Small documentation updates

tpronk · Jun 25, 2021 · 011136e · 011136e
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,10 +1,10 @@
 Package: splithalfr
 Title: Estimate Split-Half Reliabilities
-Version: 2.1.0
-Date: 2021-03-11
+Version: 2.1.1
+Date: 2021-06-25
 Author: Thomas Pronk [aut, cre]
 Authors@R: person("Thomas", "Pronk", email = "[email protected]", role = c("aut", "cre"))
-Description: Estimates split-half reliabilities for scoring algorithms of reaction time (RT) tasks and questionnaires. The 'splithalfr' supports researcher-provided scoring algorithms, with six vignettes illustrating how on included datasets. The package provides four splitting methods (first-second, odd-even, permutated, Monte Carlo), the option to stratify splits by task design, a number of reliability coefficients, and the option to sub-sample data.
+Description: Estimates split-half reliabilities for scoring algorithms of cognitive tasks and questionnaires. The 'splithalfr' supports researcher-provided scoring algorithms, with six vignettes illustrating how on included datasets. The package provides four splitting methods (first-second, odd-even, permutated, Monte Carlo), the option to stratify splits by task design, a number of reliability coefficients, and the option to sub-sample data.
 Depends: R (>= 3.6.0)
 License: GPL-3
 Encoding: UTF-8
@@ -15,11 +15,12 @@ Suggests:
     testthat (>= 2.1.0),
     MASS (>= 7.3.51)
 Imports:
-    dplyr (>= 0.8.1),
+    dplyr (>= 1.0.7),
     tibble (>= 2.1.1),
     psych (>= 1.8.12),
     bcaboot (>= 0.2.1),
     rlang (>= 0.4.0)
 RoxygenNote: 7.1.1
 VignetteBuilder: knitr
 URL: https://github.com/tpronk/splithalfr
+BugReports: https://github.com/tpronk/splithalfr/issues
diff --git a/R/data.R b/R/data.R
@@ -1,7 +1,7 @@
 #' Example Implicit Association Task (IAT) Data in JASMIN2 Format
 #'
 #' The JASMIN2 IAT closely followed the original IAT procedure
-#' (\doi{10.1037/0022-3514.74.6.1464}{Greenwald, McGhee, & Schwartz, 1998}),
+#' (\href{https://pubmed.ncbi.nlm.nih.gov/9654756/}{Greenwald, McGhee, & Schwartz, 1998}),
 #' except that target and attribute trials did not alternate.  Upon a correct response, the next trial started.
 #' Upon an incorrect response, the current trial was repeated. The response to each trial was logged.
 #' This particular dataset is from a Ethnicity-Valence IAT, which was administered (and described in detail) in 
@@ -97,7 +97,7 @@
 #' Example 23-item Rutgers Alcohol Problem Inventory (RAPI) data
 #'
 #' The RAPI is a questionnaire which asks how often a participant experienced each of 23 alcohol-related
-#' problems within the last year (\href{http://www.emcdda.europa.eu/html.cfm/index4200EN.html}{White & Labouvie, 1989}).
+#' problems within the last year (\doi{https://doi.org/10.15288/jsa.1989.50.30}{White & Labouvie, 1989}).
 #' The dataset contains one row per participant.
 #' 
 #' The dataset contains the following columns:

