R Dataset / Package psych / Schmid

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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Regression

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Stem and Leaf Plots

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Visual Summaries

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<iframe src="https://embed.picostat.com/r-dataset-package-psych-schmid.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-11795.csv 1020 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 Schmid data set which pertains to 12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation. The Schmid data set is found in the psych R package. You can load the Schmid data set in R by issuing the following command at the console data("Schmid"). This will load the data into a variable called Schmid. If R says the Schmid 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 Schmid R data set. The size of this file is about 1,020 bytes.


12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation

Description

John Schmid and John M. Leiman (1957) discuss how to transform a hierarchical factor structure to a bifactor structure. Schmid contains the example 12 x 12 correlation matrix. schmid.leiman is a 12 x 12 correlation matrix with communalities on the diagonal. This can be used to show the effect of correcting for attenuation. Two additional data sets are taken from Chen et al. (2006).

Usage

data(Schmid)

Details

Two artificial correlation matrices from Schmid and Leiman (1957). One real and one artificial covariance matrices from Chen et al. (2006).

  • Schmid: a 12 x 12 artificial correlation matrix created to show the Schmid-Leiman transformation.

  • schmid.leiman: A 12 x 12 matrix with communalities on the diagonal. Treating this as a covariance matrix shows the 6 x 6 factor solution

  • Chen: An 18 x 18 covariance matrix of health related quality of life items from Chen et al. (2006). Number of observations = 403. The first item is a measure of the quality of life. The remaining 17 items form four subfactors: The items are (a) Cognition subscale: “Have difficulty reasoning and solving problems?" “React slowly to things that were said or done?"; “Become confused and start several actions at a time?" “Forget where you put things or appointments?"; “Have difficulty concentrating?" (b) Vitality subscale: “Feel tired?" “Have enough energy to do the things you want?" (R) “Feel worn out?" ; “Feel full of pep?" (R). (c) Mental health subscale: “Feel calm and peaceful?"(R) “Feel downhearted and blue?"; “Feel very happy"(R) ; “Feel very nervous?" ; “Feel so down in the dumps nothing could cheer you up? (d) Disease worry subscale: “Were you afraid because of your health?"; “Were you frustrated about your health?"; “Was your health a worry in your life?" .

  • West: A 16 x 16 artificial covariance matrix from Chen et al. (2006).

Source

John Schmid Jr. and John. M. Leiman (1957), The development of hierarchical factor solutions.Psychometrika, 22, 83-90.

F.F. Chen, S.G. West, and K.H. Sousa.(2006) A comparison of bifactor and second-order models of quality of life. Multivariate Behavioral Research, 41(2):189-225, 2006.

References

Y.-F. Yung, D.Thissen, and L.D. McLeod. (1999) On the relationship between the higher-order factor model and the hierarchical factor model. Psychometrika, 64(2):113-128, 1999.

Examples

data(Schmid)
cor.plot(Schmid,TRUE)
print(fa(Schmid,6,rotate="oblimin"),cut=0)  #shows an oblique solution
round(cov2cor(schmid.leiman),2)
cor.plot(cov2cor(West),TRUE)

--

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