User Avatar Bond Discrepancy Questionnaire
Taylor Brown
2022-04-06
EFA completed via Jamovi - No syntax was used
First Step for CFA run packages if already installed
## This is lavaan 0.6-11
## lavaan is FREE software! Please report any bugs.## ## ################################################################################# This is semTools 0.5-5## All users of R (or SEM) are invited to submit functions or ideas for functions.## ################################################################################# -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.1.1 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x readr::clipboard() masks semTools::clipboard()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha## The following objects are masked from 'package:semTools':
##
## reliability, skew## The following object is masked from 'package:lavaan':
##
## cor2covSecond - import the dataset and define model
CFAdata<-haven::read_sav("CFA-Data.sav")%>%
mutate_if(is.character, as.factor)
describe(CFAdata)## vars n mean sd median trimmed mad min max range
## ID 1 236 4979.19 2773.48 4862.5 5038.55 3656.83 1 9458 9457
## w2_res1 2 236 3.43 1.27 3.0 3.45 1.48 1 6 5
## w2_res2 3 236 3.35 1.30 3.0 3.32 1.48 1 7 6
## w2_res3 4 235 4.52 1.29 5.0 4.60 1.48 1 7 6
## w2_res3R 5 235 3.48 1.29 3.0 3.40 1.48 1 7 6
## w2_res4 6 234 4.52 1.60 5.0 4.57 1.48 1 7 6
## w2_res5 7 236 3.77 1.72 4.0 3.76 2.97 1 7 6
## w2_res6 8 236 3.62 1.55 4.0 3.59 1.48 1 7 6
## w2_res7 9 236 2.74 1.43 3.0 2.60 1.48 1 7 6
## w2_res8 10 234 4.28 1.33 4.0 4.34 1.48 1 7 6
## w2_res8R 11 234 3.72 1.33 4.0 3.66 1.48 1 7 6
## w2_res9 12 236 3.89 1.40 4.0 3.87 1.48 1 7 6
## w2_res10 13 236 4.70 1.70 5.0 4.79 1.48 1 7 6
## w2_res10R 14 236 3.30 1.70 3.0 3.21 1.48 1 7 6
## w2_res11 15 234 2.27 1.48 2.0 2.00 1.48 1 7 6
## w2_res11R 16 234 5.73 1.48 6.0 6.00 1.48 1 7 6
## w2_res12 17 236 2.51 1.21 2.0 2.41 1.48 1 6 5
## w2_res13 18 234 2.30 1.07 2.0 2.21 1.48 1 6 5
## w2_res14 19 236 3.65 1.42 4.0 3.60 1.48 1 7 6
## w2_res14R 20 236 4.35 1.42 4.0 4.40 1.48 1 7 6
## w2_res15 21 235 4.32 1.12 4.0 4.33 1.48 1 7 6
## w2_res15R 22 235 3.68 1.12 4.0 3.67 1.48 1 7 6
## w2_av1 23 235 2.37 1.33 2.0 2.21 1.48 1 7 6
## w2_av2 24 235 2.60 1.41 2.0 2.44 1.48 1 7 6
## w2_av3 25 236 5.61 1.42 6.0 5.78 1.48 1 7 6
## w2_av3R 26 236 2.39 1.42 2.0 2.22 1.48 1 7 6
## w2_av4 27 236 4.17 1.90 4.0 4.21 2.97 1 7 6
## w2_av5 28 235 3.89 1.87 4.0 3.86 2.97 1 7 6
## w2_av6 