diff --git a/R/splithalfr.R b/R/splithalfr.R
@@ -1,20 +1,20 @@
 #' splithalfr: Split-Half Reliabilities
 #'
-#' Estimates split-half reliabilities for scoring algorithms of reaction time (RT) tasks and questionnaires. 
+#' Estimates split-half reliabilities for scoring algorithms of cognitive tasks and questionnaires. 
 #' 
 #' @section Getting started:
 #' We've got six short vignettes to help you get started. You can open a vignette bij running the corresponding code snippets (\code{vignette(...)}) in the R console.
 #' \itemize{
-#'   \item \code{vignette("rapi_sum")} Sum-score for data of the 23-item version of the Rutgers Alcohol Problem Index (\href{https://www.emcdda.europa.eu/html.cfm/index4200EN.html}{White & Labouvie, 1989})
+#'   \item \code{vignette("rapi_sum")} Sum-score for data of the 23-item version of the Rutgers Alcohol Problem Index (\doi{https://doi.org/10.15288/jsa.1989.50.30}{White & Labouvie, 1989})
 #'   \item \code{vignette("vpt_diff_of_means")} Difference of mean RTs for correct responses, after removing RTs below 200 ms and above 520 ms, on Visual Probe Task data (Mogg & Bradley, 1999 <\doi{10.1080/026999399379050}>)
 #'   \item \code{vignette("aat_double_diff_of_medians")} Double difference of medians for correct responses on Approach Avoidance Task data (Heuer, Rinck, & Becker, 2007 <\doi{10.1016/j.brat.2007.08.010}>)
-#'   \item \code{vignette("iat_dscore_ri")} Improved d-score algorithm for data of an Implicit Association Task that requires a correct response in order to continue to the next trial (Greenwald, Nosek, & Banaji, 2003 <\doi{10.1037/0022-3514.85.2.197}>)
+#'   \item \code{vignette("iat_dscore_ri")} Improved d-score algorithm for data of an Implicit Association Task that requires a correct response in order to continue to the next trial (\href{https://pubmed.ncbi.nlm.nih.gov/12916565/}{Greenwald, Nosek, & Banaji, 2003})
 #'   \item \code{vignette("sst_ssrti")} Stop-Signal Reaction Time integration method for data of a Stop Signal Task (\href{http://www.psy.vanderbilt.edu/faculty/logan/Logan(1981).pdf}{Logan, 1981})
 #'   \item \code{vignette("gng_dprime")} D-prime for data of a Go/No Go task (Miller, 1996 <\doi{10.3758/BF03205476}>)
 #' }
 #' 
 #' @section Splitting methods:
-#' The splithalfr supports a variety of methods for splitting your data. We review and assess each  method in the compendium paper, currently in pre-print (Pronk, Molenaar, Wiers, & Murre, 2020 <\doi{10.31234/osf.io/ywste}>). This vignette illustrates how to apply each splitting method via the splithalfr: \code{vignette("splitting_methods")}
+#' The splithalfr supports a variety of methods for splitting your data. We review and assess each  method in the compendium paper (Pronk et al., 2021 <\doi{https://doi.org/10.3758/s13423-021-01948-3}>). This vignette illustrates how to apply each splitting method via the splithalfr: \code{vignette("splitting_methods")}
 #' \itemize{
 #'   \item first-second and odd-even (Green et al., 2016 <\doi{10.3758/s13423-015-0968-3}>; Webb, Shavelson, & Haertel, 1996 <\doi{10.1016/S0169-7161(06)26004-8}>; Williams & Kaufmann, 2012 <\doi{10.1016/j.jesp.2012.03.001}>)
 #'   \item stratified (Green et al., 2016 <\doi{10.3758/s13423-015-0968-3}>)

diff --git a/README.md b/README.md
@@ -1,18 +1,18 @@
 # splithalfr: Split-Half Reliabilities
-Estimates split-half reliabilities for scoring algorithms of reaction time (RT) tasks and questionnaires. 
+Estimates split-half reliabilities for scoring algorithms of cognitive tasks and questionnaires. 
 
 ## Getting started
 We've got six short vignettes to help you get started. You can open a vignette bij running the corresponding code snippets `vignette(...)` in the R console.
 
