mtscr_prepare {mtscr}

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)