rm(list=ls())

一、資料整理

資料結構

library(psychometric)

先看資料樣態

pacman::p_load(TAM,psych)
describe(dta)
##     vars   n   mean     sd median trimmed    mad min max range  skew kurtosis
## k1     1 655   1.00   0.82      1    0.88   0.00   0   3     3  1.05     1.03
## k2     2 655   1.33   0.90      1    1.29   1.48   0   3     3  0.41    -0.56
## k3     3 655   1.22   0.87      1    1.15   1.48   0   3     3  0.44    -0.39
## k4     4 655   1.36   0.60      1    1.35   0.00   0   3     3  0.29    -0.09
## k5     5 655   1.02   0.76      1    0.92   0.00   0   3     3  1.14     1.65
## k6     6 655   1.29   0.84      1    1.25   1.48   0   3     3  0.38    -0.36
## k7     7 655   1.36   0.92      1    1.32   1.48   0   3     3  0.44    -0.62
## k8     8 655   0.94   0.93      1    0.81   1.48   0   3     3  0.85    -0.06
## k9     9 655   1.35   0.61      1    1.36   0.00   0   3     3  0.08    -0.22
## k10   10 655   1.31   0.72      1    1.32   0.00   0   3     3  0.36     0.01
## k11   11 655   1.31   0.78      1    1.30   0.00   0   3     3  0.31    -0.24
## a1    12 655   5.03   1.55      5    5.08   1.48   1   7     6 -0.08    -1.04
## a2    13 655   3.72   1.87      4    3.65   2.97   1   7     6  0.41    -0.89
## a3    14 655   4.54   1.45      4    4.53   1.48   1   7     6  0.22    -0.38
## a4    15 655   3.95   1.56      4    3.85   1.48   1   7     6  0.41    -0.44
## a5    16 655   4.02   1.95      4    4.00   2.97   1   7     6  0.19    -1.22
## a6    17 655   5.02   1.44      5    5.08   1.48   1   7     6 -0.10    -0.63
## a7    18 655   5.05   1.34      5    5.06   1.48   1   7     6  0.08    -0.75
## a8    19 655   5.07   1.43      5    5.14   1.48   1   7     6 -0.15    -0.65
## a9    20 655   5.22   1.36      5    5.26   1.48   1   7     6 -0.14    -0.70
## a10   21 655   5.12   1.44      5    5.18   1.48   1   7     6 -0.12    -0.91
## a11   22 655   5.19   1.43      5    5.28   1.48   1   7     6 -0.20    -0.78
## a12   23 655   4.93   1.40      5    4.97   1.48   1   7     6  0.01    -0.57
## id    24 655 328.00 189.23    328  328.00 243.15   1 655   654  0.00    -1.21
##       se
## k1  0.03
## k2  0.04
## k3  0.03
## k4  0.02
## k5  0.03
## k6  0.03
## k7  0.04
## k8  0.04
## k9  0.02
## k10 0.03
## k11 0.03
## a1  0.06
## a2  0.07
## a3  0.06
## a4  0.06
## a5  0.08
## a6  0.06
## a7  0.05
## a8  0.06
## a9  0.05
## a10 0.06
## a11 0.06
## a12 0.05
## id  7.39

二、題目分析

進行題目分析,共有23題,知識11題+態度12題

item1<- item.exam(dta[,1:23], discrim = TRUE)
head(item1)
##    Sample.SD Item.total Item.Tot.woi Difficulty Discrimination Item.Criterion
## k1 0.8202334 -0.1341884   -0.2105598   1.000000     -0.1651376             NA
## k2 0.8963435 -0.1841439   -0.2655693   1.332824     -0.3807339             NA
## k3 0.8666566 -0.1729588   -0.2521997   1.216794     -0.3532110             NA
## k4 0.5997525 -0.2640985   -0.3167135   1.361832     -0.3715596             NA
## k5 0.7639636 -0.1203025   -0.1919036   1.021374     -0.1605505             NA
## k6 0.8387876 -0.1398733   -0.2177575   1.294656     -0.2660550             NA
##    Item.Reliab Item.Rel.woi Item.Validity
## k1 -0.10998175   -0.1725763            NA
## k2 -0.16493015   -0.2378595            NA
## k3 -0.14978141   -0.2184036            NA
## k4 -0.15827275   -0.1898047            NA
## k5 -0.09183654   -0.1464954            NA
## k6 -0.11723439   -0.1825128            NA

