library(haven)
library(foreign)
library(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
library(polycor)
## Warning: package 'polycor' was built under R version 3.5.3
library(corrplot)
## Warning: package 'corrplot' was built under R version 3.5.3
## corrplot 0.84 loaded
library(psych)
## Warning: package 'psych' was built under R version 3.5.3
##
## Attaching package: 'psych'
## The following object is masked from 'package:polycor':
##
## polyserial
BSGCANM6 <- read_sav("BSGCANM6.sav")
df = BSGCANM6 %>% select("BSBM19A", "BSBM19B", "BSBM19C", "BSBM19D", "BSBM17A", "BSBM20A", "BSBM20B", "BSBM20C", "BSBM20E", "BSBM20D", "BSBG01", "BSBG07A", "BSBG07B", "BSBG10A", "BSMMAT01")
df1 <- na.omit(df)
df = df1 %>% select(- BSBG01, - BSBG07A, - BSBG07B, -BSBG10A, -BSMMAT01)
attach(df)
summary(df)
## BSBM19A BSBM19B BSBM19C BSBM19D
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000
## Median :2.000 Median :3.000 Median :3.000 Median :2.000
## Mean :1.791 Mean :2.884 Mean :2.757 Mean :2.045
## 3rd Qu.:2.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## BSBM17A BSBM20A BSBM20B BSBM20C
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :1.000 Median :2.000 Median :1.000
## Mean :2.026 Mean :1.655 Mean :1.801 Mean :1.385
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
## BSBM20E BSBM20D
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000
## Median :2.000 Median :1.000
## Mean :2.458 Mean :1.587
## 3rd Qu.:3.000 3rd Qu.:2.000
## Max. :4.000 Max. :4.000
df <- as.data.frame(lapply(df, as.numeric))
hist(df$BSBM19A, main = "Распределение переменной \n 'Обычно у меня все хорошо получается в математике' ", xlab = "Степень согласия", ylab = "Частота ответа")
hist(df$BSBM19B, main = "Распределение переменной 'Математика для меня более \n тяжелый предмет, чем для большинства моих одноклассников' ", xlab = "Степень согласия", ylab = "Частота ответа")
hist(df$BSBM19C, main = "Распределение переменной \n 'Математика - не одна из моих сильных сторон' ", xlab = "Степень согласия", ylab = "Частота ответа")
hist(df$BSBM19D, main = "Распределение переменной \n 'Я быстро учусь математике' ", xlab = "Степень согласия", ylab = "Частота ответа")
hist(df$BSBM17A, main = "Распределение переменной \n 'Мне нравится изучать математику' ", xlab = "Степень согласия", ylab = "Частота ответа")
hist(df$BSBM20A, main = "Распределение переменной 'Я думаю, что изучение математики \n поможет мне в моей повседневной жизни' ", xlab = "Степень согласия", ylab = "Частота ответа")
hist(df$BSBM20B, main = "Распределение переменной 'Мне нужна математика, \n чтобы изучать другие школьные предметы' ", xlab = "Степень согласия", ylab = "Частота ответа")
hist(df$BSBM20C, main = "Распределение переменной 'Мне нужно хорошо разбираться \n в математике, чтобы попасть в тот университет, в который хочу' ", xlab = "Степень согласия", ylab = "Частота ответа")
hist(df$BSBM20D, main = "Распределение переменной 'Мне нужно преуспеть в математике, \n чтобы получить работу, которую я хочу' ", xlab = "Степень согласия", ylab = "Частота ответа")
hist(df$BSBM20E, main = "Распределение переменной 'Я хотел бы работу, \n которая включает в себя использование математики' ", xlab = "Степень согласия", ylab = "Частота ответа")
df <- as.data.frame(lapply(df, as.factor))
df.cor = hetcor(df)
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
## Warning in log(P): созданы NaN
df.cor