-* `vignette("rapi_sum")` Sum-score for data of the 23-item version of the Rutgers Alcohol Problem Index ([White & Labouvie, 1989](http://www.emcdda.europa.eu/html.cfm/index4200EN.html))
+* `vignette("rapi_sum")` Sum-score for data of the 23-item version of the Rutgers Alcohol Problem Index ([White & Labouvie, 1989](https://doi.org/10.15288/jsa.1989.50.30))
 * `vignette("vpt_diff_of_means")` Difference of mean RTs for correct responses, after removing RTs below 200 ms and above 520 ms, on Visual Probe Task data ([Mogg & Bradley, 1999](https://doi.org/10.1080/026999399379050))
 * `vignette("aat_double_diff_of_medians")` Double difference of medians for correct responses on Approach Avoidance Task data ([Heuer, Rinck, & Becker, 2007](https://doi.org/10.1016/j.brat.2007.08.010))
-* `vignette("iat_dscore_ri")` Improved d-score algorithm for data of an Implicit Association Task that requires a correct response in order to continue to the next trial ([Greenwald, Nosek, & Banaji, 2003](https://doi.org/10.1037/0022-3514.85.2.197))
+* `vignette("iat_dscore_ri")` Improved d-score algorithm for data of an Implicit Association Task that requires a correct response in order to continue to the next trial ([Greenwald, Nosek, & Banaji, 2003](https://pubmed.ncbi.nlm.nih.gov/12916565/))
 * `vignette("sst_ssrti")` Stop-Signal Reaction Time integration method for data of a Stop Signal Task ([Logan, 1981](http://www.psy.vanderbilt.edu/faculty/logan/Logan(1981).pdf))
 * `vignette("gng_dprime")` D-prime for data of a Go/No Go task ([Miller, 1996](https://doi.org/10.3758/BF03205476))
 
 ## Splitting Methods
-The splithalfr supports a variety of methods for splitting your data. We review and assess each  method in the compendium paper, currently in pre-print ([Pronk, Molenaar, Wiers, & Murre, 2020](https://doi.org/10.31234/osf.io/ywste)). This vignette illustrates how to apply each splitting method via the splithalfr: `vignette("splitting_methods")` 
+The splithalfr supports a variety of methods for splitting your data. We review and assess each  method in the compendium paper ([Pronk et al., 2021](https://doi.org/10.3758/s13423-021-01948-3)). This vignette illustrates how to apply each splitting method via the splithalfr: `vignette("splitting_methods")` 
 * first-second and odd-even ([Green et al., 2016](https://doi.org/10.3758/s13423-015-0968-3); [Webb, Shavelson, & Haertel, 1996](https://doi.org/10.1016/S0169-7161(06)26004-8); [Williams & Kaufmann, 1996](https://doi.org/10.1016/j.jesp.2012.03.001))
 * stratified ([Green et al., 2016](https://doi.org/10.3758/s13423-015-0968-3))
 * permutated/bootstrapped/random sample of split halves ([Kopp, Lange, & Steinke, 2021](https://doi.org/10.1177/1073191119866257), [Parsons, Kruijt, & Fox, 2019](https://doi.org/10.1177/2515245919879695); [Williams & Kaufmann, 1996](https://doi.org/10.1016/j.jesp.2012.03.001))

diff --git a/inst/CITATION b/inst/CITATION
@@ -0,0 +1,16 @@
+citHeader("To cite the splithalfr, please use:")
+citEntry(
+  entry = "Article",
+  author = c(
+    person("Pronk", "Thomas"),
+    person("Molenaar", "Dylan"),
+    person("Wiers", "Reinout"),
+    person("Murre", "Jaap")
+  ),
+  title = "Methods to split cognitive task data for estimating split-half reliability: A comprehensive review and systematic assessment",
+  journal = "Psychonomic Bulletin & Review",
+  doi = "10.3758/s13423-021-01948-3",
+  pages = "1-11",
+  year = "2021",
+  textVersion  = "Pronk, T., Molenaar, D., Wiers, R. W., & Murre, J. (2021). Methods to split cognitive task data for estimating split-half reliability: A comprehensive review and systematic assessment. Psychonomic Bulletin & Review, 1-11. https://doi.org/10.3758/s13423-021-01948-3",
+)
diff --git a/tests/README.md b/tests/README.md
@@ -2,7 +2,7 @@ Tests and demonstrations of the splithalfr:
 * **test-rapi_sum.R** Sum-score for data of the 23-item version of the Rutgers Alcohol Problem Index ([White & Labouvie, 1989](https://research.alcoholstudies.rutgers.edu/rapi))
 * **test-vpt_diff_of_medians.R** Difference of mean RTs for correct responses, after removing RTs below 200 ms and above 520 ms, on Visual Probe Task data ([Mogg & Bradley, 1999](https://doi.org/10.1080/026999399379050))
 * **test-aat_double_diff_of_medians.R** Double-difference-of-medians for correct responses on Approach Avoidance Task data ([Heuer, Rinck, & Becker, 2007](http://doi.org/10.1016/j.brat.2007.08.010))
-* **test-iat_dscore_ri.R** Improved d-score algorithm for data of an Implicit Association Task that requires a correct response in order to continue to the next trial ([Greenwald, Nosek, & Banaji, 2003](http://dx.doi.org/10.1037/0022-3514.85.2.197))
+* **test-iat_dscore_ri.R** Improved d-score algorithm for data of an Implicit Association Task that requires a correct response in order to continue to the next trial ([Greenwald, Nosek, & Banaji, 2003](https://pubmed.ncbi.nlm.nih.gov/12916565/))
 