Sample.SD–> 題項標準差

Item.total–>題項和量表總分的相關

Item.Tot.woi–>題項和量表總分的相關(扣掉本身題項分數)

Difficulty–>均值

Discrimination–>前25%-33%(U)和後25%-33%(L)之差異

Item.Reliab Item reliability index–>題項一致性信度

Item.Rel.woi Item reliability index–>題項一致性信度(扣掉本身題項分數)

item2 <-item1$Item.Rel.woi

信度系數α

detach("package:psychometric", unload = TRUE)
library(psych)
item3 <- alpha(dta[,1:23],check.keys=TRUE)$alpha.drop[,'raw_alpha']
## Warning in alpha(dta[, 1:23], check.keys = TRUE): Some items were negatively correlated with total scale and were automatically reversed.
##  This is indicated by a negative sign for the variable name.
item4 <- as.data.frame(t(rbind(item3,  item2)))
names(item4) <- c('總量表信度(刪題)',  '題目信度')
head(item4)
##   總量表信度(刪題)   題目信度
## 1          0.9482378 -0.1725763
## 2          0.9466286 -0.2378595
## 3          0.9470480 -0.2184036
## 4          0.9471807 -0.1898047
## 5          0.9486693 -0.1464954
## 6          0.9470123 -0.1825128
row.names(item4) <- names(dta[,1:23])
round(item4, 3)
##     總量表信度(刪題) 題目信度
## k1               0.948   -0.173
## k2               0.947   -0.238
## k3               0.947   -0.218
## k4               0.947   -0.190
## k5               0.949   -0.146
## k6               0.947   -0.183
## k7               0.947   -0.234
## k8               0.947   -0.249
## k9               0.947   -0.186
## k10              0.947   -0.205
## k11              0.947   -0.204
## a1               0.945    0.912
## a2               0.950    0.603
## a3               0.948    0.833
## a4               0.947    0.809
## a5               0.947    0.767
## a6               0.948    0.633
## a7               0.946    0.954
## a8               0.946    1.031
## a9               0.945    0.972
## a10              0.945    0.913
## a11              0.945    0.876
## a12              0.947    0.821

三、因素分析

利用PCA探索因素數量,圖建議取2個因素

fa.parallel(dta[, 1:23], fa = "pc", show.legend = FALSE)

