AEI unidim
AEI Construct (Fall 2021 and Spring 2022)
Design Computational Solutions:
Identify a problem and develop (creative) solutions by identifying input, output, and algorithms
AEI construct (public URL):
https://cr4cr.bear-apps.com/constructs/public/u1JW9pXGW4QlVnjYGwnaF1PzwKMXNr
Fall 2021 report (requires login):
https://cr4cr.bear-apps.com/table_of_reports?scheduled_activities=141
Fall 2021 (AFE) report (requires login):
https://cr4cr.bear-apps.com/table_of_reports?scheduled_activities=219
Spring 2022 report (requires login):
https://cr4cr.bear-apps.com/table_of_reports?scheduled_activities=267
BASS page:
1 Construct levels
AEI6: Step Beyond/Strategic:
AEI5: Integrated Relational - Complex:
AEI4: Integrated Relational - Simple:
AEI3: Multi-step Solutions:
AEI2: One-step Solutions:
AEI1: Attempting/Partial:
AEI0: Not Evident:
2 Analyses
Unidimensional Rasch calibration of the AEI items (pre and post, 3 common items)
3 Datasets (raw files)
There are n=933 respondents at Pretest and n=295 at Posttest. Raw data file is available here:
4 Calibration
## $Delivery.03d_NU_F
##
## AEI1 AEI4
## 363 24
##
## $Elevator.04c_OE_F
##
## AEI0 AEI1 AEI2 AEI3 AEI4
## 69 117 122 46 3
##
## $Park.01b_MC_F
##
## AEI1 AEI4
## 306 82
##
## $Shipping.03abc_MCOE2_F
##
## AEI0 AEI1 AEI2 AEI3
## 181 14 99 82
##
## $Sorting.03b_MC_F
##
## AEI1 AEI2 AEI3
## 184 157 38
##
## $Sorting.03c_MC_F
##
## AEI1 AEI2 AEI3
## 126 51 202
##
## $TicTac.01ab_MCOE_F
##
## AEI0 AEI1 AEI2 AEI3
## 74 39 397 262
##
## $Delivery.03ab_MCMC_C
##
## AEI1 AEI3
## 334 140
##
## $SignUp.01ab_MCRO_C
##
## AEI0 AEI1 AEI3
## 24 432 92
##
## $Travel.01_NU_C
##
## AEI0 AEI2
## 336 662
##
## $Delivery.01b_MC_S
##
## AEI0 AEI1 AEI2
## 15 10 59
##
## $Delivery.01c_MC_S
##
## AEI0 AEI2
## 46 38
##
## $Shipping.03abc_MCMCOE_S
##
## AEI0 AEI1 AEI2 AEI3
## 29 9 21 21## ------------------------------------------------------------
## TAM 4.1-4 (2022-08-28 16:03:54)
## R version 4.2.2 (2022-10-31) x86_64, darwin17.0 | nodename=Permans-MacBook-Pro-4.local | login=root
##
## Date of Analysis: 2023-02-22 15:53:02
## Time difference of 0.267741 secs
## Computation time: 0.267741
##
## Multidimensional Item Response Model in TAM
##
## IRT Model: PCM2
## Call:
## TAM::tam.mml(resp = aei[, c(6:18)], Y = aei[, 5], irtmodel = "PCM2",
## verbose = FALSE)
##
## ------------------------------------------------------------
## Number of iterations = 73
## Numeric integration with 21 integration points
##
## Deviance = 8259.96
## Log likelihood = -4129.98
## Number of persons = 1228
## Number of persons used = 1103
## Number of items = 13
## Number of estimated parameters = 28
## Item threshold