Add data origination from 2014

R/ordered-pswc-data.R

@@ -42,4 +42,11 @@
 #' A sequential exploratory diagnostic model using a Pólya-gamma data
 #' augmentation strategy. *British Journal of Mathematical and Statistical Psychology*,
 #' *76*, 513–538. \doi{10.1111/bmsp.12307}
+#'
+#' **Data originated from:**
+#'
+#' - Culpepper, S. A. (2014).
+#' If at first you don’t succeed, try, try again: Applications of sequential
+#' IRT models to cognitive assessments. *Applied Psychological Measurement*,
+#' 38(8), 632–644. \doi{10.1177/0146621614536464}
 "items_ordered_pswc_hw"

README.Rmd

@@ -127,7 +127,7 @@ library(edmdata)
  Student Background Survey Item Responses [@TIMSS:2015:Background].
  - `items_ordered_timss15_background`: N = `r nrow(items_ordered_timss15_background)` subject responses
  to J = `r ncol(items_ordered_timss15_background)` items.
-- Calculus-based probability and statistics course homework problems [@Jimenez:2023:OPGEDM]
+- Calculus-based probability and statistics course homework problems [@Culpepper:2014:SequentialIRT, @Jimenez:2023:OPGEDM]
  - `items_ordered_pswc_hw`: N = `r nrow(items_ordered_pswc_hw)`
  subject responses to J = `r ncol(items_ordered_pswc_hw)` items.
 - Programme for International Student Assessment (PISA) 2012

README.md

@@ -111,8 +111,8 @@ head(items_revised_psvtr)
  - `items_ordered_timss15_background`: N = 9672 subject responses to J
  = 16 items.
 - Calculus-based probability and statistics course homework problems
- (Jimenez et al., 2023)
- - `items_ordered_pswc_hw`: N = 288 subject responses to J = 30 items.
+ Jimenez et al. (2023)
+ - `items_ordered_pswc_hw`: N = 288 subject responses to J = 29 items.
 - Programme for International Student Assessment (PISA) 2012 U.S.
  Student Questionnaire Problem-Solving Vignettes (Culpepper & Balamuta,
  2021).
@@ -209,6 +209,15 @@ the number of attributes for the exploratory DINA model.
 
 </div>
 
+<div id="ref-Culpepper:2014:SequentialIRT" class="csl-entry">
+
+Culpepper, S. A. (2014). If at first you don’t succeed, try, try again:
+Applications of sequential IRT models to cognitive assessments. *Applied
+Psychological Measurement*, *38*(8), 632–644.
+<https://doi.org/10.1177/0146621614536464>
+
+</div>
+
 <div id="ref-Culpepper:2015:BayesianDINA" class="csl-entry">
 
 Culpepper, S. A. (2015). Bayesian estimation of the DINA model with
@@ -228,9 +237,9 @@ responses with binary attributes: Identifiability and estimation.
 
 <div id="ref-Culpepper:2019:EGDM" class="csl-entry">
 
-Culpepper, S. A. (2019b). Estimating the cognitive diagnosis $Q$ matrix
-with expert knowledge: Application to the fraction-subtraction dataset.
-*Psychometrika*, *84*(2), 333–357.
+Culpepper, S. A. (2019b). Estimating the cognitive diagnosis $`Q`$
+matrix with expert knowledge: Application to the fraction-subtraction
+dataset. *Psychometrika*, *84*(2), 333–357.
 <https://doi.org/10.1007/s11336-018-9643-8>
 
 </div>

_pkgdown.yml

@@ -50,7 +50,8 @@ reference:
 - title: Calculus-based probability and statistics homework
  desc: >
  Data from a calculus-based probability and statistics course homework
- assignment.
+ assignment originating from [Culpepper, S.A. (2014)](https://doi.org/10.1177/0146621614536464)
+ and used in [Jimenez, A., Balamuta, J. J., & Culpepper, S. A. (2023)](https://doi.org/10.1111/bmsp.12307).
  contents:
  - items_ordered_pswc_hw
 - title: "Human Connectome Project's Penn Progressive Matrices Fluid Intelligence Assessment"

bibliography.bib

@@ -280,3 +280,16 @@ @article{Jimenez:2023:OPGEDM
  volume = {76},
 }
 
+@article{Culpepper:2014:SequentialIRT,
+ author = {Culpepper, Steven Andrew},
+ url = {https://doi.org/10.1177/0146621614536464},
+ year = {2014},
+ doi = {10.1177/0146621614536464},
+ eprint = {https://doi.org/10.1177/0146621614536464},
+ journal = {Applied Psychological Measurement},
+ number = {8},
+ pages = {632--644},
+ title = {If at First You Don’t Succeed, Try, Try Again: Applications of Sequential IRT Models to Cognitive Assessments},
+ volume = {38},
+}
+