Construct Confounding

This threat to construct validity occurs when other constructs mask the effects of the measured construct.

For example, self-esteem is affected by self-confidence and self-worth. The effect of these constructs needs to be incorporated into the research.

Interaction of Different Treatments

This particular threat is where more than one treatment influences the final outcome.

For example, a researcher tests an intensive counseling program as a way of helping smokers give up cigarettes. At the end of the study, the results show that 64% of the subjects successfully gave up.

Sadly, the researcher then finds that some of the subjects also used nicotine patches and gum, or electronic cigarettes. The construct validity is now too low for the results to have any meaning. Only good planning and monitoring of the subjects can prevent this.

Unreliable Scores

Variance in scores is a very easy trap to fall into.

For example, an educational researcher devises an intelligence test that provides excellent results in the UK, and shows high construct validity.

However, when the test is used upon immigrant children, with English as a second language, the scores are lower.

The test measures their language ability rather than intelligence.

Mono-Operation Bias

This threat involves the independent variable, and is a situation where a single manipulation is used to influence a construct.

For example, a researcher may want to find out whether an anti-depression drug works. They divide patients into two groups, one given the drug and a control given a placebo.

The problem with this is that it is limited (e.g. random sampling error), and a solid design would use multi-groups given different doses.

The other option is to conduct a pre-study that calculates the optimum dose, an equally acceptable way to preserve construct validity.

Mono-Method Bias

This threat to construct validity involves the dependent variable, and occurs when only a single method of measurement is used.

For example, in an experiment to measure self-esteem, the researcher uses a single method to determine the level of that construct, but then discovers that it actually measures self-confidence.

Using a variety of methods, such as questionnaires, self-rating, physiological tests, and observation minimizes the chances of this particular threat affecting construct validity.

Don't Panic

These are just a few of the threats to construct validity, and most experts agree that there are at least 24 different types. These are the main ones, and good experimental design, as well as seeking feedback from experts during the planning stage, will see you avoid them.

For the ‘hard' scientists, who think that social and behavioral science students have an easy time, you could not be more wrong!

Convergent Validity

Convergent validity and discriminant validity are commonly regarded as subsets of construct validity.

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Convergent validity tests that constructs that are expected to be related are, in fact, related.

Discriminant validity (or divergent validity) tests that constructs that should have no relationship do, in fact, not have any relationship.

If a research program is shown to possess both of these types of validity, it can also be regarded as having excellent construct validity.

In many areas of research, mainly the social sciences, psychology, education and medicine, researchers need to analyze non-quantitative and abstract concepts, such as level of pain, anxiety or educational achievement.

A researcher needs to define exactly what trait they are measuring if they are to maintain good construct validity.

Constructs very rarely exist independently, because the human brain is not a simple machine and is made up of an interlinked web of emotions, reasoning and senses. Any research program must untangle these complex interactions and establish that you are only testing the desired construct.

This is practically impossible to prove beyond doubt, so researchers gather enough evidence to defend their findings from criticism.

The basic difference between convergent and discriminant validity is that convergent validity tests whether constructs that should be related, are related. Discriminant validity tests whether believed unrelated constructs are, in fact, unrelated.

Imagine that a researcher wants to measure self-esteem, but she also knows that the other four constructs are related to self-esteem and have some overlap. The ultimate goal is to maker an attempt to isolate self-esteem.

In this example, convergent validity would test that the four other constructs are, in fact, related to self-esteem in the study. The researcher would also check that self-worth and confidence, and social skills and self-appraisal, are also related.

Discriminant validity would ensure that, in the study, the non-overlapping factors do not overlap. For example, self esteem and intelligence should not relate (too much) in most research projects.

As you can see, separating and isolating constructs is difficult, and it is one of the factors that makes social science extremely difficult.

Social science rarely produces research that gives a yes or no answer, and the process of gathering knowledge is slow and steady, building on top of what is already known.