# par3f: Three-Factor Parallel Covariance Matrix In JackStat/Lambda4: Collection of Internal Consistency Reliability Coefficients.

## Description

This Covariance matrix was used as the population model for one set of simulations. It was used to represent a parallel data structure in which all factor loadings and error variances are set at .6 and the latent variables are correlated at .3.

## Usage

 `1` ```data(par3f) ```

## Format

A covariance matrix of 12 theoretical items.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29``` ```###---Loadings fx<-t(matrix(c( .6,0,0, .6,0,0, .6,0,0, .6,0,0, 0,.6,0, 0,.6,0, 0,.6,0, 0,.6,0, 0,0,.6, 0,0,.6, 0,0,.6, 0,0,.6), nrow=3)) ###--Error Variances err<-diag(c( .6^2,.6^2,.6^2,.6^2, .6^2,.6^2,.6^2,.6^2, .6^2,.6^2,.6^2,.6^2)) ###---3x3 matrix of factor covariances phi<-matrix(c(rep(.3, 9)), nrow=3) diag(phi)<-1 ###---Reliability Calculation---### t1<-matrix(c(rep(1,12)), nrow=1) t1t<-matrix(c(rep(1,12)), ncol=1) (fx%*%phi%*%t(fx)+err) ```

JackStat/Lambda4 documentation built on May 7, 2019, 10:16 a.m.