In creativity research, we often want to extract person-level divergent thinking indices from response-level scores. In the field there are discussions whether grand mean or top-scoring is more valid way of summarising these scores. A new method called Multidimensional Top Scoring presented by Forthmann, Karwowski and Beaty (2023) combines the strengths of both approaches. This package is an R adaptation of that method.
Install mtscr with:
install.packages("mtscr")
You can install the development version of mtscr from GitHub with:
# install.packages("devtools")
::install_github("jakub-jedrusiak/mtscr") devtools
The basic usage involves fitting a model with mtscr()
function and using it to predict some scores. Note that you
still need some response-level scores! This package only
aggegates them into person-level scores. For automatic scoring see Ocsai and openscoring
package. This 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 model(s) can be fitted with mtscr()
. It takes a
dataframe with responses, an ID column, a score column, and (optionally)
an item column as arguments. See the help page (?mtscr()
)
for more details.
library("mtscr")
data("mtscr_creativity", package = "mtscr")
<- mtscr(mtscr_creativity, id, SemDis_MEAN, item, top = 1:3) fit
The model can be summarised to obtain the parameters and reliability estimates.
summary(fit)
#> # A tibble: 3 × 9
#> model nobs sigma logLik AIC BIC df.residual emp_rel FDI
#> <chr> <int> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 top1 4585 0.736 -5298. 10657. 10850. 4555 0.877 0.936
#> 2 top2 4585 0.767 -5472. 11003. 11196. 4555 0.892 0.944
#> 3 top3 4585 0.825 -5777. 11613. 11806. 4555 0.896 0.947
Then you can add your socres to your database or extract them by
person using predict()
.
# For a single model
predict(fit$top1)
#> # A tibble: 4,585 × 5
#> id response item SemDis_MEAN top1
#> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 84176 ruler pencil 0.876 0.142
#> 2 84176 nose picker pencil 0.959 0.142
#> 3 84176 scratch ear pencil 1.02 0.142
#> 4 84176 hammer clock 0.871 0.142
#> 5 84176 table clock 0.837 0.142
#> 6 84176 direction clock 0.9 0.142
#> 7 84176 coaster clock 0.938 0.142
#> 8 84176 latter clock 0.979 0.142
#> 9 84176 ladder bucket 0.763 0.142
#> 10 84176 seat bucket 0.823 0.142
#> # ℹ 4,575 more rows
# For a whole list of models
predict(fit)
#> # A tibble: 4,585 × 7
#> id response item SemDis_MEAN top1 top2 top3
#> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 84176 ruler pencil 0.876 0.142 0.0681 -0.0516
#> 2 84176 nose picker pencil 0.959 0.142 0.0681 -0.0516
#> 3 84176 scratch ear pencil 1.02 0.142 0.0681 -0.0516
#> 4 84176 hammer clock 0.871 0.142 0.0681 -0.0516
#> 5 84176 table clock 0.837 0.142 0.0681 -0.0516
#> 6 84176 direction clock 0.9 0.142 0.0681 -0.0516
#> 7 84176 coaster clock 0.938 0.142 0.0681 -0.0516
#> 8 84176 latter clock 0.979 0.142 0.0681 -0.0516
#> 9 84176 ladder bucket 0.763 0.142 0.0681 -0.0516
#> 10 84176 seat bucket 0.823 0.142 0.0681 -0.0516
#> # ℹ 4,575 more rows
You can also extract person-level scores only by setting
minimal = TRUE
.
predict(fit, minimal = TRUE)
#> # A tibble: 149 × 4
#> id top1 top2 top3
#> <dbl> <dbl> <dbl> <dbl>
#> 1 84176 0.142 0.0681 -0.0516
#> 2 84177 -0.508 -0.494 -0.444
#> 3 84178 -0.0733 -0.0995 -0.0774
#> 4 84188 0.529 0.527 0.455
#> 5 84193 -0.299 -0.350 -0.256
#> 6 84206 -0.312 -0.301 -0.371
#> 7 84211 -0.0464 0.0356 0.129
#> 8 84226 0.238 0.210 0.0902
#> 9 84228 0.137 0.139 0.102
#> 10 84236 0.459 0.422 0.409
#> # ℹ 139 more rows
You can achieve more standard behaviour of predict()
by
also setting id_col = FALSE
. Then you can assign the result
to a column manually.
$score <- predict(fit, id_col = FALSE)
mtscr_creativity
|>
mtscr_creativity ::unnest_wider(score, names_sep = "_") # Use to expand list-col
tidyr#> # A tibble: 4,585 × 11
#> id response item SemDis_MEAN score_id score_response score_item
#> <dbl> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 84176 ruler pencil 0.876 84176 ruler pencil
#> 2 84176 nose picker pencil 0.959 84176 nose picker pencil
#> 3 84176 scratch ear pencil 1.02 84176 scratch ear pencil
#> 4 84176 hammer clock 0.871 84176 hammer clock
#> 5 84176 table clock 0.837 84176 table clock
#> 6 84176 direction clock 0.9 84176 direction clock
#> 7 84176 coaster clock 0.938 84176 coaster clock
#> 8 84176 latter clock 0.979 84176 latter clock
#> 9 84176 ladder bucket 0.763 84176 ladder bucket
#> 10 84176 seat bucket 0.823 84176 seat bucket
#> # ℹ 4,575 more rows
#> # ℹ 4 more variables: score_SemDis_MEAN <dbl>, score_top1 <dbl>,
#> # score_top2 <dbl>, score_top3 <dbl>
This 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 the functions. Consult the
documentation for more details (execute ?mtscr
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.