29 235 2.60 1.62 2.0 2.38 1.48 1 7 6
## w2_av7 30 234 2.69 1.56 2.0 2.51 1.48 1 7 6
## w2_av8 31 236 5.35 1.50 6.0 5.51 1.48 1 7 6
## w2_av8R 32 236 2.65 1.50 2.0 2.49 1.48 1 7 6
## w2_av9 33 235 3.02 1.75 3.0 2.85 1.48 1 7 6
## w2_av10 34 234 4.97 1.46 5.0 5.03 1.48 1 7 6
## w2_av10R 35 234 3.03 1.46 3.0 2.97 1.48 1 7 6
## w2_av11 36 236 2.90 2.33 2.0 2.63 1.48 1 7 6
## w2_av11R 37 236 5.10 2.33 6.0 5.37 1.48 1 7 6
## w2_av12 38 235 2.77 1.68 2.0 2.57 1.48 1 7 6
## w2_av13 39 236 2.73 1.61 2.0 2.55 1.48 1 7 6
## w2_av14 40 234 4.80 1.57 4.0 4.91 1.48 1 7 6
## w2_av14R 41 234 3.20 1.57 4.0 3.09 1.48 1 7 6
## w2_av15 42 236 5.68 1.33 6.0 5.82 1.48 1 7 6
## w2_av15R 43 236 2.32 1.33 2.0 2.18 1.48 1 7 6
## W2_Dis3Rev 44 235 1.09 1.63 1.0 1.17 1.48 -5 5 10
## W2_Dis1 45 235 1.07 1.68 1.0 1.15 1.48 -5 4 9
## W2_Dis2 46 235 0.75 1.71 1.0 0.81 1.48 -5 5 10
## W2_Dis3 47 235 -1.09 1.63 -1.0 -1.17 1.48 -5 5 10
## W2_Dis4 48 234 0.35 2.37 0.0 0.34 1.48 -6 6 12
## W2_Dis5 49 235 -0.12 2.32 0.0 -0.15 1.48 -5 6 11
## W2_Dis6 50 235 1.02 2.08 1.0 1.03 1.48 -5 6 11
## W2_Dis7 51 234 0.06 1.94 0.0 0.12 1.48 -6 6 12
## W2_Dis8 52 234 -1.06 1.81 -1.0 -1.04 1.48 -6 6 12
## W2_Dis9 53 235 0.88 2.07 1.0 0.89 1.48 -5 6 11
## W2_Dis10 54 234 -0.26 1.99 0.0 -0.20 1.48 -6 6 12
## W2_Dis11 55 234 -0.63 2.27 0.0 -0.46 1.48 -6 6 12
## W2_Dis12 56 235 -0.26 1.87 0.0 -0.18 1.48 -6 5 11
## W2_Dis13 57 234 -0.43 1.71 0.0 -0.34 1.48 -6 3 9
## W2_Dis14 58 234 -1.15 1.99 -1.0 -1.17 1.48 -6 4 10
## W2_Dis15 59 235 -1.35 1.52 -1.0 -1.39 1.48 -6 4 10
## W2_Dis8Rev 60 234 1.06 1.81 1.0 1.04 1.48 -6 6 12
## W2_Dis10Rev 61 234 0.26 1.99 0.0 0.20 1.48 -6 6 12
## W2_Dis14Rev 62 234 1.15 1.99 1.0 1.17 1.48 -4 6 10
## W2_Dis15Rev 63 235 1.35 1.52 1.0 1.39 1.48 -4 6 10
## skew kurtosis se
## ID -0.13 -1.29 180.54
## w2_res1 -0.06 -0.67 0.08
## w2_res2 0.23 -0.58 0.08
## w2_res3 -0.39 -0.35 0.08
## w2_res3R 0.39 -0.35 0.08
## w2_res4 -0.30 -0.72 0.10
## w2_res5 0.07 -1.17 0.11
## w2_res6 0.18 -0.59 0.10
## w2_res7 0.75 0.11 0.09
## w2_res8 -0.26 0.23 0.09
## w2_res8R 0.26 0.23 0.09
## w2_res9 0.10 -0.28 0.09
## w2_res10 -0.44 -0.78 0.11
## w2_res10R 0.44 -0.78 0.11
## w2_res11 1.37 1.24 0.10
## w2_res11R -1.37 1.24 0.10
## w2_res12 0.78 0.34 0.08
## w2_res13 0.64 -0.04 0.07
## w2_res14 0.15 -0.46 0.09
## w2_res14R -0.15 -0.46 0.09
## w2_res15 -0.16 0.60 0.07
## w2_res15R 0.16 0.60 0.07
## w2_av1 1.06 1.00 0.09
## w2_av2 0.72 0.03 0.09
## w2_av3 -0.84 