data.ml

data.ml1

data.ml2

Datasets for conducting multilevel IRT analysis. This dataset is used
in the examples of the function <code>mcmc.2pno.ml</code>.

Supplementary functions for item response models aiming
to complement existing R packages. The functionality includes among others
multidimensional compensatory and noncompensatory IRT models
(Reckase, 2009, <doi:10.1007/978-0-387-89976-3>),
MCMC for hierarchical IRT models and testlet models
(Fox, 2010, <doi:10.1007/978-1-4419-0742-4>),
NOHARM (McDonald, 1982, <doi:10.1177/014662168200600402>),
Rasch copula model (Braeken, 2011, <doi:10.1007/s11336-010-9190-4>;
Schroeders, Robitzsch & Schipolowski, 2014, <doi:10.1111/jedm.12054>),
faceted and hierarchical rater models (DeCarlo, Kim & Johnson, 2011,
<doi:10.1111/j.1745-3984.2011.00143.x>),
ordinal IRT model (ISOP; Scheiblechner, 1995, <doi:10.1007/BF02301417>),
DETECT statistic (Stout, Habing, Douglas & Kim, 1996,
<doi:10.1177/014662169602000403>), local structural equation modeling
(LSEM; Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016,
<doi:10.1080/00273171.2016.1142856>).

Alexander Robitzsch

sirt

Supplementary Item Response Theory Models

data.ml function

<ul>
<li><code>data.ml1</code>
A data frame with 2000 student observations in 100 classes on 17 variables.
 The first variable <code>group</code> contains the class identifier.
 The remaining 16 variables are dichotomous test items.
<code>'data.frame': 2000 obs. of 17 variables:</code> 
<code> $ group: num 1001 1001 1001 1001 1001 ...</code> 
<code> $ X1 : num 1 1 1 1 1 1 1 1 1 1 ...</code> 
<code> $ X2 : num 1 1 1 0 1 1 1 1 1 1 ...</code> 
<code> $ X3 : num 0 1 1 0 1 0 1 0 1 0 ...</code> 
<code> $ X4 : num 1 1 1 0 0 1 1 1 1 1 ...</code> 
<code> $ X5 : num 0 0 0 1 1 1 0 0 1 1 ...</code> 
<code>[...]</code> 
<code> $ X16 : num 0 0 1 0 0 0 1 0 0 0 ...</code> </li>
<li><code>data.ml2</code>
A data frame with 2000 student observations in 100 classes on 6 variables.
 The first variable <code>group</code> contains the class identifier.
 The remaining 5 variables are polytomous test items.
<code>'data.frame': 2000 obs. of 6 variables:</code> 
<code> $ group: num 1 1 1 1 1 1 1 1 1 1 ...</code> 
<code> $ X1 : num 2 3 4 3 3 3 1 4 4 3 ...</code> 
<code> $ X2 : num 2 2 4 3 3 2 2 3 4 3 ...</code> 
<code> $ X3 : num 3 4 5 4 2 3 3 4 4 2 ...</code> 
<code> $ X4 : num 2 3 3 2 1 3 1 4 4 3 ...</code> 
<code> $ X5 : num 2 3 3 2 3 3 1 3 2 2 ...</code> </li>
</ul>

Format

Multilevel Datasets — data.ml

<ul>
<li><code>data.ml1</code>
A data frame with 2000 student observations in 100 classes on 17 variables.
 The first variable <code>group</code> contains the class identifier.
 The remaining 16 variables are dichotomous test items.
<code>'data.frame': 2000 obs. of 17 variables:</code> 
<code> $ group: num 1001 1001 1001 1001 1001 ...</code> 
<code> $ X1 : num 1 1 1 1 1 1 1 1 1 1 ...</code> 
<code> $ X2 : num 1 1 1 0 1 1 1 1 1 1 ...</code> 
<code> $ X3 : num 0 1 1 0 1 0 1 0 1 0 ...</code> 
<code> $ X4 : num 1 1 1 0 0 1 1 1 1 1 ...</code> 
<code> $ X5 : num 0 0 0 1 1 1 0 0 1 1 ...</code> 
<code>[...]</code> 
<code> $ X16 : num 0 0 1 0 0 0 1 0 0 0 ...</code> </li>
<li><code>data.ml2</code>
A data frame with 2000 student observations in 100 classes on 6 variables.
 The first variable <code>group</code> contains the class identifier.
 The remaining 5 variables are polytomous test items.
<code>'data.frame': 2000 obs. of 6 variables:</code> 
<code> $ group: num 1 1 1 1 1 1 1 1 1 1 ...</code> 
<code> $ X1 : num 2 3 4 3 3 3 1 4 4 3 ...</code> 
<code> $ X2 : num 2 2 4 3 3 2 2 3 4 3 ...</code> 
<code> $ X3 : num 3 4 5 4 2 3 3 4 4 2 ...</code> 
<code> $ X4 : num 2 3 3 2 1 3 1 4 4 3 ...</code> 
<code> $ X5 : num 2 3 3 2 3 3 1 3 2 2 ...</code> </li>
</ul>

data.ml: Multilevel Datasets

Description

Usage

Arguments

Format