iqitems {psych} | R Documentation |
14 multiple choice ability items were included as part of the Synthetic Aperture Personality Assessment (SAPA) web based personality assessment project. The data from 1000 subjects are included here as a demonstration set for scoring multiple choice inventories and doing basic item statistics.
data(iqitems)
A data frame with 1000 observations on the following 14 variables.
iq1
In the following number series, what number comes next?
iq2
In the following number series, what number comes next? 1 2 4 7 12
iq8
Please mark the word that does not match the other words:
iq10
If you rearrange the letters ATNHIDLA, you will have the name of a:
iq11
The opposite of a 'stubborn' person is a ' ' person.
iq15
If Jerks are Perks and some Perks are Lerks, then some Jerks are definitely Lerks. This statement is:
iq16
Zach is taller than Matt and Richard is shorter than Zach. Which of the following statements would be most accurate?
iq20
How many total legs do two ducks and three dogs have?
iq32
If the day before yesterday is three days after Saturday then what day is today?
iq37
In the following alphanumeric series, what letter comes next? Q, S, N, P, L
iq43
Matrix Reasoning 1
iq44
Matrix reasoning 2
iq47
Matrix reasoning 5
iq49
Matrix Reasoning 9
14 items were sampled from 54 items given as part of the SAPA project (Revelle, Wilt and Rosenthal, 2009) to develop online measures of ability.
This data set may be used to demonstrate item response functions, tetrachoric
correlations, or irt.fa
.
The example data set is taken from the Synthetic Aperture Personality Assessment personality and ability test at http://test.personality-project.org.
Revelle, William, Wilt, Joshua, and Rosenthal, Allen (2010) Personality and Cognition: The Personality-Cognition Link. In Gruszka, Alexandra and Matthews, Gerald and Szymura, Blazej (Eds.) Handbook of Individual Differences in Cognition: Attention, Memory and Executive Control, Springer.
data(iqitems) iq.keys <- c(4,4,3,1,4,3,2,3,1,4,1,3,4,3) score.multiple.choice(iq.keys,iqitems) #convert them to true false iq.scrub <- scrub(iqitems,isvalue=0) #first get rid of the zero responses iq.tf <- score.multiple.choice(iq.keys,iq.scrub,score=FALSE) #convert to wrong (0) and correct (1) for analysis describe(iq.tf)