0.04 0.09
## w2_av3R 0.84 0.04 0.09
## w2_av4 -0.13 -1.00 0.12
## w2_av5 0.02 -1.02 0.12
## w2_av6 0.94 0.17 0.11
## w2_av7 0.82 0.16 0.10
## w2_av8 -0.79 0.20 0.10
## w2_av8R 0.79 0.20 0.10
## w2_av9 0.50 -0.62 0.11
## w2_av10 -0.17 -0.54 0.10
## w2_av10R 0.17 -0.54 0.10
## w2_av11 0.81 -0.96 0.15
## w2_av11R -0.81 -0.96 0.15
## w2_av12 0.70 -0.26 0.11
## w2_av13 0.72 -0.06 0.10
## w2_av14 -0.36 -0.18 0.10
## w2_av14R 0.36 -0.18 0.10
## w2_av15 -0.88 0.35 0.09
## w2_av15R 0.88 0.35 0.09
## W2_Dis3Rev -0.43 0.48 0.11
## W2_Dis1 -0.55 0.69 0.11
## W2_Dis2 -0.28 0.37 0.11
## W2_Dis3 0.43 0.48 0.11
## W2_Dis4 0.03 0.01 0.16
## W2_Dis5 0.11 -0.01 0.15
## W2_Dis6 -0.05 0.24 0.14
## W2_Dis7 -0.29 0.69 0.13
## W2_Dis8 0.01 0.87 0.12
## W2_Dis9 -0.01 0.18 0.13
## W2_Dis10 -0.35 0.42 0.13
## W2_Dis11 -0.63 0.34 0.15
## W2_Dis12 -0.42 0.61 0.12
## W2_Dis13 -0.65 0.44 0.11
## W2_Dis14 0.00 -0.06 0.13
## W2_Dis15 0.16 0.87 0.10
## W2_Dis8Rev -0.01 0.87 0.12
## W2_Dis10Rev 0.35 0.42 0.13
## W2_Dis14Rev 0.00 -0.06 0.13
## W2_Dis15Rev -0.16 0.87 0.10Specify the different models
Fit the model and produce the results
fit1 <- cfa(onefactormodel, data = CFAdata, estimator= "DWLS")
summary(fit1, fit.measures=TRUE, standardized=TRUE)## lavaan 0.6-11 ended normally after 42 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 16
##
## Used Total
## Number of observations 225 236
##
## Model Test User Model:
##
## Test statistic 30.368
## Degrees of freedom 20
## P-value (Chi-square) 0.064
##
## Model Test Baseline Model:
##
## Test statistic 273.349
## Degrees of freedom 28
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.958
## Tucker-Lewis Index (TLI) 0.941
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.048
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.081
## P-value RMSEA <= 0.05 0.499
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.071
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## useravatar =~
## W2_Dis1 1.000 0.757 0.458
## W2_Dis2 1.014 0.196 5.177 0.000 0.768 0.456
## W2_Dis6 1.083 0.229 4.734 0.000 0.820 0.394
## W2_Dis7 0.826 0.199 4.152 0.000 0.625 0.320
## W2_Dis8Rev 1.322 0.259 5.106 0.000 1.001 0.555
## W2_Dis9 1.352 0.263 5.143 0.000 1.023 0.507
## W2_Dis10Rev 1.198 0.243 4.926 0.000 0.907 0.462
## W2_Dis15Rev 1.180 0.226 5.221 0.000 0.893 0.589
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .W2_Dis1 2.164 0.349 6.209 0.000 2.164 0.791