##
## Two-Step Estimates
##
## Correlations/Type of Correlation:
## BSBM19A BSBM19B BSBM19C BSBM19D BSBM17A BSBM20A
## BSBM19A 1 Polychoric Polychoric Polychoric Polychoric Polychoric
## BSBM19B -0.7295 1 Polychoric Polychoric Polychoric Polychoric
## BSBM19C -0.7723 0.8176 1 Polychoric Polychoric Polychoric
## BSBM19D 0.7936 -0.6789 -0.7247 1 Polychoric Polychoric
## BSBM17A 0.6374 -0.5014 -0.6111 0.6473 1 Polychoric
## BSBM20A 0.3275 -0.2075 -0.2639 0.3301 0.4843 1
## BSBM20B 0.2808 -0.1683 -0.2077 0.2765 0.4088 0.6929
## BSBM20C 0.2775 -0.2003 -0.2155 0.2881 0.3397 0.5278
## BSBM20E 0.4744 -0.3853 -0.4855 0.5067 0.5941 0.5331
## BSBM20D 0.2635 -0.1749 -0.2336 0.2761 0.356 0.5046
## BSBM20B BSBM20C BSBM20E BSBM20D
## BSBM19A Polychoric Polychoric Polychoric Polychoric
## BSBM19B Polychoric Polychoric Polychoric Polychoric
## BSBM19C Polychoric Polychoric Polychoric Polychoric
## BSBM19D Polychoric Polychoric Polychoric Polychoric
## BSBM17A Polychoric Polychoric Polychoric Polychoric
## BSBM20A Polychoric Polychoric Polychoric Polychoric
## BSBM20B 1 Polychoric Polychoric Polychoric
## BSBM20C 0.5358 1 Polychoric Polychoric
## BSBM20E 0.4583 0.4853 1 Polychoric
## BSBM20D 0.4899 0.7664 0.6613 1
##
## Standard Errors:
## BSBM19A BSBM19B BSBM19C BSBM19D BSBM17A BSBM20A BSBM20B
## BSBM19A
## BSBM19B 0.006166
## BSBM19C 0.005388 0.004302
## BSBM19D 0.005086 0.006844 0.006063
## BSBM17A 0.007927 0.009666 0.008067 0.007481
## BSBM20A 0.01259 0.01321 0.01286 0.01223 0.01053
## BSBM20B 0.01265 0.01309 0.01289 0.01233 0.0111 0.007204
## BSBM20C 0.01433 0.01462 0.01454 0.01391 0.0135 0.01135 0.01099
## BSBM20E 0.0102 0.0109 0.009761 0.009422 0.008205 0.009769 0.01035
## BSBM20D 0.01345 0.01374 0.01341 0.01303 0.01233 0.01089 0.0108
## BSBM20C BSBM20E
## BSBM19A
## BSBM19B
## BSBM19C
## BSBM19D
## BSBM17A
## BSBM20A
## BSBM20B
## BSBM20C
## BSBM20E 0.01175
## BSBM20D 0.006636 0.00807
##
## n = 8068
##
## P-values for Tests of Bivariate Normality:
## BSBM19A BSBM19B BSBM19C BSBM19D BSBM17A BSBM20A
## BSBM19A
## BSBM19B 4.625e-118
## BSBM19C 1.828e-149 8.95e-122
## BSBM19D 8.278e-46 5.831e-111 1.05e-165
## BSBM17A 2.463e-24 1.245e-29 1.504e-55 2.635e-36
## BSBM20A 1.578e-06 2.823e-12 8.815e-10 3.362e-10 5.454e-13
## BSBM20B 1.098e-12 6.915e-18 3.42e-12 6.989e-16 3.241e-15 2.958e-61
## BSBM20C 4.886e-15 5.963e-26 1.662e-22 2.989e-24 4.626e-24 1.075e-51
## BSBM20E 4.86e-17 1.806e-25 1.489e-26 1.236e-26 7.295e-33 3.059e-19
## BSBM20D 1.426e-10 1.4e-15 4.868e-11 4.75e-18 5.513e-14 1.378e-39
## BSBM20B BSBM20C BSBM20E
## BSBM19A
## BSBM19B
## BSBM19C
## BSBM19D
## BSBM17A
## BSBM20A
## BSBM20B
## BSBM20C 7.407e-58
## BSBM20E 2.092e-18 5.788e-31
## BSBM20D 2.479e-41 9.628e-102 1.53e-58
cor.plot(df.cor)
df <- as.data.frame(lapply(df, as.numeric))
fa.parallel(df.cor$correlations, n.obs=8191, fa="both", n.iter=100)
## Parallel analysis suggests that the number of factors = 4 and the number of components = 2
fa1 = fa(df.cor$correlations, nfactors=3, rotate="none", fm="ml")
fa.diagram(fa(df.cor$correlations, nfactors=3, rotate="none", fm="ml"))
fa1
## Factor Analysis using method = ml
## Call: fa(r = df.cor$correlations, nfactors = 3, rotate = "none", fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML2 ML1 ML3 h2 u2 com
## BSBM19A 0.83 0.29 -0.04 0.77 0.225 1.2
## BSBM19B -0.81 -0.20 0.18 0.73 0.266 1.2
## BSBM19C -0.85 -0.26 0.15 0.82 0.180 1.2
## BSBM19D 0.79 0.30 -0.02 0.71 0.285 1.3
## BSBM17A 0.64 0.38 0.20 0.59 0.410 1.9
## BSBM20A 0.26 0.52 0.64 0.75 0.255 2.3
## BSBM20B 0.19 0.50 0.59 0.63 0.365 2.2
## BSBM20C 0.06 0.77 0.17 0.63 0.370 1.1
## BSBM20E 0.37 0.68 0.12 0.61 0.391 1.6
## BSBM20D -0.03 1.00 -0.01 1.00 0.005 1.0
##
## ML2 ML1 ML3
## SS loadings 3.36 2.99 0.89
## Proportion Var 0.34 0.30 0.09
## Cumulative Var 0.34 0.64 0.72
## Proportion Explained 0.46 0.41 0.12
## Cumulative Proportion 0.46 0.88 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 45 and the objective function was 7.15
## The degrees of freedom for the model are 18 and the objective function was 0.28
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## ML2 ML1 ML3
