Abstract and Keywords
General issues involved in (1) building causal theories, (2) translating those theories into a set of mathematical representations, (3) choosing an analytic strategy to estimate parameters in the equations implied by the theory, and (4) choosing the “best” model from a set of competing models are discussed. Theory construction fundamentally relies on six relationship types, including direct effects, indirect effects, moderated effects, reciprocal effects, spurious effects and unanalyzed relationships. Once specified, each type of effect can be represented mathematically, thereby translating a path diagram into a set of (linear) equations. Parameters in the equations are then estimated using either limited information estimation approaches or full information estimation approaches, taking into account measurement properties, population distributions, and matters of robustness. Choices between competing models are based on indices of relative fit with the data and relative fit with competing models, as well as more general theoretical criteria (e.g., parsimony, consistency with related theories).
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