fall19 spring 20 mirt
This script cleans & analyzes the fall 20 ACI data
Contents:
Part 1 - load test data & prep for analysis
Part 2 - basic psychometric analyses
Part 3 - multidimensional analyses
Part 1
load libraries and data
library(mirt)## Warning: package 'mirt' was built under R version 4.1.3## Loading required package: stats4## Loading required package: lattice## Warning: package 'lattice' was built under R version 4.1.3library(psych)
library(mokken)## Warning: package 'mokken' was built under R version 4.1.3## Loading required package: poLCA## Warning: package 'poLCA' was built under R version 4.1.3## Loading required package: scatterplot3d## Loading required package: MASS## Warning: package 'MASS' was built under R version 4.1.3##
## Attaching package: 'mokken'## The following object is masked from 'package:psych':
##
## ICCf19s20 <- read.csv("G:/My Drive/EACI/R files/f19s20_data.csv")clean up some variables
itemNames <- c("se1", "se2", "se3", "se4", "se5", "se6", "se7", "se8", "se9",
"se10","se11","se12","se13","se14","se15","se16","se17","se18",
"se19","se20","se21","se22","se23","se24","se25","se26","se27",
"se28", "se29", "se30", "se31", "se32", "se33", "se34", "se35",
"se36", "se37", "se38", "se39", "se40", "se41", "se42", "se43",
"se44", "se45", "se46", "se47", "se48", "se49", "se50", "se51",
"se52", "se53", "se54", "se55", "se56", "ss1", "ss2", "ss3",
"ss4", "ss5", "ss6", "ss7", "ss8","ss9", "ss10", "ss11",
"ss12", "ss13", "ss14", "ss15", "ss16", "ss17", "ss18", "ss19",
"ss20", "ss21", "ss22", "ss23", "ss24", "ss25", "ss26", "ss27",
"ss28", "ss29", "ss30", "ss31", "ss32", "ss33", "ss34", "ss35",
"ss36", "ss37", "ss38", "ss39", "ss40", "ss41a", "ss41b",
"ss41c", "ss41d","ss42a","ss42b","ss42c","ss42d","ss43a",
"ss43b", "ss43c","ss43d","ss44a","ss44b","ss44c","ss44d","ss45a",
"ss45b", "ss45c","ss45d","ss46a","ss46b","ss46c","ss46d","ss47a",
"ss47b", "ss47c", "ss47d", "ss47e", "ss47f", "ss48a","ss48b",
"ss48c", "ss48d", "ss48e", "ss48f", "ss49a", "ss49b","ss49c",
"ss49d", "ss49e", "ss49f", "ss50a", "ss50b", "ss50c","ss50d",
"ss50e", "ss50f", "ss51a", "ss51b", "ss51c", "ss51d","ss51e",
"ss51f", "ss52a", "ss52b", "ss52c", "ss52d", "ss52e","ss52f",
"ss53", "ss54", "ss55", "ss56", "ss57", "ss58", "ss59", "ss60",
"ss61", "ss62", "ss63", "ss64", "anchor1", "anchor2","anchor3",
"anchor4", "anchor5", "anchor6")
f19s20[f19s20 == -9] <- NA
f19s20 <- f19s20[,-c(10,12,13,16,18,24,25,27,49,72,91,95,97:156,159)]
describe(f19s20)# basic descriptives for all variables## vars n mean sd median trimmed mad min max range skew kurtosis
## se1 1 329 0.87 0.33 1.0 0.96 0.00 0 1 1 -2.22 2.94
## se2 2 317 0.88 0.33 1.0 0.97 0.00 0 1 1 -2.28 3.23
## se3 3 293 0.84 0.37 1.0 0.92 0.00 0 1 1 -1.81 1.27
## se4 4 307 0.83 0.37 1.0 0.91 0.00 0 1 1 -1.79 1.19
## se5 5 294 0.79 0.41 1.0 0.86 0.00 0 1 1 -1.39 -0.08
## se6 6 273 0.85 0.36 1.0 0.93 0.00 0 1 1 -1.91 1.65
## se7 7 317 0.85 0.36 1.0 0.94 0.00 0 1 1 -1.97 1.89
## se8 8 327 0.85 0.36 1.0 0.94 0.00 0 1 1 -1.95 1.82
## se9 9 290 0.50 0.50 0.0 0.50 0.00 0 1 1 0.01 -2.01
## se11 10 289 0.52 0.50 1.0 0.53 0.00 0 1 1 -0.09 -2.00
## se14 11 310 0.34 0.48 0.0 0.30 0.00 0 1 1 0.66 -1.57
## se15 12 283 0.34 0.47 0.0 0.30 0.00 0 1 1 0.69 -1.53
## se17 13 284 0.52 0.50 1.0 0.52 0.00 0 1 1 -0.07 -2.00
## se19 14 324 0.39 0.49 0.0 0.36 0.00 0 1 1 0.47 -1.79
## se20 15 312 0.63 0.48 1.0 0.66 0.00 0 1 1 -0.53 -1.73
## se21 16 284 0.56 0.50 1.0 0.58 0.00 0 1 1 -0.25 -1.94
## se22 17 260 0.35 0.48 0.0 0.31 0.00 0 1 1 0.63 -1.62
## se23 18 282 0.35 0.48 0.0 0.32 0.00 0 1 1 0.60 -1.64
## se26 19 306 0.54 0.50 1.0 0.54 0.00 0 1 1 -0.14 -1.99
## se28 20 244 0.48 0.50 0.0 0.47 0.00 0 1 1 0.10 -2.00
## se29 21 268 0.53 0.50 1.0 0.54 0.00 0 1 1 -0.13 -1.99
## se30 22 276 0.47 0.50 0.0 0.46 0.00 0 1 1 0.13 -1.99
## se31 23 305 0.62 0.49 1.0 0.65 0.00 0 1 1 -0.50 -1.75
## se32 24 284 0.48 0.50 0.0 0.47 0.00 0 1 1 0.08 -2.00
## se33 25 273 0.56 0.50 1.0 0.58 0.00 0 1 1 -0.26 -1.94
## se34 26 249 0.75 0.44 1.0 0.81 0.00 0 1 1 -1.13 -0.73
## se35 27 273 0.51 0.50 1.0 0.52 0.00 0 1 1 -0.05 -2.00
## se36 28 278 0.81 0.39 1.0 0.88 0.00 0 1 1 -1.57 0.46
## se37 29 301 0.39 0.49 0.0 0.36 0.00 0 1 1 0.47 -1.79
## se38 30 280 0.48 0.50 0.0 0.48 0.00 0 1 1 0.07 -2.00
## se39 31 271 0.49 0.50 0.0 0.49 0.00 0 1 1 0.02 -2.01
## se40 32 240 0.45 0.50 0.0 0.44 0.00 0 1 1 0.18 -1.97
## se41 33 268 0.37 0.48 0.0 0.34 0.00 0 1 1 0.54 -1.72
## se42 34 273 0.34 0.47 0.0 0.30 0.00 0 1 1 0.69 -1.54
## se43 35 301 0.68 0.47 1.0 0.73 0.00 0 1 1 -0.77 -1.41
## se44 36 281 0.39 0.49 0.0 0.36 0.00 0 1 1 0.44 -1.81
## se45 37 238 0.47 0.50 0.0 0.46 0.00 0 1 1 0.12 -1.99
## se46 38 270 0.46 0.50 0.0 0.45 0.00 0 1 1 0.16 -1.98
## se47 39 269 0.38 0.49 0.0 0.35 0.00 0 1 1 0.48 -1.78
## se48 40 270 0.40 0.49 0.0 0.37 0.00 0 1 1 0.42 -1.83
## se50 41 271 0.44 0.50 0.0 0.43 0.00 0 1 1 0.23 -1.95
## se51 42 265 0.28 0.45 0.0 0.23 0.00 0 1 1 0.98 -1.05
## se52 43 235 0.34 0.47 0.0 0.30 0.00 0 1 1 0.69 -1.53
## se53 44 259 0.27 0.44 0.0 0.22 0.00 0 1 1 1.03 -0.95
## se54 45 267 0.32 0.47 0.0 0.27 0.00 0 1 1 0.78 -1.40
## se55 46 294 0.35 0.48 0.0 0.31 0.00 0 1 1 0.64 -1.60
## se56 47 272 0.45 0.50 0.0 0.44 0.00 0 1 1 0.19 -1.97
## ss1 48 319 0.82 0.38 1.0 0.90 0.00 0 1 1 -1.70 0.88
## ss2 49 321 0.73 0.45 1.0 0.79 0.00 0 1 1 -1.03 -0.95
## ss3 50 311 0.68 0.47 1.0 0.72 0.00 0 1 1 -0.76 -1.43
## ss4 51 306 0.41 0.49 0.0 0.39 0.00 0 1 1 0.37 -1.87
## ss5 52 287 0.90 0.31 1.0 0.99 0.00 0 1 1 -2.57 4.63
## ss6 53 302 0.84 0.37 1.0 0.93 0.00 0 1 1 -1.86 1.45
## ss7 54 316 0.62 0.49 1.0 0.65 0.00 0 1 1 -0.49 -1.76
## ss8 55 324 0.66 0.47 1.0 0.70 0.00 0 1 1 -0.67 -1.55
## ss9 56 312 0.82 0.39 1.0 0.90 0.00 0 1 1 -1.63 0.67
## ss10 57 307 0.55 0.50 1.0 0.57 0.00 0 1 1 -0.22 -1.96
## ss11 58 298 0.73 0.44 1.0 0.79 0.00 0 1 1 -1.06 -0.88
## ss12 59 287 0.84 0.36 1.0 0.93 0.00 0 1 1 -1.88 1.53
## ss13 60 317 0.40 0.49 0.0 0.38 0.00 0 1 1 0.39 -1.85
## ss14 61 324 0.60 0.49 1.0 0.63 0.00 0 1 1 -0.43 -1.82
## ss15 62 308 0.67 0.47 1.0 0.71 0.00 0 1 1 -0.73 -1.47
## ss17 63 285 0.32 0.47 0.0 0.28 0.00 0 1 1 0.75 -1.44
## ss18 64 302 0.91 0.28 1.0 1.00 0.00 0 1 1 -2.94 6.65
## ss19 65 319 0.56 0.50 1.0 0.58 0.00 0 1 1 -0.26 -1.94
## ss20 66 321 0.63 0.48 1.0 0.67 0.00 0 1 1 -0.55 -1.71
## ss21 67 309 0.22 0.42 0.0 0.16 0.00 0 1 1 1.32 -0.25
## ss22 68 305 0.92 0.27 1.0 1.00 0.00 0 1 1 -3.03 7.22
## ss23 69 286 0.90 0.30 1.0 1.00 0.00 0 1 1 -2.63 4.92
## ss24 70 303 0.86 0.35 1.0 0.95 0.00 0 1 1 -2.08 2.34
## ss25 71 303 0.83 0.38 1.0 0.91 0.00 0 1 1 -1.70 0.90
## ss26 72 310 0.87 0.34 1.0 0.96 0.00 0 1 1 -2.16 2.68
## ss27 73 294 0.83 0.38 1.0 0.91 0.00 0 1 1 -1.72 0.95
## ss28 74 284 0.79 0.41 1.0 0.86 0.00 0 1 1 -1.38 -0.09
## ss29 75 275 0.88 0.33 1.0 0.97 0.00 0 1 1 -2.33 3.42
## ss30 76 283 0.87 0.34 1.0 0.96 0.00 0 1 1 -2.18 2.76
## ss31 77 306 0.51 0.50 1.0 0.52 0.00 0 1 1 -0.05 -2.00
## ss32 78 307 0.79 0.41 1.0 0.85 0.00 0 1 1 -1.38 -0.09
## ss33 79 291 0.60 0.49 1.0 0.62 0.00 0 1 1 -0.40 -1.85
## ss34 80 282 0.71 0.45 1.0 0.76 0.00 0 1 1 -0.92 -1.16
## ss36 81 282 0.70 0.46 1.0 0.75 0.00 0 1 1 -0.88 -1.23
## ss37 82 301 0.75 0.44 1.0 0.81 0.00 0 1 1 -1.13 -0.72
## ss38 83 304 0.60 0.49 1.0 0.63 0.00 0 1 1 -0.41 -1.83
## ss40 84 280 0.66 0.47 1.0 0.70 0.00 0 1 1 -0.68 -1.55
## ss53 85 271 0.65 0.48 1.0 0.69 0.00 0 1 1 -0.62 -1.62
## ss54 86 280 0.56 0.50 1.0 0.57 0.00 0 1 1 -0.23 -1.95
## ss56 87 286 0.50 0.50 0.5 0.50 0.74 0 1 1 0.00 -2.01
## ss57 88 265 0.38 0.49 0.0 0.35 0.00 0 1 1 0.50 -1.75
## ss58 89 270 0.49 0.50 0.0 0.49 0.00 0 1 1 0.04 -2.01
