Most measurement assumes effect indicators, so misspecifying causal indicators yields biased estimates.

Evidence

5Good methodologists have always been conscious of the issue of causal direction in concepts and measures: “Nearly all measurement in psychology and the other social sciences assumes effect indicators. Factor analysis, reliability tests, and latent class analysis are examples of techniques that assume effect indicators. However, there are situations in which indicators are more realistically thought of as causes of the latent variable rather than the reverse. Tests for causal versus effect indicators have recently become available (Bollen and Ting 2000), but most empirical research implicitly assumes effect indicators. Incorrectly specifying indicators as causal or effect indicators leads to a misspecified model and holds the potential for inconsistent parameter estimates and misleading conclusions (Bollen and Lennox 1991)” (Bollen 2002, 616–17; see also Blalock 1964, 162–69, who uses the name “cause” indicators). — Gary Goertz, Social Science Concepts, 135

Categorization

• QUESTION: How often do published political-science models actually mis-specify indicator direction, and would re-estimation flip their substantive conclusions?