k_model_fit

Model fit indices extracted from k-folds

Provides functions to identify plausible and replicable factor
structures for a set of variables via k-fold cross validation. The process
combines the exploratory and confirmatory factor analytic approach to scale
development (Flora & Flake, 2017) <doi:10.1037/cbs0000069> with a cross validation
technique that maximizes the available data (Hastie, Tibshirani, & Friedman, 2009)
<isbn:978-0-387-21606-5>. Also available are functions to determine k by drawing
on power analytic techniques for covariance structures (MacCallum, Browne, &
Sugawara, 1996) <doi:10.1037/1082-989X.1.2.130>, generate model syntax, and
summarize results in a report.

Kyle Nickodem

K-Fold Cross Validation for Factor Analysis

k_model_fit function

<dl><dt>models</dt>
<dd>an object returned from <code>kfa</code></dd>
<dt>index</dt>
<dd>character; one or more fit indices to summarize in the report. Use <code>index_available</code> to see choices.
Chi-square value and degrees of freedom are always reported. Default is CFI and RMSEA (naive, scaled, or robust version depends on estimator used in <code>models</code>).</dd>
<dt>by.fold</dt>
<dd>Should each element in the returned lists be a fold (default) or a factor model?</dd></dl>

Arguments

Extract model fit — k_model_fit

<dl>

<dt>models</dt>
<dd>an object returned from <code>kfa</code></dd>


<dt>index</dt>
<dd>character; one or more fit indices to summarize in the report. Use <code>index_available</code> to see choices.
Chi-square value and degrees of freedom are always reported. Default is CFI and RMSEA (naive, scaled, or robust version depends on estimator used in <code>models</code>).</dd>


<dt>by.fold</dt>
<dd>Should each element in the returned lists be a fold (default) or a factor model?</dd>

</dl>

k_model_fit: Extract model fit

Description

Usage

Value

Arguments

Examples