## ss59 90 266 0.42 0.49 0.0 0.40 0.00 0 1 1 0.33 -1.90
## ss60 91 259 0.64 0.48 1.0 0.68 0.00 0 1 1 -0.60 -1.64
## ss61 92 265 0.42 0.49 0.0 0.39 0.00 0 1 1 0.34 -1.89
## ss62 93 262 0.42 0.50 0.0 0.40 0.00 0 1 1 0.31 -1.91
## ss63 94 257 0.15 0.36 0.0 0.07 0.00 0 1 1 1.93 1.73
## ss64 95 265 0.15 0.36 0.0 0.07 0.00 0 1 1 1.94 1.77
## anchor1 96 1577 0.62 0.48 1.0 0.65 0.00 0 1 1 -0.51 -1.74
## anchor2 97 1578 0.60 0.49 1.0 0.62 0.00 0 1 1 -0.39 -1.85
## anchor3 98 1562 0.55 0.50 1.0 0.56 0.00 0 1 1 -0.20 -1.96
## anchor4 99 1562 0.21 0.41 0.0 0.14 0.00 0 1 1 1.43 0.04
## anchor5 100 1564 0.41 0.49 0.0 0.39 0.00 0 1 1 0.35 -1.88
## anchor6 101 1570 0.78 0.41 1.0 0.85 0.00 0 1 1 -1.37 -0.13
## se
## se1 0.02
## se2 0.02
## se3 0.02
## se4 0.02
## se5 0.02
## se6 0.02
## se7 0.02
## se8 0.02
## se9 0.03
## se11 0.03
## se14 0.03
## se15 0.03
## se17 0.03
## se19 0.03
## se20 0.03
## se21 0.03
## se22 0.03
## se23 0.03
## se26 0.03
## se28 0.03
## se29 0.03
## se30 0.03
## se31 0.03
## se32 0.03
## se33 0.03
## se34 0.03
## se35 0.03
## se36 0.02
## se37 0.03
## se38 0.03
## se39 0.03
## se40 0.03
## se41 0.03
## se42 0.03
## se43 0.03
## se44 0.03
## se45 0.03
## se46 0.03
## se47 0.03
## se48 0.03
## se50 0.03
## se51 0.03
## se52 0.03
## se53 0.03
## se54 0.03
## se55 0.03
## se56 0.03
## ss1 0.02
## ss2 0.02
## ss3 0.03
## ss4 0.03
## ss5 0.02
## ss6 0.02
## ss7 0.03
## ss8 0.03
## ss9 0.02
## ss10 0.03
## ss11 0.03
## ss12 0.02
## ss13 0.03
## ss14 0.03
## ss15 0.03
## ss17 0.03
## ss18 0.02
## ss19 0.03
## ss20 0.03
## ss21 0.02
## ss22 0.02
## ss23 0.02
## ss24 0.02
## ss25 0.02
## ss26 0.02
## ss27 0.02
## ss28 0.02
## ss29 0.02
## ss30 0.02
## ss31 0.03
## ss32 0.02
## ss33 0.03
## ss34 0.03
## ss36 0.03
## ss37 0.03
## ss38 0.03
## ss40 0.03
## ss53 0.03
## ss54 0.03
## ss56 0.03
## ss57 0.03
## ss58 0.03
## ss59 0.03
## ss60 0.03
## ss61 0.03
## ss62 0.03
## ss63 0.02
## ss64 0.02
## anchor1 0.01
## anchor2 0.01
## anchor3 0.01
## anchor4 0.01
## anchor5 0.01
## anchor6 0.01Part 2
itemDesc <- describe(f19s20) # get item descriptives
itemDesc## vars n mean sd median trimmed mad min max range skew kurtosis
## se1 1 329 0.87 0.33 1.0 0.96 0.00 0 1 1 -2.22 2.94
## se2 2 317 0.88 0.33 1.0 0.97 0.00 0 1 1 -2.28 3.23
## se3 3 293 0.84 0.37 1.0 0.92 0.00 0 1 1 -1.81 1.27
## se4 4 307 0.83 0.37 1.0 0.91 0.00 0 1 1 -1.79 1.19
## se5 5 294 0.79 0.41 1.0 0.86 0.00 0 1 1 -1.39 -0.08
## se6 6 273 0.85 0.36 1.0 0.93 0.00 0 1 1 -1.91 1.65
## se7 7 317 0.85 0.36 1.0 0.94 0.00 0 1 1 -1.97 1.89
## se8 8 327 0.85 0.36 1.0 0.94 0.00 0 1 1 -1.95 1.82
## se9 9 290 0.50 0.50 0.0 0.50 0.00 0 1 1 0.01 -2.01
## se11 10 289 0.52 0.50 1.0 0.53 0.00 0 1 1 -0.09 -2.00
## se14 11 310 0.34 0.48 0.0 0.30 0.00 0 1 1 0.66 -1.57
## se15 12 283 0.34 0.47 0.0 0.30 0.00 0 1 1 0.69 -1.53
## se17 13 284 0.52 0.50 1.0 0.52 0.00 0 1 1 -0.07 -2.00
## se19 14 324 0.39 0.49 0.0 0.36 0.00 0 1 1 0.47 -1.79
## se20 15 312 0.63 0.48 1.0 0.66 0.00 0 1 1 -0.53 -1.73
## se21 16 284 0.56 0.50 1.0 0.58 0.00 0 1 1 -0.25 -1.94
## se22 17 260 0.35 0.48 0.0 0.31 0.00 0 1 1 0.63 -1.62
## se23 18 282 0.35 0.48 0.0 0.32 0.00 0 1 1 0.60 -1.64
## se26 19 306 0.54 0.50 1.0 0.54 0.00 0 1 1 -0.14 -1.99
## se28 20 244 0.48 0.50 0.0 0.47 0.00 0 1 1 0.10 -2.00
## se29 21 268 0.53 0.50 1.0 0.54 0.00 0 1 1 -0.13 -1.99
## se30 22 276 0.47 0.50 0.0 0.46 0.00 0 1 1 0.13 -1.99
## se31 23 305 0.62 0.49 1.0 0.65 0.00 0 1 1 -0.50 -1.75
## se32 24 284 0.48 0.50 0.0 0.47 0.00 0 1 1 0.08 -2.00
## se33 25 273 0.56 0.50 1.0 0.58 0.00 0 1 1 -0.26 -1.94
## se34 26 249 0.75 0.44 1.0 0.81 0.00 0 1 1 -1.13 -0.73
## se35 27 273 0.51 0.50 1.0 0.52 0.00 0 1 1 -0.05 -2.00
## se36 28 278 0.81 0.39 1.0 0.88 0.00 0 1 1 -1.57 0.46
## se37 29 301 0.39 0.49 0.0 0.36 0.00 0 1 1 0.47 -1.79
## se38 30 280 0.48 0.50 0.0 0.48 0.00 0 1 1 0.07 -2.00
## se39 31 271 0.49 0.50 0.0 0.49 0.00 0 1 1 0.02 -2.01
## se40 32 240 0.45 0.50 0.0 0.44 0.00 0 1 1 0.18 -1.97
## se41 33 268 0.37 0.48 0.0 0.34 0.00 0 1 1 0.54 -1.72
## se42 34 273 0.34 0.47 0.0 0.30 0.00 0 1 1 0.69 -1.54
## se43 35 301 0.68 0.47 1.0 0.73 0.00 0 1 1 -0.77 -1.41
## se44 36 281 0.39 0.49 0.0 0.36 0.00 0 1 1 0.44 -1.81
## se45 37 238 0.47 0.50 0.0 0.46 0.00 0 1 1 0.12 -1.99
## se46 38 270 0.46 0.50 0.0 0.45 0.00 0 1 1 0.16 -1.98
## se47 39 269 0.38 0.49 0.0 0.35 0.00 0 1 1 0.48 -1.78
## se48 40 270 0.40 0.49 0.0 0.37 0.00 0 1 1 0.42 -1.83
## se50 41 271 0.44 0.50 0.0 0.43 0.00 0 1 1 0.23 -1.95
## se51 42 265 0.28 0.45 0.0 0.23 0.00 0 1 1 0.98 -1.05
## se52 43 235 0.34 0.47 0.0 0.30 0.00 0 1 1 0.69 -1.53
## se53 44 259 0.27 0.44 0.0 0.22 0.00 0 1 1 1.03 -0.95
## se54 45 267 0.32 0.47 0.0 0.27 0.00 0 1 1 0.78 -1.40
## se55 46 294 0.35 0.48 0.0 0.31 0.00 0 1 1 0.64 -1.60
## se56 47 272 0.45 0.50 0.0 0.44 0.00 0 1 1 0.19 -1.97
## ss1 48 319 0.82 0.38 1.0 0.90 0.00 0 1 1 -1.70 0.88
## ss2 49 321 0.73 0.45 1.0 0.79 0.00 0 1 1 -1.03 -0.95
## ss3 50 311 0.68 0.47 1.0 0.72 0.00 0 1 1 -0.76 -1.43
## ss4 51 306 0.41 0.49 0.0 0.39 0.00 0 1 1 0.37 -1.87
## ss5 52 287 0.90 0.31 1.0 0.99 0.00 0 1 1 -2.57 4.63
## ss6 53 302 0.84 0.37 1.0 0.93 0.00 0 1 1 -1.86 1.45
## ss7 54 316 0.62 0.49 1.0 0.65 0.00 0 1 1 -0.49 -1.76
## ss8 55 324 0.66 0.47 1.0 0.70 0.00 0 1 1 -0.67 -1.55
## ss9 56 312 0.82 0.39 1.0 0.90 0.00 0 1 1 -1.63 0.67
## ss10 57 307 0.55 0.50 1.0 0.57 0.00 0 1 1 -0.22 -1.96
## ss11 58 298 0.73 0.44 1.0 0.79 0.00 0 1 1 -1.06 -0.88
## ss12 59 287 0.84 0.36 1.0 0.93 0.00 0 1 1 -1.88 1.53
## ss13 60 317 0.40 0.49 0.0 0.38 0.00 0 1 1 0.39 -1.85
## ss14 61 324 0.60 0.49 1.0 0.63 0.00 0 1 1 -0.43 -1.82
## ss15 62 308 0.67 0.47 1.0 0.71 0.00 0 1 1 -0.73 -1.47
## ss17 63 285 0.32 0.47 0.0 0.28 0.00 0 1 1 0.75 -1.44
## ss18 64 302 0.91 0.28 1.0 1.00 0.00 0 1 1 -2.94 6.65
## ss19 65 319 0.56 0.50 1.0 0.58 0.00 0 1 1 -0.26 -1.94
## ss20 66 321 0.63 0.48 1.0 0.67 0.00 0 1 1 -0.55 -1.71
## ss21 67 309 0.22 0.42 0.0 0.16 0.00 0 1 1 1.32 -0.25
## ss22 68 305 0.92 0.27 1.0 1.00 0.00 0 1 1 -3.03 7.22
## ss23 69 286 0.90 0.30 1.0 1.00 0.00 0 1 1 -2.63 4.92
## ss24 70 303 0.86 0.35 1.0 0.95 0.00 0 1 1 -2.08 2.34
## ss25 71 303 0.83 0.38 1.0 0.91 0.00 0 1 1 -1.70 0.90
## ss26 72 310 0.87 0.34 1.0 0.96 0.00 0 1 1 -2.16 2.68
## ss27 73 294 0.83 0.38 1.0 0.91 0.00 0 1 1 -1.72 0.95
## ss28 74 284 0.79 0.41 1.0 0.86 0.00 0 1 1 -1.38 -0.09
## ss29 75 275 0.88 0.33 1.0 0.97 0.00 0 1 1 -2.33 3.42
## ss30 76 283 0.87 0.34 1.0 0.96 0.00 0 1 1 -2.18 2.76
## ss31 77 306 0.51 0.50 1.0 0.52 0.00 0 1 1 -0.05 -2.00
## ss32 78 307 0.79 0.41 1.0 0.85 0.00 0 1 1 -1.38 -0.09
## ss33 79 291 0.60 0.49 1.0 0.62 0.00 0 1 1 -0.40 -1.85
## ss34 80 282 0.71 0.45 1.0 0.76 0.00 0 1 1 -0.92 -1.16
## ss36 81 282 0.70 0.46 1.0 0.75 0.00 0 1 1 -0.88 -1.23
## ss37 82 301 0.75 0.44 1.0 0.81 0.00 0 1 1 -1.13 -0.72
## ss38 83 304 0.60 0.49 1.0 0.63 0.00 0 1 1 -0.41 -1.83
## ss40 84 280 0.66 0.47 1.0 0.70 0.00 0 1 1 -0.68 -1.55
## ss53 85 271 0.65 0.48 1.0 0.69 0.00 0 1 1 -0.62 -1.62
## ss54 86 280 0.56 0.50 1.0 0.57 0.00 0 1 1 -0.23 -1.95
## ss56 87 286 0.50 0.50 0.5 0.50 0.74 0 1 1 0.00 -2.01
## ss57 88 265 0.38 0.49 0.0 0.35 0.00 0 1 1 0.50 -1.75
## ss58 89 270 0.49 0.50 0.0 0.49 0.00 0 1 1 0.04 -2.01
## ss59 90 266 0.42 0.49 0.0 0.40 0.00 0 1 1 0.33 -1.90
## ss60 91 259 0.64 0.48 1.0 0.68 0.00 0 1 1 -0.60 -1.64
## ss61 92 265 0.42 0.49 0.0 0.39 0.00 0 1 1 0.34 -1.89
## ss62 93 262 0.42 0.50 0.0 0.40 0.00 0 1 1 0.31 -1.91
## ss63 94 257 0.15 0.36 0.0 0.07 0.00 0 1 1 1.93 1.73
## ss64 95 265 0.15 0.36 0.0 0.07 0.00 0 1 1 1.94 1.77
## anchor1 96 1577 0.62 0.48 1.0 0.65 0.00 0 1 1 -0.51 -1.74
## anchor2 97 1578 0.60 0.49 