Package 'mtscr'

Title: Multidimensional Top Scoring for Creativity Research
Description: Implementation of Multidimensional Top Scoring method for creativity assessment proposed in Boris Forthmann, Maciej Karwowski, Roger E. Beaty (2023) <doi:10.1037/aca0000571>.
Authors: Jakub Jędrusiak [aut, cre, cph] (<https://orcid.org/0000-0002-6481-8210>, University of Wrocław), Boris Forthmann [aut, rev] (<https://orcid.org/0000-0001-9755-7304>, University of Münster), Roger E. Beaty [aut] (<https://orcid.org/0000-0001-6114-5973>, Pennsylvania State University), Maciej Karwowski [aut] (<https://orcid.org/0000-0001-6974-1673>, University of Wrocław)
Maintainer: Jakub Jędrusiak <[email protected]>
License: MIT + file LICENSE
Version: 1.0.2
Built: 2024-10-31 06:07:04 UTC
Source: https://github.com/jakub-jedrusiak/mtscr

Help Index

Shiny GUI for mtscr

Description

Shiny app used as graphical interface for mtscr. Simply invoke mtscr_app() to run.

Usage

mtscr_app()

Details

To use the GUI you need to have the following packages installed: DT, broom.mixed, datamods, writexl.

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 .all_max and .all_top2 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.

Value

Runs the app. No explicit return value.

See Also

mtscr_score() for more information on the arguments.

mtscr_creativity for more information about the example dataset.

Forthmann, B., Karwowski, M., & Beaty, R. E. (2023). Don’t throw the “bad” ideas away! Multidimensional top scoring increases reliability of divergent thinking tasks. Psychology of Aesthetics, Creativity, and the Arts. doi:10.1037/aca0000571

Examples

if(interactive()){
mtscr_app()
}

Creativity assessment through semantic distance dataset

Description

A dataset from Forthmann, Karwowski & Beaty (2023) paper. It contains a set of responses in Alternative Uses Task for different items with their semantic distance assessment.

Usage

mtscr_creativity

Format

mtscr_creativity

A tibble with 4585 rows and 10 columns:

id

patricipant's unique identification number

response

response in AUT

item

item for which alternative uses were searched for

SemDis_MEAN

mean semantic distance

Value

a tibble

Source

https://osf.io/7rgsp/

References

doi:10.1037/aca0000571

Create MTS model

Description

Create MTS model for creativity analysis.

Usage

mtscr_model(
  df,
  id_column,
  item_column = NULL,
  score_column,
  top = 1,
  prepared = FALSE,
  ties_method = c("random", "average"),
  normalise = TRUE,
  self_ranking = NULL
)

Arguments

df

Data frame in long format.
id_column

Name of the column containing participants' id.
item_column

Optional, name of the column containing distinct trials (e.g. names of items in AUT).
score_column

Name of the column containing divergent thinking scores (e.g. semantic distance).
top

Integer or vector of integers (see examples), number of top answers to include in the model. Default is 1, i.e. only the top answer.
prepared

Logical, is the data already prepared with mtscr_prepare()?
ties_method

Character string specifying how ties are treated when ordering. Can be "average" (better for continuous scores like semantic distance) or "random" (default, better for ratings). See rank() for details.
normalise

Logical, should the creativity score be normalised? Default is TRUE and it's recommended to leave it as such.
self_ranking

Name of the column containing answers' self-ranking. Provide if model should be based on top answers self-chosen by the participant. Every item should have its own ranks. The top answers should have a value of 1, and the other answers should have a value of 0. In that case, the top argument doesn't change anything and should be left as top = 1. ties_method is not used if self_ranking was provided. See mtscr_self_rank for example.

Value

The return value depends on length of the top argument. If top is a single integer, a glmmTMB model is returned. If top is a vector of integers, a list of glmmTMB models is returned, with names corresponding to the top values, e.g. top1, top2, etc.

Examples

data("mtscr_creativity", package = "mtscr")

mtscr_creativity <- mtscr_creativity |>
  dplyr::slice_sample(n = 300) # for performance, ignore

mtscr_model(mtscr_creativity, id, item, SemDis_MEAN) |>
  summary()

# three models for top 1, 2, and 3 answers
mtscr_model(mtscr_creativity, id, item, SemDis_MEAN, top = 1:3) |>
  mtscr_model_summary()

# you can prepare data first
data <- mtscr_prepare(mtscr_creativity, id, item, SemDis_MEAN)
mtscr_model(data, id, item, SemDis_MEAN, prepared = TRUE)

# extract effects for creativity score by hand
model <- mtscr_model(mtscr_creativity, id, item, SemDis_MEAN, top = 1)
creativity_score <- glmmTMB::ranef(model)$cond$id[, 1]

Summarise a model

Description

Summarise a model generated with mtscr_model with some basic statistics; calculate the empirical reliability and the first difference of the empirical reliability.

Usage

mtscr_model_summary(model)

Arguments

model

A model generated with mtscr_model. Can be a list of models.