## Correlation of (regression) scores with factors 0.97 1.00 0.87
## Multiple R square of scores with factors 0.93 1.00 0.75
## Minimum correlation of possible factor scores 0.86 0.99 0.51
fa12 = fa(df.cor$correlations, nfactors=2, rotate="none", fm="ml")
fa.diagram(fa(df.cor$correlations, nfactors=2, rotate="none", fm="ml"))
fa12
## Factor Analysis using method = ml
## Call: fa(r = df.cor$correlations, nfactors = 2, rotate = "none", fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML1 ML2 h2 u2 com
## BSBM19A 0.85 -0.24 0.78 0.22 1.2
## BSBM19B -0.78 0.35 0.72 0.28 1.4
## BSBM19C -0.84 0.32 0.81 0.19 1.3
## BSBM19D 0.82 -0.19 0.72 0.28 1.1
## BSBM17A 0.75 0.04 0.56 0.44 1.0
## BSBM20A 0.51 0.47 0.48 0.52 2.0
## BSBM20B 0.45 0.49 0.45 0.55 2.0
## BSBM20C 0.50 0.64 0.66 0.34 1.9
## BSBM20E 0.69 0.36 0.61 0.39 1.5
## BSBM20D 0.51 0.69 0.74 0.26 1.9
##
## ML1 ML2
## SS loadings 4.73 1.80
## Proportion Var 0.47 0.18
## Cumulative Var 0.47 0.65
## Proportion Explained 0.72 0.28
## Cumulative Proportion 0.72 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 45 and the objective function was 7.15
## The degrees of freedom for the model are 26 and the objective function was 0.76
##
## The root mean square of the residuals (RMSR) is 0.05
## The df corrected root mean square of the residuals is 0.07
##
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## ML1 ML2
## Correlation of (regression) scores with factors 0.97 0.92
## Multiple R square of scores with factors 0.94 0.85
## Minimum correlation of possible factor scores 0.89 0.70
fa1 = fa(df.cor$correlations, nfactors=4, rotate="none", fm="ml")
fa.diagram(fa(df.cor$correlations, nfactors=4, rotate="none", fm="ml"))
fa2 = fa(df.cor$correlations, nfactors=3, rotate="varimax", fm="ml")
fa.diagram(fa(df.cor$correlations, nfactors=3, rotate="varimax", fm="ml"))
fa2
## Factor Analysis using method = ml
## Call: fa(r = df.cor$correlations, nfactors = 3, rotate = "varimax",
## fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML2 ML1 ML3 h2 u2 com
## BSBM19A 0.85 0.13 0.18 0.77 0.225 1.1
## BSBM19B -0.85 -0.08 -0.03 0.73 0.266 1.0
## BSBM19C -0.89 -0.13 -0.07 0.82 0.180 1.1
## BSBM19D 0.81 0.15 0.20 0.71 0.285 1.2
## BSBM17A 0.62 0.20 0.40 0.59 0.410 2.0
## BSBM20A 0.18 0.31 0.79 0.75 0.255 1.4
## BSBM20B 0.12 0.32 0.72 0.63 0.365 1.4
## BSBM20C 0.12 0.69 0.37 0.63 0.370 1.6
## BSBM20E 0.42 0.55 0.35 0.61 0.391 2.6
## BSBM20D 0.11 0.96 0.24 1.00 0.005 1.2
##
## ML2 ML1 ML3
## SS loadings 3.54 2.00 1.70
## Proportion Var 0.35 0.20 0.17
## Cumulative Var 0.35 0.55 0.72
## Proportion Explained 0.49 0.28 0.23
## Cumulative Proportion 0.49 0.77 1.00
##
## Mean item complexity = 1.5
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 45 and the objective function was 7.15
## The degrees of freedom for the model are 18 and the objective function was 0.28
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## ML2 ML1 ML3
## Correlation of (regression) scores with factors 0.96 0.99 0.88
## Multiple R square of scores with factors 0.92 0.98 0.77
## Minimum correlation of possible factor scores 0.85 0.96 0.55
fa3 = fa(df.cor$correlations, nfactors=3, rotate="oblimin", fm="ml")
## Loading required namespace: GPArotation
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : A loading greater than abs(1) was detected. Examine the loadings