1.0 0.62 0.00 0 1 1 -0.39 -1.85
## anchor3 98 1562 0.55 0.50 1.0 0.56 0.00 0 1 1 -0.20 -1.96
## anchor4 99 1562 0.21 0.41 0.0 0.14 0.00 0 1 1 1.43 0.04
## anchor5 100 1564 0.41 0.49 0.0 0.39 0.00 0 1 1 0.35 -1.88
## anchor6 101 1570 0.78 0.41 1.0 0.85 0.00 0 1 1 -1.37 -0.13
## se
## se1 0.02
## se2 0.02
## se3 0.02
## se4 0.02
## se5 0.02
## se6 0.02
## se7 0.02
## se8 0.02
## se9 0.03
## se11 0.03
## se14 0.03
## se15 0.03
## se17 0.03
## se19 0.03
## se20 0.03
## se21 0.03
## se22 0.03
## se23 0.03
## se26 0.03
## se28 0.03
## se29 0.03
## se30 0.03
## se31 0.03
## se32 0.03
## se33 0.03
## se34 0.03
## se35 0.03
## se36 0.02
## se37 0.03
## se38 0.03
## se39 0.03
## se40 0.03
## se41 0.03
## se42 0.03
## se43 0.03
## se44 0.03
## se45 0.03
## se46 0.03
## se47 0.03
## se48 0.03
## se50 0.03
## se51 0.03
## se52 0.03
## se53 0.03
## se54 0.03
## se55 0.03
## se56 0.03
## ss1 0.02
## ss2 0.02
## ss3 0.03
## ss4 0.03
## ss5 0.02
## ss6 0.02
## ss7 0.03
## ss8 0.03
## ss9 0.02
## ss10 0.03
## ss11 0.03
## ss12 0.02
## ss13 0.03
## ss14 0.03
## ss15 0.03
## ss17 0.03
## ss18 0.02
## ss19 0.03
## ss20 0.03
## ss21 0.02
## ss22 0.02
## ss23 0.02
## ss24 0.02
## ss25 0.02
## ss26 0.02
## ss27 0.02
## ss28 0.02
## ss29 0.02
## ss30 0.02
## ss31 0.03
## ss32 0.02
## ss33 0.03
## ss34 0.03
## ss36 0.03
## ss37 0.03
## ss38 0.03
## ss40 0.03
## ss53 0.03
## ss54 0.03
## ss56 0.03
## ss57 0.03
## ss58 0.03
## ss59 0.03
## ss60 0.03
## ss61 0.03
## ss62 0.03
## ss63 0.02
## ss64 0.02
## anchor1 0.01
## anchor2 0.01
## anchor3 0.01
## anchor4 0.01
## anchor5 0.01
## anchor6 0.01itemDiff <- itemDesc$mean # save the difficulties as a vector for plotting
items <- itemDesc$vars # save item names as a vector for plotting
# plot item difficulties
plot(items,itemDiff, type = "l")
points(items,itemDiff)
IRT Analyses
m_1dimension <- 'math.skill = 1-101' # make the 1 dimensional model
# fit the Rasch
results.1pl <- mirt(data=f19s20, model=m_1dimension, itemtype="Rasch", SE=TRUE, verbose=FALSE)
# this ^ is the rasch, i've been fitting the 1pl; not sure if you can do that in MIRT
coef.1pl <- coef(results.1pl, IRTpars=TRUE, simplify=TRUE)
# fit the 2pl
results.2pl <- mirt(data=f19s20, model=m_1dimension, itemtype="2PL", SE=TRUE, verbose=FALSE)
coef.2pl <- coef(results.2pl, IRTpars=TRUE, simplify=TRUE)
param.num.3pl <- mirt(data=f19s20, model=m_1dimension, itemtype="3PL", SE=TRUE, verbose=FALSE,
pars='values') # get the parameter numbers
m <- 'F = 1-101
PRIOR = (1-101, g, norm, -1.1,3)'
model <- mirt.model(m)
results.3pl <- mirt(data=f19s20, model=model, itemtype="3PL", SE=TRUE, verbose=FALSE) # fit the 3pl## EM cycles terminated after 500 iterations.coef.3pl <- coef(results.3pl, IRTpars=TRUE, simplify=TRUE)
coef.3pl## $items
## a b g u
## se1 1.528 -1.534 0.142 1
## se2 1.952 -0.650 0.596 1
## se3 2.651 -0.203 0.638 1
## se4 1.314 -1.526 0.087 1
## se5 1.192 -0.535 0.414 1
## se6 2.951 0.001 0.697 1
## se7 16.017 -0.057 0.671 1
## se8 2.905 -0.773 0.377 1
## se9 5.327 1.371 0.437 1
## se11 10.817 1.542 0.493 1
## se14 8.987 1.714 0.311 1
## se15 0.673 3.241 0.244 1
## se17 1.629 0.988 0.369 1
## se19 0.723 2.537 0.267 1
## se20 0.254 -0.957 0.154 1
## se21 3.121 1.068 0.459 1
## se22 5.330 1.930 0.333 1
## se23 0.727 1.590 0.137 1
## se26 1.885 0.856 0.361 1
## se28 0.375 0.818 0.097 1
## se29 0.613 0.040 0.097 1
## se30 0.846 1.013 0.223 1
## se31 0.460 -0.046 0.233 1
## se32 2.197 1.393 0.391 1
## se33 2.370 0.501 0.319 1
## se34 0.674 -1.572 0.113 1
## se35 3.363 0.560 0.312 1
## se36 0.898 -1.572 0.187 1
## se37 2.080 1.087 0.218 1
## se38 15.152 1.059 0.388 1
## se39 1.663 0.628 0.229 1
## se40 4.882 1.036 0.361 1
## se41 19.222 1.259 0.299 1
## se42 15.324 1.066 0.234 1
## se43 1.290 -0.088 0.320 1
## se44 3.285 1.792 0.354 1
## se45 3.497 1.306 0.407 1
## se46 1.123 0.917 0.212 1
## se47 16.813 1.368 0.327 1
## se48 7.322 1.148 0.311 1
## se50 7.730 1.283 0.374 1
## se51 5.800 1.825 0.250 1
## se52 2.017 1.258 0.218 1
## se53 -5.544 -2.128 0.246 1
## se54 3.650 1.528 0.263 1
## se55 6.477 1.790 0.318 1
## se56 1.001 1.359 0.274 1
## ss1 3.188 -0.246 0.568 1
## ss2 4.020 0.082 0.484 1
## ss3 4.627 0.582 0.539 1
## ss4 2.764 1.097 0.272 1
## ss5 4.817 -1.157 0.252 1
## ss6 2.421 -1.283 0.037 1
## ss7 1.535 0.640 0.423 1
## ss8 1.799 0.226 0.401 1
## ss9 1.323 -0.891 0.362 1
## ss10 1.565 0.438 0.265 1
## ss11 1.469 -0.736 0.194 1
## ss12 3.691 -0.214 0.630 1
## ss13 1.465 1.308 0.253 1
## ss14 2.928 0.145 0.280 1
## ss15 1.598 -0.018 0.321 1
## ss17 1.217 0.947 0.053 1
## ss18 13.688 -1.320 0.231 1
## ss19 1.268 0.040 0.136 1
## ss20 2.392 0.530 0.446 1
## ss21 1.951 1.577 0.116 1
## ss22 3.409 -1.463 0.123 1
## ss23 2.363 -1.303 0.315 1
## ss24 1.498 -1.586 0.142 1
## ss25 9.273 0.204 0.689 1
## ss26 8.010 -0.221 0.679 1
## ss27 1.801 -1.196 0.095 1
## ss28 0.913 -1.103 0.259 1
## ss29 2.972 -1.153 0.280 1
## ss30 2.344 -1.459 0.043 1
## ss31 2.549 1.226 0.418 1
## ss32 8.602 -0.062 0.540 1
## ss33 0.630 -0.310 0.112 1
## ss34 1.843 -0.634 0.062 1
## ss36 1.803 0.240 0.492 1
## ss37 1.880 -0.029 0.474 1
## ss38 5.141 0.533 0.411 1
## ss40 2.981 0.632 0.507 1
## ss53 1.907 0.580 0.478 1
## ss54 0.977 -0.275 0.043 1
## ss56 1.252 0.068 0.025 1
## ss57 1.661 0.535 0.019 1
## ss58 7.366 0.812 0.343 1
## ss59 0.543 1.291 0.131 1
## ss60 0.517 -0.847 0.116 1
## ss61 1.592 1.604 0.319 1
## ss62 1.880 0.708 0.164 1
## ss63 3.341 1.917 0.118 1
## ss64 0.640 3.640 0.061 1
## anchor1 2.813 -0.284 0.075 1
## anchor2 2.383 -0.191 0.085 1
## anchor3 2.233 0.167 0.188 1
## anchor4 0.621 2.364 0.009 1
## anchor5 0.321 1.375 0.035 1
## anchor6 1.778 -0.550 0.375 1
##
## $means
## F
## 0
##
## $cov
## F
## F 1# lr tests
anova(results.1pl, results.2pl)##
## Model 1: mirt(data = f19s20, model = m_1dimension, itemtype = "Rasch",
## SE = TRUE, verbose = FALSE)
## Model 2: mirt(data = f19s20, model = m_1dimension, itemtype = "2PL", SE = TRUE,
## verbose = FALSE)## AIC SABIC HQ BIC logLik X2 df p
## 1 41990.30 42238.35 42200.28 42562.41 -20893.15 NaN NaN NaN
## 2 41162.17 41653.40 41578.01 42295.16 -20379.09 1028.132 100 0anova(results.2pl, results.3pl)##
## Model 1: mirt(data = f19s20, model = m_1dimension, itemtype = "2PL", SE = TRUE,
## verbose = FALSE)
## Model 2: mirt(data = f19s20, model = model, itemtype = "3PL", SE = TRUE,
## verbose = FALSE)## AIC SABIC HQ BIC logLik logPost df
## 1 41162.17 41653.40 41578.01 42295.16 -20379.09 -20379.09 NaN
## 2 41038.34 41775.17 41662.10 42737.82 -20216.17 -20426.41 101firstFive <- c(1:5)
itemplot(results.3pl, 1)
plot(results.3pl, type='trace', auto.key=F) # all item trace lines
plot(results.3pl, type='infotrace',auto.key=T)
plot(results.3pl, type='info', auto.key=T) # all item trace lines
theta <- fscores(results.3pl)Part 3
2 factor model
#parnums.multi2.3pl <- mirt(data=f19s20, model=2, itemtype="3PL",
# pars = 'values', SE=TRUE, verbose=FALSE) # get param numbers
#results.multi2.3pl.s20 <- mirt(data=f19s20, model=2, itemtype="3PL",
# parprior = list(c(seq(4,505,5), 'norm', -1.1,3),
# c(seq(1,505,5), 'norm', 0.0,3),
# c(seq(2,505,5), 'norm', 0.0,3),
# c(seq(3,505,5), 'norm', 0.0,3)),
# SE=TRUE, verbose=FALSE) # fit the 3pl
#save(results.multi2.3pl.s20, file = "results.multi2.3pl.s20")
load('C:/Users/Sydne/Documents/results.multi2.3pl.s20')
coef.multi2.3pl.s20 <- coef(results.multi2.3pl.s20, simplify = T, rotate = 'bifactorT')##
