supposed to measure. |
| How is it assessed? | By checking the consistency of results across time, across different observers, and across parts of the test itself. | By checking how well the results correspond to established theories and other measures of the same concept. |
| How do they relate? | A reliable measurement is not always valid: the results might be , but they’re not necessarily correct. | A valid measurement is generally reliable: if a test produces accurate results, they should be reproducible. |
Table of contents
Understanding reliability vs validity, how are reliability and validity assessed, how to ensure validity and reliability in your research, where to write about reliability and validity in a thesis, other interesting articles.
Reliability and validity are closely related, but they mean different things. A measurement can be reliable without being valid. However, if a measurement is valid, it is usually also reliable.
What is reliability?
Reliability refers to how consistently a method measures something. If the same result can be consistently achieved by using the same methods under the same circumstances, the measurement is considered reliable.
What is validity?
Validity refers to how accurately a method measures what it is intended to measure. If research has high validity, that means it produces results that correspond to real properties, characteristics, and variations in the physical or social world.
High reliability is one indicator that a measurement is valid. If a method is not reliable, it probably isn’t valid.
If the thermometer shows different temperatures each time, even though you have carefully controlled conditions to ensure the sample’s temperature stays the same, the thermometer is probably malfunctioning, and therefore its measurements are not valid.
However, reliability on its own is not enough to ensure validity. Even if a test is reliable, it may not accurately reflect the real situation.
Validity is harder to assess than reliability, but it is even more important. To obtain useful results, the methods you use to collect data must be valid: the research must be measuring what it claims to measure. This ensures that your discussion of the data and the conclusions you draw are also valid.
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Reliability can be estimated by comparing different versions of the same measurement. Validity is harder to assess, but it can be estimated by comparing the results to other relevant data or theory. Methods of estimating reliability and validity are usually split up into different types.
Types of reliability
Different types of reliability can be estimated through various statistical methods.
| Type of reliability | What does it assess? | Example |
|---|---|---|
| The consistency of a measure : do you get the same results when you repeat the measurement? | A group of participants complete a designed to measure personality traits. If they repeat the questionnaire days, weeks or months apart and give the same answers, this indicates high test-retest reliability. | |
| The consistency of a measure : do you get the same results when different people conduct the same measurement? | Based on an assessment criteria checklist, five examiners submit substantially different results for the same student project. This indicates that the assessment checklist has low inter-rater reliability (for example, because the criteria are too subjective). | |
| The consistency of : do you get the same results from different parts of a test that are designed to measure the same thing? | You design a questionnaire to measure self-esteem. If you randomly split the results into two halves, there should be a between the two sets of results. If the two results are very different, this indicates low internal consistency. |
Types of validity
The validity of a measurement can be estimated based on three main types of evidence. Each type can be evaluated through expert judgement or statistical methods.
| Type of validity | What does it assess? | Example |
|---|---|---|
| The adherence of a measure to of the concept being measured. | A self-esteem questionnaire could be assessed by measuring other traits known or assumed to be related to the concept of self-esteem (such as social skills and ). Strong correlation between the scores for self-esteem and associated traits would indicate high construct validity. | |
| The extent to which the measurement of the concept being measured. | A test that aims to measure a class of students’ level of Spanish contains reading, writing and speaking components, but no listening component. Experts agree that listening comprehension is an essential aspect of language ability, so the test lacks content validity for measuring the overall level of ability in Spanish. | |
| The extent to which the result of a measure corresponds to of the same concept. | A is conducted to measure the political opinions of voters in a region. If the results accurately predict the later outcome of an election in that region, this indicates that the survey has high criterion validity. |
To assess the validity of a cause-and-effect relationship, you also need to consider internal validity (the design of the experiment ) and external validity (the generalizability of the results).
The reliability and validity of your results depends on creating a strong research design , choosing appropriate methods and samples, and conducting the research carefully and consistently.
Ensuring validity
If you use scores or ratings to measure variations in something (such as psychological traits, levels of ability or physical properties), it’s important that your results reflect the real variations as accurately as possible. Validity should be considered in the very earliest stages of your research, when you decide how you will collect your data.
- Choose appropriate methods of measurement
Ensure that your method and measurement technique are high quality and targeted to measure exactly what you want to know. They should be thoroughly researched and based on existing knowledge.
For example, to collect data on a personality trait, you could use a standardized questionnaire that is considered reliable and valid. If you develop your own questionnaire, it should be based on established theory or findings of previous studies, and the questions should be carefully and precisely worded.
- Use appropriate sampling methods to select your subjects
To produce valid and generalizable results, clearly define the population you are researching (e.g., people from a specific age range, geographical location, or profession). Ensure that you have enough participants and that they are representative of the population. Failing to do so can lead to sampling bias and selection bias .
Ensuring reliability
Reliability should be considered throughout the data collection process. When you use a tool or technique to collect data, it’s important that the results are precise, stable, and reproducible .
- Apply your methods consistently
Plan your method carefully to make sure you carry out the same steps in the same way for each measurement. This is especially important if multiple researchers are involved.
