ipsatize

Rescore each circumplex item using deviation scoring across variables. In
other words, subtract each observation's mean response from each response.
This effectively removes the presence of a general factor, which can make
certain circumplex fit analyses more powerful.

Circumplex models, which organize constructs in a circle around two
underlying dimensions, are popular for studying interpersonal functioning,
mood/affect, and vocational preferences/environments. This package provides
tools for analyzing and visualizing circular data, including scoring
functions for relevant instruments and a generalization of the bootstrapped
structural summary method from Zimmermann & Wright (2017)
<doi:10.1177/1073191115621795> and functions for creating publication-ready
tables and figures from the results.

Jeffrey Girard

circumplex

Analysis and Visualization of Circular Data

Johannes Zimmermann

Aidan Wright

ipsatize function

<dl><dt>.data</dt>
<dd>Required. A data frame containing at least circumplex scales.</dd>
<dt>items</dt>
<dd>Required. The variable names or column numbers for the
variables in <code>.data</code> that contain circumplex items to be ipsatized.</dd>
<dt>na.rm</dt>
<dd>Optional. A logical that determines whether missing values
should be ignored during the calculation of the mean during ipsatization
(default = TRUE).</dd>
<dt>overwrite</dt>
<dd>Optional. A logical that determines whether the variables
specified in <code>items</code> should be overwritten with ipsatized versions
or alternatively preserved and new variables ending with "_i" should be
added to the data frame (default = FALSE).</dd></dl>

Arguments

Ipsatize circumplex items using deviation scoring across variables — ipsatize

<dl>

<dt>.data</dt>
<dd>Required. A data frame containing at least circumplex scales.</dd>


<dt>items</dt>
<dd>Required. The variable names or column numbers for the
variables in <code>.data</code> that contain circumplex items to be ipsatized.</dd>


<dt>na.rm</dt>
<dd>Optional. A logical that determines whether missing values
should be ignored during the calculation of the mean during ipsatization
(default = TRUE).</dd>


<dt>overwrite</dt>
<dd>Optional. A logical that determines whether the variables
specified in <code>items</code> should be overwritten with ipsatized versions
or alternatively preserved and new variables ending with "_i" should be
added to the data frame (default = FALSE).</dd>

</dl>

ipsatize: Ipsatize circumplex items using deviation scoring across variables

Description

Usage

Value

Arguments

See Also

Examples