## Parallel analysis suggests that the number of factors =  NA  and the number of components =  2

四、Partial Credit Model

DAFS 中文版同時包含二元計分和多重計分題型,因此選擇單參數的部分給分模式 (partial credit model, PCM)作為分析模式。

pacman::p_load(readr,eRm,WrightMap)# For plot the variable map
# Run the Partial Credit Model
PC_model <- PCM(PC_dta)
# Check the result
summary(PC_model)
## 
## Results of PCM estimation: 
## 
## Call:  PCM(X = PC_dta) 
## 
## Conditional log-likelihood: -18140.21 
## Number of iterations: 394 
## Number of parameters: 104 
## 
## Item (Category) Difficulty Parameters (eta): with 0.95 CI:
##        Estimate Std. Error lower CI upper CI
## k1.c2     2.788      0.203    2.391    3.185
## k1.c3     2.777      0.170    2.445    3.109
## k2.c1    -0.605      0.116   -0.832   -0.378
## k2.c2     0.761      0.138    0.490    1.031
## k2.c3     1.956      0.165    1.631    2.280
## k3.c1    -0.383      0.107   -0.593   -0.172
## k3.c2     1.002      0.131    0.744    1.259
## k3.c3     2.538      0.174    2.198    2.879
## k4.c1    -2.123      0.199   -2.514   -1.733
## k4.c2    -1.048      0.207   -1.454   -0.642
## k4.c3     2.370      0.327    1.729    3.012
## k5.c1    -0.708      0.103   -0.910   -0.505
## k5.c2     2.711      0.217    2.286    3.137
## k5.c3     2.661      0.178    2.312    3.010
## k6.c1    -0.670      0.117   -0.899   -0.441
## k6.c2     0.605      0.137    0.337    0.873
## k6.c3     2.248      0.179    1.897    2.599
## k7.c1    -0.681      0.117   -0.912   -0.451
## k7.c2     0.830      0.143    0.550    1.110
## k7.c3     1.751      0.162    1.433    2.069
## k8.c1     0.372      0.092    0.193    0.552
## k8.c2     2.392      0.146    2.107    2.678
## k8.c3     3.168      0.162    2.850    3.485
## k9.c1    -1.772      0.173   -2.111   -1.433
## k9.c2    -0.760      0.182   -1.116   -0.403
## k9.c3     3.000      0.349    2.316    3.683
## k10.c1   -1.242      0.139   -1.514   -0.969
## k10.c2   -0.022      0.154   -0.323    0.280
## k10.c3    2.309      0.221    1.877    2.742
## k11.c1   -0.896      0.126   -1.143   -0.650
## k11.c2    0.262      0.142   -0.016    0.539
## k11.c3    2.316      0.198    1.929    2.703
## a1.c1     0.230      0.482   -0.714    1.175
## a1.c2    -1.592      0.363   -2.303   -0.880
## a1.c3    -1.968      0.352   -2.658   -1.278
## a1.c4    -0.644      0.360   -1.349    0.062
## a1.c5     0.196      0.365   -0.521    0.912
## a1.c6    -0.064      0.358   -0.766    0.638
## a2.c1    -0.209      0.149   -0.501    0.083
## a2.c2     0.363      0.156    0.058    0.669
## a2.c3     0.772      0.162    0.456    1.089
## a2.c4     2.560      0.213    2.142    2.978
## a2.c5     3.153      0.223    2.716    3.589
## a2.c6     3.339      0.222    2.905    3.774
## a3.c1    -0.659      0.348   -1.341    0.023
## a3.c2    -1.118      0.318   -1.741   -0.496
## a3.c3    -1.875      0.303   -2.469   -1.282
## a3.c4    -0.341      0.314   -0.957    0.275
## a3.c5     1.087      0.335    0.430    1.745
## a3.c6     0.989      0.325    0.352    1.626
## a4.c1    -0.851      0.229   -1.300   -0.401
## a4.c2    -1.014      0.218   -1.441   -0.586
## a4.c3    -0.596      0.221   -1.029   -0.163
## a4.c4     0.899      0.244    0.421    1.377
## a4.c5     2.095      0.270    1.567    2.623
## a4.c6     2.506      0.271    1.975    3.037
## a5.c1    -0.400      0.160   -0.714   -0.086
## a5.c2     0.265      0.169   -0.066    0.596
## a5.c3     0.716      0.175    0.374    1.059
## a5.c4     2.080      0.210    1.668    2.493
## a5.c5     2.403      0.212    1.987    2.819
## a5.c6     2.797      0.222    2.363    3.232
## a6.c1    -0.549      0.445   -1.421    0.323
## a6.c2    -1.056      0.400   -1.840   -0.271
## a6.c3    -2.176      0.374   -2.910   -1.442
## a6.c4    -1.228      0.376   -1.966   -0.490
## a6.c5     