parameters = 26
## Item slope parameters = 0
## Regression parameters = 1
## Variance/covariance parameters = 1
##
## AIC = 8316 | penalty=56 | AIC=-2*LL + 2*p
## AIC3 = 8344 | penalty=84 | AIC3=-2*LL + 3*p
## BIC = 8456 | penalty=196.16 | BIC=-2*LL + log(n)*p
## aBIC = 8367 | penalty=107.13 | aBIC=-2*LL + log((n-2)/24)*p (adjusted BIC)
## CAIC = 8484 | penalty=224.16 | CAIC=-2*LL + [log(n)+1]*p (consistent AIC)
## AICc = 8317 | penalty=57.51 | AICc=-2*LL + 2*p + 2*p*(p+1)/(n-p-1) (bias corrected AIC)
## GHP = 0.78364 | GHP=( -LL + p ) / (#Persons * #Items) (Gilula-Haberman log penalty)
##
## ------------------------------------------------------------
## EAP Reliability
## [1] 0.478
## ------------------------------------------------------------
## Covariances and Variances
## [,1]
## [1,] 0.639
## ------------------------------------------------------------
## Correlations and Standard Deviations (in the diagonal)
## [,1]
## [1,] 0.8
## ------------------------------------------------------------
## Regression Coefficients
## [,1]
## [1,] 0.000
## [2,] 0.542
## ------------------------------------------------------------
## Standardized Coefficients
## parm dim est StdYX StdX StdY
## 1 Intercept 1 0.000 NA NA NA
## 2 Y1 1 0.542 0.2783 0.2317 0.6511
##
## ** Explained Variance R^2
## [1] 0.0774
## ** SD Theta
## [1] 0.8325
## ** SD Predictors
## Intercept Y1
## 0.0000 0.4274
## ------------------------------------------------------------
## Item Parameters -A*Xsi
## item N M xsi.item AXsi_.Cat1 AXsi_.Cat2 AXsi_.Cat3
## 1 Delivery.03d_NU_F 387 0.062 2.975 2.975 NA NA
## 2 Elevator.04c_OE_F 357 1.431 1.071 -0.870 -0.835 0.637
## 3 Park.01b_MC_F 388 0.211 1.466 1.466 NA NA
## 4 Shipping.03abc_MCOE2_F 376 1.218 0.411 2.344 0.536 1.234
## 5 Sorting.03b_MC_F 379 0.615 1.007 0.137 2.014 NA
## 6 Sorting.03c_MC_F 379 1.201 -0.293 0.637 -0.585 NA
## 7 TicTac.01ab_MCOE_F 772 2.097 -0.686 -0.059 -2.655 -2.058
## 8 Delivery.03ab_MCMC_C 474 0.295 1.061 1.061 NA NA
## 9 SignUp.01ab_MCRO_C 548 1.124 -0.601 -3.149 -1.202 NA
## 10 Travel.01_NU_C 998 0.663 -0.646 -0.646 NA NA
## 11 Delivery.01b_MC_S 84 1.524 -0.486 0.366 -0.973 NA
## 12 Delivery.01c_MC_S 84 0.452 0.679 0.679 NA NA
## 13 Shipping.03abc_MCMCOE_S 80 1.425 0.766 1.369 1.181 2.299
## AXsi_.Cat4 B.Cat1.Dim1 B.Cat2.Dim1 B.Cat3.Dim1 B.Cat4.Dim1
## 1 NA 1 0 0 0
## 2 4.286 1 2 3 4
## 3 NA 1 0 0 0
## 4 NA 1 2 3 0
## 5 NA 1 2 0 0
## 6 NA 1 2 0 0
## 7 NA 1 2 3 0
## 8 NA 1 0 0 0
## 9 NA 1 2 0 0
## 10 NA 1 0 0 0
## 11 NA 1 2 0 0
## 12 NA 1 0 0 0
## 13 NA 1 2 3 0
##
## Item Parameters Xsi
## xsi se.xsi
## Delivery.03d_NU_F 2.975 0.216
## Elevator.04c_OE_F 1.071 0.065
## Park.01b_MC_F 1.466 