Value

A data frame with the following columns:

model

The model number

nobs

Number of observations

sigma

The square root of the estimated residual variance

logLik

The log-likelihood of the model

AIC

The Akaike information criterion

BIC

The Bayesian information criterion

df.residual

The residual degrees of freedom

emp_rel

The empirical reliability

FDI

The first difference of the empirical reliability

Examples

data("mtscr_creativity", package = "mtscr")
mtscr_model(mtscr_creativity, id, item, SemDis_MEAN, top = 1:3) |>
  mtscr_model_summary()

Prepare database for MTS

Description

Prepare database for MTS analysis.

Usage

mtscr_prepare(
  df,
  id_column,
  item_column = NULL,
  score_column,
  top = 1,
  minimal = FALSE,
  ties_method = c("random", "average"),
  normalise = TRUE,
  self_ranking = NULL
)

Arguments

df

Data frame in long format.
id_column

Name of the column containing participants' id.
item_column

Optional, name of the column containing distinct trials (e.g. names of items in AUT).
score_column

Name of the column containing divergent thinking scores (e.g. semantic distance).
top

Integer or vector of integers (see examples), number of top answers to prepare indicators for. Default is 1, i.e. only the top answer.
minimal

Logical, append columns to df (FALSE) or return only id, item, and the new columns (TRUE).
ties_method

Character string specifying how ties are treated when ordering. Can be "average" (better for continuous scores like semantic distance) or "random" (default, better for ratings). See rank() for details.
normalise

Logical, should the creativity score be normalised? Default is TRUE and it's recommended to leave it as such.
self_ranking

Name of the column containing answers' self-ranking. Provide if model should be based on top answers self-chosen by the participant. Every item should have its own ranks. The top answers should have a value of 1, and the other answers should have a value of 0. In that case, the top argument doesn't change anything and should be left as top = 1. ties_method is not used if self_ranking was provided. See mtscr_self_rank for example.

Value

The input data frame with additional columns:

.z_score

Numerical, z-score of the creativity score

.ordering

Numerical, ranking of the answer relative to participant and item

.ordering_topX

Numerical, 0 for X top answers, otherwise value of .ordering

Number of .ordering_topX columns depends on the top argument. If minimal = TRUE, only the new columns and the item and id columns are returned. The values are relative to the participant AND item, so the values for different participants scored for different tasks (e.g. uses for "brick" and "can") are distinct.

Examples

data("mtscr_creativity", package = "mtscr")
# Indicators for top 1 and top 2 answers
mtscr_prepare(mtscr_creativity, id, item, SemDis_MEAN, top = 1:2, minimal = TRUE)

Score creativity with MTS

Description

Score creativity with MTS

Usage

mtscr_score(
  df,
  id_column,
  item_column = NULL,
  score_column,
  top = 1,
  format = c("minimal", "full"),
  ties_method = c("random", "average"),
  normalise = TRUE,
  self_ranking = NULL
)

Arguments

df

Data frame in long format.
id_column

Name of the column containing participants' id.
item_column

Optional, name of the column containing distinct trials (e.g. names of items in AUT).
score_column

Name of the column containing divergent thinking scores (e.g. semantic distance).
top

Integer or vector of integers (see examples), number of top answers to prepare indicators for. Default is 1, i.e. only the top answer.
format

Character, controls the format of the output data frame. Accepts:

"minimal"

default, returns only the creativity scores and id columns.

"full"

returns the original data frame with creativity scores columns added.
ties_method

Character string specifying how ties are treated when ordering. Can be "average" (better for continuous scores like semantic distance) or "random" (default, better for ratings). See rank() for details.
normalise

Logical, should the creativity score be normalised? Default is TRUE and it's recommended to leave it as such.
self_ranking

Name of the column containing answers' self-ranking. Provide if model should be based on top answers self-chosen by the participant. Every item should have its own ranks. The top answers should have a value of 1, and the other answers should have a value of 0. In that case, the top argument doesn't change anything and should be left as top = 1. ties_method is not used if self_ranking was provided. See mtscr_self_rank for example.

Value

A tibble with creativity scores. If format = "full", the original data frame is returned with scores columns added. Otherwise, only the scores and id columns are returned. number of creativity scores columns (e.g. creativity_score_top2) depends on the top argument.

See Also

tidyr::pivot_wider() for converting the output to wide format by yourself.

Examples

data("mtscr_creativity", package = "mtscr")
mtscr_score(mtscr_creativity, id, item, SemDis_MEAN, top = 1:2)

# add scores to the original data frame
mtscr_score(mtscr_creativity, id, item, SemDis_MEAN, format = "full")

# use self-chosen best answers
data("mtscr_self_rank", package = "mtscr")
mtscr_score(mtscr_self_rank, subject, task, avr, self_ranking = top_two)

Self-chosen best answers

Description

An example dataset with best answers self-chosen by the participant. Use with self_ranking argument in mtscr_model.

Usage

mtscr_self_rank

Format

mtscr_self_rank

A tibble with 3225 rows and 4 columns:

subject

patricipant's unique identification number

task

divergent thinking task number

avr

average judges' raiting

top_two

indicator of self-chosen two best answer; 1 if chosen, 0 if not

Source

https://osf.io/7rgsp/

References

doi:10.1037/aca0000571