## .W2_Dis2 2.245 0.343 6.553 0.000 2.245 0.792
## .W2_Dis6 3.663 0.484 7.571 0.000 3.663 0.845
## .W2_Dis7 3.426 0.443 7.734 0.000 3.426 0.898
## .W2_Dis8Rev 2.253 0.444 5.071 0.000 2.253 0.692
## .W2_Dis9 3.025 0.484 6.252 0.000 3.025 0.743
## .W2_Dis10Rev 3.028 0.466 6.499 0.000 3.028 0.786
## .W2_Dis15Rev 1.500 0.329 4.558 0.000 1.500 0.653
## useravatar 0.573 0.161 3.555 0.000 1.000 1.000Produce the modification indices
## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 20 W2_Dis1 ~~ W2_Dis7 6.074 -0.644 -0.644 -0.237 -0.237
## 31 W2_Dis6 ~~ W2_Dis7 4.889 0.806 0.806 0.228 0.228
## 23 W2_Dis1 ~~ W2_Dis10Rev 4.720 -0.603 -0.603 -0.236 -0.236
## 24 W2_Dis1 ~~ W2_Dis15Rev 3.902 0.514 0.514 0.285 0.285
## 32 W2_Dis6 ~~ W2_Dis8Rev 2.928 -0.525 -0.525 -0.183 -0.183
## 44 W2_Dis9 ~~ W2_Dis15Rev 2.679 -0.504 -0.504 -0.236 -0.236
## 18 W2_Dis1 ~~ W2_Dis2 1.973 0.390 0.390 0.177 0.177
## 37 W2_Dis7 ~~ W2_Dis9 1.584 0.414 0.414 0.128 0.128
## 29 W2_Dis2 ~~ W2_Dis10Rev 1.457 -0.341 -0.341 -0.131 -0.131
## 42 W2_Dis8Rev ~~ W2_Dis15Rev 1.372 0.349 0.349 0.190 0.190
## 34 W2_Dis6 ~~ W2_Dis10Rev 1.303 0.426 0.426 0.128 0.128
## 38 W2_Dis7 ~~ W2_Dis10Rev 1.088 0.367 0.367 0.114 0.114
## 28 W2_Dis2 ~~ W2_Dis9 1.021 0.289 0.289 0.111 0.111
## 26 W2_Dis2 ~~ W2_Dis7 1.016 -0.272 -0.272 -0.098 -0.098
## 35 W2_Dis6 ~~ W2_Dis15Rev 0.957 -0.295 -0.295 -0.126 -0.126
## 41 W2_Dis8Rev ~~ W2_Dis10Rev 0.940 0.332 0.332 0.127 0.127
## 27 W2_Dis2 ~~ W2_Dis8Rev 0.782 -0.227 -0.227 -0.101 -0.101
## 39 W2_Dis7 ~~ W2_Dis15Rev 0.519 -0.186 -0.186 -0.082 -0.082
## 25 W2_Dis2 ~~ W2_Dis6 0.513 0.206 0.206 0.072 0.072
## 43 W2_Dis9 ~~ W2_Dis10Rev 0.373 0.192 0.192 0.063 0.063
## 19 W2_Dis1 ~~ W2_Dis6 0.368 0.186 0.186 0.066 0.066
## 33 W2_Dis6 ~~ W2_Dis9 0.255 -0.174 -0.174 -0.052 -0.052
## 21 W2_Dis1 ~~ W2_Dis8Rev 0.136 0.101 0.101 0.046 0.046
## 36 W2_Dis7 ~~ W2_Dis8Rev 0.091 0.098 0.098 0.035 0.035
## 22 W2_Dis1 ~~ W2_Dis9 0.037 -0.058 -0.058 -0.022 -0.022
## 40 W2_Dis8Rev ~~ W2_Dis9 0.027 -0.061 -0.061 -0.023 -0.023
## 30 W2_Dis2 ~~ W2_Dis15Rev 0.002 0.010 0.010 0.005 0.005
## 45 W2_Dis10Rev ~~ W2_Dis15Rev 0.000 0.002 0.002 0.001 0.001Produce the reliability indices
## useravatar
## alpha 0.6930769
## omega 0.6842130
## omega2 0.6842130
## omega3 0.6679165
## avevar 0.2167208Graph the model via lavaan
lavaanPlot(model = fit1, edge_options = list(color = "grey"))Graph the model via semtools
#To Repeat the procedure with a different estimator, change the estimator (ML).
