DCS unidim
DCS Construct (Fall 2021 and Spring 2022)
Design Computational Solutions:
Identify a problem and develop (creative) solutions by identifying input, output, and algorithms
DCS construct (public URL):
(please copy and paste these links)
https://cr4cr.bear-apps.com/constructs/public/5xXhDYnYmUve9c9DkpCgIMRsh6nCLI
Spring 2022 report (requires login):
https://cr4cr.bear-apps.com/scores_reports?scheduled_activities=267
BASS page:
1 Construct levels
DCS6: Strategic/Step Beyond:
DCS5: Integrated Relational - Complex:
DCS4: Integrated Relational - Simple:
DCS3: Multi-Step Solution:
DCS2: One-Step Solution:
DCS1: Attempting/Partial:
2 Analyses
Unidimensional Rasch calibration of the DCS items (pre and post, 2 common items across)
3 Datasets (raw files)
There are n=947 respondents at Pretest and n=296 at Posttest. Raw data file is available here:
4 Calibration
## ------------------------------------------------------------
## 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 15:54:14
## Time difference of 0.188365 secs
## Computation time: 0.188365
##
## Multidimensional Item Response Model in TAM
##
## IRT Model: PCM2
## Call:
## TAM::tam.mml(resp = dcs[, c(6:16)], Y = dcs[, 5], irtmodel = "PCM2",
## verbose = FALSE)
##
## ------------------------------------------------------------
## Number of iterations = 45
## Numeric integration with 21 integration points
##
## Deviance = 10799.31
## Log likelihood = -5399.66
## Number of persons = 1242
## Number of persons used = 1117
## Number of items = 11
## Number of estimated parameters = 45
## Item threshold parameters = 43
## Item slope parameters = 0
## Regression parameters = 1
## Variance/covariance parameters = 1
##
## AIC = 10889 | penalty=90 | AIC=-2*LL + 2*p
## AIC3 = 10934 | penalty=135 | AIC3=-2*LL + 3*p
## BIC = 11115 | penalty=315.83 | BIC=-2*LL + log(n)*p
## aBIC = 10972 | penalty=172.74 | aBIC=-2*LL + log((n-2)/24)*p (adjusted BIC)
## CAIC = 11160 | penalty=360.83 | CAIC=-2*LL + [log(n)+1]*p (consistent AIC)
## AICc = 10893 | penalty=93.87 | AICc=-2*LL + 2*p + 2*p*(p+1)/(n-p-1) (bias corrected AIC)
## GHP = 1.29481 | GHP=( -LL + p ) / (#Persons * #Items) (Gilula-Haberman log penalty)
##
## ------------------------------------------------------------
## EAP Reliability
## [1] 0.627
## ------------------------------------------------------------
## Covariances and Variances
## [,1]
## [1,] 1.116
## ------------------------------------------------------------
## Correlations and Standard Deviations (in the diagonal)
## [,1]
## [1,] 1.056
## ------------------------------------------------------------
## Regression Coefficients
## [,1]
## [1,] 0.00000
## [2,] 0.06899
## ------------------------------------------------------------
## Standardized Coefficients
## parm dim est StdYX StdX StdY
## 1 Intercept 1 0.000 NA NA NA
## 2 Y1 1 0.069 0.0278 0.0294 0.0653
##
## ** Explained