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What is validity and why is it important in research?

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❶However, just because a measure is reliable, it is not necessarily valid eg. On this basis it is concluded that there is no relationship between the two.

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As such, "scientific or statistical validity" is not a deductive claim that is necessarily truth preserving, but is an inductive claim that remains true or false in an undecided manner. This is why "scientific or statistical validity" is a claim that is qualified as being either strong or weak in its nature, it is never necessarily nor certainly true.

This has the effect of making claims of "scientific or statistical validity" open to interpretation as to what, in fact, the facts of the matter mean. Validity is important because it can help determine what types of tests to use, and help to make sure researchers are using methods that are not only ethical, and cost-effective, but also a method that truly measures the idea or construct in question.

Validity [3] of an assessment is the degree to which it measures what it is supposed to measure. This is not the same as reliability , which is the extent to which a measurement gives results that are very consistent. Within validity, the measurement does not always have to be similar, as it does in reliability.

However, just because a measure is reliable, it is not necessarily valid eg. A scale that is 5 pounds off is reliable but not valid. A test cannot be valid unless it is reliable. Validity is also dependent on the measurement measuring what it was designed to measure, and not something else instead.

There are many different types of validity. Construct validity refers to the extent to which operationalizations of a construct e. It subsumes all other types of validity. For example, the extent to which a test measures intelligence is a question of construct validity. A measure of intelligence presumes, among other things, that the measure is associated with things it should be associated with convergent validity , not associated with things it should not be associated with discriminant validity.

Construct validity evidence involves the empirical and theoretical support for the interpretation of the construct. Such lines of evidence include statistical analyses of the internal structure of the test including the relationships between responses to different test items. They also include relationships between the test and measures of other constructs. As currently understood, construct validity is not distinct from the support for the substantive theory of the construct that the test is designed to measure.

As such, experiments designed to reveal aspects of the causal role of the construct also contribute to construct validity evidence. For example, does an IQ questionnaire have items covering all areas of intelligence discussed in the scientific literature?

Content validity evidence involves the degree to which the content of the test matches a content domain associated with the construct. For example, a test of the ability to add two numbers should include a range of combinations of digits. A test with only one-digit numbers, or only even numbers, would not have good coverage of the content domain.

Content related evidence typically involves a subject matter expert SME evaluating test items against the test specifications. Before going to final administration of questionnaires, the researcher should consult the validity of items against each of the constructs or variables and accordingly modify measurement instruments on the basis of SME's opinion. Items are chosen so that they comply with the test specification which is drawn up through a thorough examination of the subject domain.

The experts will be able to review the items and comment on whether the items cover a representative sample of the behaviour domain. Face validity is an estimate of whether a test appears to measure a certain criterion; it does not guarantee that the test actually measures phenomena in that domain. Measures may have high validity, but when the test does not appear to be measuring what it is, it has low face validity. Indeed, when a test is subject to faking malingering , low face validity might make the test more valid.

Considering one may get more honest answers with lower face validity, it is sometimes important to make it appear as though there is low face validity whilst administering the measures. Face validity is very closely related to content validity. While content validity depends on a theoretical basis for assuming if a test is assessing all domains of a certain criterion e.

To answer this you have to know, what different kinds of arithmetic skills mathematical skills include face validity relates to whether a test appears to be a good measure or not.

This judgment is made on the "face" of the test, thus it can also be judged by the amateur. Face validity is a starting point, but should never be assumed to be probably valid for any given purpose, as the "experts" have been wrong before—the Malleus Malificarum Hammer of Witches had no support for its conclusions other than the self-imagined competence of two "experts" in "witchcraft detection," yet it was used as a "test" to condemn and burn at the stake tens of thousands men and women as "witches.

Criterion validity evidence involves the correlation between the test and a criterion variable or variables taken as representative of the construct. In other words, it compares the test with other measures or outcomes the criteria already held to be valid. For example, employee selection tests are often validated against measures of job performance the criterion , and IQ tests are often validated against measures of academic performance the criterion. If the test data and criterion data are collected at the same time, this is referred to as concurrent validity evidence.