After having confirmed the model, we will proceed with the IRT Step 1 pachages run (if aleardy installed)
## Loading required package: stats4## Loading required package: latticeDefine data
data<-haven::read_sav("Data.sav")%>%
mutate_if(is.character, as.factor)
describe(data)## vars n mean sd median trimmed mad min max range
## ID 1 477 4835.31 2726.94 4802 4868.40 3398.12 1 9458 9457
## w2_res1 2 475 3.39 1.31 3 3.39 1.48 1 7 6
## w2_res2 3 477 3.32 1.32 3 3.28 1.48 1 7 6
## w2_res3 4 476 4.63 1.30 5 4.69 1.48 1 7 6
## w2_res3R 5 476 3.37 1.30 3 3.31 1.48 1 7 6
## w2_res4 6 473 4.33 1.66 4 4.36 1.48 1 7 6
## w2_res5 7 476 3.53 1.74 3 3.48 1.48 1 7 6
## w2_res6 8 477 3.46 1.53 3 3.41 1.48 1 7 6
## w2_res7 9 477 2.76 1.36 3 2.63 1.48 1 7 6
## w2_res8 10 474 4.32 1.34 4 4.37 1.48 1 7 6
## w2_res8R 11 474 3.68 1.34 4 3.63 1.48 1 7 6
## w2_res9 12 476 3.88 1.43 4 3.85 1.48 1 7 6
## w2_res10 13 476 4.78 1.64 5 4.88 1.48 1 7 6
## w2_res10R 14 476 3.22 1.64 3 3.12 1.48 1 7 6
## w2_res11 15 475 2.24 1.39 2 2.00 1.48 1 7 6
## w2_res11R 16 475 5.76 1.39 6 6.00 1.48 1 7 6
## w2_res12 17 477 2.59 1.26 2 2.46 1.48 1 7 6
## w2_res13 18 474 2.34 1.12 2 2.24 1.48 1 6 5
## w2_res14 19 475 3.79 1.45 4 3.76 1.48 1 7 6
## w2_res14R 20 475 4.21 1.45 4 4.24 1.48 1 7 6
## w2_res15 21 476 4.35 1.17 4 4.40 1.48 1 7 6
## w2_res15R 22 476 3.65 1.17 4 3.60 1.48 1 7 6
## w2_av1 23 476 2.33 1.31 2 2.18 1.48 1 7 6
## w2_av2 24 476 2.41 1.29 2 2.27 1.48 1 7 6
## w2_av3 25 477 5.63 1.43 6 5.81 1.48 1 7 6
## w2_av3R 26 477 2.37 1.43 2 2.19 1.48 1 7 6
## w2_av4 27 474 4.13 1.87 4 4.17 1.48 1 7 6
## w2_av5 28 474 3.76 1.92 4 3.70 2.97 1 7 6
## w2_av6 29 476 2.41 1.49 2 2.19 1.48 1 7 6
## w2_av7 30 475 2.61 1.55 2 2.42 1.48 1 7 6
## w2_av8 31 475 5.40 1.50 6 5.57 1.48 1 7 6
## w2_av8R 32 475 2.60 1.50 2 2.43 1.48 1 7 6
## w2_av9 33 476 2.96 1.66 3 2.80 1.48 1 7 6
## w2_av10 34 475 5.01 1.45 5 5.07 1.48 1 7 6
## w2_av10R 35 475 2.99 1.45 3 2.93 1.48 1 7 6
## w2_av11 36 477 2.81 2.26 2 2.52 1.48 1 7 6
## w2_av11R 37 477 5.19 2.26 6 5.48 1.48 1 7 6
## w2_av12 38 475 2.87 1.74 3 2.66 1.48 1 7 6
## w2_av13 39 475 2.72 1.59 2 2.54 1.48 1 7 6
## w2_av14 40 475 4.85 1.59 5 4.98 1.48 1 7 6
## w2_av14R 41 475 3.15 1.59 3 3.02 1.48 1 7 6
## w2_av15 42 477 5.79 1.26 6 5.92 1.48 1 7 6
## w2_av15R 43 477 2.21 1.26 2 2.08 1.48 1 7 6
## W2_Dis3Rev 44 476 1.00 1.74 1 1.05 1.48 -5 6 11
## W2_Dis1 45 474 1.07 1.61 1 1.10 1.48 -5 6 11
## W2_Dis2 46 476 0.91 1.63 1 0.93 1.48 -5 6 11
## W2_Dis3 47 476 -1.00 1.74 -1 -1.05 1.48 -6 5 11
## W2_Dis4 48 470 0.18 2.31 0 0.22 1.48 -6 6 12
## W2_Dis5 49 473 -0.23 2.39 0 -0.21 2.97 -6 6 12
## W2_Dis6 50 476 