Variance R^2
## [1] 8e-04
## ** SD Theta
## [1] 1.0567
## ** SD Predictors
## Intercept Y1
## 0.0000 0.4257
## ------------------------------------------------------------
## Item Parameters -A*Xsi
## item N M xsi.item AXsi_.Cat1 AXsi_.Cat2 AXsi_.Cat3
## 1 Delivery.00ab_OEOE_F 404 1.903 0.094 -2.402 -2.467 -1.708
## 2 Elevator.00ab_OE2_F 395 1.711 0.526 -1.130 -2.494 -0.204
## 3 Ticket.02_OE_F 764 2.009 0.271 -1.767 -2.479 -2.073
## 4 Travel.00ab_OEOE_F 773 1.558 -0.162 -2.328 -2.184 -0.487
## 5 Elevator.02abc_OE3_C 528 1.591 0.471 -1.453 -0.952 0.099
## 6 Market.01abc_OE_C 874 1.630 0.348 -1.692 -0.974 0.019
## 7 Delivery.00abc_OEOEOE_S 84 1.619 0.879 -1.862 -2.046 -0.310
## 8 Elevator.00abc_OEOEOE_S 72 1.750 0.588 -0.639 -1.723 -0.216
## 9 Market.00abc_OEOEOE_S 155 1.787 0.281 -1.793 -2.199 -0.455
## 10 Market.01abc_MCOEOE_S 71 1.859 -0.395 -4.507 -4.677 -2.511
## 11 Travel.00abc_OEOEOE_S 85 1.882 0.481 -1.597 -2.737 -1.427
## AXsi_.Cat4 B.Cat1.Dim1 B.Cat2.Dim1 B.Cat3.Dim1 B.Cat4.Dim1
## 1 0.376 1 2 3 4
## 2 2.102 1 2 3 4
## 3 1.085 1 2 3 4
## 4 NA 1 2 3 0
## 5 1.882 1 2 3 4
## 6 1.393 1 2 3 4
## 7 3.515 1 2 3 4
## 8 2.351 1 2 3 4
## 9 1.123 1 2 3 4
## 10 -1.581 1 2 3 4
## 11 1.923 1 2 3 4
##
## Item Parameters Xsi
## xsi se.xsi
## Delivery.00ab_OEOE_F 0.094 0.061
## Elevator.00ab_OE2_F 0.526 0.070
## Ticket.02_OE_F 0.271 0.046
## Travel.00ab_OEOE_F -0.162 0.053
## Elevator.02abc_OE3_C 0.471 0.051
## Market.01abc_OE_C 0.348 0.038
## Delivery.00abc_OEOEOE_S 0.879 0.152
## Elevator.00abc_OEOEOE_S 0.588 0.148
## Market.00abc_OEOEOE_S 0.281 0.100
## Market.01abc_MCOEOE_S -0.395 0.158
## Travel.00abc_OEOEOE_S 0.481 0.148
## Delivery.00ab_OEOE_F_step1 -2.496 0.150
## Delivery.00ab_OEOE_F_step2 -0.158 0.109
## Delivery.00ab_OEOE_F_step3 0.665 0.129
## Elevator.00ab_OE2_F_step1 -1.656 0.156
## Elevator.00ab_OE2_F_step2 -1.889 0.123
## Elevator.00ab_OE2_F_step3 1.764 0.178
## Ticket.02_OE_F_step1 -2.039 0.119
## Ticket.02_OE_F_step2 -0.983 0.088
## Ticket.02_OE_F_step3 0.134 0.085
## Travel.00ab_OEOE_F_step1 -2.166 0.091
## Travel.00ab_OEOE_F_step2 0.307 0.078
## Elevator.02abc_OE3_C_step1 -1.923 0.110
## Elevator.02abc_OE3_C_step2 0.030 0.099
## Elevator.02abc_OE3_C_step3 0.581 0.130
## Market.01abc_OE_C_step1 -2.040 0.085
## Market.01abc_OE_C_step2 0.370 0.077
## Market.01abc_OE_C_step3 0.645 0.104
## Delivery.00abc_OEOEOE_S_step1 -2.740 0.369
## Delivery.00abc_OEOEOE_S_step2 -1.063 0.252
## Delivery.00abc_OEOEOE_S_step3 0.858 0.340
## Elevator.00abc_OEOEOE_S_step1 -1.227 0.329
## Elevator.00abc_OEOEOE_S_step2 -1.672 0.286
## Elevator.00abc_OEOEOE_S_step3 0.919 0.329
## Market.00abc_OEOEOE_S_step1 -2.073 0.218
## Market.00abc_OEOEOE_S_step2 -0.688 0.178
## Market.00abc_OEOEOE_S_step3 1.464 0.252
## Market.01abc_MCOEOE_S_step1 -4.112 0.415
## Market.01abc_MCOEOE_S_step2 0.225 0.254
## Market.01abc_MCOEOE_S_step3 2.562 0.439
