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"Rock the MIC: The Matrix of Implied Causation for Design and Model Checking"

2/13/2020

 
Advances in Methods and Practices in Psychological Science has accepted an article by Associate Professor Drew Bailey (picture right) and colleague Timothy Brick (Pennsylvania State University): "Rock the MIC: The Matrix of Implied Causation for Design and Model Checking."

Bailey studies mathematical development, individual differences, and longitudinal methods. Brick is interested in the way that humans interact with their environment and with each other, the way those interactions change and are changed by internal states, and how an understanding of those processes can help nudge people towards thriving.

Abstract

Path modeling and the extended Structural Equation Modeling (SEM) framework for statistical analysis is in increasingly common use in modern behavioral science. Path models such as SEMs provide a flexible means of defining complex models in a way that can be easily visualized, specified, and fit to data. 
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​While causality cannot be determined simply by fitting a path model, researchers often use these models as a representation of an underlying causal process model. Indeed, causal implications are a vital characteristic of the model’s explanatory value, but these implications are rarely examined directly. When models are hypothesized to be causal, they can be differentiated from one another by examining their causal implications as defined by a combination of model assumptions, data, and estimation procedure. However, the implied causal relationships may not be immediately obvious to researchers, both because of norms against making causal inferences on the basis of non-experimental data in psychology, and because intricate or long-chain causal structures (as in longitudinal panel model designs) can make the necessary mental arithmetic difficult. We present Matrices of Implied Causation (MICs), a tool for easily understanding and reporting a model’s implications for the causal influence of one variable on another, and illustrate their use in model checking and design with examples from the literature. We argue that MICs should become a routine element of interpretation when examining models with complex causal implications, and that they may provide an additional tool for differentiating among models with otherwise similar fit.

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