## Rotation: bifactorTcoef.multi2.3pl.s20## $items
## a1 a2 d g u
## se1 1.537 -0.924 2.591 0.105 1
## se2 2.041 0.331 1.250 0.608 1
## se3 2.292 -0.002 0.646 0.618 1
## se4 1.247 -0.764 2.095 0.090 1
## se5 1.086 -0.775 0.665 0.416 1
## se6 2.123 -0.328 0.429 0.657 1
## se7 4.467 0.983 1.150 0.622 1
## se8 2.418 -0.964 2.607 0.220 1
## se9 1.012 -0.787 -1.788 0.363 1
## se11 1.328 -1.408 -3.592 0.479 1
## se14 0.497 -0.983 -2.514 0.259 1
## se15 0.138 -0.445 -0.975 0.068 1
## se17 1.348 -0.212 -1.325 0.351 1
## se19 0.296 -0.253 -0.845 0.110 1
## se20 0.280 0.121 0.205 0.171 1
## se21 0.757 -0.516 -0.328 0.219 1
## se22 1.551 0.980 -3.267 0.275 1
## se23 0.556 -0.225 -0.868 0.078 1
## se26 1.413 -0.382 -1.098 0.318 1
## se28 0.372 0.024 -0.296 0.090 1
## se29 0.660 0.054 -0.066 0.113 1
## se30 1.491 0.420 -1.761 0.323 1
## se31 0.449 -0.169 -0.099 0.275 1
## se32 0.847 -0.636 -1.153 0.270 1
## se33 2.602 -0.683 -1.275 0.323 1
## se34 0.662 0.031 1.021 0.126 1
## se35 2.626 -0.543 -1.319 0.290 1
## se36 1.136 -1.050 0.879 0.472 1
## se37 2.064 -0.963 -2.399 0.218 1
## se38 2.114 -2.538 -2.477 0.304 1
## se39 2.721 0.057 -1.744 0.275 1
## se40 2.134 -0.924 -2.423 0.337 1
## se41 1.322 -3.001 -3.728 0.256 1
## se42 3.302 -0.830 -3.728 0.216 1
## se43 1.573 -0.511 -0.219 0.395 1
## se44 0.070 -1.700 -2.292 0.263 1
## se45 0.639 -1.158 -0.946 0.249 1
## se46 1.279 -0.376 -1.195 0.231 1
## se47 0.880 -2.774 -3.831 0.293 1
## se48 2.951 -0.836 -3.711 0.300 1
## se50 1.401 -3.206 -2.707 0.263 1
## se51 0.011 -0.788 -1.407 0.068 1
## se52 1.426 -0.311 -1.843 0.183 1
## se53 1.381 -0.949 -4.668 0.248 1
## se54 2.431 0.444 -3.808 0.245 1
## se55 1.261 -0.486 -3.084 0.287 1
## se56 0.593 -0.127 -0.743 0.175 1
## ss1 2.169 -0.776 1.308 0.458 1
## ss2 2.919 0.287 0.306 0.422 1
## ss3 1.969 -0.745 -0.595 0.447 1
## ss4 1.063 -1.041 -1.266 0.167 1
## ss5 3.258 -0.156 4.181 0.204 1
## ss6 2.436 -0.660 3.188 0.030 1
## ss7 1.169 -0.548 -0.543 0.366 1
## ss8 1.386 -0.100 0.010 0.327 1
## ss9 1.292 -0.148 1.094 0.387 1
## ss10 1.102 -0.626 -0.097 0.133 1
## ss11 1.801 -0.766 0.993 0.280 1
## ss12 2.912 -0.297 0.911 0.606 1
## ss13 1.636 0.394 -2.042 0.252 1
## ss14 2.511 0.261 -0.122 0.243 1
## ss15 1.209 -0.501 0.583 0.143 1
## ss17 1.317 -0.561 -1.292 0.068 1
## ss18 3.758 -0.228 5.844 0.082 1
## ss19 1.212 -0.383 -0.005 0.127 1
## ss20 2.301 -0.195 -1.162 0.442 1
## ss21 0.929 -1.257 -2.142 0.051 1
## ss22 2.498 0.656 4.048 0.105 1
## ss23 2.408 -1.542 3.925 0.188 1
## ss24 1.512 0.022 2.472 0.091 1
## ss25 3.391 0.440 0.114 0.636 1
## ss26 3.570 -1.039 2.068 0.577 1
## ss27 1.729 -0.070 2.145 0.075 1
## ss28 0.857 -0.089 1.128 0.195 1
## ss29 2.465 -0.426 3.384 0.147 1
## ss30 2.310 -0.923 3.504 0.046 1
## ss31 2.192 -1.543 -2.787 0.397 1
## ss32 3.174 -0.140 1.112 0.436 1
## ss33 0.638 -0.252 0.253 0.087 1
## ss34 1.828 -0.078 1.211 0.044 1
## ss36 1.475 -0.152 -0.214 0.463 1
## ss37 1.690 -0.240 0.259 0.440 1
## ss38 3.306 -0.186 -1.553 0.390 1
## ss40 0.964 -1.166 0.212 0.265 1
## ss53 0.927 0.302 0.233 0.227 1
## ss54 1.037 -0.421 0.237 0.066 1
## ss56 1.285 0.023 -0.072 0.025 1
## ss57 1.598 -0.654 -0.904 0.028 1
## ss58 2.758 -1.765 -2.184 0.291 1
## ss59 0.661 -0.883 -1.192 0.214 1
## ss60 0.624 0.463 0.540 0.072 1
## ss61 0.430 -0.483 -0.601 0.075 1
## ss62 1.486 -0.823 -0.995 0.124 1
## ss63 0.296 -0.873 -2.329 0.044 1
## ss64 0.863 -0.771 -2.821 0.078 1
## anchor1 2.802 -0.086 0.827 0.074 1
## anchor2 2.842 -0.086 0.346 0.130 1
## anchor3 2.116 -0.693 -0.260 0.174 1
## anchor4 2.360 3.093 -4.083 0.048 1
## anchor5 1.729 3.821 -1.862 0.106 1
## anchor6 1.652 0.000 1.111 0.329 1
##
## $means
## F1 F2
## 0 0
##
## $cov
## F1 F2
## F1 1 0
## F2 0 13 factor model
#parnums.multi3.3pl <- mirt(data=f19s20, model=3, itemtype="3PL",
# pars = 'values', SE=TRUE, verbose=FALSE) # get param numbers
#results.multi3.3pl.s20 <- mirt(data=f19s20, model=3, itemtype="3PL",
# parprior = list(c(seq(5,606,6), 'norm', -1.1,3),
# c(seq(1,606,6), 'norm', 0.0,3),
# c(seq(2,606,6), 'norm', 0.0,3),
# c(seq(3,606,6), 'norm', 0.0,3),
# c(seq(4,606,6), 'norm', 0.0,3)),
# SE=TRUE, verbose=FALSE) # fit the 3pl
#save(results.multi3.3pl.s20, file = "results.multi3.3pl.s20")
load('C:/Users/Sydne/Documents/results.multi3.3pl.s20')
coef.multi3.3pl.s20 <- coef(results.multi3.3pl.s20, simplify = T, rotate = 'bifactorT')##
## Rotation: bifactorTcoef.multi3.3pl.s20## $items
## a1 a2 a3 d g u
## se1 1.740 -0.471 0.032 2.597 0.105 1
## se2 2.408 0.762 -0.375 1.479 0.593 1
## se3 1.853 0.398 0.958 1.084 0.551 1
## se4 1.296 -0.200 -0.791 2.151 0.080 1
## se5 1.114 -0.331 0.023 1.087 0.263 1
## se6 2.143 0.303 -0.150 0.365 0.665 1
## se7 3.628 1.863 0.832 1.301 0.599 1
## se8 2.825 -0.505 0.109 2.544 0.295 1
## se9 1.913 -0.148 -0.940 -2.337 0.368 1
## se11 1.276 0.206 -1.097 -2.786 0.455 1
## se14 -0.163 -0.649 2.334 -2.558 0.186 1
## se15 0.320 -0.362 -0.300 -1.037 0.081 1
## se17 1.773 -0.173 0.767 -1.679 0.363 1
## se19 1.349 -0.224 1.272 -3.084 0.301 1
## se20 0.979 0.337 -1.403 -0.126 0.265 1
## se21 0.752 -0.355 0.221 -0.110 0.150 1
## se22 0.804 1.624 -1.072 -3.081 0.244 1
## se23 0.612 -0.152 0.041 -0.979 0.104 1
## se26 1.660 -0.110 0.195 -1.313 0.336 1
## se28 0.346 0.129 0.014 -0.299 0.090 1
## se29 0.577 0.193 0.073 -0.072 0.113 1
## se30 1.417 0.991 -0.633 -1.915 0.323 1
## se31 0.797 -0.122 -0.275 -0.794 0.429 1
## se32 0.905 -0.417 0.366 -1.108 0.263 1
## se33 2.536 -0.676 2.281 -1.150 0.271 1
## se34 0.648 0.071 0.463 1.056 0.133 1
## se35 2.740 0.464 -0.185 -1.353 0.286 1
## se36 1.788 -0.851 -1.756 0.740 0.543 1
## se37 2.264 -0.234 -0.245 -2.422 0.221 1
## se38 2.588 -2.042 0.704 -2.608 0.312 1
## se39 2.699 0.390 2.960 -1.925 0.229 1
## se40 2.677 -0.186 -0.740 -2.814 0.340 1
## se41 2.508 -0.872 -2.943 -3.826 0.228 1
## se42 3.857 -0.456 1.031 -3.990 0.199 1
## se43 1.574 0.042 -0.069 -0.038 0.361 1
## se44 0.533 -2.184 -0.177 -2.782 0.271 1
## se45 1.057 -1.202 -0.133 -1.144 0.268 1
## se46 1.938 -0.229 2.008 -2.463 0.289 1
## se47 1.970 -0.879 -2.052 -3.985 0.297 1
## se48 3.487 -0.434 0.613 -4.071 0.293 1
## se50 1.919 -2.836 0.855 -2.800 0.270 1
## se51 0.313 -0.629 -0.691 -1.409 0.060 1
## se52 1.167 0.038 0.303 -1.472 0.145 1
## se53 1.349 -0.312 -0.521 -4.471 0.249 1
## se54 2.340 1.259 -0.728 -3.991 0.240 1
## se55 1.247 0.048 -1.671 -3.280 0.263 1
## se56 0.600 0.009 0.197 -0.828 0.194 1
## ss1 1.649 -0.323 0.649 2.087 0.120 1
## ss2 2.223 0.772 1.198 0.507 0.383 1
## ss3 1.524 -0.307 0.895 -0.225 0.399 1
## ss4 1.037 -0.358 -0.956 -0.736 0.057 1
## ss5 3.170 0.722 0.221 4.146 0.213 1
## ss6 2.540 0.280 -1.261 3.423 0.035 1
## ss7 1.027 -0.127 -0.515 -0.065 0.261 1
## ss8 1.359 0.005 0.904 -0.164 0.359 1
## ss9 1.696 -0.054 2.182 0.760 0.551 1
## ss10 2.076 0.041 -2.100 0.118 0.077 1
## ss11 2.108 0.080 -1.133 0.931 0.316 1
## ss12 3.162 1.007 -0.499 1.264 0.579 1
## ss13 1.273 1.518 -1.812 -1.965 0.187 1
## ss14 2.617 0.618 1.510 -0.448 0.286 1
## ss15 1.212 -0.237 0.390 0.609 0.139 1
## ss17 1.360 -0.072 -0.282 -1.274 0.068 1
## ss18 3.421 1.055 -1.334 5.866 0.081 1
## ss19 1.430 0.039 -0.350 -0.195 0.185 1
## ss20 1.769 0.099 2.136 -0.838 0.387 1
## ss21 0.848 -1.056 1.041 -2.080 0.035 1
## ss22 2.306 1.403 -0.052 4.171 0.104 1
## ss23 2.989 -0.722 -0.621 4.296 0.164 1
## ss24 1.313 0.850 -0.891 2.663 0.089 1
## ss25 3.321 1.749 -0.873 0.504 0.615 1
## ss26 3.476 -0.502 1.899 2.221 0.568 1
## ss27 1.571 0.296 1.490 2.522 0.064 1
## ss28 0.807 0.169 -0.180 1.211 0.149 1
## ss29 2.477 0.326 0.064 3.362 0.159 1
## ss30 2.361 0.117 -1.832 3.930 0.049 1
## ss31 0.945 -0.218 -2.070 -0.335 0.121 1
## ss32 2.080 0.388 2.237 2.418 0.119 1
## ss33 0.484 -0.115 0.708 0.270 0.092 1
## ss34 1.741 0.392 0.115 1.205 0.042 1
## ss36 1.053 0.027 0.421 0.599 0.250 1
## ss37 1.227 0.284 -0.611 1.100 0.181 1
## ss38 2.783 0.367 1.435 -1.391 0.377 1
## ss40 1.135 -0.760 -0.251 0.324 0.227 1
## ss53 0.891 0.498 0.710 0.197 0.246 1
## ss54 1.527 0.257 -1.668 -0.030 0.146 1
## ss56 1.333 0.364 0.026 -0.073 0.025 1
## ss57 1.725 -0.194 0.050 -0.917 0.027 1
## ss58 2.977 -1.358 1.285 -2.312 0.294 1
## ss59 0.699 -0.549 -0.091 -0.656 0.105 1
## ss60 1.073 -0.017 3.385 1.344 0.053 1
## ss61 0.909 -0.497 -0.521 -0.966 0.141 1
## ss62 1.745 -0.523 0.190 -1.111 0.134 1
## ss63 0.491 -0.176 -0.908 -2.276 0.028 1
## ss64 1.232 -1.268 0.557 -3.217 0.064 1
## anchor1 2.749 0.571 0.464 0.877 0.067 1
## anchor2 2.922 0.542 0.626 0.363 0.133 1
## anchor3 2.250 -0.265 0.372 -0.247 0.171 1
## anchor4 1.532 3.832 0.424 -4.372 0.052 1
## anchor5 0.814 4.859 0.850 -2.380 0.126 1
## anchor6 1.510 0.532 0.151 1.261 0.276 1
##
## $means
## F1 F2 F3
## 0 0 0
##
## $cov
## F1 F2 F3
## F1 1 0 0
## F2 0 1 0
## F3 0 0 14 factor model
#parnums.multi4.3pl <- mirt(data=f19s20, model=4, itemtype="3PL",