## carefully.
fa.diagram(fa(df.cor$correlations, nfactors=3, rotate="oblimin", fm="ml"))
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : A loading greater than abs(1) was detected. Examine the loadings
## carefully.
fa3 = fa(df, nfactors = 3, cor="mixed", fm="mle")
##
## mixed.cor is deprecated, please use mixedCor.
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : A loading greater than abs(1) was detected. Examine the loadings
## carefully.
fa3
## Factor Analysis using method = ml
## Call: fa(r = df, nfactors = 3, fm = "mle", cor = "mixed")
##
## Warning: A Heywood case was detected.
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML2 ML1 ML3 h2 u2 com
## BSBM19A 0.86 -0.01 0.06 0.77 0.225 1.0
## BSBM19B -0.89 0.01 0.11 0.73 0.266 1.0
## BSBM19C -0.92 -0.02 0.07 0.82 0.180 1.0
## BSBM19D 0.81 0.00 0.09 0.71 0.285 1.0
## BSBM17A 0.57 0.00 0.34 0.59 0.410 1.6
## BSBM20A 0.02 0.02 0.85 0.75 0.255 1.0
## BSBM20B -0.03 0.06 0.77 0.63 0.365 1.0
## BSBM20C 0.00 0.65 0.21 0.63 0.370 1.2
## BSBM20E 0.34 0.46 0.19 0.61 0.391 2.2
## BSBM20D -0.02 1.03 -0.05 1.00 0.005 1.0
##
## ML2 ML1 ML3
## SS loadings 3.60 1.89 1.76
## Proportion Var 0.36 0.19 0.18
## Cumulative Var 0.36 0.55 0.72
## Proportion Explained 0.50 0.26 0.24
## Cumulative Proportion 0.50 0.76 1.00
##
## With factor correlations of
## ML2 ML1 ML3
## ML2 1.00 0.3 0.37
## ML1 0.30 1.0 0.60
## ML3 0.37 0.6 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 45 and the objective function was 7.15 with Chi Square of 57637.49
## The degrees of freedom for the model are 18 and the objective function was 0.28
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 8068 with the empirical chi square 487.04 with prob < 5.5e-92
## The total number of observations was 8068 with Likelihood Chi Square = 2253.06 with prob < 0
##
## Tucker Lewis Index of factoring reliability = 0.903
## RMSEA index = 0.124 and the 90 % confidence intervals are 0.12 0.128
## BIC = 2091.14
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## ML2 ML1 ML3
## Correlation of (regression) scores with factors 0.97 1.00 0.92
## Multiple R square of scores with factors 0.94 1.00 0.85
## Minimum correlation of possible factor scores 0.87 0.99 0.70
fa.diagram(fa(df, nfactors=3, cor="mixed", fm="mle"))
##
## mixed.cor is deprecated, please use mixedCor.
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : A loading greater than abs(1) was detected. Examine the loadings
## carefully.
f1 <- df[c('BSBM20D', 'BSBM20C', 'BSBM20E')]
f2 <- df[c('BSBM19C', 'BSBM19B', 'BSBM19A', 'BSBM19D', 'BSBM17A')]
f3 <- df[c('BSBM20A', 'BSBM20B')]
alpha(f1, check.keys = TRUE)
##
## Reliability analysis
## Call: alpha(x = f1, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.73 0.75 0.7 0.5 3 0.0051 1.8 0.67 0.53
##
## lower alpha upper 95% confidence boundaries
## 0.72 0.73 0.74
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## BSBM20D 0.49 0.52 0.36 0.36 1.1 0.0102 NA 0.36
## BSBM20C 0.68 0.69 0.53 0.53 2.2 0.0069 NA 0.53
## BSBM20E 0.76 0.77 0.63 0.63 3.4 0.0051 NA 0.63
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## BSBM20D 8068 0.86 0.88 0.81 0.68 1.6 0.80
## BSBM20C 8068 0.75 0.81 0.67 0.54 1.4 0.65
## BSBM20E 8068 0.83 0.77 0.57 0.50 2.5 1.01
##
## Non missing response frequency for each item
## 1 2 3 4 miss
## BSBM20D 0.58 0.29 0.10 0.03 0
## BSBM20C 0.69 0.25 0.04 0.02 0
## BSBM20E 0.20 0.32 0.29 0.18 0
alpha(f2, check.keys = TRUE)
## Warning in alpha(f2, check.keys = TRUE): Some items were negatively correlated with total scale and were automatically reversed.
