Both are very important in analysing the appropriateness, meaningfulness and usefulness of a research study. However, here I will focus on the validity of the measurement technique i.
There are 4 main types of validity used when assessing internal validity. Each type views validity from a different perspective and evaluates different relationships between measurements. Face validity- This refers to whether a technique looks as if it should measure the variable it intends to measure. For example, a method where a participant is required to click a button as soon as a stimulus appears and this time is measured appears to have face validity for measuring reaction time.
An example of analysing research for face validity by Hardesty and Bearden can be found here. Concurrent validity- This compares the results from a new measurement technique to those of a more established technique that claims to measure the same variable to see if they are related. Often two measurements will behave in the same way, but are not necessarily measuring the same variable, therefore this kind of validity must be examined thoroughly.
An example and some weakness associated with this type of validity can be found here Shuttleworth, Predictive validity- This is when the results obtained from measuring a construct can be accurately used to predict behaviour.
There are obvious limitations to this as behaviour cannot be fully predicted to great depths, but this validity helps predict basic trends to a certain degree. A meta-analysis by van IJzendoorn examines the predictive validity of the Adult Attachment Interview. Construct validity- This is whether the measurements of a variable in a study behave in exactly the same way as the variable itself.
This involves examining past research regarding different aspects of the same variable. The use of construct validity in psychology is examined by Cronbach and Meehl here. A research study will often have one or more types of these validities but maybe not them all so caution should be taken. For example, using measurements of weight to measure the variable height has concurrent validity as weight generally increases as height increases, however it lacks construct validity as weight fluctuates based on food deprivation whereas height does not.
What are the threats to Internal Validity? Factors that can effect internal validity can come in many forms, and it is important that these are controlled for as much as possible during research to reduce their impact on validity. The term history refers to effects that are not related to the treatment that may result in a change of performance over time.
Instrumental bias refers to a change in the measuring instrument over time which may change the results. This is often evident in behavioural observations where the practice and experience of the experimenter influences their ability to notice certain things and changes their standards.
A main threat to internal validity is testing effects. Often participants can become tired or bored during an experiment, and previous tests may influence their performance. This is often counterbalanced in experimental studies so that participants receive the tasks in a different order to reduce their impact on validity. If the results of a study are not deemed to be valid then they are meaningless to our study.
If it does not measure what we want it to measure then the results cannot be used to answer the research question, which is the main aim of the study. These results cannot then be used to generalise any findings and become a waste of time and effort. It is important to remember that just because a study is valid in one instance it does not mean that it is valid for measuring something else.
Validity is harder to assess than reliability, but it is even more important. To obtain useful results, the methods you use to collect your 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. 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.
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. 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.
Validity should be considered in the very earliest stages of your research, when you decide how you will collect your data. 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. To produce valid generalizable results, clearly define the population you are researching e.
Ensure that you have enough participants and that they are representative of the population. Reliability should be considered throughout the data collection process. 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 behaviours or responses will be counted, and make sure questions are phrased the same way each time. 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.
Showing that you have taken them into account in planning your research and interpreting the results makes your work more credible and trustworthy. Have a language expert improve your writing. Check your paper for plagiarism in 10 minutes. Do the check.
Generate your APA citations for free! APA Citation Generator. Reliability vs validity 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? Validity can be demonstrated by showing a clear relationship between the test and what it is meant to measure. Ever wonder what your personality type means? Sign up to find out more in our Healthy Mind newsletter.
Standards for talking and thinking about validity. Psychol Methods. Cizek GJ. Defining and distinguishing validity: Interpretations of score meaning and justifications of test use.
Psychological Testing in the Service of Disability Determination. Washington, DC; Lin WL. Criterion validity. In: Michalos AC, ed. Springer, Dordrecht; Concurrent validity. Predictive validity.
In: Michalos AC, eds. Ginty AT. Construct validity. Encyclopedia of Behavioral Medicine. Springer, New York, NY; Johnson E. Face validity. In: Volkmar FR, ed. Encyclopedia of Autism Spectrum Disorders. Evaluation of methods used for estimating content validity. Res Social Adm Pharm. Your Privacy Rights. To change or withdraw your consent choices for VerywellMind. At any time, you can update your settings through the "EU Privacy" link at the bottom of any page.
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I Accept Show Purposes. Table of Contents View All. Table of Contents. Content Validity. Criterion-Related Validity. Construct Validity. Face Validity.
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