# pars = 'values', SE=TRUE, verbose=FALSE) # get param numbers
#results.multi4.3pl.s20 <- mirt(data=f19s20, model=4, itemtype="3PL",
# parprior = list(c(seq(6,707,7), 'norm', -1.1,3),
# c(seq(1,707,7), 'norm', 0.0,3),
# c(seq(2,707,7), 'norm', 0.0,3),
# c(seq(3,707,7), 'norm', 0.0,3),
# c(seq(4,707,7), 'norm', 0.0,3),
# c(seq(5,707,7), 'norm', 0.0,3)),
# SE=TRUE, verbose=FALSE, method = 'QMCEM') # fit the 3pl
#save(results.multi4.3pl.s20, file = "results.multi4.3pl.s20")
load('C:/Users/Sydne/Documents/results.multi4.3pl.s20')
coef.multi4.3pl.s20 <- coef(results.multi4.3pl.s20, simplify = T, rotate = 'bifactorT')##
## Rotation: bifactorTcoef.multi4.3pl.s20## $items
## a1 a2 a3 a4 d g u
## se1 1.725 -0.191 1.196 0.710 2.966 0.099 1
## se2 2.245 0.648 1.292 -0.357 2.818 0.306 1
## se3 2.943 -0.068 -1.344 -0.561 2.316 0.410 1
## se4 0.976 -0.215 -0.026 1.013 2.017 0.108 1
## se5 1.674 -0.787 -0.511 1.623 -0.170 0.576 1
## se6 3.126 0.258 0.540 -0.550 0.653 0.654 1
## se7 4.319 1.882 0.203 0.048 1.428 0.598 1
## se8 3.009 0.087 1.892 0.052 3.630 0.131 1
## se9 1.964 -0.250 0.849 1.809 -3.000 0.377 1
## se11 1.785 -0.045 1.525 -0.198 -3.988 0.475 1
## se14 0.331 -0.583 -1.995 -1.661 -2.279 0.147 1
## se15 -0.015 -0.461 0.193 1.371 -1.187 0.045 1
## se17 1.021 -0.036 -0.094 -0.207 -0.489 0.207 1
## se19 0.339 -0.204 0.187 0.186 -0.907 0.122 1
## se20 1.378 0.980 2.644 0.592 -1.455 0.412 1
## se21 0.834 -0.329 -0.218 0.446 -0.471 0.251 1
## se22 0.375 1.559 1.249 0.816 -2.814 0.218 1
## se23 0.520 -0.048 0.180 0.286 -0.904 0.085 1
## se26 1.906 0.098 1.590 0.190 -1.188 0.281 1
## se28 0.320 0.152 -0.125 0.087 -0.320 0.095 1
## se29 0.506 0.203 -0.080 0.437 -0.105 0.128 1
## se30 2.040 0.705 -0.657 -0.809 -2.311 0.328 1
## se31 0.469 -0.077 0.054 0.216 -0.180 0.298 1
## se32 1.813 -0.534 0.389 -1.591 -1.697 0.258 1
## se33 2.727 -0.526 -1.923 0.002 -1.055 0.261 1
## se34 0.702 0.042 -0.538 -0.025 1.069 0.142 1
## se35 2.838 0.469 0.691 0.129 -1.242 0.268 1
## se36 0.912 -0.454 0.124 1.179 0.784 0.488 1
## se37 2.372 -0.550 -0.100 0.120 -2.698 0.232 1
## se38 2.438 -2.115 -0.371 0.001 -2.316 0.291 1
## se39 2.917 0.539 -2.904 0.587 -1.921 0.221 1
## se40 3.101 -0.159 0.922 -1.885 -2.452 0.271 1
## se41 1.363 -0.616 4.298 -0.730 -1.853 0.029 1
## se42 2.672 0.189 3.212 0.463 -3.149 0.134 1
## se43 1.634 -0.240 -0.124 0.574 -0.175 0.381 1
## se44 0.338 -1.224 0.049 -0.414 -1.834 0.240 1
## se45 1.312 -1.151 0.076 -0.884 -1.028 0.234 1
## se46 2.039 -0.153 -1.846 0.115 -2.352 0.280 1
## se47 2.240 -0.577 2.812 -1.260 -3.925 0.250 1
## se48 2.275 0.231 2.956 0.607 -3.465 0.249 1
## se50 1.907 -2.838 -0.779 -0.064 -2.785 0.267 1
## se51 0.215 -0.680 0.557 0.285 -1.436 0.065 1
## se52 1.159 0.075 -0.202 0.276 -1.506 0.151 1
## se53 -1.563 0.924 0.565 -0.127 -3.654 0.203 1
## se54 1.884 1.000 1.384 -0.935 -3.511 0.215 1
## se55 0.887 -0.714 -1.443 -0.973 -2.489 0.199 1
## se56 0.683 0.036 -0.188 -0.082 -1.028 0.232 1
## ss1 1.806 0.091 1.329 0.628 2.109 0.254 1
## ss2 2.006 0.522 -0.810 0.089 0.854 0.299 1
## ss3 1.393 -0.261 -0.719 0.022 0.058 0.335 1
## ss4 0.989 -0.427 1.032 0.136 -0.683 0.047 1
## ss5 3.061 0.554 -0.731 0.990 4.208 0.243 1
## ss6 1.497 0.603 0.577 2.559 3.641 0.033 1
## ss7 0.885 -0.163 0.255 -0.321 0.366 0.100 1
## ss8 1.526 0.302 0.173 -1.336 0.769 0.141 1
## ss9 1.917 0.000 -1.826 -0.357 1.170 0.480 1
## ss10 1.856 -0.062 2.018 0.306 0.126 0.081 1
## ss11 1.588 0.240 -0.261 2.774 1.013 0.330 1
## ss12 2.817 0.755 0.517 0.785 1.010 0.603 1
## ss13 1.798 0.577 -1.232 -1.010 -2.080 0.203 1
## ss14 2.352 0.825 -0.349 -0.560 0.062 0.208 1
## ss15 1.187 -0.182 -0.425 0.479 0.366 0.227 1
## ss17 1.805 -0.215 0.507 1.351 -1.950 0.107 1
## ss18 2.218 1.178 -0.617 2.967 6.085 0.062 1
## ss19 1.506 -0.045 0.433 -0.138 -0.226 0.188 1
## ss20 2.102 0.074 -1.433 -0.809 -0.531 0.354 1
## ss21 1.085 -1.250 -1.236 0.397 -2.555 0.059 1
## ss22 2.389 1.425 0.283 0.232 4.258 0.106 1
## ss23 2.334 -0.723 0.271 1.821 4.382 0.085 1
## ss24 0.813 0.802 0.477 1.221 2.629 0.088 1
## ss25 3.214 1.086 0.419 -0.905 1.056 0.556 1
## ss26 3.913 -0.231 -0.195 -1.314 2.900 0.506 1
## ss27 1.494 0.337 -1.298 0.830 2.512 0.074 1
## ss28 1.605 0.091 0.247 -1.348 1.838 0.131 1
## ss29 2.600 0.210 0.053 0.623 3.537 0.175 1
## ss30 1.224 0.003 0.009 2.753 3.969 0.044 1
## ss31 1.711 -1.123 -0.848 -1.678 -0.627 0.147 1
## ss32 2.905 0.351 -1.190 -0.090 1.624 0.343 1
## ss33 0.425 -0.117 -0.852 0.429 0.272 0.103 1
## ss34 1.798 0.405 0.018 0.293 1.227 0.044 1
## ss36 1.402 0.018 0.240 -0.145 0.801 0.209 1
## ss37 1.865 0.170 -0.109 -0.522 0.560 0.381 1
## ss38 2.784 0.360 -0.716 -0.097 -1.214 0.368 1
## ss40 1.291 -0.872 0.230 -0.024 0.150 0.288 1
## ss53 1.322 0.587 -0.197 -1.209 0.806 0.091 1
## ss54 0.585 0.212 0.472 1.032 0.295 0.039 1
## ss56 1.315 0.327 0.297 0.217 -0.077 0.023 1
## ss57 1.721 -0.253 -0.003 0.559 -0.969 0.035 1
## ss58 3.100 -1.336 -0.998 -0.451 -1.990 0.256 1
## ss59 0.766 -0.650 0.831 1.988 -2.566 0.291 1
## ss60 1.775 0.129 -3.351 -0.860 0.573 0.224 1
## ss61 1.300 -0.581 0.901 -0.651 -0.753 0.037 1
## ss62 1.897 -0.570 -0.098 0.797 -1.368 0.160 1
## ss63 0.378 -0.218 1.015 0.051 -2.234 0.020 1
## ss64 1.046 -0.307 0.845 1.478 -4.048 0.098 1
## anchor1 2.790 0.662 -0.337 1.263 0.877 0.077 1
## anchor2 3.162 0.704 -0.405 1.791 0.417 0.127 1
## anchor3 2.561 -0.314 -0.171 0.641 -0.418 0.196 1
## anchor4 1.459 3.609 -0.337 0.045 -4.075 0.046 1
## anchor5 0.723 3.973 -0.416 -0.289 -1.614 0.084 1
## anchor6 1.493 0.352 -0.171 0.359 1.283 0.261 1
##
## $means
## F1 F2 F3 F4
## 0 0 0 0
##
## $cov
## F1 F2 F3 F4
## F1 1 0 0 0
## F2 0 1 0 0
## F3 0 0 1 0
## F4 0 0 0 1compare fit
anova(results.3pl, results.multi2.3pl.s20, results.multi3.3pl.s20,
results.multi4.3pl.s20)## AIC SABIC HQ BIC logLik logPost df
## 1 41038.34 41775.17 41662.10 42737.82 -20216.17 -20426.41 NaN
## 2 40845.57 41825.59 41675.20 43105.95 -20019.79 -20890.06 100
## 3 40792.03 42012.79 41825.46 43607.68 -19894.01 -20978.14 99
## 4 40784.17 42243.25 42019.34 44149.49 -19792.08 -21085.21 98calculate IECV 2 factor
sum.multi2.s20 <- summary(results.multi2.3pl.s20, rotate = "bifactorT")##