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.

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. It is important to ensure that validity and reliability do not get confused.

Reliability is the consistency of results when the experiment is replicated under the same conditions, which is very different to validity. These two evaluations of research studies are independent factors, therefore a study can be reliable without being valid, and vice versa, as demonstrated here this resource also provides more information on types of validity and threats. However, a good study will be both reliable and valid. So to conclude, validity is very important in a research study to ensure that our results can be used effectively, and variables that may threaten validity should be controlled as much as possible.

Validity is possibly the most important aspect of research and if anything is to be achieved it should be relibiltiy and validity or findings are in sense worthless. For instance, if we are testing a new educational program, we have an idea of what it would look like ideally. Similarly, on the effect side, we have an idea of what we are ideally trying to affect and measure the effect construct. But each of these, the cause and the effect, has to be translated into real things, into a program or treatment and a measure or observational method.

We use the term operationalization to describe the act of translating a construct into its manifestation. In effect, we take our idea and describe it as a series of operations or procedures.

Now, instead of it only being an idea in our minds, it becomes a public entity that anyone can look at and examine for themselves. It is one thing, for instance, for you to say that you would like to measure self-esteem a construct.

But when you show a ten-item paper-and-pencil self-esteem measure that you developed for that purpose, others can look at it and understand more clearly what you intend by the term self-esteem. Now, back to explaining the four validity types. They build on one another, with two of them conclusion and internal referring to the land of observation on the bottom of the figure, one of them construct emphasizing the linkages between the bottom and the top, and the last external being primarily concerned about the range of our theory on the top.

Assume that we took these two constructs, the cause construct the WWW site and the effect understanding , and operationalized them -- turned them into realities by constructing the WWW site and a measure of knowledge of the course material. Here are the four validity types and the question each addresses:. In this study, is there a relationship between the two variables?

In the context of the example we're considering, the question might be worded: There are several conclusions or inferences we might draw to answer such a question.

We could, for example, conclude that there is a relationship. We might conclude that there is a positive relationship. We might infer that there is no relationship. We can assess the conclusion validity of each of these conclusions or inferences. Assuming that there is a relationship in this study, is the relationship a causal one? Just because we find that use of the WWW site and knowledge are correlated, we can't necessarily assume that WWW site use causes the knowledge.

Both could, for example, be caused by the same factor. For instance, it may be that wealthier students who have greater resources would be more likely to use have access to a WWW site and would excel on objective tests.

When we want to make a claim that our program or treatment caused the outcomes in our study, we can consider the internal validity of our causal claim. Assuming that there is a causal relationship in this study , can we claim that the program reflected well our construct of the program and that our measure reflected well our idea of the construct of the measure?

Ensuring the Validity of Research

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Internal validity and reliability are at the core of any experimental design. External validity is the process of examining the results and questioning whether there are any other possible causal relationships.

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Internal validity - the instruments or procedures used in the research measured what they were supposed to measure. Example: As part of a stress experiment, people are shown photos of war atrocities. Example: As part of a stress experiment, people are shown photos of war atrocities.

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Validity: the best available approximation to the truth of a given proposition, inference, or conclusion. The first thing we have to ask is: "validity of what?" When we think about validity in research, most of us think about research components. "Any research can be affected by different kinds of factors which, while extraneous to the concerns of the research, can invalidate the findings" (Seliger & Shohamy , 95). Controlling all possible factors that threaten the research's validity is a primary responsibility of every good researcher.

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Internal consistency reliability is a measure of reliability used to evaluate the degree to which different test items that probe the same construct produce similar results. Average inter-item correlation is a subtype of internal consistency reliability. Research validity in surveys relates to the extent at which the survey measures right elements that need to be measured. In simple terms, validity refers to how well an instrument as measures what it is intended to measure.