0.131
## Shipping.03abc_MCOE2_F 0.411 0.052
## Sorting.03b_MC_F 1.007 0.086
## Sorting.03c_MC_F -0.293 0.066
## TicTac.01ab_MCOE_F -0.686 0.048
## Delivery.03ab_MCMC_C 1.061 0.107
## SignUp.01ab_MCRO_C -0.601 0.103
## Travel.01_NU_C -0.646 0.071
## Delivery.01b_MC_S -0.486 0.161
## Delivery.01c_MC_S 0.679 0.234
## Shipping.03abc_MCMCOE_S 0.766 0.120
## Elevator.04c_OE_F_step1 -1.941 0.143
## Elevator.04c_OE_F_step2 -1.037 0.118
## Elevator.04c_OE_F_step3 0.400 0.169
## Shipping.03abc_MCOE2_F_step1 1.932 0.116
## Shipping.03abc_MCOE2_F_step2 -2.219 0.122
## Sorting.03b_MC_F_step1 -0.870 0.107
## Sorting.03c_MC_F_step1 0.930 0.151
## TicTac.01ab_MCOE_F_step1 0.627 0.075
## TicTac.01ab_MCOE_F_step2 -1.910 0.074
## SignUp.01ab_MCRO_C_step1 -2.548 0.108
## Delivery.01b_MC_S_step1 0.852 0.340
## Shipping.03abc_MCMCOE_S_step1 0.603 0.238
## Shipping.03abc_MCMCOE_S_step2 -0.955 0.262
##
## Item Parameters in IRT parameterization
## item alpha beta tau.Cat1 tau.Cat2 tau.Cat3 tau.Cat4
## 1 Delivery.03d_NU_F 1 2.975 NA NA NA NA
## 2 Elevator.04c_OE_F 1 1.071 -1.941 -1.037 0.400 2.578
## 3 Park.01b_MC_F 1 1.466 NA NA NA NA
## 4 Shipping.03abc_MCOE2_F 1 0.411 1.932 -2.219 0.287 NA
## 5 Sorting.03b_MC_F 1 1.007 -0.870 0.870 NA NA
## 6 Sorting.03c_MC_F 1 -0.293 0.930 -0.930 NA NA
## 7 TicTac.01ab_MCOE_F 1 -0.686 0.627 -1.910 1.283 NA
## 8 Delivery.03ab_MCMC_C 1 1.061 NA NA NA NA
## 9 SignUp.01ab_MCRO_C 1 -0.601 -2.548 2.548 NA NA
## 10 Travel.01_NU_C 1 -0.646 NA NA NA NA
## 11 Delivery.01b_MC_S 1 -0.486 0.852 -0.852 NA NA
## 12 Delivery.01c_MC_S 1 0.679 NA NA NA NA
## 13 Shipping.03abc_MCMCOE_S 1 0.766 0.603 -0.955 0.352 NA## Item parameters
## |**********|
## |----------|
## Regression parameters
## |**|
## ||
## |--|## est.Dim1 se.Dim1
## 1 0.0000000 0.00000000
## 2 0.5420039 0.08824013Please see this link below for the calibration of the same dataset in ConQuest:
https://www.dropbox.com/sh/u3sy2wk3zg66vs5/AAA9oZRXvTKNlxNs0rptzqWJa?dl=0
4.1 Model specification
## [1] "item+item*step"4.2 Latent regression coefficient (gain from pre to post)
## hs
## Main dimension 0.542
## S. errors 0.058—> ## Reliability
## EAP/PV RELIABILITY: 0.4854.3 Variance
## errors
## [1,] 0.64 0.0264.4 Item parameters
The parameter difficulty parameters may be thought of as a kind of “average” item difficulty for a partial credit item. This may be useful, if one wishes to have one indicative difficulty parameter for a partial credit item as a whole. Otherwise, to describe the difficulty of a partial credit item, one needs to describe the difficulties of individual score categories within the item, such as using the Thurstonian thresholds (Wu et al., 2016).
