Package 'mtscr'

Create MTS model

Description

Create MTS model for creativity analysis. Use with summary.mtscr() and predict.mtscr().

Usage

mtscr(
  df,
  id_column,
  score_column,
  item_column = NULL,
  top = 1,
  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.
score_column	Name of the column containing divergent thinking scores (e.g. semantic distance).
item_column	Optional, name of the column containing distinct trials (e.g. names of items in AUT).
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.
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 mtscr model is returned. If top is a vector of integers, a mtscr_list object is returned, with names corresponding to the top values, e.g. top1, top2, etc.

See Also

• summary.mtscr() for the fit measures of the model.

• predict.mtscr() for getting the scores.

Examples

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

mtscr_creativity <- mtscr_creativity |>
  dplyr::slice_sample(n = 500) # for performance, ignore

# single model for top 1 answer
mtscr(mtscr_creativity, id, SemDis_MEAN, item) |>
  summary()

# three models for top 1, 2, and 3 answers
fit3 <- mtscr(
  mtscr_creativity,
  id,
  SemDis_MEAN,
  item,
  top = 1:3,
  ties_method = "average"
)

# add the scores to the database
predict(fit3)

# get the socres only
predict(fit3, minimal = TRUE)

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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, shinyWidgets, bslib, rstatix.

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.

Value

Runs the app. No explicit return value.

See Also

mtscr() 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

---

Self-chosen best answers

Description

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

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 answers; 1 if chosen, 0 if not

Source

https://osf.io/7rgsp/

References

doi:10.1037/aca0000571

---

Extract scores from mtscr model

Description

Extract the scores from a model fitted with mtscr().

Usage

## S3 method for class 'mtscr'
predict(object, ..., minimal = FALSE, id_col = TRUE)

## S3 method for class 'mtscr_list'
predict(object, ..., minimal = FALSE, id_col = TRUE)

Arguments

object	A model or a model list fitted with mtscr().
...	Additional arguments. Currently not used.
minimal	If TRUE, returns only the person-level scores without the original data.
id_col	If TRUE, returns the id column in the result. If FALSE, the id column is not returned. Only has an impact when minimal = TRUE.

Value

The return value is always a tibble but its content depends mainly on the minimal argument:

• If minimal = FALSE (default), the original data frame is returned with the creativity scores columns added.

• If minimal = TRUE, only the creativity scores are returned (i.e., one row per person).

Functions

• predict(mtscr_list): Extract scores from a model list fitted with mtscr().

Examples

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

mtscr_creativity <- mtscr_creativity |>
  dplyr::slice_sample(n = 500) # for performance, ignore

fit <- mtscr(mtscr_creativity, id, SemDis_MEAN, item, top = 1:3)

# for a single model from a list
predict(fit$top1)

# for a whole list of models
predict(fit)

# person-level scores only
predict(fit, minimal = TRUE)

# you can also achieve more classic predict() behaviour
mtscr_creativity$score <- predict(fit, id_col = FALSE)

mtscr_creativity |>
  tidyr::unnest_wider(score, names_sep = "_") # Use to expand list-col

---

Fit measures for mtscr model

Description

Summarise the overall fit of a single model fitted with mtscr().

Usage

## S3 method for class 'mtscr'
summary(object, ...)

## S3 method for class 'mtscr_list'
summary(object, ...)

Arguments

object	mtscr model or a mtscr_list object.
...	Additional arguments. Currently not used.

Value

A tibble with the following columns:

model

The model number (only if a list of models is provided)

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

Functions

• summary(mtscr_list): Get fit measures for a list of models fitted with mtscr().

Examples

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

mtscr_creativity <- mtscr_creativity |>
  dplyr::slice_sample(n = 500) # for performance, ignore

fit1 <- mtscr(mtscr_creativity, id, SemDis_MEAN, item, ties_method = "average")
fit3 <- mtscr(mtscr_creativity, id, SemDis_MEAN, item, top = 1:3, ties_method = "average")

summary(fit1)

summary(fit3)

---

Simple top-scoring for creativity research

Description

Get creativity measures using simple top-scoring, i.e., calculate a single index based only on top-X best scores.

Usage

top_scoring(
  df,
  id_column,
  score_column,
  item_column = NULL,
  top = 1,
  by_item = FALSE,
  na_if_less = FALSE,
  append = FALSE,
  aggregate_function = mean,
  top_all = TRUE
)

Arguments

df	Data frame in long format.
id_column	Name of the column containing participant's unique id.
score_column	Name of the column containing idea-level scores.
item_column	Name of the column containing separate trials for the task (e.g., AUT items). Optional. Supplying this argument changes the way the scores are calculated. See Deatils.
top	A number or an integer vector specifying on how many best ideas the final score should be based.
by_item	Boolean specifying whether the return value should aggregate scores from different items.
na_if_less	Whether to return NA if the number of ideas is less than top. Otherwise will calculate the scores based on the available number of ideas (default).
append	Boolean specifying whether the return value should be a new data frame with person-level scores (FALSE, default) or the original data frame with scores appended as new columns (TRUE).
aggregate_function	The function that should be used to aggregate idea-level scores into person-level scores. Should be a function, not a call (e.g., aggregate_function = mean and not aggregate_function = mean())
top_all	Whether to calculate the top score based on all ideas, not only the top-X.

Details

The way the top-X scores are calculated is based mainly on the aggregate_function and on whether the item_column was supplied. If the item_column wasn't supplied, the top-X scores are the participants' best ideas across all trials. For example, if top = 2, then the score is based on the person's 2 best ideas, even if there were 3 different items and both best ideas were uses for a brick.

Now if the item_column was supplied and by_item = FALSE (default), the score will be based on X best ideas per item. For example, if top = 2 and there were 3 different items, the final score will be the mean of 6 best scores – 2 per item. Set by_item = TRUE to get separate scores for each item.

Value

The return value is a dataframe. By default, it contains an id column and a series of score columns named top1, top2 etc. for each element of the vector given in the top argument. If by_item = TRUE, the return value also contains an item column with item indices. A separate score for each item is calculated. If append = TRUE, the return value is the original dataframe with the score columns appended.

Examples

data("mtscr_creativity", package = "mtscr")
mtscr_creativity <- mtscr_creativity |>
  dplyr::slice_sample(n = 500) # for performance, ignore

# Get top1, top2, and top3 scores for each participant
top_scoring(mtscr_creativity, id, SemDis_MEAN, item, top = 1:3)

# Get top2 scores ignoring items
top_scoring(mtscr_creativity, id, SemDis_MEAN, top = 2)

# Get top2-top4 scores for each item separately
top_scoring(mtscr_creativity, id, SemDis_MEAN, item, top = 2:4, by_item = TRUE)

# Add the scores to the original data frame
top_scoring(mtscr_creativity, id, SemDis_MEAN, item, top = 2:4, append = TRUE)

# Get scores by the sum of 3 top scores (note no parentheses after the function)
top_scoring(
  mtscr_creativity,
  id,
  SemDis_MEAN,
  item,
  top = 3,
  aggregate_function = sum
)

# Create a custom aggregate function (here: scale by 100, round and then get the mean)
top_scoring(
  mtscr_creativity,
  id,
  SemDis_MEAN,
  item,
  top = 1:3,
  aggregate_function = \(x) mean(round(x * 100))
)

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