R Dataset / Package psych / epi.dictionary

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Embed
<iframe src="https://embed.picostat.com/r-dataset-package-psych-epidictionary.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-39520.csv 3.4 KB
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Dataset Help

On this Picostat.com statistics page, you will find information about the epi.dictionary data set which pertains to Eysenck Personality Inventory (EPI) data for 3570 participants. The epi.dictionary data set is found in the psych R package. You can load the epi.dictionary data set in R by issuing the following command at the console data("epi.dictionary"). This will load the data into a variable called epi.dictionary. If R says the epi.dictionary data set is not found, you can try installing the package by issuing this command install.packages("psych") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the epi.dictionary R data set. The size of this file is about 3,479 bytes.

Documentation

Eysenck Personality Inventory (EPI) data for 3570 participants

Description

The EPI is and has been a very frequently administered personality test with 57 measuring two broad dimensions, Extraversion-Introversion and Stability-Neuroticism, with an additional Lie scale. Developed by Eysenck and Eysenck, 1964. Eventually replaced with the EPQ which measures three broad dimensions. This data set represents 3570 observations collected in the early 1990s at the Personality, Motivation and Cognition lab at Northwestern. The data are included here as demonstration of scale construction.

Usage

data(epi)
data(epi.dictionary)

Format

A data frame with 3570 observations on the following 57 variables.

V1

a numeric vector

V2

a numeric vector

V3

a numeric vector

V4

a numeric vector

V5

a numeric vector

V6

a numeric vector

V7

a numeric vector

V8

a numeric vector

V9

a numeric vector

V10

a numeric vector

V11

a numeric vector

V12

a numeric vector

V13

a numeric vector

V14

a numeric vector

V15

a numeric vector

V16

a numeric vector

V17

a numeric vector

V18

a numeric vector

V19

a numeric vector

V20

a numeric vector

V21

a numeric vector

V22

a numeric vector

V23

a numeric vector

V24

a numeric vector

V25

a numeric vector

V26

a numeric vector

V27

a numeric vector

V28

a numeric vector

V29

a numeric vector

V30

a numeric vector

V31

a numeric vector

V32

a numeric vector

V33

a numeric vector

V34

a numeric vector

V35

a numeric vector

V36

a numeric vector

V37

a numeric vector

V38

a numeric vector

V39

a numeric vector

V40

a numeric vector

V41

a numeric vector

V42

a numeric vector

V43

a numeric vector

V44

a numeric vector

V45

a numeric vector

V46

a numeric vector

V47

a numeric vector

V48

a numeric vector

V49

a numeric vector

V50

a numeric vector

V51

a numeric vector

V52

a numeric vector

V53

a numeric vector

V54

a numeric vector

V55

a numeric vector

V56

a numeric vector

V57

a numeric vector

Details

The original data were collected in a group testing framework for screening participants for subsequent studies. The participants were enrolled in an introductory psychology class between Fall, 1991 and Spring, 1995.

The structure of the E scale has been shown by Rocklin and Revelle (1981) to have two subcomponents, Impulsivity and Sociability. These were subsequently used by Revelle, Humphreys, Simon and Gilliland to examine the relationship between personality, caffeine induced arousal, and cognitive performance.

Source

Data from the PMC laboratory at Northwestern.

References

Eysenck, H.J. and Eysenck, S. B.G. (1968). Manual for the Eysenck Personality Inventory.Educational and Industrial Testing Service, San Diego, CA.

Rocklin, T. and Revelle, W. (1981). The measurement of extraversion: A comparison of the Eysenck Personality Inventory and the Eysenck Personality Questionnaire. British Journal of Social Psychology, 20(4):279-284.

Examples

data(epi)
epi.keys <- make.keys(epi,list(E = c(1, 3, -5, 8, 10, 13, -15, 17, -20, 22, 25, 27,
                -29, -32, -34, -37, 39, -41, 44, 46, 49, -51, 53, 56),
   N=c(2, 4, 7, 9, 11, 14, 16, 19, 21, 23, 26, 28, 31, 33, 35, 38, 40,
     43, 45, 47, 50, 52, 55, 57),
   L = c(6, -12, -18, 24, -30, 36, -42, -48, -54),
   I =c(1, 3, -5, 8, 10, 13, 22, 39, -41), 
   S = c(-11, -15, 17, -20, 25, 27, -29, -32, -37, 44, 46, -51, 53)))
scores <- scoreItems(epi.keys,epi)
  N <- epi[abs(epi.keys[,"N"]) >0]
  E <- epi[abs(epi.keys[,"E"]) >0]
  fa.lookup(epi.keys[,1:3],epi.dictionary) #show the items and keying information
--

Dataset imported from https://www.r-project.org.

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