Confirmatory Factor Analysis Homework for Stat 5600: Applied Multivariate Statistics, Assignments of Descriptive statistics

Instructions for completing homework 6 in the stat 5600: applied multivariate statistics course. Students are required to perform confirmatory factor analysis on two different datasets using r and the sem package. The first dataset, psych tests, is a triangular correlation matrix for nine psychological tests on 145 children. The second dataset, cognition, is obtained from a website and requires the removal of observations with missing values and reversing the direction of certain variables before analysis.

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Pre 2010

Uploaded on 07/30/2009

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Stat 5600 Applied Multivariate Statistics
Homework 6
Confirmatory Factor Analysis
Due 5:00 Friday, March 9
1. Recall, the PSYCH TESTS dataset from homework 5 is a triangular correlation ma-
trix for nine psychological/intelligence tests on 145 children. Use read.tri to import
this matrix and use the sem package to run a confirmatory factor analysis on the
PSYCH TESTS data to answer question 6.9.
2. Import the COGNITION dataset from the website and remove the observations with
missing values, as in homework 4. Use Table 4.1 to identify the variables which are
reverse-coded, and multiply those columns by -1, so that all variables are measured
in the same direction. For example, to reverse item 3, use something like
cog[,3] <- -1*cog[,3] .
Use this modified data set and sem to answer question 6.8. (The modification is
necessary for part b. The sem function can fix loadings to be equal, but not to be
equal in absolute value but with different signs.)

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Stat 5600 — Applied Multivariate Statistics Homework 6 Confirmatory Factor Analysis Due 5:00 Friday, March 9

  1. Recall, the PSYCH TESTS dataset from homework 5 is a triangular correlation ma- trix for nine psychological/intelligence tests on 145 children. Use read.tri to import this matrix and use the sem package to run a confirmatory factor analysis on the PSYCH TESTS data to answer question 6.9.
  2. Import the COGNITION dataset from the website and remove the observations with missing values, as in homework 4. Use Table 4.1 to identify the variables which are reverse-coded, and multiply those columns by -1, so that all variables are measured in the same direction. For example, to reverse item 3, use something like cog[,3] <- -1*cog[,3]. Use this modified data set and sem to answer question 6.8. (The modification is necessary for part b. The sem function can fix loadings to be equal, but not to be equal in absolute value but with different signs.)