Parsimony in model selection: tools for assessing fit propensity

Carl F. Falk and Michael Muthukrishna

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Theories can be represented as statistical models for empirical testing. There is a vast literature on model selection and multimodel inference that focuses on how to assess which statistical model, and therefore which theory, best fits the available data. For example, given some data, one can compare models on various information criterion or other fit statistics. However, what these indices fail to capture is the full range of counterfactuals. That is, some models may fit the given data better not because they represent a more correct theory, but simply because these models have more fit propensity - a tendency to fit a wider range of data, even nonsensical data, better. Current approaches fall short in considering the principle of parsimony (Occam’s Razor), often equating it with the number of model parameters. Here we offer a toolkit for researchers to better study and understand parsimony through the fit propensity of Structural Equation Models. We provide an R package (ockhamSEM) built on the popular lavaan package. To illustrate the importance of evaluating fit propensity, we use ockhamSEM to investigate the factor structure of the Rosenberg Self-Esteem Scale.

Keywords: fit indices; parsimony; model fit; structural equation modeling; formal theory; SEM (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published in Psychological Methods, 2021, 28(1), pp. 123-136. ISSN: 1082-989X

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:110856

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