4.1 Lecture

How do you know if you have measured the putative hypothetical construct that you intend to measure?

  • The methods introduced in this lecture (namely, latent variables, factor analysis, and reliability analysis) can shed empirical light on this issue.

In the social and behavioral sciences we’re often forced to measure key concepts indirectly. For example, we have no way of directly quantifying a person’s current level of depression, or their innate motivation, or their risk-aversion, or any of the other myriad psychological features that comprise the human mental state. In truth, we cannot really measure these hypothetical constructs at all, we must estimate latent representations thereof (though, psychometricians still use the language of physical measurement to describe this process).

Furthermore, we can rarely estimate an adequate representation with only a single observed variable (e.g., question on a survey, score on a test, reading from a sensor). We generally need several observed variables to reliably represent a single hypothetical construct. For example, we cannot accurately determine someone’s IQ or socio-economic status based on their response to a single question; we need several questions that each tap into slightly different aspects of IQ or SES.

Given multiple items measuring the same construct, we can use the methods discussed in this lecture (i.e., factor analysis and reliability analysis) to evaluate the quality of our measurement (i.e., how well we have estimated the underlying hypothetical construct). If we do well enough in this estimation task, we will be able to combine these estimated latent variables with the path analysis methods discussed in previous two weeks to produce the full structural equation models that we will cover at the end of this course.

4.1.1 Recording

Notes:

  • This week, we’ll be re-using Caspar van Lissa’s old slides and lecture recording. So, you’ll see Caspar in the following video, and the slides will have a notably different flavor than our usual materials.

  • Don’t be confused by any mention of “model fit” in the lecture. We haven’t covered model fit yet, but we will do so next week.

4.1.2 Slides

You can download the lecture slides here.