Holzinger and Swineford (1939) Ability data in 301 children from two schools

Description

This classic data set contains of intelligence-test scores from 301 children on 26 distinct tests. The data are also available in the MBESS package.

The tests cover mental speed, memory, mathematical-ability, spatial, and verbal ability as listed below.

Usage

data("HS.ability.data")

Format

A data frame with 301 observations on the following 2 variables.

id

student ID number (int)

Gender

Sex (Factor w/ 2 levels “Female”,“Male”

grade

Grade in school (integer 7 or 8)

agey

Age in years (integer)

agem

Age in months (integer)

school

School attended (Factor w/2 levels “Grant-White” and “Pasteur”)

addition

A speed test (numeric)

code

A speed test (numeric)

counting

A speed test (numeric)

straight

A speed test (numeric)

wordr

A memory subtest

numberr

A memory subtest

figurer

A memory subtest

object

A memory subtest

numberf

A memory subtest

figurew

A memory subtest

deduct

A mathematical subtest

numeric

A mathematical subtest

problemr

A mathematical subtest

series

A mathematical subtest

arithmet

A mathematical subtest

visual

A spatial subtest

cubes

A spatial subtest

paper

A spatial subtest

flags

A spatial subtest

paperrev

A spatial subtest

flagssub

A spatial subtest

general

A verbal subtest

paragrap

A verbal subtest

sentence

A verbal subtest

wordc

A verbal subtest

wordm

A verbal subtest

Details

The data are from children who differ in grade (seventh- and eighth-grade) and are nested in one of two schools (Pasteur and Grant-White). You will see it in use elsewhere, both in R (lavaan, MBESS), and in Joreskog (1969) reporting a cfa on the Grant-White school subject subset).

The last two tests are substitute versions for other tests. paperrev (a paper form board test) can substitute for paper and flagssub for the lozenges test flags.

Source

Holzinger, K., and Swineford, F. (1939).

References

Holzinger, K., and Swineford, F. (1939). A study in factor analysis: The stability of a bifactor solution. Supplementary Educational Monograph, no. 48. Chicago: University of Chicago Press. Joreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34, 183-202.

Examples

data(HS.ability.data)
str(HS.ability.data)
levels(HS.ability.data$school)
plot(flags ~ flagssub, data = HS.ability.data)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(OpenMx)
Loading required package: digest
Loading required package: MASS
Loading required package: Matrix
Loading required package: Rcpp
Loading required package: parallel

Attaching package: 'OpenMx'

The following objects are masked from 'package:Matrix':

    %&%, expm

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/OpenMx/HS.ability.data.Rd_%03d_medium.png", width=480, height=480)
> ### Name: HS.ability.data
> ### Title: Holzinger and Swineford (1939) Ability data in 301 children from
> ###   two schools
> ### Aliases: HS.ability.data HS.fake.data
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(HS.ability.data)
> str(HS.ability.data)
'data.frame':	301 obs. of  32 variables:
 $ id      : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Gender  : Factor w/ 2 levels "Female","Male": 2 2 2 1 1 1 2 2 1 1 ...
 $ grade   : int  8 8 8 8 7 8 7 8 7 8 ...
 $ agey    : int  14 14 13 14 14 12 13 15 13 11 ...
 $ agem    : int  6 0 4 3 -3 2 9 7 3 9 ...
 $ school  : Factor w/ 2 levels "Grant-White",..: 1 2 1 1 1 1 2 2 2 1 ...
 $ visual  : int  27 37 42 25 39 16 36 21 30 34 ...
 $ cubes   : int  24 24 33 24 32 30 19 24 20 26 ...
 $ paper   : int  17 16 13 16 17 12 16 12 10 13 ...
 $ flags   : int  37 25 24 14 24 20 19 9 7 24 ...
 $ general : int  49 46 60 30 35 29 28 50 48 32 ...
 $ paragrap: int  11 12 8 10 11 8 11 7 14 8 ...
 $ sentence: int  18 20 16 23 13 22 15 16 28 16 ...
 $ wordc   : int  27 31 26 31 29 27 15 22 34 19 ...
 $ wordm   : int  18 26 14 16 16 17 15 19 27 11 ...
 $ addition: int  48 92 93 128 87 102 103 138 83 92 ...
 $ code    : int  58 79 91 97 70 82 77 71 81 63 ...
 $ counting: int  71 147 114 166 109 102 100 115 107 95 ...
 $ straight: int  166 211 205 246 173 195 218 172 195 237 ...
 $ wordr   : int  173 168 185 184 154 171 174 157 172 172 ...
 $ numberr : int  89 90 96 84 97 77 80 86 80 83 ...
 $ figurer : int  105 104 112 113 99 102 101 105 100 105 ...
 $ object  : int  8 10 11 14 10 4 10 10 11 7 ...
 $ numberf : int  13 13 16 8 6 8 1 8 9 8 ...
 $ figurew : int  12 19 19 20 9 18 11 15 11 9 ...
 $ deduct  : int  32 42 74 43 47 42 -2 38 35 36 ...
 $ numeric : int  9 12 20 14 19 15 19 15 15 14 ...
 $ problemr: int  31 40 41 18 27 22 17 29 43 24 ...
 $ series  : int  21 37 27 20 6 13 16 16 25 19 ...
 $ arithmet: int  23 30 28 30 18 25 22 29 25 28 ...
 $ paperrev: int  17 21 22 16 13 11 15 11 19 20 ...
 $ flagssub: int  44 37 51 39 42 38 34 24 43 37 ...
> levels(HS.ability.data$school)
[1] "Grant-White" "Pasteur"    
> plot(flags ~ flagssub, data = HS.ability.data)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>