## Travel.00abc_OEOEOE_S_step1 -2.078 0.370
## Travel.00abc_OEOEOE_S_step2 -1.620 0.268
## Travel.00abc_OEOEOE_S_step3 0.829 0.289
##
## Item Parameters in IRT parameterization
## item alpha beta tau.Cat1 tau.Cat2 tau.Cat3 tau.Cat4
## 1 Delivery.00ab_OEOE_F 1 0.094 -2.496 -0.158 0.665 1.990
## 2 Elevator.00ab_OE2_F 1 0.526 -1.656 -1.889 1.764 1.781
## 3 Ticket.02_OE_F 1 0.271 -2.039 -0.983 0.134 2.887
## 4 Travel.00ab_OEOE_F 1 -0.162 -2.166 0.307 1.859 NA
## 5 Elevator.02abc_OE3_C 1 0.471 -1.923 0.030 0.581 1.313
## 6 Market.01abc_OE_C 1 0.348 -2.040 0.370 0.645 1.026
## 7 Delivery.00abc_OEOEOE_S 1 0.879 -2.740 -1.063 0.858 2.946
## 8 Elevator.00abc_OEOEOE_S 1 0.588 -1.227 -1.672 0.919 1.979
## 9 Market.00abc_OEOEOE_S 1 0.281 -2.073 -0.688 1.464 1.297
## 10 Market.01abc_MCOEOE_S 1 -0.395 -4.112 0.225 2.562 1.325
## 11 Travel.00abc_OEOEOE_S 1 0.481 -2.078 -1.620 0.829 2.869## Item parameters
## |**********|
## |----------|
## Regression parameters
## |**|
## ||
## |--|## est.Dim1 se.Dim1
## 1 0.00000000 0.00000000
## 2 0.06899431 0.08407034Please see this link below for the calibration of the same dataset in ConQuest:
https://www.dropbox.com/sh/azla5iji33plzeg/AACtlbCBccSuAV86i-Rthw_na?dl=0
4.1 Model specification
## [1] "item+item*step"4.2 Latent regression coefficient (gain from pre to post)
## hs
## Main dimension -0.018
## S. errors 0.047—> ## Reliability
## EAP/PV RELIABILITY: 0.6854.3 Variance
## errors
## [1,] 1.117 0.0454.4 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 Deliv.00ab_.. 0.074 0.061
## 2 2 Elev.00ab_O.. 0.502 0.070
## 3 3 Tick.02_OE_.. 0.250 0.046
## 4 4 Trav.00ab_O.. -0.184 0.053
## 5 5 Elev.02abc_.. 0.435 0.051
## 6 6 Mark.01abc_.. 0.323 0.038
## 7 7 Deliv.00abc.. 0.772 0.152
## 8 8 Elev.00abc_.. 0.514 0.148
## 9 9 Mark.00abc_.. 0.213 0.100
## 10 10 Mark.01abc_.. -0.470 0.158
## 11 11 Trav.00abc_.. 0.373 0.148## W.fit: Weighted fit (Infit)## [,1] [,2]
## [1,] "item1" "Delivery.00ab_OEOE_F"
## [2,] "item2" "Elevator.00ab_OE2_F"
## [3,] "item3" "Ticket.02_OE_F"
## [4,] "item4" "Travel.00ab_OEOE_F"
## [5,] "item5" "Elevator.02abc_OE3_C"
## [6,] "item6" "Market.01abc_OE_C"
## [7,] "item7" "Delivery.00abc_OEOEOE_S"
## [8,] "item8" "Elevator.00abc_OEOEOE_S"
## [9,] "item9" "Market.00abc_OEOEOE_S"
## [10,] "item10" "Market.01abc_MCOEOE_S"
## [11,] "item11" "Travel.00abc_OEOEOE_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 1 Deliv.00ab_.. 0 NA NA
## 2 1 Deliv.00ab_.. 1 -2.496 0.150
## 3 1 Deliv.00ab_.. 2 -0.158 0.109
## 4 1 Deliv.00ab_.. 3 0.664 0.129
## 5 1 Deliv.00ab_.. 4 1.990 NA
## 6 2 Elev.00ab_O.. 0 NA NA
## 7 2 Elev.00ab_O.. 1 -1.655 0.156
## 8 2 Elev.00ab_O.. 2 -1.889 0.123
## 9 2 Elev.00ab_O.. 3 1.764 0.178
## 10 2 Elev.00ab_O.. 4 1.780 NA
## 11 3 Tick.02_OE_.. 0 NA NA
## 12 3 Tick.02_OE_.. 1 -2.038 0.119
## 13 3 Tick.02_OE_.. 2 -0.982 0.088
## 14 3 Tick.02_OE_.. 3 0.134 0.085
## 15 3 Tick.02_OE_.. 