## Rotation: bifactorT
##
## Rotated factor loadings:
##
## F1 F2 h2
## se1 0.62174 -0.373728 0.5262
## se2 0.76215 0.123483 0.5961
## se3 0.80283 -0.000596 0.6445
## se4 0.55583 -0.340512 0.4249
## se5 0.50219 -0.358451 0.3807
## se6 0.77458 -0.119838 0.6143
## se7 0.91532 0.201449 0.8784
## se8 0.77747 -0.309968 0.7005
## se9 0.47491 -0.369345 0.3620
## se11 0.51527 -0.546211 0.5639
## se14 0.24520 -0.484880 0.2952
## se15 0.07800 -0.252281 0.0697
## se17 0.61790 -0.097278 0.3913
## se19 0.16926 -0.145170 0.0497
## se20 0.16217 0.069752 0.0312
## se21 0.39159 -0.266980 0.2246
## se22 0.61975 0.391455 0.5373
## se23 0.30815 -0.124702 0.1105
## se26 0.62951 -0.170260 0.4253
## se28 0.21345 0.014046 0.0458
## se29 0.36161 0.029508 0.1316
## se30 0.64784 0.182485 0.4530
## se31 0.25410 -0.095485 0.0737
## se32 0.42271 -0.317235 0.2793
## se33 0.81740 -0.214428 0.7141
## se34 0.36245 0.017000 0.1317
## se35 0.82678 -0.170896 0.7128
## se36 0.49394 -0.456625 0.4525
## se37 0.72585 -0.338752 0.6416
## se38 0.56884 -0.683090 0.7902
## se39 0.84766 0.017739 0.7188
## se40 0.74047 -0.320763 0.6512
## se41 0.35777 -0.812264 0.7878
## se42 0.86749 -0.217946 0.8000
## se43 0.66288 -0.215155 0.4857
## se44 0.02889 -0.706380 0.4998
## se45 0.29654 -0.537089 0.3764
## se46 0.59163 -0.173940 0.3803
## se47 0.26099 -0.822803 0.7451
## se48 0.84124 -0.238448 0.7645
## se50 0.36002 -0.823999 0.8086
## se51 0.00561 -0.420172 0.1766
## se52 0.63601 -0.138523 0.4237
## se53 0.57826 -0.397199 0.4922
## se54 0.81017 0.147976 0.6783
## se55 0.58018 -0.223577 0.3866
## se56 0.32799 -0.070438 0.1125
## ss1 0.75727 -0.271074 0.6469
## ss2 0.86079 0.084738 0.7481
## ss3 0.72727 -0.275244 0.6047
## ss4 0.47010 -0.460509 0.4331
## ss5 0.88557 -0.042285 0.7860
## ss6 0.80024 -0.216935 0.6874
## ss7 0.54731 -0.256519 0.3654
## ss8 0.63079 -0.045711 0.4000
## ss9 0.60308 -0.069119 0.3685
## ss10 0.51935 -0.294792 0.3566
## ss11 0.69435 -0.295238 0.5693
## ss12 0.86003 -0.087802 0.7474
## ss13 0.68343 0.164811 0.4942
## ss14 0.82468 0.085745 0.6874
## ss15 0.56306 -0.233330 0.3715
## ss17 0.59219 -0.252437 0.4144
## ss18 0.90955 -0.055133 0.8303
## ss19 0.57050 -0.180094 0.3579
## ss20 0.80209 -0.067912 0.6480
## ss21 0.40214 -0.543941 0.4576
## ss22 0.80759 0.212130 0.6972
## ss23 0.72361 -0.463453 0.7384
## ss24 0.66423 0.009762 0.4413
## ss25 0.88780 0.115120 0.8014
## ss26 0.87303 -0.254129 0.8268
## ss27 0.71243 -0.028762 0.5084
## ss28 0.44944 -0.046597 0.2042
## ss29 0.81469 -0.140810 0.6835
## ss30 0.76637 -0.306338 0.6812
## ss31 0.69028 -0.485971 0.7126
## ss32 0.88061 -0.038817 0.7770
## ss33 0.34767 -0.137465 0.1398
## ss34 0.73152 -0.031356 0.5361
## ss36 0.65334 -0.067210 0.4314
## ss37 0.70108 -0.099616 0.5014
## ss38 0.88797 -0.050007 0.7910
## ss40 0.42340 -0.512094 0.4415
## ss53 0.47269 0.153799 0.2471
## ss54 0.50914 -0.206746 0.3020
## ss56 0.60248 0.010961 0.3631
## ss57 0.65923 -0.269652 0.5073
## ss58 0.74740 -0.478174 0.7873
## ss59 0.32596 -0.435384 0.2958
## ss60 0.33347 0.247581 0.1725
## ss61 0.23623 -0.265297 0.1262
## ss62 0.61790 -0.342221 0.4989
## ss63 0.15291 -0.451052 0.2268
## ss64 0.41917 -0.374601 0.3160
## anchor1 0.85439 -0.026340 0.7307
## anchor2 0.85763 -0.026004 0.7362
## anchor3 0.75508 -0.247243 0.6313
## anchor4 0.55578 0.728312 0.8393
## anchor5 0.38204 0.844171 0.8586
## anchor6 0.69639 0.000000 0.4850
##
## Rotated SS loadings: 39.83 10.723
##
## Factor correlations:
##
## F1 F2
## F1 1 0
## F2 0 1f2.iecv.s20 <- sum.multi2.s20$rotF[,1]^2/sum.multi2.s20$h2
f2.summary <- as.data.frame(cbind(sum.multi2.s20$rotF[,1], sum.multi2.s20$h2,
f2.iecv.s20))
names(f2.summary) <- c("Gen Factor", "Communality", "IECV")
round(f2.summary,2)## Gen Factor Communality IECV
## se1 0.62 0.53 0.73
## se2 0.76 0.60 0.97
## se3 0.80 0.64 1.00
## se4 0.56 0.42 0.73
## se5 0.50 0.38 0.66
## se6 0.77 0.61 0.98
## se7 0.92 0.88 0.95
## se8 0.78 0.70 0.86
## se9 0.47 0.36 0.62
## se11 0.52 0.56 0.47
## se14 0.25 0.30 0.20
## se15 0.08 0.07 0.09
## se17 0.62 0.39 0.98
## se19 0.17 0.05 0.58
## se20 0.16 0.03 0.84
## se21 0.39 0.22 0.68
## se22 0.62 0.54 0.71
## se23 0.31 0.11 0.86
## se26 0.63 0.43 0.93
## se28 0.21 0.05 1.00
## se29 0.36 0.13 0.99
## se30 0.65 0.45 0.93
## se31 0.25 0.07 0.88
## se32 0.42 0.28 0.64
## se33 0.82 0.71 0.94
## se34 0.36 0.13 1.00
## se35 0.83 0.71 0.96
## se36 0.49 0.45 0.54
## se37 0.73 0.64 0.82
## se38 0.57 0.79 0.41
## se39 0.85 0.72 1.00
## se40 0.74 0.65 0.84
## se41 0.36 0.79 0.16
## se42 0.87 0.80 0.94
## se43 0.66 0.49 0.90
## se44 0.03 0.50 0.00
## se45 0.30 0.38 0.23
## se46 0.59 0.38 0.92
## se47 0.26 0.75 0.09
## se48 0.84 0.76 0.93
## se50 0.36 0.81 0.16
## se51 0.01 0.18 0.00
## se52 0.64 0.42 0.95
## se53 0.58 0.49 0.68
## se54 0.81 0.68 0.97
## se55 0.58 0.39 0.87
## se56 0.33 0.11 0.96
## ss1 0.76 0.65 0.89
## ss2 0.86 0.75 0.99
## ss3 0.73 0.60 0.87
## ss4 0.47 0.43 0.51
## ss5 0.89 0.79 1.00
## ss6 0.80 0.69 0.93
## ss7 0.55 0.37 0.82
## ss8 0.63 0.40 0.99
## ss9 0.60 0.37 0.99
## ss10 0.52 0.36 0.76
## ss11 0.69 0.57 0.85
## ss12 0.86 0.75 0.99
## ss13 0.68 0.49 0.95
## ss14 0.82 0.69 0.99
## ss15 0.56 0.37 0.85
## ss17 0.59 0.41 0.85
## ss18 0.91 0.83 1.00
## ss19 0.57 0.36 0.91
## ss20 0.80 0.65 0.99
## ss21 0.40 0.46 0.35
## ss22 0.81 0.70 0.94
## ss23 0.72 0.74 0.71
## ss24 0.66 0.44 1.00
## ss25 0.89 0.80 0.98
## ss26 0.87 0.83 0.92
## ss27 0.71 0.51 1.00
## ss28 0.45 0.20 0.99
## ss29 0.81 0.68 0.97
## ss30 0.77 0.68 0.86
## ss31 0.69 0.71 0.67
## ss32 0.88 0.78 1.00
## ss33 0.35 0.14 0.86
## ss34 0.73 0.54 1.00
## ss36 0.65 0.43 0.99
## ss37 0.70 0.50 0.98
## ss38 0.89 0.79 1.00
## ss40 0.42 0.44 0.41
## ss53 0.47 0.25 0.90
## ss54 0.51 0.30 0.86
## ss56 0.60 0.36 1.00
## ss57 0.66 0.51 0.86
## ss58 0.75 0.79 0.71
## ss59 0.33 0.30 0.36
## ss60 0.33 0.17 0.64
## ss61 0.24 0.13 0.44
## ss62 0.62 0.50 0.77
## ss63 0.15 0.23 0.10
## ss64 0.42 0.32 0.56
## anchor1 0.85 0.73 1.00
## anchor2 0.86 0.74 1.00
## anchor3 0.76 0.63 0.90
## anchor4 0.56 0.84 0.37
## anchor5 0.38 0.86 0.17
## anchor6 0.70 0.48 1.00calculate IECV 3 factor
sum.multi3.s20 <- summary(results.multi3.3pl.s20, rotate = "bifactorT")##
## Rotation: bifactorT
##
## Rotated factor loadings:
##
## F1 F2 F3 h2
## se1 0.7018 -0.19006 0.01305 0.5288
## se2 0.7847 0.24831 -0.12233 0.6924
## se3 0.6808 0.14632 0.35203 0.6088
## se4 0.5662 -0.08716 -0.34536 0.4474
## se5 0.5406 -0.16054 0.01104 0.3181
## se6 0.7772 0.10979 -0.05431 0.6190
## se7 0.8068 0.41428 0.18493 0.8568
## se8 0.8463 -0.15127 0.03274 0.7401
## se9 0.7002 -0.05402 -0.34426 0.6118
## se11 0.5310 0.08595 -0.45675 0.4980
## se14 -0.0551 -0.21895 0.78708 0.6705
## se15 0.1782 -0.20162 -0.16701 0.1003
## se17 0.6871 -0.06694 0.29718 0.5649
## se19 0.5340 -0.08866 0.50340 0.5464
## se20 0.4017 0.13843 -0.57589 0.5122
## se21 0.3945 -0.18623 0.11583 0.2037
## se22 0.2968 0.59997 -0.39597 0.6049
## se23 0.3373 -0.08345 0.02254 0.1212
## se26 0.6952 -0.04596 0.08153 0.4921
## se28 0.1988 0.07385 0.00826 0.0451
## se29 0.3191 0.10658 0.04010 0.1148
## se30 0.5652 0.39534 -0.25232 0.5394
## se31 0.4189 -0.06423 -0.14458 0.2005
## se32 0.4513 -0.20796 0.18253 0.2802
## se33 0.6551 -0.17466 0.58913 0.8068
## se34 0.3445 0.03752 0.24636 0.1808
## se35 0.8395 0.14219 -0.05655 0.7281
## se36 0.5683 -0.27050 -0.55792 0.7074
## se37 0.7937 -0.08212 -0.08592 0.6441
## se38 0.6854 -0.54064 0.18644 0.7969
## se39 0.6176 0.08927 0.67738 0.8483
## se40 0.8205 -0.05711 -0.22691 0.7279
## se41 0.5814 -0.20215 -0.68219 0.8443
## se42 0.8838 -0.10439 0.23629 0.8479
## se43 0.6786 0.01817 -0.02995 0.4617
## se44 0.1885 -0.77304 -0.06271 0.6371
## se45 0.4516 -0.51362 -0.05671 0.4710
## se46 0.5914 -0.06988 0.61283 0.7302
## se47 0.5744 -0.25639 -0.59836 0.7538
## se48 0.8823 -0.10971 0.15508 0.8146
## se50 0.4897 -0.72382 0.21825 0.8113
## se51 0.1591 -0.31992 -0.35115 0.2510
## se52 0.5594 0.01805 0.14541 0.3344
## se53 0.5981 -0.13857 -0.23090 