## n_item item est error
## 1 1 Delivery.03.. 2.975 0.216
## 2 2 Elevator.04.. 1.072 0.065
## 3 3 Park.01b_MC.. 1.467 0.131
## 4 4 Shipping.03.. 0.412 0.052
## 5 5 Sorting.03b.. 1.007 0.086
## 6 6 Sorting.03c.. -0.293 0.066
## 7 7 TicTac.01ab.. -0.686 0.048
## 8 8 Delivery.03.. 1.062 0.107
## 9 9 SignUp.01ab.. -0.601 0.103
## 10 10 Travel.01_N.. -0.646 0.071
## 11 11 Delivery.01.. -0.486 0.161
## 12 12 Delivery.01.. 0.679 0.234
## 13 13 Shipping.03.. 0.767 0.120## W.fit: Weighted fit (Infit)## [,1] [,2]
## [1,] "item1" "Delivery.03d_NU_F"
## [2,] "item2" "Elevator.04c_OE_F"
## [3,] "item3" "Park.01b_MC_F"
## [4,] "item4" "Shipping.03abc_MCOE2_F"
## [5,] "item5" "Sorting.03b_MC_F"
## [6,] "item6" "Sorting.03c_MC_F"
## [7,] "item7" "TicTac.01ab_MCOE_F"
## [8,] "item8" "Delivery.03ab_MCMC_C"
## [9,] "item9" "SignUp.01ab_MCRO_C"
## [10,] "item10" "Travel.01_NU_C"
## [11,] "item11" "Delivery.01b_MC_S"
## [12,] "item12" "Delivery.01c_MC_S"
## [13,] "item13" "Shipping.03abc_MCMCOE_S"4.5 Step parameters
Delta is the point at which the probability of being in category k − 1 and category k is equals. This mathematical fact provides an interpretation for the delta (d) parameters (Wu et al., 2016)
## n_item item step est error
## 1 2 Elevator.04.. 0 NA NA
## 2 2 Elevator.04.. 1 -1.942 0.143
## 3 2 Elevator.04.. 2 -1.037 0.118
## 4 2 Elevator.04.. 3 0.400 0.169
## 5 2 Elevator.04.. 4 2.579 NA
## 6 4 Shipping.03.. 0 NA NA
## 7 4 Shipping.03.. 1 1.932 0.117
## 8 4 Shipping.03.. 2 -2.220 0.122
## 9 4 Shipping.03.. 3 0.287 NA
## 10 5 Sorting.03b.. 0 NA NA
## 11 5 Sorting.03b.. 1 -0.870 0.107
## 12 5 Sorting.03b.. 2 0.870 NA
## 13 6 Sorting.03c.. 0 NA NA
## 14 6 Sorting.03c.. 1 0.929 0.151
## 15 6 Sorting.03c.. 2 -0.929 NA
## 16 7 TicTac.01ab.. 0 NA NA
## 17 7 TicTac.01ab.. 1 0.627 0.075
## 18 7 TicTac.01ab.. 2 -1.911 0.074
## 19 7 TicTac.01ab.. 3 1.284 NA
## 20 9 SignUp.01ab.. 0 NA NA
## 21 9 SignUp.01ab.. 1 -2.548 0.108
## 22 9 SignUp.01ab.. 2 2.548 NA
## 23 11 Delivery.01.. 0 NA NA
## 24 11 Delivery.01.. 1 0.852 0.340
## 25 11 Delivery.01.. 2 -0.852 NA
## 26 13 Shipping.03.. 0 NA NA
## 27 13 Shipping.03.. 1 0.603 0.238
## 28 13 Shipping.03.. 2 -0.955 0.262
## 29 13 Shipping.03.. 3 0.352 NA4.6 Thurstonian thresholds
For partial credit items, to achieve a score of 2, students would generally need to accomplish more tasks than for achieving a score of 1. To reflect this “cumulative achievement”, the Thurstonian thresholds are sometimes used as indicators of “score difficulties”. The Thurstonian threshold for a score category is defined as the ability at which the probability of achieving that score or higher reaches 0.50.