## This is indicated by a negative sign for the variable name.
##
## Reliability analysis
## Call: alpha(x = f2, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.87 0.6 7.6 0.0021 2 0.8 0.61
##
## lower alpha upper 95% confidence boundaries
## 0.88 0.88 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## BSBM19C- 0.84 0.85 0.81 0.58 5.5 0.0029 0.0086 0.58
## BSBM19B- 0.86 0.86 0.83 0.61 6.2 0.0026 0.0053 0.60
## BSBM19A 0.85 0.85 0.82 0.58 5.5 0.0027 0.0113 0.58
## BSBM19D 0.85 0.85 0.83 0.59 5.7 0.0027 0.0128 0.59
## BSBM17A 0.88 0.89 0.86 0.66 7.8 0.0021 0.0027 0.65
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## BSBM19C- 8068 0.88 0.86 0.83 0.78 2.2 1.11
## BSBM19B- 8068 0.83 0.82 0.77 0.72 2.1 1.03
## BSBM19A 8068 0.85 0.86 0.82 0.77 1.8 0.86
## BSBM19D 8068 0.84 0.85 0.80 0.75 2.0 0.93
## BSBM17A 8068 0.73 0.74 0.63 0.59 2.0 0.92
##
## Non missing response frequency for each item
## 1 2 3 4 miss
## BSBM19C 0.19 0.21 0.27 0.34 0
## BSBM19B 0.13 0.20 0.32 0.35 0
## BSBM19A 0.45 0.37 0.14 0.05 0
## BSBM19D 0.33 0.37 0.22 0.08 0
## BSBM17A 0.32 0.42 0.16 0.09 0
alpha(f3, check.keys = TRUE)
## Warning in matrix(unlist(drop.item), ncol = 10, byrow = TRUE): длина данных
## [16] не является множителем количества столбцов [10]
##
## Reliability analysis
## Call: alpha(x = f3, check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.74 0.74 0.58 0.58 2.8 0.0058 1.7 0.73 0.58
##
## lower alpha upper 95% confidence boundaries
## 0.73 0.74 0.75
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## BSBM20A 0.58 0.58 0.34 0.58 NA NA 0.58 0.58
## BSBM20B 0.34 0.58 NA NA NA NA 0.34 0.58
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## BSBM20A 8068 0.89 0.89 0.68 0.58 1.7 0.80
## BSBM20B 8068 0.89 0.89 0.68 0.58 1.8 0.83
##
## Non missing response frequency for each item
## 1 2 3 4 miss
## BSBM20A 0.52 0.35 0.09 0.04 0
## BSBM20B 0.42 0.39 0.14 0.04 0
a = fa3$scores
a = cbind(a, f1, f2, f3)
model = lm(df1$BSMMAT01 ~ df1$BSBG01 + df1$BSBG07A + df1$BSBG07B + df1$BSBG10A + a$ML1 + a$ML2 + a$ML3)
summary(model)
##
## Call:
## lm(formula = df1$BSMMAT01 ~ df1$BSBG01 + df1$BSBG07A + df1$BSBG07B +
## df1$BSBG10A + a$ML1 + a$ML2 + a$ML3)
##
## Residuals:
## <Labelled double>
## Min 1Q Median 3Q Max
## -220.152 -36.339 2.069 38.425 189.504
##
## Labels:
## value label
## 999 Omitted or invalid
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 539.85098 3.56229 151.546 < 2e-16 ***
## df1$BSBG01 -1.55366 1.27410 -1.219 0.2227
## df1$BSBG07A 0.52428 0.41886 1.252 0.2107
## df1$BSBG07B 0.01009 0.40237 0.025 0.9800
## df1$BSBG10A -3.69020 1.85520 -1.989 0.0467 *
## a$ML1 0.20318 0.89106 0.228 0.8196
## a$ML2 -39.23760 0.70321 -55.798 < 2e-16 ***
## a$ML3 5.12616 0.98761 5.190 2.15e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 56.77 on 8060 degrees of freedom
## Multiple R-squared: 0.3065, Adjusted R-squared: 0.3059
## F-statistic: 508.9 on 7 and 8060 DF, p-value: < 2.2e-16