1.04 2.00 1 1.06 1.48 -5 6 11
## W2_Dis7 51 475 0.15 1.83 0 0.21 1.48 -6 6 12
## W2_Dis8 52 472 -1.09 1.78 -1 -1.07 1.48 -6 6 12
## W2_Dis9 53 475 0.93 2.06 1 0.94 1.48 -5 6 11
## W2_Dis10 54 474 -0.23 1.97 0 -0.16 1.48 -6 6 12
## W2_Dis11 55 475 -0.57 2.21 0 -0.42 1.48 -6 6 12
## W2_Dis12 56 475 -0.29 1.96 0 -0.23 1.48 -6 6 12
## W2_Dis13 57 472 -0.38 1.69 0 -0.32 1.48 -6 5 11
## W2_Dis14 58 473 -1.06 2.01 -1 -1.07 1.48 -6 6 12
## W2_Dis15 59 476 -1.44 1.52 -1 -1.42 1.48 -6 4 10
## W2_Dis8Rev 60 472 1.09 1.78 1 1.07 1.48 -6 6 12
## W2_Dis10Rev 61 474 0.23 1.97 0 0.16 1.48 -6 6 12
## W2_Dis14Rev 62 473 1.06 2.01 1 1.07 1.48 -6 6 12
## W2_Dis15Rev 63 476 1.44 1.52 1 1.42 1.48 -4 6 10
## skew kurtosis se
## ID -0.07 -1.16 124.86
## w2_res1 0.10 -0.59 0.06
## w2_res2 0.30 -0.41 0.06
## w2_res3 -0.34 -0.36 0.06
## w2_res3R 0.34 -0.36 0.06
## w2_res4 -0.20 -0.86 0.08
## w2_res5 0.27 -1.05 0.08
## w2_res6 0.31 -0.55 0.07
## w2_res7 0.80 0.41 0.06
## w2_res8 -0.28 0.14 0.06
## w2_res8R 0.28 0.14 0.06
## w2_res9 0.12 -0.31 0.07
## w2_res10 -0.45 -0.71 0.08
## w2_res10R 0.45 -0.71 0.08
## w2_res11 1.35 1.45 0.06
## w2_res11R -1.35 1.45 0.06
## w2_res12 0.81 0.31 0.06
## w2_res13 0.73 0.25 0.05
## w2_res14 0.11 -0.40 0.07
## w2_res14R -0.11 -0.40 0.07
## w2_res15 -0.40 0.62 0.05
## w2_res15R 0.40 0.62 0.05
## w2_av1 0.98 0.74 0.06
## w2_av2 0.78 0.27 0.06
## w2_av3 -1.03 0.66 0.07
## w2_av3R 1.03 0.66 0.07
## w2_av4 -0.08 -0.95 0.09
## w2_av5 0.11 -1.10 0.09
## w2_av6 1.12 0.80 0.07
## w2_av7 0.88 0.24 0.07
## w2_av8 -0.81 0.17 0.07
## w2_av8R 0.81 0.17 0.07
## w2_av9 0.50 -0.52 0.08
## w2_av10 -0.17 -0.52 0.07
## w2_av10R 0.17 -0.52 0.07
## w2_av11 0.89 -0.78 0.10
## w2_av11R -0.89 -0.78 0.10
## w2_av12 0.70 -0.30 0.08
## w2_av13 0.75 0.04 0.07
## w2_av14 -0.42 -0.17 0.07
## w2_av14R 0.42 -0.17 0.07
## w2_av15 -0.88 0.36 0.06
## w2_av15R 0.88 0.36 0.06
## W2_Dis3Rev -0.25 0.35 0.08
## W2_Dis1 -0.26 0.58 0.07
## W2_Dis2 -0.13 0.58 0.07
## W2_Dis3 0.25 0.35 0.08
## W2_Dis4 -0.12 0.13 0.11
## W2_Dis5 -0.05 -0.08 0.11
## W2_Dis6 -0.15 0.52 0.09
## W2_Dis7 -0.39 0.95 0.08
## W2_Dis8 -0.02 0.72 0.08
## W2_Dis9 -0.03 0.11 0.09
## W2_Dis10 -0.30 0.38 0.09
## W2_Dis11 -0.63 0.41 0.10
## W2_Dis12 -0.24 0.55 0.09
## W2_Dis13 -0.42 0.53 0.08
## W2_Dis14 0.07 0.39 0.09
## W2_Dis15 -0.17 0.76 0.07
## W2_Dis8Rev 0.02 0.72 0.08
## W2_Dis10Rev 0.30 0.38 0.09
## W2_Dis14Rev -0.07 0.39 0.09
## W2_Dis15Rev 0.17 0.76 0.07view(data)Then we select only the variables we need for our analysis
myvars <- c("W2_Dis1", "W2_Dis2", "W2_Dis6", "W2_Dis7", "W2_Dis8Rev", "W2_Dis9", "W2_Dis10Rev", "W2_Dis15Rev"
)
data1<-data[myvars]