## [,1] [,2] [,3] [,4]
## Delivery.03d_NU_F 2.976 NA NA NA
## Elevator.04c_OE_F -1.153 0.116 1.566 3.75
## Park.01b_MC_F 1.467 NA NA NA
## Shipping.03abc_MCOE2_F 0.013 0.100 0.968 NA
## Sorting.03b_MC_F -0.005 2.019 NA NA
## Sorting.03c_MC_F -0.489 -0.097 NA NA
## TicTac.01ab_MCOE_F -1.517 -1.270 0.653 NA
## Delivery.03ab_MCMC_C 1.062 NA NA NA
## SignUp.01ab_MCRO_C -3.155 1.953 NA NA
## Travel.01_NU_C -0.646 NA NA NA
## Delivery.01b_MC_S -0.698 -0.275 NA NA
## Delivery.01c_MC_S 0.680 NA NA NA
## Shipping.03abc_MCMCOE_S 0.227 0.549 1.440 NA## AEI1 AEI2 AEI3 AEI4
## Delivery.03d_NU_F 2.976 NA NA NA
## Elevator.04c_OE_F -1.153 0.116 1.566 3.75
## Park.01b_MC_F 1.467 NA NA NA
## Shipping.03abc_MCOE2_F 0.013 0.100 0.968 NA
## Sorting.03b_MC_F -0.005 2.019 NA NA
## Sorting.03c_MC_F -0.489 -0.097 NA NA
## TicTac.01ab_MCOE_F -1.517 -1.270 0.653 NA
## Delivery.03ab_MCMC_C 1.062 NA NA NA
## SignUp.01ab_MCRO_C -3.155 1.953 NA NA
## Travel.01_NU_C -0.646 NA NA NA
## Delivery.01b_MC_S -0.698 -0.275 NA NA
## Delivery.01c_MC_S 0.680 NA NA NA
## Shipping.03abc_MCMCOE_S 0.227 0.549 1.440 NA## AEI1 AEI2 AEI3 AEI4
## Min. :-3.15500 Min. :-1.2700 Min. :0.6530 Min. :3.75
## 1st Qu.:-0.69800 1st Qu.:-0.1415 1st Qu.:0.8892 1st Qu.:3.75
## Median :-0.00500 Median : 0.1080 Median :1.2040 Median :3.75
## Mean :-0.09523 Mean : 0.3869 Mean :1.1567 Mean :3.75
## 3rd Qu.: 0.68000 3rd Qu.: 0.9000 3rd Qu.:1.4715 3rd Qu.:3.75
## Max. : 2.97600 Max. : 2.0190 Max. :1.5660 Max. :3.75
## NA's :5 NA's :9 NA's :12## X AEI1 AEI2 AEI3 AEI4
## 1 Delivery.03d_NU_F NA NA NA 2.976
## 2 Elevator.04c_OE_F -1.153 0.116 1.566 3.750
## 3 Park.01b_MC_F NA NA NA 1.467
## 4 Shipping.03abc_MCOE2_F 0.013 0.100 0.968 NA
## 5 Sorting.03b_MC_F NA -0.005 2.019 NA
## 6 Sorting.03c_MC_F NA -0.489 -0.097 NA
## 7 TicTac.01ab_MCOE_F -1.517 -1.270 0.653 NA
## 8 Delivery.03ab_MCMC_C NA NA 1.062 NA
## 9 SignUp.01ab_MCRO_C -3.155 NA 1.953 NA
## 10 Travel.01_NU_C NA -0.646 NA NA
## 11 Delivery.01b_MC_S -0.698 -0.275 NA NA
## 12 Delivery.01c_MC_S NA 0.680 NA NA
## 13 Shipping.03abc_MCMCOE_S 0.227 0.549 1.440 NA5 Links to CQ files (please copy/paste these links)
https://www.dropbox.com/sh/vr3f9anqhy5im7c/AADIvwaK5vPUPdw1BZBepbQSa?dl=0
6 Item fit graph
7 Wright Map
8 Category Characteristic Curves (CCC)
9 Calibration summary from TAM
