setwd("~/Dropbox/Teaching_MRes_Northumbria/Lecture7")
require(haven)
## Loading required package: haven
in_class<-read_spss('https://stats.idre.ucla.edu/wp-content/uploads/2016/02/M255.sav')
require(dplyr)
## Loading required package: dplyr
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
in_class_red<- select(in_class, num_range("ITEM",13:24))
require(skimr)
## Loading required package: skimr
##
## Attaching package: 'skimr'
## The following object is masked from 'package:stats':
##
## filter
skim(in_class_red)
require(psych)
## Loading required package: psych
KMO(in_class_red)
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = in_class_red)
## Overall MSA = 0.94
## MSA for each item =
## ITEM13 ITEM14 ITEM15 ITEM16 ITEM17 ITEM18 ITEM19 ITEM20 ITEM21 ITEM22
## 0.93 0.92 0.94 0.94 0.96 0.92 0.91 0.96 0.96 0.96
## ITEM23 ITEM24
## 0.92 0.91
fa_6<-fa(in_class_red,6, fm = 'minres', rotate='varimax', fa = 'fa')
sink('fa6_Sidanius.txt')
fa_6
## Factor Analysis using method = minres
## Call: fa(r = in_class_red, nfactors = 6, rotate = "varimax", fm = "minres",
## fa = "fa")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR6 MR3 MR4 MR1 MR5 h2 u2 com
## ITEM13 0.19 0.66 0.24 0.28 0.12 0.08 0.63 0.37 2.0
## ITEM14 0.22 0.84 0.19 0.15 -0.04 -0.12 0.83 0.17 1.4
## ITEM15 0.30 0.64 0.21 0.20 0.16 0.10 0.62 0.38 2.1
## ITEM16 0.22 0.36 0.17 0.80 0.05 0.00 0.85 0.15 1.7
## ITEM17 0.42 0.46 0.28 0.33 0.21 0.13 0.64 0.36 4.2
## ITEM18 0.78 0.26 0.21 0.12 -0.03 0.11 0.74 0.26 1.5
## ITEM19 0.73 0.14 0.13 0.11 0.06 -0.07 0.59 0.41 1.2
## ITEM20 0.54 0.20 0.16 0.12 0.04 0.08 0.37 0.63 1.6
## ITEM21 0.52 0.32 0.29 0.17 0.38 0.00 0.63 0.37 3.6
## ITEM22 0.55 0.17 0.28 0.14 0.15 -0.06 0.46 0.54 2.1
## ITEM23 0.40 0.44 0.59 0.12 0.15 0.08 0.75 0.25 3.0
## ITEM24 0.30 0.26 0.75 0.17 0.03 -0.01 0.76 0.24 1.7
##
## MR2 MR6 MR3 MR4 MR1 MR5
## SS loadings 2.64 2.41 1.41 1.02 0.29 0.08
## Proportion Var 0.22 0.20 0.12 0.09 0.02 0.01
## Cumulative Var 0.22 0.42 0.54 0.62 0.65 0.65
## Proportion Explained 0.34 0.31 0.18 0.13 0.04 0.01
## Cumulative Proportion 0.34 0.64 0.82 0.95 0.99 1.00
##
## Mean item complexity = 2.2
## Test of the hypothesis that 6 factors are sufficient.
##
## The degrees of freedom for the null model are 66 and the objective function was 6.43 with Chi Square of 9147.29
## The degrees of freedom for the model are 9 and the objective function was 0.01
##
## The root mean square of the residuals (RMSR) is 0
## The df corrected root mean square of the residuals is 0.01
##
## The harmonic number of observations is 1412 with the empirical chi square 2.01 with prob < 0.99
## The total number of observations was 1428 with Likelihood Chi Square = 8.36 with prob < 0.5
##
## Tucker Lewis Index of factoring reliability = 1.001
## RMSEA index = 0 and the 90 % confidence intervals are 0 0.028
## BIC = -57.01
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR2 MR6 MR3 MR4
## Correlation of (regression) scores with factors 0.88 0.89 0.83 0.87
## Multiple R square of scores with factors 0.77 0.80 0.68 0.76
## Minimum correlation of possible factor scores 0.54 0.60 0.36 0.52
## MR1 MR5
## Correlation of (regression) scores with factors 0.57 0.43
## Multiple R square of scores with factors 0.33 0.19
## Minimum correlation of possible factor scores -0.35 -0.63
sink()
fa.parallel(in_class_red, fm = 'minres', fa = 'fa')
## Parallel analysis suggests that the number of factors = 3 and the number of components = NA
require(labelled)
## Loading required package: labelled
fa_3_sid<-fa(in_class_red,3, fm = 'minres', rotate='varimax', fa = 'fa')
sink('fa_3_sidanius_output.txt')
fa_3_sid
## Factor Analysis using method = minres
## Call: fa(r = in_class_red, nfactors = 3, rotate = "varimax", fm = "minres",
## fa = "fa")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 MR3 h2 u2 com
## ITEM13 0.77 0.18 0.23 0.67 0.33 1.3
## ITEM14 0.74 0.21 0.21 0.64 0.36 1.3
## ITEM15 0.69 0.30 0.22 0.61 0.39 1.6
## ITEM16 0.58 0.28 0.19 0.45 0.55 1.7
## ITEM17 0.59 0.44 0.29 0.62 0.38 2.4
## ITEM18 0.29 0.74 0.22 0.68 0.32 1.5
## ITEM19 0.17 0.74 0.13 0.59 0.41 1.2
## ITEM20 0.23 0.54 0.16 0.37 0.63 1.6
## ITEM21 0.40 0.53 0.33 0.54 0.46 2.6
## ITEM22 0.22 0.56 0.30 0.45 0.55 1.9
## ITEM23 0.46 0.38 0.66 0.78 0.22 2.4
## ITEM24 0.33 0.32 0.66 0.64 0.36 2.0
##
## MR1 MR2 MR3
## SS loadings 2.98 2.65 1.42
## Proportion Var 0.25 0.22 0.12
## Cumulative Var 0.25 0.47 0.59
## Proportion Explained 0.42 0.38 0.20
## Cumulative Proportion 0.42 0.80 1.00
##
## Mean item complexity = 1.8
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 66 and the objective function was 6.43 with Chi Square of 9147.29
## The degrees of freedom for the model are 33 and the objective function was 0.1
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.02
##
## The harmonic number of observations is 1412 with the empirical chi square 47.53 with prob < 0.049
## The total number of observations was 1428 with Likelihood Chi Square = 140.51 with prob < 3.2e-15
##
## Tucker Lewis Index of factoring reliability = 0.976
## RMSEA index = 0.048 and the 90 % confidence intervals are 0.04 0.056
## BIC = -99.2
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1 MR2 MR3
## Correlation of (regression) scores with factors 0.88 0.87 0.79
## Multiple R square of scores with factors 0.78 0.75 0.63
## Minimum correlation of possible factor scores 0.55 0.50 0.25
sink()