describe(data1)## vars n mean sd median trimmed mad min max range skew kurtosis
## W2_Dis1 1 474 1.07 1.61 1 1.10 1.48 -5 6 11 -0.26 0.58
## W2_Dis2 2 476 0.91 1.63 1 0.93 1.48 -5 6 11 -0.13 0.58
## W2_Dis6 3 476 1.04 2.00 1 1.06 1.48 -5 6 11 -0.15 0.52
## W2_Dis7 4 475 0.15 1.83 0 0.21 1.48 -6 6 12 -0.39 0.95
## W2_Dis8Rev 5 472 1.09 1.78 1 1.07 1.48 -6 6 12 0.02 0.72
## W2_Dis9 6 475 0.93 2.06 1 0.94 1.48 -5 6 11 -0.03 0.11
## W2_Dis10Rev 7 474 0.23 1.97 0 0.16 1.48 -6 6 12 0.30 0.38
## W2_Dis15Rev 8 476 1.44 1.52 1 1.42 1.48 -4 6 10 0.17 0.76
## se
## W2_Dis1 0.07
## W2_Dis2 0.07
## W2_Dis6 0.09
## W2_Dis7 0.08
## W2_Dis8Rev 0.08
## W2_Dis9 0.09
## W2_Dis10Rev 0.09
## W2_Dis15Rev 0.07re-view data and define the scale
glimpse(data1)## Rows: 477
## Columns: 8
## $ W2_Dis1 <dbl> 4, 4, 3, -2, 2, 1, 2, 1, 2, 1, 0, 0, -1, 3, 3, 3, 2, 1, 0,~
## $ W2_Dis2 <dbl> 4, 3, 6, 3, 4, 4, 3, 0, 1, 0, -1, 0, -1, 1, 2, 3, 1, 0, 0,~
## $ W2_Dis6 <dbl> 2, 3, 4, 2, 2, 0, 0, -1, 0, 0, 0, 0, 0, 1, 2, 4, 0, 4, 2, ~
## $ W2_Dis7 <dbl> 0, 3, 1, -5, 2, 0, -1, 0, -2, 1, -1, 2, 0, 1, 1, 1, 0, -1,~
## $ W2_Dis8Rev <dbl> 0, 3, 2, 3, 1, 1, 2, -1, 2, 1, 1, 1, -1, 2, 1, 1, 3, 0, -1~
## $ W2_Dis9 <dbl> 1, 3, 5, 2, 4, -1, 0, -1, -1, -2, 0, 3, 1, 1, -2, 2, 2, 2,~
## $ W2_Dis10Rev <dbl> 0, 4, 0, -4, 4, -5, -2, -1, 0, -2, -2, 0, 0, 0, 0, 2, 3, -~
## $ W2_Dis15Rev <dbl> 3, 3, 3, 3, 0, 2, 2, 2, 0, 2, -1, 2, 2, 3, 3, 2, 4, 1, 2, ~scale <-(data1)
head(scale, 8) ## # A tibble: 8 x 8
## W2_Dis1 W2_Dis2 W2_Dis6 W2_Dis7 W2_Dis8Rev W2_Dis9 W2_Dis10Rev W2_Dis15Rev
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 4 4 2 0 0 1 0 3
## 2 4 3 3 3 3 3 4 3
## 3 3 6 4 1 2 5 0 3
## 4 -2 3 2 -5 3 2 -4 3
## 5 2 4 2 2 1 4 4 0
## 6 1 4 0 0 1 -1 -5 2
## 7 2 3 0 -1 2 0 -2 2
## 8 1 0 -1 0 -1 -1 -1 2Fit the graded response model using the ‘mirt’ R package.
## "W2_Dis8Rev" re-mapped to ensure all categories have a distance of 1## Sample size after row-wise response data removal: 459## M2 df p RMSEA RMSEA_5 RMSEA_95 SRMSR
## stats 94.52615 20 1.185985e-11 0.09020001 0.07228525 0.1087782 0.06674021itemfit(mod1, na.rm = TRUE)## Sample size after row-wise response data removal: 459## item S_X2 df.S_X2 RMSEA.S_X2 p.S_X2
## 1 W2_Dis1 90.312 99 0.000 