Question

Want to preform profile analysis on groups that are not independent. Suppose that the data, spouse,  from profileR in R, were a sample of couples, with husbands and wives each rating their own spouses. Formulate and test the same first two hypotheses (parallel and coincident) as were discussed in class. Suggest a way to formulate and test the third hypothesis(level).

Will rate positively, thanks

Answer 1

The pbg function implements three hypothesis tests. These tests are whether the profiles are parallel, have equal levels, and are flat across groups defined by the grouping variable. If parallelism is rejected, the other two tests are not necessary. In that case, flatness may be assessed within each group, and various within- and between-group contrasts may be analyzed.

here flat mean concident.

Formate of code :
pbg(data, group, original.names = FALSE, profile.plot = FALSE)

Object of class profg is returned, listing the following components:

data.summary - Means of observed variables by the grouping variable

corr.table - A matrix of correlations among observed variables splitted by the grouping variable

profile.test - Results of F-tests for testing parallel, coincidential, and level profiles across two groups.

### R - Code
Load package :
library(ggplot2)
library (RcolorBrewer)
library (reshape)
library (lavaan)

#Note : if above package is not available in your r package directory. then please install them using following command
install.package("package name", dependencies = TRUE) .then load.

data(spouse)
mod <- pbg(data=spouse[,1:4], group=spouse[,5], original.names=TRUE, profile.plot=TRUE)
print(mod) #prints average scores in the profile across two groups
summary(mod) #prints the results of three profile by group hypothesis tests
####
run above code in r you will get output as below.

# OUTPUT
Loading required package: ggplot2
Loading required package: RColorBrewer
Loading required package: reshape
Loading required package: lavaan
This is lavaan 0.5-23.1097
lavaan is BETA software! Please report any bugs.

Data Summary:
       Husband     Wife
item1 3.900000 3.833333
item2 3.966667 4.100000
item3 4.333333 4.633333
item4 4.400000 4.533333
Call:
pbg(data = spouse[, 1:4], group = spouse[, 5], original.names = TRUE, 
    profile.plot = TRUE)

Hypothesis Tests:
$`Ho: Profiles are parallel`
  Multivariate.Test Statistic Approx.F num.df den.df    p.value
1             Wilks 0.8785726 2.579917      3     56 0.06255945
2            Pillai 0.1214274 2.579917      3     56 0.06255945
3  Hotelling-Lawley 0.1382099 2.579917      3     56 0.06255945
4               Roy 0.1382099 2.579917      3     56 0.06255945

$`Ho: Profiles have equal levels`
            Df Sum Sq Mean Sq F value Pr(>F)
group        1  0.234  0.2344   1.533  0.221
Residuals   58  8.869  0.1529               

$`Ho: Profiles are flat`
         F df1 df2      p-value
1 24.82071   3  57  0.0001554491