An R adaptation of Multidimensional Top Scoring method presented by Forthmann, Karwowski and Beaty (2023) using the code from the OSF database. The code was adapted to use the tidyverse framework for greater flexibility.
Install mtscr with:
install.packages("mtscr")You can install the development version of mtscr from GitHub with:
# install.packages("devtools")
devtools::install_github("jakub-jedrusiak/mtscr")Basic usage involves scoring participants’ responses to a divergent
thinking task. The package includes a sample dataset
mtscr_creativity with 4652 responses to the Alternative
Uses Task with semantic distance scored. The dataset comes from the
original paper (Forthmann, Karwowski and Beaty, 2023).
The main function is mtscr_scores() which returns a
dataframe with scored responses. It takes a dataframe with responses, an
ID column, an item column and a score column as arguments. The score
column should contain semantic distance scores for each response. The
function adds columns with scores for each person. The number of
creativity scores is based on a given number of top answers provided by
the top argument.
library("mtscr")
data("mtscr_creativity", package = "mtscr")
mtscr_score(mtscr_creativity, id, item, SemDis_MEAN, top = 1:2)
#> # A tibble: 149 × 3
#> id .creativity_score_top1 .creativity_score_top2
#> <chr> <dbl> <dbl>
#> 1 84176 0.142 0.0681
#> 2 84177 -0.508 -0.494
#> 3 84178 -0.0733 -0.0995
#> 4 84188 0.529 0.527
#> 5 84193 -0.299 -0.350
#> 6 84206 -0.312 -0.301
#> 7 84211 -0.0464 0.0356
#> 8 84226 0.238 0.210
#> 9 84228 0.137 0.139
#> 10 84236 0.459 0.422
#> # ℹ 139 more rowsmtscr_score() does everything automatically. You can
also use mtscr_prepare() to get your data prepared for
modelling by hand and mtscr_model() to get the model
object. See the functions’ documentation for more details.
The model can be summarised to obtain the parameters and reliability estimates.
mtscr_model(mtscr_creativity, id, item, SemDis_MEAN, top = 1:3) |>
mtscr_model_summary()
#> # A tibble: 3 × 10
#> model nobs sigma logLik AIC BIC deviance df.residual emp_rel FDI
#> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 top1 4585 0.736 -5298. 10657. 10850. 2383. 4555 0.877 0.936
#> 2 top2 4585 0.767 -5472. 11003. 11196. 2597. 4555 0.892 0.944
#> 3 top3 4585 0.825 -5777. 11613. 11806. 3024. 4555 0.896 0.947This package includes a Shiny app which can be used as a GUI. You can
find “mtscr GUI” option in RStudio’s Addins menu. Alternatively execute
mtscr_app() to run it.
Try web based version here!
First thing you see after running the app is datamods
window for importing your data. You can use the data already loaded in
your environment or any other option. Then you’ll see four dropdown
lists used to choose arguments for mtscr_model() and
mtscr_score() functions. Consult these functions’
documentation for more details (execute ?mtscr_score in the
console). When the parameters are chosen, click “Generate model” button.
After a while (up to a dozen or so seconds) models’ parameters and are
shown along with a scored dataframe.
You can download your data as a .csv or an .xlsx file using buttons in the sidebar. You can either download the scores only (i.e. the dataframe you see displayed) or your whole data with scores columns added.
For testing purposes, you may use mtscr_creativity
dataframe. In the importing window change “Global Environment” to
“mtscr” and our dataframe should appear in the upper dropdown list. Use
id for the ID column, item for the item column
and SemDis_MEAN for the score column.
Correspondence concerning the meritorical side of these solutions should be addressed to Boris Forthmann, Institute of Psychology, University of Münster, Fliednerstrasse 21, 48149 Münster, Germany. Email: boris.forthmann@wwu.de.
The maintainer of the R package is Jakub Jędrusiak and the technical concerns should be directed to him. Well, me. Best way is to open a discussion on GitHub. Technical difficulties may deserve an issue.