KYPSitemsim

A simulated dataset that replicates the dataset item-level
data from KYPS.

datasets

Utilizes the 'lme4' and 'optimx' packages (previously the optim()
function from 'stats') to estimate (generalized) linear mixed models (GLMM)
with factor structures using a profile likelihood approach, as outlined in
Jeon and Rabe-Hesketh (2012) <doi:10.3102/1076998611417628> and
Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>.
Factor analysis and item response models can be extended to allow
for an arbitrary number of nested and crossed random effects,
making it useful for multilevel and cross-classified models.

Nicholas Rockwood

PLmixed

Estimate (Generalized) Linear Mixed Models with Factor
Structures

Minjeong Jeon

KYPSitemsim function

A data frame with 66947 rows and 6 variables:<dl>
 <dt>id</dt>
<dd>Student ID</dd> <dt>time</dt>
<dd>Time Identifier</dd> <dt>item</dt>
<dd>Item ID</dd> <dt>mid</dt>
<dd>Middle School ID</dd> <dt>hid</dt>
<dd>High School ID</dd> <dt>response</dt>
<dd>Item Response</dd>
</dl>

Format

Simulated KYPS item-level dataset. — KYPSitemsim

A data frame with 66947 rows and 6 variables:<dl>
 <dt>id</dt>
<dd>Student ID</dd>

 <dt>time</dt>
<dd>Time Identifier</dd>

 <dt>item</dt>
<dd>Item ID</dd>

 <dt>mid</dt>
<dd>Middle School ID</dd>

 <dt>hid</dt>
<dd>High School ID</dd>

 <dt>response</dt>
<dd>Item Response</dd>


</dl>

KYPSitemsim: Simulated KYPS item-level dataset.

Description

Usage

Arguments

Format