| Abstract: | One common application of structural equation modeling (SEM) involves expressing and empirically investigating causal explanations. Nonetheless, several aspects of causal explanation that have an impact on behavioral science methodology remain poorly understood. It remains unclear whether applications of SEM should attempt to provide complete explanations or partial explanations. Moreover, it remains unclear what sorts of things researchers can best take as causes and effects. Finally, the meaning of causal assertions itself remains poorly understood. Attempting to clarify the use of structural equations as causal explanations by addressing these issues has implications for behavioral science methodology because applications of SEM typically remain vague about causation and thus about their substantive conclusions. Research aimed at clarifying these issues can lead to a sharper and more refined use of SEM for causal explanation, and by extension, clarify behavioral science methodology more generally. |