0.069      0.386   -0.687    0.825
## a6.c6    -0.039      0.379   -0.781    0.704
## a7.c1    -0.081      0.638   -1.332    1.169
## a7.c2    -1.489      0.512   -2.494   -0.485
## a7.c3    -2.862      0.484   -3.812   -1.913
## a7.c4    -1.805      0.482   -2.750   -0.860
## a7.c5    -0.677      0.483   -1.625    0.270
## a7.c6    -0.552      0.476   -1.485    0.381
## a8.c1    -0.287      0.465   -1.197    0.624
## a8.c2    -1.111      0.399   -1.893   -0.328
## a8.c3    -2.146      0.375   -2.881   -1.411
## a8.c4    -1.201      0.377   -1.940   -0.462
## a8.c5    -0.152      0.383   -0.902    0.598
## a8.c6    -0.069      0.379   -0.811    0.674
## a9.c1     0.120      0.601   -1.059    1.298
## a9.c2    -1.087      0.475   -2.018   -0.156
## a9.c3    -2.504      0.441   -3.367   -1.640
## a9.c4    -1.701      0.439   -2.561   -0.841
## a9.c5    -0.552      0.442   -1.419    0.314
## a9.c6    -0.598      0.435   -1.450    0.255
## a10.c1   -0.771      0.570   -1.889    0.347
## a10.c2   -1.915      0.504   -2.903   -0.928
## a10.c3   -2.646      0.486   -3.599   -1.694
## a10.c4   -1.731      0.483   -2.679   -0.784
## a10.c5   -0.707      0.484   -1.655    0.241
## a10.c6   -0.735      0.476   -1.667    0.197
## a11.c1    0.161      0.552   -0.921    1.243
## a11.c2   -1.416      0.424   -2.247   -0.584
## a11.c3   -2.214      0.404   -3.007   -1.421
## a11.c4   -1.389      0.405   -2.182   -0.596
## a11.c5   -0.378      0.408   -1.178    0.422
## a11.c6   -0.417      0.403   -1.206    0.373
## a12.c1   -0.352      0.458   -1.251    0.547
## a12.c2   -1.095      0.399   -1.877   -0.314
## a12.c3   -2.272      0.374   -3.004   -1.540
## a12.c4   -1.148      0.377   -1.886   -0.410
## a12.c5   -0.002      0.384   -0.755    0.750
## a12.c6    0.132      0.380   -0.612    0.877
## 
## Item Easiness Parameters (beta) with 0.95 CI:
##             Estimate Std. Error lower CI upper CI
## beta k1.c1     0.399      0.097    0.209    0.590
## beta k1.c2    -2.788      0.203   -3.185   -2.391
## beta k1.c3    -2.777      0.170   -3.109   -2.445
## beta k2.c1     0.605      0.116    0.378    0.832
## beta k2.c2    -0.761      0.138   -1.031   -0.490
## beta k2.c3    -1.956      0.165   -2.280   -1.631
## beta k3.c1     0.383      0.107    0.172    0.593
## beta k3.c2    -1.002      0.131   -1.259   -0.744
## beta k3.c3    -2.538      0.174   -2.879   -2.198
## beta k4.c1     2.123      0.199    1.733    2.514
## beta k4.c2     1.048      0.207    0.642    1.454
## beta k4.c3    -2.370      0.327   -3.012   -1.729
## beta k5.c1     0.708      0.103    0.505    0.910
## beta k5.c2    -2.711      0.217   -3.137   -2.286
## beta k5.c3    -2.661      0.178   -3.010   -2.312
## beta k6.c1     0.670      0.117    0.441    0.899
## beta k6.c2    -0.605      0.137   -0.873   -0.337
## beta k6.c3    -2.248      0.179   -2.599   -1.897
## beta k7.c1     0.681      0.117    0.451    0.912
## beta k7.c2    -0.830      0.143   -1.110   -0.550
## beta k7.c3    -1.751      0.162   -2.069   -1.433
## beta k8.c1    -0.372      0.092   -0.552   -0.193
## beta k8.c2    -2.392      0.146   -2.678   -2.107
## beta k8.c3    -3.168      0.162   -3.485   -2.850
## beta k9.c1     1.772      0.173    1.433    2.111
## beta k9.c2     0.760      0.182    0.403    1.116
## beta k9.c3    -3.000      0.349   -3.683   -2.316
## beta k10.c1    1.242      0.139    0.969    1.514
## beta k10.c2    0.022      0.154   -0.280    0.323
## beta k10.c3   -2.309      0.221   -2.742   -1.877
## beta k11.c1    0.896      0.126    0.650    1.143
## beta k11.c2   -0.262      0.142   -0.539    0.016
## beta k11.c3   -2.316      0.198   -2.703   -1.929
## beta a1.c1    -0.230      0.482   -1.175    0.714
## beta a1.c2     1.592      0.363    0.880    2.303
## beta a1.c3     1.968      0.352    1.278    