0.4303
## se54 0.7225 0.38890 -0.22494 0.7238
## se55 0.4633 0.01798 -0.62075 0.6003
## se56 0.3307 0.00515 0.10839 0.1211
## ss1 0.6654 -0.13018 0.26176 0.5282
## ss2 0.7077 0.24558 0.38135 0.7065
## ss3 0.6163 -0.12397 0.36205 0.5263
## ss4 0.4632 -0.15982 -0.42700 0.4225
## ss5 0.8623 0.19629 0.06009 0.7857
## ss6 0.7653 0.08430 -0.37990 0.7371
## ss7 0.4990 -0.06158 -0.25040 0.3155
## ss8 0.5762 0.00195 0.38339 0.4790
## ss9 0.5224 -0.01669 0.67224 0.7251
## ss10 0.6090 0.01198 -0.61614 0.7507
## ss11 0.7175 0.02729 -0.38579 0.6644
## ss12 0.8403 0.26771 -0.13268 0.7954
## ss13 0.4004 0.47760 -0.57002 0.7133
## ss14 0.7430 0.17553 0.42856 0.7665
## ss15 0.5667 -0.11098 0.18236 0.3668
## ss17 0.6188 -0.03293 -0.12827 0.4004
## ss18 0.8179 0.25230 -0.31905 0.8344
## ss19 0.6354 0.01739 -0.15553 0.4282
## ss20 0.5434 0.03044 0.65605 0.7266
## ss21 0.3518 -0.43772 0.43179 0.5018
## ss22 0.7225 0.43974 -0.01616 0.7156
## ss23 0.8375 -0.20237 -0.17390 0.7725
## ss24 0.5300 0.34295 -0.35969 0.5279
## ss25 0.7884 0.41507 -0.20726 0.8368
## ss26 0.8009 -0.11561 0.43747 0.8462
## ss27 0.5670 0.10687 0.53796 0.6224
## ss28 0.4247 0.08921 -0.09463 0.1973
## ss29 0.8192 0.10784 0.02110 0.6832
## ss30 0.6862 0.03389 -0.53238 0.7554
## ss31 0.3314 -0.07659 -0.72641 0.6434
## ss32 0.5912 0.11031 0.63585 0.7660
## ss33 0.2533 -0.06035 0.37077 0.2053
## ss34 0.7051 0.15897 0.04642 0.5247
## ss36 0.5149 0.01308 0.20569 0.3076
## ss37 0.5566 0.12904 -0.27746 0.4035
## ss38 0.7767 0.10241 0.40069 0.7743
## ss40 0.5167 -0.34609 -0.11407 0.3997
## ss53 0.4229 0.23615 0.33700 0.3482
## ss54 0.5373 0.09036 -0.58697 0.6414
## ss56 0.6081 0.16600 0.01180 0.3975
## ss57 0.7094 -0.07970 0.02046 0.5100
## ss58 0.7622 -0.34772 0.32909 0.8101
## ss59 0.3635 -0.28582 -0.04711 0.2160
## ss60 0.2726 -0.00439 0.85956 0.8132
## ss61 0.4414 -0.24148 -0.25277 0.3170
## ss62 0.6979 -0.20910 0.07592 0.5365
## ss63 0.2455 -0.08789 -0.45432 0.2744
## ss64 0.4894 -0.50401 0.22127 0.5425
## anchor1 0.8290 0.17221 0.13991 0.7365
## anchor2 0.8393 0.15565 0.17995 0.7610
## anchor3 0.7873 -0.09274 0.13027 0.6453
## anchor4 0.3417 0.85452 0.09447 0.8559
## anchor5 0.1542 0.92004 0.16088 0.8961
## anchor6 0.6450 0.22709 0.06465 0.4717
##
## Rotated SS loadings: 37.127 7.231 12.762
##
## Factor correlations:
##
## F1 F2 F3
## F1 1 0 0
## F2 0 1 0
## F3 0 0 1f3.iecv.s20 <- sum.multi3.s20$rotF[,1]^2/sum.multi3.s20$h2
f3.summary <- as.data.frame(cbind(sum.multi3.s20$rotF[,1], sum.multi3.s20$h2,
f3.iecv.s20))
names(f3.summary) <- c("Gen Factor", "Communality", "IECV")
round(f3.summary,2)## Gen Factor Communality IECV
## se1 0.70 0.53 0.93
## se2 0.78 0.69 0.89
## se3 0.68 0.61 0.76
## se4 0.57 0.45 0.72
## se5 0.54 0.32 0.92
## se6 0.78 0.62 0.98
## se7 0.81 0.86 0.76
## se8 0.85 0.74 0.97
## se9 0.70 0.61 0.80
## se11 0.53 0.50 0.57
## se14 -0.06 0.67 0.00
## se15 0.18 0.10 0.32
## se17 0.69 0.56 0.84
## se19 0.53 0.55 0.52
## se20 0.40 0.51 0.32
## se21 0.39 0.20 0.76
## se22 0.30 0.60 0.15
## se23 0.34 0.12 0.94
## se26 0.70 0.49 0.98
## se28 0.20 0.05 0.88
## se29 0.32 0.11 0.89
## se30 0.57 0.54 0.59
## se31 0.42 0.20 0.88
## se32 0.45 0.28 0.73
## se33 0.66 0.81 0.53
## se34 0.34 0.18 0.66
## se35 0.84 0.73 0.97
## se36 0.57 0.71 0.46
## se37 0.79 0.64 0.98
## se38 0.69 0.80 0.59
## se39 0.62 0.85 0.45
## se40 0.82 0.73 0.92
## se41 0.58 0.84 0.40
## se42 0.88 0.85 0.92
## se43 0.68 0.46 1.00
## se44 0.19 0.64 0.06
## se45 0.45 0.47 0.43
## se46 0.59 0.73 0.48
## se47 0.57 0.75 0.44
## se48 0.88 0.81 0.96
## se50 0.49 0.81 0.30
## se51 0.16 0.25 0.10
## se52 0.56 0.33 0.94
## se53 0.60 0.43 0.83
## se54 0.72 0.72 0.72
## se55 0.46 0.60 0.36
## se56 0.33 0.12 0.90
## ss1 0.67 0.53 0.84
## ss2 0.71 0.71 0.71
## ss3 0.62 0.53 0.72
## ss4 0.46 0.42 0.51
## ss5 0.86 0.79 0.95
## ss6 0.77 0.74 0.79
## ss7 0.50 0.32 0.79
## ss8 0.58 0.48 0.69
## ss9 0.52 0.73 0.38
## ss10 0.61 0.75 0.49
## ss11 0.72 0.66 0.77
## ss12 0.84 0.80 0.89
## ss13 0.40 0.71 0.22
## ss14 0.74 0.77 0.72
## ss15 0.57 0.37 0.88
## ss17 0.62 0.40 0.96
## ss18 0.82 0.83 0.80
## ss19 0.64 0.43 0.94
## ss20 0.54 0.73 0.41
## ss21 0.35 0.50 0.25
## ss22 0.72 0.72 0.73
## ss23 0.84 0.77 0.91
## ss24 0.53 0.53 0.53
## ss25 0.79 0.84 0.74
## ss26 0.80 0.85 0.76
## ss27 0.57 0.62 0.52
## ss28 0.42 0.20 0.91
## ss29 0.82 0.68 0.98
## ss30 0.69 0.76 0.62
## ss31 0.33 0.64 0.17
## ss32 0.59 0.77 0.46
## ss33 0.25 0.21 0.31
## ss34 0.71 0.52 0.95
## ss36 0.51 0.31 0.86
## ss37 0.56 0.40 0.77
## ss38 0.78 0.77 0.78
## ss40 0.52 0.40 0.67
## ss53 0.42 0.35 0.51
## ss54 0.54 0.64 0.45
## ss56 0.61 0.40 0.93
## ss57 0.71 0.51 0.99
## ss58 0.76 0.81 0.72
## ss59 0.36 0.22 0.61
## ss60 0.27 0.81 0.09
## ss61 0.44 0.32 0.61
## ss62 0.70 0.54 0.91
## ss63 0.25 0.27 0.22
## ss64 0.49 0.54 0.44
## anchor1 0.83 0.74 0.93
## anchor2 0.84 0.76 0.93
## anchor3 0.79 0.65 0.96
## anchor4 0.34 0.86 0.14
## anchor5 0.15 0.90 0.03
## anchor6 0.64 0.47 0.88calculate IECV 4 factor
sum.multi4.s20 <- summary(results.multi4.3pl.s20, rotate = "bifactorT")##
## Rotation: bifactorT
##
## Rotated factor loadings:
##
## F1 F2 F3 F4 h2
## se1 0.61594 -6.83e-02 0.42717 0.253421 0.6308
## se2 0.70452 2.03e-01 0.40546 -0.111983 0.7147
## se3 0.79555 -1.83e-02 -0.36339 -0.151680 0.7883
## se4 0.43994 -9.69e-02 -0.01153 0.456726 0.4117
## se5 0.55150 -2.59e-01 -0.16833 0.534746 0.6856
## se6 0.85628 7.07e-02 0.14780 -0.150522 0.7827
## se7 0.86145 3.75e-01 0.04050 0.009510 0.8848
## se8 0.76328 2.20e-02 0.47995 0.013105 0.8136
## se9 0.59740 -7.61e-02 0.25815 0.550175 0.7320
## se11 0.61399 -1.56e-02 0.52466 -0.068272 0.6572
## se14 0.10430 -1.84e-01 -0.62819 -0.523097 0.7129
## se15 -0.00684 -2.06e-01 0.08607 0.611384 0.4235
## se17 0.51083 -1.81e-02 -0.04715 -0.103754 0.2743
## se19 0.19194 -1.16e-01 0.10582 0.105505 0.0725
## se20 0.38071 2.71e-01 0.73062 0.163645 0.7789
## se21 0.41978 -1.66e-01 -0.10993 0.224462 0.2661
## se22 0.13529 5.62e-01 0.45018 0.294025 0.6235
## se23 0.28675 -2.65e-02 0.09918 0.157988 0.1177
## se26 0.63183 3.26e-02 0.52685 0.063112 0.6818
## se28 0.18318 8.72e-02 -0.07145 0.050126 0.0488
## se29 0.27467 1.11e-01 -0.04370 0.237066 0.1458
## se30 0.69399 2.40e-01 -0.22362 -0.275166 0.6648
## se31 0.26354 -4.31e-02 0.03052 0.121174 0.0869
## se32 0.59931 -1.76e-01 0.12874 -0.525951 0.6835
## se33 0.72089 -1.39e-01 -0.50848 0.000511 0.7975
## se34 0.36579 2.18e-02 -0.28020 -0.013095 0.2130
## se35 0.83096 1.37e-01 0.20240 0.037700 0.7517
## se36 0.39472 -1.96e-01 0.05374 0.510233 0.4576
## se37 0.79732 -1.85e-01 -0.03355 0.040360 0.6726
## se38 0.66464 -5.77e-01 -0.10123 0.000210 0.7847
## se39 0.64462 1.19e-01 -0.64183 0.129751 0.8585
## se40 0.75330 -3.87e-02 0.22406 -0.458082 0.8290
## se41 0.27735 -1.25e-01 0.87479 -0.148671 0.8800
## se42 0.58865 4.16e-02 0.70764 0.102031 0.8594
## se43 0.66874 -9.83e-02 -0.05086 0.235081 0.5147
## se44 0.15634 -5.66e-01 0.02284 -0.191337 0.3814
## se45 0.50564 -4.44e-01 0.02945 -0.340758 0.5694
## se46 0.62922 -4.71e-02 -0.56982 0.035503 0.7241
## se47 0.53184 -1.37e-01 0.66761 -0.299093 0.8368
## se48 0.54808 5.56e-02 0.71207 0.146301 0.8319
## se50 0.48922 -7.28e-01 -0.19984 -0.016374 0.8094
## se51 0.11028 -3.49e-01 0.28577 0.146429 0.2370
## se52 0.55473 3.57e-02 -0.09693 0.132131 0.3359
## se53 -0.61153 3.62e-01 0.22128 -0.049582 0.5563
## se54 0.58893 3.13e-01 0.43263 -0.292138 0.7170
## se55 0.33013 -2.66e-01 -0.53682 -0.362193 0.5990
## se56 0.37005 1.96e-02 -0.10170 -0.044365 0.1496
## ss1 0.62585 3.16e-02 0.46056 0.217566 0.6521
## ss2 0.71568 1.86e-01 -0.28902 0.031705 