psych (version 1.8.12)

epi: 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. An additional data set (epiR) has test and retest information for 474 participants. The data are included here as demonstration of scale construction and test-retest reliability.

Usage

data(epi)
data(epi.dictionary)
data(epiR)

Format

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

id

The identification number within the study

time

First (group testing) or 2nd time (before a lab experiment) for the epiR data set.

study

Four lab based studies and their pretest data

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 actual items may be found in the epi.dictionary.

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.

The epiR data include the original group testing data and matched data for 474 participants collected several weeks later. This is useful for showing that internal consistency estimates (e.g. alpha or omega) can be low even though the test is stable across time. For more demonstrations of the distinction between immediate internal consistency and delayed test-retest reliability see the msqR and sai data sets and testRetest.

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

# NOT RUN {
data(epi)
epi.keys <- list(E = c("V1",  "V3",  "V8",  "V10", "V13", "V17", "V22", "V25", "V27", "V39",
  "V44", "V46", "V49", "V53", "V56", "-V5", "-V15", "-V20", "-V29", "-V32", "-V34","-V37",
   "-V41", "-V51"),
N = c( "V2", "V4", "V7", "V9", "V11", "V14", "V16", "V19", "V21", "V23", "V26", "V28", 
"V31", "V33", "V35", "V38", "V40","V43", "V45", "V47", "V50", "V52","V55", "V57"),
L = c("V6",  "V24", "V36", "-V12", "-V18", "-V30", "-V42", "-V48", "-V54"),
Imp = c( "V1",  "V3",  "V8",  "V10", "V13", "V22", "V39", "-V5", "-V41"),
Soc = c( "V17", "V25", "V27", "V44", "V46", "V53", "-V11", "-V15", "-V20", 
"-V29", "-V32", "-V37", "-V51")
)
scores <- scoreItems(epi.keys,epi)

keys <- make.keys(epi,epi.keys)   #the old way of making keys is to make a matrix
fa.lookup(keys[,1:3],epi.dictionary) #show the items and keying information

#a variety of demonstrations (not run) of test retest reliability versus alpha versus omega

E <- selectFromKeys(epi.keys$E)
#omega(epi[E])  #to show the low omega but high alpha of Extraversion 
#testRetest(epiR,select=E)  #test retest of the extraversion scale (.82) is higher than 
#alpha  for either the first (.77) or second administration (.74)
#Imp <- selectFromKeys(epi.keys$Imp)
#testRetest(epiR,select=Imp)  #Similarly test retest = .68 but alpha = .48 and .50.
#Soc <- selectFromKeys(epi.keys$Soc)
#testRetest(epiR,select=Soc)  # test retest =.83, alpha = .76, .75
#N <- selectFromKeys(epi.keys$N)
#testRetest(epiR, select=N) #Test retest = .8, alpha = .81, .80
# }