data_mod_cat

A moderation model with
a categorical moderator.

datasets

Computes indirect effects, conditional effects, and conditional
indirect effects in a structural equation model or path model after model
fitting, with no need to define any user parameters or label any paths in
the model syntax, using the approach presented in Cheung and Cheung
(2024) <doi:10.3758/s13428-023-02224-z>. Can also form bootstrap
confidence intervals by doing bootstrapping only once and reusing the
bootstrap estimates in all subsequent computations. Supports bootstrap
confidence intervals for standardized (partially or completely) indirect
effects, conditional effects, and conditional indirect effects as described
in Cheung (2009) <doi:10.3758/BRM.41.2.425> and Cheung, Cheung, Lau, Hui,
and Vong (2022) <doi:10.1037/hea0001188>. Model fitting can be done by
structural equation modeling using lavaan() or regression using lm().

Shu Fai Cheung

manymome

Mediation, Moderation and Moderated-Mediation After Model
Fitting

Sing-Hang Cheung

data_mod_cat function

A data frame with 300 rows
and 5 variables:<dl>
<dt>x</dt>
<dd>Predictor. Numeric.</dd><dt>w</dt>
<dd>Moderator. String. Values: "group1", "group2", "group3"</dd><dt>y</dt>
<dd>Outcome variable. Numeric.</dd><dt>c1</dt>
<dd>Control variable. Numeric.</dd><dt>c2</dt>
<dd>Control variable. Numeric.</dd>
</dl>

Format

Sample Dataset: Moderation
with One Categorical Moderator — data_mod_cat

A data frame with 300 rows
and 5 variables:<dl>
<dt>x</dt>
<dd>Predictor. Numeric.</dd>

<dt>w</dt>
<dd>Moderator. String. Values: "group1", "group2", "group3"</dd>

<dt>y</dt>
<dd>Outcome variable. Numeric.</dd>

<dt>c1</dt>
<dd>Control variable. Numeric.</dd>

<dt>c2</dt>
<dd>Control variable. Numeric.</dd>


</dl>

data_mod_cat: Sample Dataset: Moderation with One Categorical Moderator

Description

Usage

Arguments

Format

Examples