## ------------------------------------------------------------
## TAM 4.1-4 (2022-08-28 16:03:54)
## R version 4.2.2 (2022-10-31) x86_64, darwin17.0 | nodename=Permans-MacBook-Pro-4.local | login=root
##
## Date of Analysis: 2023-02-22 15:53:02
## Time difference of 0.267741 secs
## Computation time: 0.267741
##
## Multidimensional Item Response Model in TAM
##
## IRT Model: PCM2
## Call:
## TAM::tam.mml(resp = aei[, c(6:18)], Y = aei[, 5], irtmodel = "PCM2",
## verbose = FALSE)
##
## ------------------------------------------------------------
## Number of iterations = 73
## Numeric integration with 21 integration points
##
## Deviance = 8259.96
## Log likelihood = -4129.98
## Number of persons = 1228
## Number of persons used = 1103
## Number of items = 13
## Number of estimated parameters = 28
## Item threshold parameters = 26
## Item slope parameters = 0
## Regression parameters = 1
## Variance/covariance parameters = 1
##
## AIC = 8316 | penalty=56 | AIC=-2*LL + 2*p
## AIC3 = 8344 | penalty=84 | AIC3=-2*LL + 3*p
## BIC = 8456 | penalty=196.16 | BIC=-2*LL + log(n)*p
## aBIC = 8367 | penalty=107.13 | aBIC=-2*LL + log((n-2)/24)*p (adjusted BIC)
## CAIC = 8484 | penalty=224.16 | CAIC=-2*LL + [log(n)+1]*p (consistent AIC)
## AICc = 8317 | penalty=57.51 | AICc=-2*LL + 2*p + 2*p*(p+1)/(n-p-1) (bias corrected AIC)
## GHP = 0.78364 | GHP=( -LL + p ) / (#Persons * #Items) (Gilula-Haberman log penalty)
##
## ------------------------------------------------------------
## EAP Reliability
## [1] 0.478
## ------------------------------------------------------------
## Covariances and Variances
## [,1]
## [1,] 0.639
## ------------------------------------------------------------
## Correlations and Standard Deviations (in the diagonal)
## [,1]
## [1,] 0.8
## ------------------------------------------------------------
## Regression Coefficients
## [,1]
## [1,] 0.000
## [2,] 0.542
## ------------------------------------------------------------
## Standardized Coefficients
## parm dim est StdYX StdX StdY
## 1 Intercept 1 0.000 NA NA NA
## 2 Y1 1 0.542 0.2783 0.2317 0.6511
##
## ** Explained Variance R^2
## [1] 0.0774
## ** SD Theta
## [1] 0.8325
## ** SD Predictors
## Intercept Y1
## 0.0000 0.4274
## ------------------------------------------------------------
## Item Parameters -A*Xsi
## item N M xsi.item AXsi_.Cat1 AXsi_.Cat2 AXsi_.Cat3
## 1 Delivery.03d_NU_F 387 0.062 2.975 2.975 NA NA
## 2 Elevator.04c_OE_F 357 1.431 1.071 -0.870 -0.835 0.637
## 3 Park.01b_MC_F 388 0.211 1.466 1.466 NA NA
## 4 Shipping.03abc_MCOE2_F 376 1.218 0.411 2.344 0.536 1.234
## 5 Sorting.03b_MC_F 379 0.615 1.007 0.137 2.014 NA
## 6 