R Dataset / Package psych / Gleser

How To Create a Barplot

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How To Create a Stacked Barplot

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How To Create a Pie Chart

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How To Compute the Mean

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How To Create a Plot

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How to Compute the Median

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Boxplot

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Correlation Coefficient

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Cumulative Frequency Histogram

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Dotplot

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Hollow Histogram

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Numerical Summaries

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Pie Chart

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Plot

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Ratio

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Regression

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Stem and Leaf Plots

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Visual Summaries

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Attachment Size
dataset-75737.csv 360 bytes
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Documentation

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. Try to load the Gleser data set in R by issuing the following command at the console data("Gleser"). This may 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 with library("psych") followed by data("Gleser"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing Gleser at the command-line which should display the entire dataset.

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.


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)

--

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