4 2.887 NA
## 16 4 Trav.00ab_O.. 0 NA NA
## 17 4 Trav.00ab_O.. 1 -2.166 0.091
## 18 4 Trav.00ab_O.. 2 0.307 0.078
## 19 4 Trav.00ab_O.. 3 1.859 NA
## 20 5 Elev.02abc_.. 0 NA NA
## 21 5 Elev.02abc_.. 1 -1.924 0.110
## 22 5 Elev.02abc_.. 2 0.031 0.099
## 23 5 Elev.02abc_.. 3 0.581 0.130
## 24 5 Elev.02abc_.. 4 1.311 NA
## 25 6 Mark.01abc_.. 0 NA NA
## 26 6 Mark.01abc_.. 1 -2.040 0.085
## 27 6 Mark.01abc_.. 2 0.370 0.077
## 28 6 Mark.01abc_.. 3 0.645 0.104
## 29 6 Mark.01abc_.. 4 1.025 NA
## 30 7 Deliv.00abc.. 0 NA NA
## 31 7 Deliv.00abc.. 1 -2.742 0.369
## 32 7 Deliv.00abc.. 2 -1.063 0.252
## 33 7 Deliv.00abc.. 3 0.858 0.340
## 34 7 Deliv.00abc.. 4 2.947 NA
## 35 8 Elev.00abc_.. 0 NA NA
## 36 8 Elev.00abc_.. 1 -1.232 0.329
## 37 8 Elev.00abc_.. 2 -1.672 0.286
## 38 8 Elev.00abc_.. 3 0.922 0.329
## 39 8 Elev.00abc_.. 4 1.983 NA
## 40 9 Mark.00abc_.. 0 NA NA
## 41 9 Mark.00abc_.. 1 -2.081 0.218
## 42 9 Mark.00abc_.. 2 -0.688 0.178
## 43 9 Mark.00abc_.. 3 1.467 0.252
## 44 9 Mark.00abc_.. 4 1.302 NA
## 45 10 Mark.01abc_.. 0 NA NA
## 46 10 Mark.01abc_.. 1 -4.119 0.415
## 47 10 Mark.01abc_.. 2 0.225 0.254
## 48 10 Mark.01abc_.. 3 2.566 0.440
## 49 10 Mark.01abc_.. 4 1.329 NA
## 50 11 Trav.00abc_.. 0 NA NA
## 51 11 Trav.00abc_.. 1 -2.080 0.370
## 52 11 Trav.00abc_.. 2 -1.621 0.268
## 53 11 Trav.00abc_.. 3 0.830 0.289
## 54 11 Trav.00abc_.. 4 2.870 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]
## Deliv.00ab_OE2_F -2.510 -0.299 0.831 2.275
## Elev.00ab_OE2_F -1.707 -0.870 1.819 2.764
## Tick.02_OE_F -2.046 -0.726 0.570 3.196
## Trav.00ab_OE2_F -2.426 0.034 1.842 NA
## Elev.02abc_OE3_C -1.615 0.204 1.069 2.090
## Mark.01abc_OE_C -1.802 0.295 1.007 1.805
## Deliv.00abc_OE3_S -2.122 -0.266 1.649 3.826
## Elev.00abc_OE3_S -1.349 -0.632 1.273 2.741
## Mark.00abc_OE3_S -2.059 -0.401 1.270 2.066
## Mark.01abc_MCOE2_S -4.602 -0.339 1.329 1.790
## Trav.00abc_OE3_S -2.079 -0.967 1.174 3.355## DCS1 DCS2 DCS3 DCS4
## Deliv.00ab_OE2_F -2.510 -0.299 0.831 2.275
## Elev.00ab_OE2_F -1.707 -0.870 1.819 2.764
## Tick.02_OE_F -2.046 -0.726 0.570 3.196
## Trav.00ab_OE2_F -2.426 0.034 1.842 NA
## Elev.02abc_OE3_C -1.615 0.204 1.069 2.090
## Mark.01abc_OE_C -1.802 0.295 1.007 1.805
## Deliv.00abc_OE3_S -2.122 -0.266 1.649 3.826
## Elev.00abc_OE3_S -1.349 -0.632 1.273 2.741
## Mark.00abc_OE3_S -2.059 -0.401 1.270 2.066
## Mark.01abc_MCOE2_S -4.602 -0.339 1.329 1.790
## Trav.00abc_OE3_S -2.079 -0.967 1.174 3.355## DCS1 DCS2 DCS3 DCS4
## Min. :-4.602 Min. :-0.9670 Min. :0.570 Min. :1.790
## 1st Qu.:-2.274 1st Qu.:-0.6790 1st Qu.:1.038 1st Qu.:2.072
## Median :-2.059 Median :-0.3390 Median :1.270 Median :2.508
## Mean :-2.211 Mean :-0.3606 Mean :1.258 Mean :2.591
## 3rd Qu.:-1.754 3rd Qu.:-0.1160 3rd Qu.:1.489 3rd Qu.:3.088
## Max. :-1.349 Max. : 0.2950 Max. :1.842 Max. :3.826
## NA's :15 Links to CQ files (please copy/paste these links)
https://www.dropbox.com/sh/azla5iji33plzeg/AACtlbCBccSuAV86i-Rthw_na?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 15:54:14
