Reliability and Validity

Reliability and Validity

We have seen in Chapter 4 that there are a wide variety of self-report and behavioral measured variables that scientists can use to assess conceptual variables. And we have seen that because changes in conceptual variables are assumed to cause changes in measured variables, the measured variables are used to make inferences about the conceptual variables. But how do we know whether the measures that we have chosen actually assess the conceptual variables they are designed to measure? This chapter discusses techniques for evaluating the relationship between measured and conceptual variables. 

          In some cases, demonstrating the adequacy of a measure is rather straightforward because there is a clear way to check whether it is measuring what it is supposed to. For instance, when a physiological psychologist investigates  perceptions of the brightness or color of a light source, she or he can compare the participants’ judgments with objective measurements of light intensity and wavelength. Similarly, when we ask people to indicate their sex or their current college grade-point average, we can check up on whether their reports are correct. 

           In many cases within behavioral science, however, assessing the effectiveness of a measured variable is more difficult. For instance, a researcher who has created a new Likert scale designed to measure “anxiety” assumes that an individual’s score on this scale will refl ect, at least to some extent, his or her actual level of anxiety. But because the researcher does not know how to measure anxiety in any better way, there is no obvious way to “check” the responses of the individual against any type of factual standard.