  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.