 All tests come with an Excel sheet in which the score for a single participant is calculated manually.
 
diff --git a/vignettes/iat_dscore_ri.Rmd b/vignettes/iat_dscore_ri.Rmd
@@ -20,7 +20,7 @@ knitr::opts_chunk$set(
 library(splithalfr)
 library(dplyr)
 ```
-This vignette describes a scoring method introduced by [Greenwald, Nosek, and Banaji (2003)](https://doi.org/10.1037/0022-3514.85.2.197); the improved d-score for Implicit Association Task (IATs) that require a correct response in order to continue to the next trial. This version of the d-score algorithm adds up all response times of all responses per trial. As this algorithm also specifies which participants to keep and to drop, functions from the [dplyr package](https://dplyr.tidyverse.org/) will be used to produce relevant summary statistics. Note that this vignette is more advanced that the others included in the `splithalfr` package, so it is not recommended as a first introduction on to how to use the splithalfr.
+This vignette describes a scoring method introduced by [Greenwald, Nosek, and Banaji (2003)](https://pubmed.ncbi.nlm.nih.gov/1291656); the improved d-score for Implicit Association Task (IATs) that require a correct response in order to continue to the next trial. This version of the d-score algorithm adds up all response times of all responses per trial. As this algorithm also specifies which participants to keep and to drop, functions from the [dplyr package](https://dplyr.tidyverse.org/) will be used to produce relevant summary statistics. Note that this vignette is more advanced that the others included in the `splithalfr` package, so it is not recommended as a first introduction on to how to use the splithalfr.
 
 <br />
 

diff --git a/vignettes/rapi_sum.Rmd b/vignettes/rapi_sum.Rmd
@@ -18,7 +18,7 @@ knitr::opts_chunk$set(
 library(splithalfr)
 ```
 This vignette describes a sum score of answers on questions from the 23-item Rutgers Alcohol Problem Inventory (RAPI)
-([White & Labouvie, 1989](http://www.emcdda.europa.eu/html.cfm/index4200EN.html));
+([White & Labouvie, 1989](https://doi.org/10.15288/jsa.1989.50.30));
 
 <br />
 

diff --git a/vignettes/splitting_methods.Rmd b/vignettes/splitting_methods.Rmd
@@ -17,7 +17,7 @@ knitr::opts_chunk$set(
 ```{r setup}
 library(splithalfr)
 ```
-This vignette demonstrates the methods of splitting data that are supported by the `splithalfr`. Each splitting method is illustrated by calling `by_split` with the right arguments, printing to the terminal what data is in each of the two parts produced by a split.
+This vignette demonstrates the methods of splitting data that are supported by the `splithalfr`. Each splitting method is illustrated by calling `by_split` with the right arguments, printing to the terminal what data is in each of the two parts produced by a split. For a comprehensive review of each splitting method, see [Pronk et al. (2021)](https://doi.org/10.3758/s13423-021-01948-3).
 
 
 # Example data