BARTLETT

<a href="https://lifecycle.r-lib.org/articles/stages.html#superseded"><img src="figures/lifecycle-superseded.svg?package=EFAtools&version=1.0.0" alt="[Superseded]"></a>
<code>BARTLETT()</code> has been superseded by <code>efa_bartlett()</code>, which is the recommended
interface going forward. It remains available and unchanged so existing code
keeps working.

Provides a complete workflow for exploratory factor analysis
(EFA). It covers data screening and factorability checks, a suite of
factor retention criteria for choosing the number of factors, and factor
extraction by principal axis factoring, maximum likelihood, unweighted
least squares, or diagonally weighted least squares from Pearson,
Spearman, Kendall, polychoric, tetrachoric, or two-stage
full-information maximum likelihood correlations. A built-in rotation
engine offers a range of orthogonal and oblique rotations, and standard
errors for loadings and related quantities can be obtained by analytic,
robust, or bootstrap methods. Further tools support model averaging
across analytic choices, multigroup EFA with factor congruence, EFA on
multiply imputed data, Schmid-Leiman transformation, reliability
coefficients (including McDonald's omegas), factor score estimation,
data simulation, and power analysis. Computationally intensive
procedures are implemented in 'C++' for speed.

Markus Steiner

EFAtools

Fast and Flexible Implementations of Exploratory Factor Analysis
Tools

BARTLETT function

<dl><dt>x</dt>
<dd>data.frame or matrix. Dataframe or matrix of raw data or matrix with
correlations.</dd>
<dt>N</dt>
<dd>numeric. The number of observations. Needs only be specified if a
correlation matrix is used.</dd>
<dt>use</dt>
<dd>character. Passed to <code><a href="/link/stats%3A%3Acor()?package=EFAtools&version=1.0.0" data-mini-rdoc="EFAtools::stats::cor()">stats::cor()</a></code> if raw data
is given as input. Default is "pairwise.complete.obs".</dd>
<dt>cor_method</dt>
<dd>character. Correlation computed from raw data: <code>"pearson"</code>,
<code>"spearman"</code>, or <code>"kendall"</code> (passed to <code><a href="/link/stats%3A%3Acor()?package=EFAtools&version=1.0.0" data-mini-rdoc="EFAtools::stats::cor()">stats::cor()</a></code>), or <code>"poly"</code> /
<code>"tetra"</code> for polychoric / tetrachoric correlations of ordinal / binary data
(a two-step estimator with no empty-cell continuity correction).
Default is "pearson".</dd></dl>

Arguments

<a href='https://lifecycle.r-lib.org/articles/stages.html#superseded'><img src='figures/lifecycle-superseded.svg' alt='[Superseded]' /></a>
<code>BARTLETT()</code> has been superseded by <code>efa_bartlett()</code>, which is the recommended
interface going forward. It remains available and unchanged so existing code
keeps working.

Bartlett's test of sphericity — BARTLETT

<dl>

<dt>x</dt>
<dd>data.frame or matrix. Dataframe or matrix of raw data or matrix with
correlations.</dd>


<dt>N</dt>
<dd>numeric. The number of observations. Needs only be specified if a
correlation matrix is used.</dd>


<dt>use</dt>
<dd>character. Passed to <code><a href='https://rdrr.io/r/stats/cor.html'>stats::cor()</a></code> if raw data
is given as input. Default is "pairwise.complete.obs".</dd>


<dt>cor_method</dt>
<dd>character. Correlation computed from raw data: <code>"pearson"</code>,
<code>"spearman"</code>, or <code>"kendall"</code> (passed to <code><a href='https://rdrr.io/r/stats/cor.html'>stats::cor()</a></code>), or <code>"poly"</code> /
<code>"tetra"</code> for polychoric / tetrachoric correlations of ordinal / binary data
(a two-step estimator with no empty-cell continuity correction).
Default is "pearson".</dd>

</dl>

BARTLETT: Bartlett's test of sphericity

Description

Usage

Value

Arguments

See Also