# R Dataset / Package psych / Gleser

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On this R-data 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. ## Example data from Gleser, Cronbach and Rajaratnam (1965) to show basic principles of generalizability theory.## DescriptionGleser, 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. ## Usagedata(Gleser) ## FormatA 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
## DetailsGeneralizability 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. ## SourceGleser, 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. ## ReferencesGleser, 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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Attachment | Size |
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dataset-75737.csv | 360 bytes |