For example, if you are conducting interviews or observations , clearly define how specific behaviors or responses will be counted, and make sure questions are phrased the same way each time. Failing to do so can lead to errors such as omitted variable bias or information bias .
- Standardize the conditions of your research
When you collect your data, keep the circumstances as consistent as possible to reduce the influence of external factors that might create variation in the results.
For example, in an experimental setup, make sure all participants are given the same information and tested under the same conditions, preferably in a properly randomized setting. Failing to do so can lead to a placebo effect , Hawthorne effect , or other demand characteristics . If participants can guess the aims or objectives of a study, they may attempt to act in more socially desirable ways.
It’s appropriate to discuss reliability and validity in various sections of your thesis or dissertation or research paper . Showing that you have taken them into account in planning your research and interpreting the results makes your work more credible and trustworthy.
| Section | Discuss |
|---|---|
| What have other researchers done to devise and improve methods that are reliable and valid? | |
| How did you plan your research to ensure reliability and validity of the measures used? This includes the chosen sample set and size, sample preparation, external conditions and measuring techniques. | |
| If you calculate reliability and validity, state these values alongside your main results. | |
| This is the moment to talk about how reliable and valid your results actually were. Were they consistent, and did they reflect true values? If not, why not? | |
| If reliability and validity were a big problem for your findings, it might be helpful to mention this here. |
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
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Registration of Amyotrophic Lateral Sclerosis: Validity in the Danish National Patient Registry
- L. Levison , Peter Jepsen , Henning Andersen
- Published in Clinical Epidemiology 1 June 2024
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Validation of discharge diagnosis coding for amyotrophic lateral sclerosis in an italian regional healthcare database, validity of hospital discharge diagnoses for the assessment of the prevalence and incidence of amyotrophic lateral sclerosis., comparison of diagnoses of amyotrophic lateral sclerosis by use of death certificates and hospital discharge data in the danish population, the accuracy of discharge diagnosis coding for amyotrophic lateral sclerosis in a large teaching hospital, epidemiology of amyotrophic lateral sclerosis: an update of recent literature, gold coast diagnostic criteria increase sensitivity in amyotrophic lateral sclerosis, current pathways for epidemiological research in amyotrophic lateral sclerosis, [data validity and coverage in the danish national health registry. a literature review]., the changing picture of amyotrophic lateral sclerosis: lessons from european registers, comparison of the five danish regions regarding demographic characteristics, healthcare utilization, and medication use—a descriptive cross-sectional study, related papers.
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Effects of Plyometric Training Volume on Physical Performance in Youth Basketball Players
Aztarain-Cardiel, Kike 1 ; Garatachea, Nuria 1,2,3,4 ; Pareja-Blanco, Fernando 5
1 Faculty of Health and Sport Sciences, University of Zaragoza, Huesca, Spain;
2 Growth, Exercise, Nutrition and Development (GENUD) Research Group, University of Zaragoza, Zaragoza, Spain;
3 Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBER- Obn), Madrid, Spain;
4 Instituto Agroalimentario de Aragon-IA2-CITA, University of Zaragoza, Zaragoza, Spain; and
5 Physical Performance & Sports Research Center, University of Pablo de Olavide, Sevilla, Spain
Address correspondence to Kike Aztarain-Cardiel, [email protected] .
Aztarain-Cardiel, K, Garatachea, N, and Pareja-Blanco, F. Effects of plyometric training volume on physical performance in youth basketball players. J Strength Cond Res 38(7): 1275–1279, 2024—The aim of this study was to analyze the effects of different plyometric training volumes on jumping, sprinting, and change-of-direction performances in youth basketball players. Thirty-one young male basketball players (age: 15.1 ± 1.8 years) from 4 squads belonging to an elite basketball club were randomly assigned to 1 of 3 groups: (a) low-volume (LV) plyometric training, (b) high-volume (HV) plyometric training, and (c) control group (CG). The subjects followed a plyometric training program twice a week for 6 weeks, differing in the training volume controlled through the number of jumps per session. Both experimental groups performed 8 repetitions in each set of bilateral vertical jumps with free arms, but HV completed twice the training volume of LV (98.7 vs. 49.3 jumps per session). Pretraining and Posttraining measurements included the following: (a) squat jump (SJ); (b) countermovement jump (CMJ); (c) horizontal jump (HJ); (d) straight-line sprint in 20 m (Sprint); and (e) V-Cut change-of-direction test (V-Cut). The LV and HV groups showed similar increases in vertical jump performance. The LV group significantly increased its performance in SJ, CMJ, and HJ ( p < 0.001–0.05), whereas the HV group showed significant improvements only in SJ and CMJ ( p < 0.001). Furthermore, neither group increased their sprint or change of direction performance. Control group did not improve in any performance variable analyzed. Therefore, the LV and HV plyometric training programs produce similar benefits on jump performance variables, but LV is more repetition-efficient than HV in young basketball players. This supports the statement that there is a nonlinear relationship between training volume and performance improvements.
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