## Time difference of 0.188365 secs
## Computation time: 0.188365
##
## Multidimensional Item Response Model in TAM
##
## IRT Model: PCM2
## Call:
## TAM::tam.mml(resp = dcs[, c(6:16)], Y = dcs[, 5], irtmodel = "PCM2",
## verbose = FALSE)
##
## ------------------------------------------------------------
## Number of iterations = 45
## Numeric integration with 21 integration points
##
## Deviance = 10799.31
## Log likelihood = -5399.66
## Number of persons = 1242
## Number of persons used = 1117
## Number of items = 11
## Number of estimated parameters = 45
## Item threshold parameters = 43
## Item slope parameters = 0
## Regression parameters = 1
## Variance/covariance parameters = 1
##
## AIC = 10889 | penalty=90 | AIC=-2*LL + 2*p
## AIC3 = 10934 | penalty=135 | AIC3=-2*LL + 3*p
## BIC = 11115 | penalty=315.83 | BIC=-2*LL + log(n)*p
## aBIC = 10972 | penalty=172.74 | aBIC=-2*LL + log((n-2)/24)*p (adjusted BIC)
## CAIC = 11160 | penalty=360.83 | CAIC=-2*LL + [log(n)+1]*p (consistent AIC)
## AICc = 10893 | penalty=93.87 | AICc=-2*LL + 2*p + 2*p*(p+1)/(n-p-1) (bias corrected AIC)
## GHP = 1.29481 | GHP=( -LL + p ) / (#Persons * #Items) (Gilula-Haberman log penalty)
##
## ------------------------------------------------------------
## EAP Reliability
## [1] 0.627
## ------------------------------------------------------------
## Covariances and Variances
## [,1]
## [1,] 1.116
## ------------------------------------------------------------
## Correlations and Standard Deviations (in the diagonal)
## [,1]
## [1,] 1.056
## ------------------------------------------------------------
## Regression Coefficients
## [,1]
## [1,] 0.00000
## [2,] 0.06899
## ------------------------------------------------------------
## Standardized Coefficients
## parm dim est StdYX StdX StdY
## 1 Intercept 1 0.000 NA NA NA
## 2 Y1 1 0.069 0.0278 0.0294 0.0653
##
## ** Explained Variance R^2
## [1] 8e-04
## ** SD Theta
## [1] 1.0567
## ** SD Predictors
## Intercept Y1
## 0.0000 0.4257
## ------------------------------------------------------------
## Item Parameters -A*Xsi
## item N M xsi.item AXsi_.Cat1 AXsi_.Cat2 AXsi_.Cat3
## 1 Delivery.00ab_OEOE_F 404 1.903 0.094 -2.402 -2.467 -1.708
## 2 Elevator.00ab_OE2_F 395 1.711 0.526 -1.130 -2.494 -0.204
## 3 Ticket.02_OE_F 764 2.009 0.271 -1.767 -2.479 -2.073
## 4 Travel.00ab_OEOE_F 773 1.558 -0.162 -2.328 -2.184 -0.487
## 5 Elevator.02abc_OE3_C 528 1.591 0.471 -1.453 -0.952 0.099
## 6 Market.01abc_OE_C 874 1.630 0.348 -1.692 -0.974 0.019
## 7 Delivery.00abc_OEOEOE_S 84 1.619 0.879 -1.862 -2.046 -0.310
## 8 Elevator.00abc_OEOEOE_S 72 1.750 0.588 -0.639 -1.723 -0.216
## 9 Market.00abc_OEOEOE_S 155 1.787 0.281 -1.793 -2.199 -0.455
## 10 Market.01abc_MCOEOE_S 71 1.859 -0.395 -4.507 -4.677 -2.511