0.6314
## ss3 0.59821 -1.12e-01 -0.30875 0.009408 0.4658
## ss4 0.43606 -1.88e-01 0.45511 0.059854 0.4363
## ss5 0.81548 1.48e-01 -0.19479 0.263888 0.7944
## ss6 0.42547 1.71e-01 0.16405 0.727165 0.7661
## ss7 0.44954 -8.30e-02 0.12978 -0.163198 0.2525
## ss8 0.57156 1.13e-01 0.06464 -0.500147 0.5938
## ss9 0.60515 -2.92e-05 -0.57655 -0.112577 0.7113
## ss10 0.57247 -1.91e-02 0.62248 0.094440 0.7245
## ss11 0.43643 6.61e-02 -0.07160 0.762353 0.7812
## ss12 0.80372 2.15e-01 0.14744 0.224045 0.7642
## ss13 0.59933 1.92e-01 -0.41054 -0.336686 0.6781
## ss14 0.76127 2.67e-01 -0.11285 -0.181274 0.6965
## ss15 0.54470 -8.33e-02 -0.19520 0.219904 0.3901
## ss17 0.62711 -7.48e-02 0.17621 0.469519 0.6504
## ss18 0.51728 2.75e-01 -0.14393 0.691836 0.8425
## ss19 0.64978 -1.95e-02 0.18667 -0.059729 0.4610
## ss20 0.66378 2.32e-02 -0.45246 -0.255381 0.7111
## ss21 0.40100 -4.62e-01 -0.45683 0.146718 0.6043
## ss22 0.72797 4.34e-01 0.08634 0.070792 0.7310
## ss23 0.66672 -2.07e-01 0.07733 0.520138 0.7637
## ss24 0.33409 3.30e-01 0.19605 0.501776 0.5105
## ss25 0.81901 2.77e-01 0.10672 -0.230666 0.8119
## ss26 0.87441 -5.17e-02 -0.04356 -0.293582 0.8554
## ss27 0.54141 1.22e-01 -0.47032 0.300884 0.6197
## ss28 0.59166 3.36e-02 0.09101 -0.496821 0.6063
## ss29 0.81845 6.60e-02 0.01656 0.196069 0.7129
## ss30 0.35367 9.70e-04 0.00273 0.795592 0.7581
## ss31 0.52503 -3.45e-01 -0.26022 -0.514873 0.7273
## ss32 0.80936 9.77e-02 -0.33145 -0.025002 0.7751
## ss33 0.21234 -5.86e-02 -0.42579 0.214440 0.2758
## ss34 0.71180 1.60e-01 0.00698 0.116174 0.5459
## ss36 0.63060 8.00e-03 0.10807 -0.065444 0.4137
## ss37 0.72121 6.59e-02 -0.04221 -0.201737 0.5670
## ss38 0.82820 1.07e-01 -0.21309 -0.028918 0.7436
## ss40 0.55661 -3.76e-01 0.09930 -0.010512 0.4613
## ss53 0.51903 2.30e-01 -0.07735 -0.474460 0.5535
## ss54 0.27357 9.91e-02 0.22073 0.482778 0.3665
## ss56 0.59592 1.48e-01 0.13468 0.098283 0.4049
## ss57 0.68929 -1.01e-01 -0.00139 0.223881 0.5355
## ss58 0.78760 -3.39e-01 -0.25363 -0.114502 0.8130
## ss59 0.26195 -2.22e-01 0.28439 0.679833 0.6611
## ss60 0.41792 3.04e-02 -0.78913 -0.202556 0.8393
## ss61 0.52367 -2.34e-01 0.36311 -0.262371 0.5297
## ss62 0.69435 -2.09e-01 -0.03585 0.291670 0.6120
## ss63 0.18634 -1.08e-01 0.50020 0.025346 0.2971
## ss64 0.39591 -1.16e-01 0.31977 0.559302 0.5853
## anchor1 0.77897 1.85e-01 -0.09422 0.352552 0.7741
## anchor2 0.77233 1.72e-01 -0.09882 0.437495 0.8272
## anchor3 0.81014 -9.94e-02 -0.05417 0.202611 0.7102
## anchor4 0.34237 8.47e-01 -0.07914 0.010651 0.8406
## anchor5 0.16381 9.01e-01 -0.09441 -0.065395 0.8512
## anchor6 0.64209 1.51e-01 -0.07370 0.154537 0.4645
##
## Rotated SS loadings: 34.197 6.253 10.938 9.813
##
## Factor correlations:
##
## F1 F2 F3 F4
## F1 1 0 0 0
## F2 0 1 0 0
## F3 0 0 1 0
## F4 0 0 0 1f4.iecv.s20 <- sum.multi4.s20$rotF[,1]^2/sum.multi4.s20$h2
f4.summary <- as.data.frame(cbind(sum.multi4.s20$rotF[,1], sum.multi4.s20$h2,
f4.iecv.s20))
names(f4.summary) <- c("Gen Factor", "Communality", "IECV")
round(f4.summary,2)## Gen Factor Communality IECV
## se1 0.62 0.63 0.60
## se2 0.70 0.71 0.69
## se3 0.80 0.79 0.80
## se4 0.44 0.41 0.47
## se5 0.55 0.69 0.44
## se6 0.86 0.78 0.94
## se7 0.86 0.88 0.84
## se8 0.76 0.81 0.72
## se9 0.60 0.73 0.49
## se11 0.61 0.66 0.57
## se14 0.10 0.71 0.02
## se15 -0.01 0.42 0.00
## se17 0.51 0.27 0.95
## se19 0.19 0.07 0.51
## se20 0.38 0.78 0.19
## se21 0.42 0.27 0.66
## se22 0.14 0.62 0.03
## se23 0.29 0.12 0.70
## se26 0.63 0.68 0.59
## se28 0.18 0.05 0.69
## se29 0.27 0.15 0.52
## se30 0.69 0.66 0.72
## se31 0.26 0.09 0.80
## se32 0.60 0.68 0.53
## se33 0.72 0.80 0.65
## se34 0.37 0.21 0.63
## se35 0.83 0.75 0.92
## se36 0.39 0.46 0.34
## se37 0.80 0.67 0.95
## se38 0.66 0.78 0.56
## se39 0.64 0.86 0.48
## se40 0.75 0.83 0.68
## se41 0.28 0.88 0.09
## se42 0.59 0.86 0.40
## se43 0.67 0.51 0.87
## se44 0.16 0.38 0.06
## se45 0.51 0.57 0.45
## se46 0.63 0.72 0.55
## se47 0.53 0.84 0.34
## se48 0.55 0.83 0.36
## se50 0.49 0.81 0.30
## se51 0.11 0.24 0.05
## se52 0.55 0.34 0.92
## se53 -0.61 0.56 0.67
## se54 0.59 0.72 0.48
## se55 0.33 0.60 0.18
## se56 0.37 0.15 0.92
## ss1 0.63 0.65 0.60
## ss2 0.72 0.63 0.81
## ss3 0.60 0.47 0.77
## ss4 0.44 0.44 0.44
## ss5 0.82 0.79 0.84
## ss6 0.43 0.77 0.24
## ss7 0.45 0.25 0.80
## ss8 0.57 0.59 0.55
## ss9 0.61 0.71 0.51
## ss10 0.57 0.72 0.45
## ss11 0.44 0.78 0.24
## ss12 0.80 0.76 0.85
## ss13 0.60 0.68 0.53
## ss14 0.76 0.70 0.83
## ss15 0.54 0.39 0.76
## ss17 0.63 0.65 0.60
## ss18 0.52 0.84 0.32
## ss19 0.65 0.46 0.92
## ss20 0.66 0.71 0.62
## ss21 0.40 0.60 0.27
## ss22 0.73 0.73 0.72
## ss23 0.67 0.76 0.58
## ss24 0.33 0.51 0.22
## ss25 0.82 0.81 0.83
## ss26 0.87 0.86 0.89
## ss27 0.54 0.62 0.47
## ss28 0.59 0.61 0.58
## ss29 0.82 0.71 0.94
## ss30 0.35 0.76 0.17
## ss31 0.53 0.73 0.38
## ss32 0.81 0.78 0.85
## ss33 0.21 0.28 0.16
## ss34 0.71 0.55 0.93
## ss36 0.63 0.41 0.96
## ss37 0.72 0.57 0.92
## ss38 0.83 0.74 0.92
## ss40 0.56 0.46 0.67
## ss53 0.52 0.55 0.49
## ss54 0.27 0.37 0.20
## ss56 0.60 0.40 0.88
## ss57 0.69 0.54 0.89
## ss58 0.79 0.81 0.76
## ss59 0.26 0.66 0.10
## ss60 0.42 0.84 0.21
## ss61 0.52 0.53 0.52
## ss62 0.69 0.61 0.79
## ss63 0.19 0.30 0.12
## ss64 0.40 0.59 0.27
## anchor1 0.78 0.77 0.78
## anchor2 0.77 0.83 0.72
## anchor3 0.81 0.71 0.92
## anchor4 0.34 0.84 0.14
## anchor5 0.16 0.85 0.03
## anchor6 0.64 0.46 0.89plot the IECVs
# 2 factors
f2.summary <- f2.summary[order(f2.summary$IECV),]
f2.summary$color <- ifelse(f2.summary$IECV < .5, "green3",
ifelse(f2.summary$IECV > .5 & f2.summary$IECV < .8,
"gold2", "red"))
f2.summary$pch <- ifelse(f2.summary$Communality < .33, 1, 16)
plot(f2.summary$Communality, f2.summary$IECV,
xlab = "Communality", ylab = "IECV", main = "2 Dimensions",
col = f2.summary$color, pch = f2.summary$pch)
text(-.03+f2.summary$Communality, f2.summary$IECV,
labels = row.names(f2.summary))
plot(1-f2.summary$Communality, f2.summary$IECV,
xlab = "Uniqueness", ylab = "IECV", main = "2 Dimensions")
plot(f2.summary$IECV, xlab = "Item", ylab = "IECV", xaxt = "n")
axis(1, at = seq(1,101,1), las = 2, labels = row.names(f2.summary))
# 3 factors
f3.summary <- f3.summary[order(f3.summary$IECV),]
f3.summary$color <- ifelse(f3.summary$IECV < .5, "green3",
ifelse(f3.summary$IECV > .5 & f3.summary$IECV < .8,
"gold2", "red"))
f3.summary$pch <- ifelse(f3.summary$Communality < .33, 1, 16)
plot(f3.summary$Communality, f3.summary$IECV,
xlab = "Communality", ylab = "IECV", main = "3 Dimensions",
col = f3.summary$color, pch = f3.summary$pch)
text(-.03+f3.summary$Communality, f3.summary$IECV,
labels = row.names(f3.summary))
plot(1-f3.summary$Communality, f3.summary$IECV,
xlab = "Uniqueness", ylab = "IECV", main = "3 Dimensions")
plot(f3.summary$IECV, xlab = "Item", ylab = "IECV", xaxt = "n")
axis(1, at = seq(1,101,1), las = 2, labels = row.names(f3.summary))
# 4 factors
f4.summary <- f4.summary[order(f4.summary$IECV),]
f4.summary$color <- ifelse(f4.summary$IECV < .5, "green3",
ifelse(f4.summary$IECV > .5 & f4.summary$IECV < .8,
"gold2", "red"))
f4.summary$pch <- ifelse(f4.summary$Communality < .33, 1, 16)
plot(f4.summary$Communality, f4.summary$IECV,
xlab = "Communality", ylab = "IECV", main = "4 Dimensions",
col = f4.summary$color, pch = f4.summary$pch)
text(-.03+f4.summary$Communality, f4.summary$IECV,
labels = row.names(f4.summary))
plot(1-f4.summary$Communality, f4.summary$IECV,
xlab = "Uniqueness", ylab = "IECV", main = "4 Dimensions")
plot(f4.summary$IECV, xlab = "Item", ylab = "IECV", xaxt = "n")
axis(1, at = seq(1,101,1), las = 2, labels = row.names(f4.summary))