Sorting.03c_MC_F 379 1.201 -0.293 0.637 -0.585 NA
## 7 TicTac.01ab_MCOE_F 772 2.097 -0.686 -0.059 -2.655 -2.058
## 8 Delivery.03ab_MCMC_C 474 0.295 1.061 1.061 NA NA
## 9 SignUp.01ab_MCRO_C 548 1.124 -0.601 -3.149 -1.202 NA
## 10 Travel.01_NU_C 998 0.663 -0.646 -0.646 NA NA
## 11 Delivery.01b_MC_S 84 1.524 -0.486 0.366 -0.973 NA
## 12 Delivery.01c_MC_S 84 0.452 0.679 0.679 NA NA
## 13 Shipping.03abc_MCMCOE_S 80 1.425 0.766 1.369 1.181 2.299
## AXsi_.Cat4 B.Cat1.Dim1 B.Cat2.Dim1 B.Cat3.Dim1 B.Cat4.Dim1
## 1 NA 1 0 0 0
## 2 4.286 1 2 3 4
## 3 NA 1 0 0 0
## 4 NA 1 2 3 0
## 5 NA 1 2 0 0
## 6 NA 1 2 0 0
## 7 NA 1 2 3 0
## 8 NA 1 0 0 0
## 9 NA 1 2 0 0
## 10 NA 1 0 0 0
## 11 NA 1 2 0 0
## 12 NA 1 0 0 0
## 13 NA 1 2 3 0
##
## Item Parameters Xsi
## xsi se.xsi
## Delivery.03d_NU_F 2.975 0.216
## Elevator.04c_OE_F 1.071 0.065
## Park.01b_MC_F 1.466 0.131
## Shipping.03abc_MCOE2_F 0.411 0.052
## Sorting.03b_MC_F 1.007 0.086
## Sorting.03c_MC_F -0.293 0.066
## TicTac.01ab_MCOE_F -0.686 0.048
## Delivery.03ab_MCMC_C 1.061 0.107
## SignUp.01ab_MCRO_C -0.601 0.103
## Travel.01_NU_C -0.646 0.071
## Delivery.01b_MC_S -0.486 0.161
## Delivery.01c_MC_S 0.679 0.234
## Shipping.03abc_MCMCOE_S 0.766 0.120
## Elevator.04c_OE_F_step1 -1.941 0.143
## Elevator.04c_OE_F_step2 -1.037 0.118
## Elevator.04c_OE_F_step3 0.400 0.169
## Shipping.03abc_MCOE2_F_step1 1.932 0.116
## Shipping.03abc_MCOE2_F_step2 -2.219 0.122
## Sorting.03b_MC_F_step1 -0.870 0.107
## Sorting.03c_MC_F_step1 0.930 0.151
## TicTac.01ab_MCOE_F_step1 0.627 0.075
## TicTac.01ab_MCOE_F_step2 -1.910 0.074
## SignUp.01ab_MCRO_C_step1 -2.548 0.108
## Delivery.01b_MC_S_step1 0.852 0.340
## Shipping.03abc_MCMCOE_S_step1 0.603 0.238
## Shipping.03abc_MCMCOE_S_step2 -0.955 0.262
##
## Item Parameters in IRT parameterization
## item alpha beta tau.Cat1 tau.Cat2 tau.Cat3 tau.Cat4
## 1 Delivery.03d_NU_F 1 2.975 NA NA NA NA
## 2 Elevator.04c_OE_F 1 1.071 -1.941 -1.037 0.400 2.578
## 3 Park.01b_MC_F 1 1.466 NA NA NA NA
## 4 Shipping.03abc_MCOE2_F 1 0.411 1.932 -2.219 0.287 NA
## 5 Sorting.03b_MC_F 1 1.007 -0.870 0.870 NA NA
## 6 Sorting.03c_MC_F 1 -0.293 0.930 -0.930 NA NA
## 7 TicTac.01ab_MCOE_F 1 -0.686 0.627 -1.910 1.283 NA
## 8 Delivery.03ab_MCMC_C 1 1.061 NA NA NA NA
## 9 SignUp.01ab_MCRO_C 1 -0.601 -2.548 2.548 NA NA
## 10 Travel.01_NU_C 1 -0.646 NA NA NA NA
## 11 Delivery.01b_MC_S 1 -0.486 0.852 -0.852 NA NA
## 12 Delivery.01c_MC_S 1 0.679 NA NA NA NA
## 13 Shipping.03abc_MCMCOE_S 1 0.766 0.603 -0.955 0.352 NA