# R Dataset / Package psych / Gleser

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## Visual Summaries

Attachment Size
360 bytes
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
R Dataset Help

On this Picostat.com statistics page, you will find information about the Gleser data set which pertains to Example data from Gleser, Cronbach and Rajaratnam (1965) to show basic principles of generalizability theory. . The Gleser data set is found in the psych R package. You can load the Gleser data set in R by issuing the following command at the console data("Gleser"). This will load the data into a variable called Gleser. If R says the Gleser 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 Gleser R data set. The size of this file is about 360 bytes.

Documentation

## Example data from Gleser, Cronbach and Rajaratnam (1965) to show basic principles of generalizability theory.

### Description

Gleser, Cronbach and Rajaratnam (1965) discuss the estimation of variance components and their ratios as part of their introduction to generalizability theory. This is a adaptation of their "illustrative data for a completely matched G study" (Table 3). 12 patients are rated on 6 symptoms by two judges. Components of variance are derived from the ANOVA.

### Usage

data(Gleser)

### Format

A data frame with 12 observations on the following 12 variables. J item by judge:

J11

a numeric vector

J12

a numeric vector

J21

a numeric vector

J22

a numeric vector

J31

a numeric vector

J32

a numeric vector

J41

a numeric vector

J42

a numeric vector

J51

a numeric vector

J52

a numeric vector

J61

a numeric vector

J62

a numeric vector

### Details

Generalizability theory is the application of a components of variance approach to the analysis of reliability. Given a G study (generalizability) the components are estimated and then may be used in a D study (Decision). Different ratios are formed as appropriate for the particular D study.

### Source

Gleser, G., Cronbach, L., and Rajaratnam, N. (1965). Generalizability of scores influenced by multiple sources of variance. Psychometrika, 30(4):395-418. (Table 3, rearranged to show increasing patient severity and increasing item severity.

### References

Gleser, G., Cronbach, L., and Rajaratnam, N. (1965). Generalizability of scores influenced by multiple sources of variance. Psychometrika, 30(4):395-418.

### Examples

#Find the MS for each component:
#First, stack the data
data(Gleser)
stack.g <- stack(Gleser)
st.gc.df <- data.frame(stack.g,Persons=rep(letters[1:12],12),
Items=rep(letters[1:6],each=24),Judges=rep(letters[1:2],each=12))
#now do the ANOVA
anov <- aov(values ~ (Persons*Judges*Items),data=st.gc.df)
summary(anov)

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

All Public Datasets File Size
Test 394 bytes
Q1 332 bytes
prova_Correlatio 593 bytes
profva
test 332 bytes
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