mentalImageryOSIQ: (simulated) results of 2,100 participants answering a Likert...

mentalImageryOSIQR Documentation

(simulated) results of 2,100 participants answering a Likert scale like questionnaire about mental imagery (called the Object-Spatial Imagery Questionnaire: OSIQ).

Description

mentalImageryOSIQ a data set with (simulated) results of 2,100 participants answering a Likert scale like questionnaire comprising 30 questions about mental imagery (called the Object-Spatial Imagery Questionnaire: OSIQ). Half of the questions concern mental imagery for object and the other half of the questions concern mental imagery for spatial locations.

Usage

data("mentalImageryOSIQ")

Format

a list containing one data frame (mentalImageryOSIQ$OSIQ) with 2,100 rows (observations) and 30 columns (questions). The first letter of a column denotes the type of imagery (s for spatial and o for object). The questions are in the same order as in the original publication by Blajenkova et al. (2006). The first letter of the name of a row can be H, M, L. This letter indicates that the participants identified themselves as having High or superior episodic memory, Medium (when they did not mention high nor low), or Low episodic memory.

Details

These data are simulated data that roughly match some real data. Details about the questionnaire and for the actual questions see the paper by Blajenkova et al. (2006). The order of the questions is the same as in the original paper.

The answers to the questions were given on a Likert type scale from 1 to 5. One question (s27) has been reversed coded so that a large number indicates a good memory.

These data were created to mimic the structure of real data and were created with the function buildRandomImage4PCA.

Author(s)

Hervé Abdi & Brian Levine

References

The original OSIQ questionnaire can be found in:

Blajenkova, O., Kozhevnikov, M., & Motes, M.A. (2006). Object-spatial imagery: a new self-report imagery questionnaire. Applied Cognitive Psychology, 20, 239–263.

These (simulated) data have been also used in Guillemot et al. (in press, 2019).


HerveAbdi/data4PCCAR documentation built on Sept. 11, 2022, 4:19 p.m.