0.722
## 2 W2_Dis2 94.430 95 0.000 0.497
## 3 W2_Dis6 123.889 118 0.010 0.337
## 4 W2_Dis7 145.904 108 0.028 0.009
## 5 W2_Dis8Rev 115.819 101 0.018 0.149
## 6 W2_Dis9 125.263 120 0.010 0.353
## 7 W2_Dis10Rev 122.641 124 0.000 0.518
## 8 W2_Dis15Rev 90.106 84 0.013 0.305Examine IRT parameters (0 = non discriminative; 0.01–0.34 = very low; 0.35–0.64 = low; 0.65–1.34 = moderate; 1.35–1.69 = high; >1.70 = very high; Baker, 2001)
## a b1 b2 b3 b4 b5
## W2_Dis1 1.2033812 -5.753107 -4.837215 -3.674272 -2.715240 -2.052186
## W2_Dis2 1.2298251 -5.657421 -4.526699 -3.477328 -2.668506 -1.798430
## W2_Dis6 1.0615840 -4.811033 -4.142462 -3.279788 -2.634424 -1.865611
## W2_Dis7 0.8036281 -6.705555 -5.318943 -4.580861 -3.330335 -2.401604
## W2_Dis8Rev 1.2822607 -5.418420 -4.553949 -3.252645 -2.661278 -1.865100
## W2_Dis9 1.1639431 -4.927078 -3.851845 -3.007046 -2.197782 -1.408447
## W2_Dis10Rev 0.7980509 -7.233385 -6.719474 -5.642235 -3.487192 -2.195918
## W2_Dis15Rev 1.3117336 -5.422397 -4.314520 -3.527245 -2.345960 -1.028045
## b6 b7 b8 b9 b10 b11 b12
## W2_Dis1 -0.6418868 0.4521075 1.668518 2.622101 4.532010 5.789916 NA
## W2_Dis2 -0.3500712 0.6493051 1.684483 2.874701 4.274311 5.101288 NA
## W2_Dis6 -0.4365800 0.4660534 1.457749 2.422384 3.292712 4.361987 NA
## W2_Dis7 -1.3868753 0.5066756 1.940914 3.206168 4.683504 6.671817 8.059164
## W2_Dis8Rev -0.5028195 0.3960155 1.458178 2.325062 2.965665 4.207108 NA
## W2_Dis9 -0.3167869 0.4886917 1.264591 2.362515 3.179687 3.860828 NA
## W2_Dis10Rev -0.9409435 0.6074190 1.644943 2.605570 3.800691 4.526885 6.754988
## W2_Dis15Rev 0.1435927 1.1817495 2.474795 3.260924 3.864729 NA NAExamine items parameters/loadings and communalities
summary(mod1)## F1 h2
## W2_Dis1 0.577 0.333
## W2_Dis2 0.586 0.343
## W2_Dis6 0.529 0.280
## W2_Dis7 0.427 0.182
## W2_Dis8Rev 0.602 0.362
## W2_Dis9 0.564 0.319
## W2_Dis10Rev 0.425 0.180
## W2_Dis15Rev 0.610 0.373
##
## SS loadings: 2.372
## Proportion Var: 0.297
##
## Factor correlations:
##
## F1
## F1 1Produce IRT plots - Category characteristic Curve
Produce the Item information Curves
Scale information and conditional standard errors
Produce the Conditional reliability if chosen
## [1] 0.7604046Scale characteristic Curve