## 11 Travel.00abc_OEOEOE_S 85 1.882 0.481 -1.597 -2.737 -1.427
## AXsi_.Cat4 B.Cat1.Dim1 B.Cat2.Dim1 B.Cat3.Dim1 B.Cat4.Dim1
## 1 0.376 1 2 3 4
## 2 2.102 1 2 3 4
## 3 1.085 1 2 3 4
## 4 NA 1 2 3 0
## 5 1.882 1 2 3 4
## 6 1.393 1 2 3 4
## 7 3.515 1 2 3 4
## 8 2.351 1 2 3 4
## 9 1.123 1 2 3 4
## 10 -1.581 1 2 3 4
## 11 1.923 1 2 3 4
##
## Item Parameters Xsi
## xsi se.xsi
## Delivery.00ab_OEOE_F 0.094 0.061
## Elevator.00ab_OE2_F 0.526 0.070
## Ticket.02_OE_F 0.271 0.046
## Travel.00ab_OEOE_F -0.162 0.053
## Elevator.02abc_OE3_C 0.471 0.051
## Market.01abc_OE_C 0.348 0.038
## Delivery.00abc_OEOEOE_S 0.879 0.152
## Elevator.00abc_OEOEOE_S 0.588 0.148
## Market.00abc_OEOEOE_S 0.281 0.100
## Market.01abc_MCOEOE_S -0.395 0.158
## Travel.00abc_OEOEOE_S 0.481 0.148
## Delivery.00ab_OEOE_F_step1 -2.496 0.150
## Delivery.00ab_OEOE_F_step2 -0.158 0.109
## Delivery.00ab_OEOE_F_step3 0.665 0.129
## Elevator.00ab_OE2_F_step1 -1.656 0.156
## Elevator.00ab_OE2_F_step2 -1.889 0.123
## Elevator.00ab_OE2_F_step3 1.764 0.178
## Ticket.02_OE_F_step1 -2.039 0.119
## Ticket.02_OE_F_step2 -0.983 0.088
## Ticket.02_OE_F_step3 0.134 0.085
## Travel.00ab_OEOE_F_step1 -2.166 0.091
## Travel.00ab_OEOE_F_step2 0.307 0.078
## Elevator.02abc_OE3_C_step1 -1.923 0.110
## Elevator.02abc_OE3_C_step2 0.030 0.099
## Elevator.02abc_OE3_C_step3 0.581 0.130
## Market.01abc_OE_C_step1 -2.040 0.085
## Market.01abc_OE_C_step2 0.370 0.077
## Market.01abc_OE_C_step3 0.645 0.104
## Delivery.00abc_OEOEOE_S_step1 -2.740 0.369
## Delivery.00abc_OEOEOE_S_step2 -1.063 0.252
## Delivery.00abc_OEOEOE_S_step3 0.858 0.340
## Elevator.00abc_OEOEOE_S_step1 -1.227 0.329
## Elevator.00abc_OEOEOE_S_step2 -1.672 0.286
## Elevator.00abc_OEOEOE_S_step3 0.919 0.329
## Market.00abc_OEOEOE_S_step1 -2.073 0.218
## Market.00abc_OEOEOE_S_step2 -0.688 0.178
## Market.00abc_OEOEOE_S_step3 1.464 0.252
## Market.01abc_MCOEOE_S_step1 -4.112 0.415
## Market.01abc_MCOEOE_S_step2 0.225 0.254
## Market.01abc_MCOEOE_S_step3 2.562 0.439
## Travel.00abc_OEOEOE_S_step1 -2.078 0.370
## Travel.00abc_OEOEOE_S_step2 -1.620 0.268
## Travel.00abc_OEOEOE_S_step3 0.829 0.289
##
## Item Parameters in IRT parameterization
## item alpha beta tau.Cat1 tau.Cat2 tau.Cat3 tau.Cat4
## 1 Delivery.00ab_OEOE_F 1 0.094 -2.496 -0.158 0.665 1.990
## 2 Elevator.00ab_OE2_F 1 0.526 -1.656 -1.889 1.764 1.781
## 3 Ticket.02_OE_F 1 0.271 -2.039 -0.983 0.134 2.887
## 4 Travel.00ab_OEOE_F 1 -0.162 -2.166 0.307 1.859 NA
## 5 Elevator.02abc_OE3_C 1 0.471 -1.923 0.030 0.581 1.313
## 6 Market.01abc_OE_C 1 0.348 -2.040 0.370 0.645 1.026
## 7 Delivery.00abc_OEOEOE_S 1 0.879 -2.740 -1.063 0.858 2.946
## 8 Elevator.00abc_OEOEOE_S 1 0.588 -1.227 -1.672 0.919 1.979
## 9 Market.00abc_OEOEOE_S 1 0.281 -2.073 -0.688 1.464 1.297
## 10 Market.01abc_MCOEOE_S 1 -0.395 -4.112 0.225 2.562 1.325
## 11 Travel.00abc_OEOEOE_S 1 0.481 -2.078 -1.620 0.829 2.869