R Dataset / Package psych / Schutz

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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Submitted by pmagunia on March 9, 2018 - 1:06 PM
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Attachment Size
dataset-38357.csv 513 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 Schutz data set which pertains to The Schutz correlation matrix example from Shapiro and ten Berge. The Schutz data set is found in the psych R package. Try to load the Schutz data set in R by issuing the following command at the console data("Schutz"). This may load the data into a variable called Schutz. If R says the Schutz 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("Schutz"). 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 Schutz 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 Schutz R data set. The size of this file is about 513 bytes.


The Schutz correlation matrix example from Shapiro and ten Berge

Description

Shapiro and ten Berge use the Schutz correlation matrix as an example for Minimum Rank Factor Analysis. The Schutz data set is also a nice example of how normal minres or maximum likelihood will lead to a Heywood case, but minrank factoring will not.

Usage

data("Schutz")

Format

The format is: num [1:9, 1:9] 1 0.8 0.28 0.29 0.41 0.38 0.44 0.4 0.41 0.8 ... - attr(*, "dimnames")=List of 2 ..$ :1] "Word meaning" "Odd Words" "Boots" "Hatchets" ... ..$ : chr [1:9] "V1" "V2" "V3" "V4" ...

Details

These are 9 cognitive variables of importance mainly because they are used as an example by Shapiro and ten Berge for their paper on Minimum Rank Factor Analysis.

The solution from the fa function with the fm='minrank' option is very close (but not exactly equal) to their solution.

Source

Richard E. Schutz,(1958) Factorial Validity of the Holzinger-Crowdeer Uni-factor tests. Educational and Psychological Measurement, 48, 873-875.

References

Alexander Shapiro and Jos M.F. ten Berge (2002) Statistical inference of minimum rank factor analysis. Psychometrika, 67. 70-94

Examples

data(Schutz)
corPlot(Schutz,numbers=TRUE,upper=FALSE)
#f4min <- fa(Schutz,4,fm="minrank")  #for an example of minimum rank factor Analysis
#compare to
#f4 <- fa(Schutz,4,fm="mle")  #for the maximum likelihood solution which has a Heywood case 

--

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