2.658
## beta a1.c4     0.644      0.360   -0.062    1.349
## beta a1.c5    -0.196      0.365   -0.912    0.521
## beta a1.c6     0.064      0.358   -0.638    0.766
## beta a2.c1     0.209      0.149   -0.083    0.501
## beta a2.c2    -0.363      0.156   -0.669   -0.058
## beta a2.c3    -0.772      0.162   -1.089   -0.456
## beta a2.c4    -2.560      0.213   -2.978   -2.142
## beta a2.c5    -3.153      0.223   -3.589   -2.716
## beta a2.c6    -3.339      0.222   -3.774   -2.905
## beta a3.c1     0.659      0.348   -0.023    1.341
## beta a3.c2     1.118      0.318    0.496    1.741
## beta a3.c3     1.875      0.303    1.282    2.469
## beta a3.c4     0.341      0.314   -0.275    0.957
## beta a3.c5    -1.087      0.335   -1.745   -0.430
## beta a3.c6    -0.989      0.325   -1.626   -0.352
## beta a4.c1     0.851      0.229    0.401    1.300
## beta a4.c2     1.014      0.218    0.586    1.441
## beta a4.c3     0.596      0.221    0.163    1.029
## beta a4.c4    -0.899      0.244   -1.377   -0.421
## beta a4.c5    -2.095      0.270   -2.623   -1.567
## beta a4.c6    -2.506      0.271   -3.037   -1.975
## beta a5.c1     0.400      0.160    0.086    0.714
## beta a5.c2    -0.265      0.169   -0.596    0.066
## beta a5.c3    -0.716      0.175   -1.059   -0.374
## beta a5.c4    -2.080      0.210   -2.493   -1.668
## beta a5.c5    -2.403      0.212   -2.819   -1.987
## beta a5.c6    -2.797      0.222   -3.232   -2.363
## beta a6.c1     0.549      0.445   -0.323    1.421
## beta a6.c2     1.056      0.400    0.271    1.840
## beta a6.c3     2.176      0.374    1.442    2.910
## beta a6.c4     1.228      0.376    0.490    1.966
## beta a6.c5    -0.069      0.386   -0.825    0.687
## beta a6.c6     0.039      0.379   -0.704    0.781
## beta a7.c1     0.081      0.638   -1.169    1.332
## beta a7.c2     1.489      0.512    0.485    2.494
## beta a7.c3     2.862      0.484    1.913    3.812
## beta a7.c4     1.805      0.482    0.860    2.750
## beta a7.c5     0.677      0.483   -0.270    1.625
## beta a7.c6     0.552      0.476   -0.381    1.485
## beta a8.c1     0.287      0.465   -0.624    1.197
## beta a8.c2     1.111      0.399    0.328    1.893
## beta a8.c3     2.146      0.375    1.411    2.881
## beta a8.c4     1.201      0.377    0.462    1.940
## beta a8.c5     0.152      0.383   -0.598    0.902
## beta a8.c6     0.069      0.379   -0.674    0.811
## beta a9.c1    -0.120      0.601   -1.298    1.059
## beta a9.c2     1.087      0.475    0.156    2.018
## beta a9.c3     2.504      0.441    1.640    3.367
## beta a9.c4     1.701      0.439    0.841    2.561
## beta a9.c5     0.552      0.442   -0.314    1.419
## beta a9.c6     0.598      0.435   -0.255    1.450
## beta a10.c1    0.771      0.570   -0.347    1.889
## beta a10.c2    1.915      0.504    0.928    2.903
## beta a10.c3    2.646      0.486    1.694    3.599
## beta a10.c4    1.731      0.483    0.784    2.679
## beta a10.c5    0.707      0.484   -0.241    1.655
## beta a10.c6    0.735      0.476   -0.197    1.667
## beta a11.c1   -0.161      0.552   -1.243    0.921
## beta a11.c2    1.416      0.424    0.584    2.247
## beta a11.c3    2.214      0.404    1.421    3.007
## beta a11.c4    1.389      0.405    0.596    2.182
## beta a11.c5    0.378      0.408   -0.422    1.178
## beta a11.c6    0.417      0.403   -0.373    1.206
## beta a12.c1    0.352      0.458   -0.547    1.251
## beta a12.c2    1.095      0.399    0.314    1.877
## beta a12.c3    2.272      0.374    1.540    3.004
## beta a12.c4    1.148      0.377    0.410    1.886
## beta a12.c5    0.002      0.384   -0.750    0.755
## beta a12.c6   -0.132      0.380   -0.877    0.612

Expected Response Curves & Item characteristic curves

# Plot the Variable Map
plotPImap(PC_model)

Item characteristic curves

plotICC(PC_model, ask = FALSE)