How to Validate a Research Instrument

In the field of Psychology, research is a necessary component of determining whether a given treatment is effective and if our current understanding of human behavior is accurate. Therefore, the instruments used to evaluate research data must be valid and precise. If they are not, the information collected from a study is likely to be biased or factually flawed, doing more harm than good.

Protect construct validity. A construct is the behavior or outcome a researcher seeks to measure within a study, often revealed by the independent variable. Therefore, it is important to operationalize or define the construct precisely. For example, if you are studying depression but only measure the number of times a person cries, your construct is not valid and your research will likely be skewed.

Protect internal validity. Internal validity refers to how well your experiment is free of outside influence that could taint its results. Thus, a research instrument that takes students’ grades into account but not their developmental age is not a valid determinant of intelligence. Because the grades on a test will vary within different age brackets, a valid instrument should control for differences and isolate true scores.

Protect external validity. External validity refers to how well your study reflects the real world and not just an artificial situation. An instrument may work perfectly with a group of white male college students but this does not mean its results are generalizable to children, blue-collar adults or those of varied gender and ethnicity. For an instrument to have high external validity, it must be applicable to a diverse group of people and a wide array of natural environments.

Protect conclusion validity. When the study is complete, researchers may still invalidate their data by making a conclusion error. Essentially, there are two types to guard against. A Type I error is concluding there is no relationship between experimental variables when, in fact, there is. Conversely, a Type II error is claiming a relationship exists when the correlation is merely the result of flawed data.