Nothing
tests/testthat/_snaps/difORD.md
In difNLR: DIF and DDF Detection by Non-Linear Regression Models
difORD - examples at help page
Code
(fit1 <- difORD(Data, group, focal.name = 1, model = "adjacent"))
Output
Detection of both types of Differential Item Functioning
for ordinal data using adjacent category logit regression
model
Likelihood-ratio Chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
R1 1.2551 0.5339
R2 5.9526 0.0510 .
R3 0.0852 0.9583
R4 0.1258 0.9390
R5 1.0432 0.5936
R6 9.8619 0.0072 **
R7 9.9535 0.0069 **
R8 1.0119 0.6029
R9 2.8220 0.2439
R10 5.2412 0.0728 .
R11 2.5074 0.2855
R12 4.0344 0.1330
R13 1.6216 0.4445
R14 0.5069 0.7761
R15 1.6559 0.4370
R16 3.9444 0.1391
R17 1.7717 0.4124
R18 0.1236 0.9401
R19 9.1928 0.0101 *
R20 11.1244 0.0038 **
R21 3.0459 0.2181
R22 3.7980 0.1497
R23 2.7844 0.2485
R24 0.5137 0.7735
R25 1.0364 0.5956
R26 0.9524 0.6211
R27 0.2938 0.8634
R28 4.3879 0.1115
R29 3.4921 0.1745
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
R6
R7
R19
R20
Code
AIC(fit1)
Output
[1] 868.7506 828.2355 762.8070 1172.1709 834.1942 1066.3088 1327.1609
[8] 1222.1158 1355.7572 698.8149 1342.9936 1431.2480 1320.1761 1413.2859
[15] 966.0284 1322.9039 511.0202 1571.5938 705.2756 912.1476 1287.1975
[22] 1051.6305 1422.2094 1246.3794 1972.2709 1450.7519 1090.9833 1380.3714
[29] 900.9954
Code
BIC(fit1)
Output
[1] 891.9565 851.4414 786.0129 1195.3768 857.4002 1098.7970 1359.6492
[8] 1245.3217 1378.9631 722.0208 1366.1995 1454.4539 1343.3820 1436.4918
[15] 989.2343 1346.1098 534.2261 1594.7997 737.7639 944.6358 1310.4034
[22] 1074.8364 1445.4153 1269.5853 1995.4768 1473.9578 1114.1892 1403.5774
[29] 924.2013
Code
logLik(fit1)
Output
[1] -429.3753 -409.1178 -376.4035 -581.0855 -412.0971 -526.1544 -656.5804
[8] -606.0579 -672.8786 -344.4075 -666.4968 -710.6240 -655.0880 -701.6429
[15] -478.0142 -656.4519 -250.5101 -780.7969 -345.6378 -449.0738 -638.5987
[22] -520.8152 -706.1047 -618.1897 -981.1354 -720.3759 -540.4916 -685.1857
[29] -445.4977
Code
AIC(fit1, item = 1)
Output
[1] 868.7506
Code
BIC(fit1, item = 1)
Output
[1] 891.9565
Code
logLik(fit1, item = 1)
Output
'log Lik.' -429.3753 (df=5)
Code
(fit2 <- difORD(Data, group, focal.name = 1, model = "adjacent",
p.adjust.method = "BH"))
Output
Detection of both types of Differential Item Functioning
for ordinal data using adjacent category logit regression
model
Likelihood-ratio Chi-square statistics
Item purification was not applied
Multiple comparisons made with Benjamini-Hochberg adjustment of p-values
Chisq-value P-value Adj. P-value
R1 1.2551 0.5339 0.7832
R2 5.9526 0.0510 0.2957
R3 0.0852 0.9583 0.9583
R4 0.1258 0.9390 0.9583
R5 1.0432 0.5936 0.7832
R6 9.8619 0.0072 0.0698 .
R7 9.9535 0.0069 0.0698 .
R8 1.0119 0.6029 0.7832
R9 2.8220 0.2439 0.5148
R10 5.2412 0.0728 0.3517
R11 2.5074 0.2855 0.5519
R12 4.0344 0.1330 0.4342
R13 1.6216 0.4445 0.7161
R14 0.5069 0.7761 0.9003
R15 1.6559 0.4370 0.7161
R16 3.9444 0.1391 0.4342
R17 1.7717 0.4124 0.7161
R18 0.1236 0.9401 0.9583
R19 9.1928 0.0101 0.0731 .
R20 11.1244 0.0038 0.0698 .
R21 3.0459 0.2181 0.5148
R22 3.7980 0.1497 0.4342
R23 2.7844 0.2485 0.5148
R24 0.5137 0.7735 0.9003
R25 1.0364 0.5956 0.7832
R26 0.9524 0.6211 0.7832
R27 0.2938 0.8634 0.9583
R28 4.3879 0.1115 0.4342
R29 3.4921 0.1745 0.4600
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
None of items is detected as DIF
Code
(fit3 <- difORD(Data, group, focal.name = 1, model = "adjacent", purify = TRUE))
Output
Detection of both types of Differential Item Functioning
for ordinal data using adjacent category logit regression
model
Likelihood-ratio Chi-square statistics
Item purification was applied with 2 iterations
No p-value adjustment for multiple comparisons
Chisq-value P-value
R1 1.6404 0.4403
R2 6.7384 0.0344 *
R3 0.0320 0.9841
R4 0.0008 0.9996
R5 0.8556 0.6520
R6 7.8654 0.0196 *
R7 9.9131 0.0070 **
R8 0.7217 0.6971
R9 2.7545 0.2523
R10 4.2581 0.1190
R11 2.8458 0.2410
R12 3.7298 0.1549
R13 1.2787 0.5276
R14 0.4712 0.7901
R15 1.9018 0.3864
R16 4.1030 0.1285
R17 2.2937 0.3176
R18 0.1617 0.9223
R19 7.4481 0.0241 *
R20 11.2324 0.0036 **
R21 2.7952 0.2472
R22 4.2428 0.1199
R23 2.1986 0.3331
R24 0.7738 0.6792
R25 0.5600 0.7558
R26 0.6024 0.7399
R27 0.7387 0.6912
R28 4.2304 0.1206
R29 3.1896 0.2030
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
R2
R6
R7
R19
R20
Code
(fit4 <- difORD(Data, group, focal.name = 1, model = "adjacent", type = "udif"))
Output
Detection of uniform Differential Item Functioning for
ordinal data using adjacent category logit regression model
Likelihood-ratio Chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
R1 0.5126 0.4740
R2 5.9329 0.0149 *
R3 0.0688 0.7932
R4 0.0093 0.9231
R5 0.0026 0.9592
R6 9.7515 0.0018 **
R7 5.2111 0.0224 *
R8 0.0037 0.9518
R9 2.7367 0.0981 .
R10 1.8363 0.1754
R11 2.0607 0.1511
R12 3.0119 0.0827 .
R13 1.1911 0.2751
R14 0.1871 0.6654
R15 0.7101 0.3994
R16 3.8040 0.0511 .
R17 1.5132 0.2186
R18 0.1219 0.7270
R19 6.9075 0.0086 **
R20 9.7346 0.0018 **
R21 2.4667 0.1163
R22 3.7512 0.0528 .
R23 1.0600 0.3032
R24 0.3149 0.5747
R25 0.3647 0.5459
R26 0.2691 0.6039
R27 0.0963 0.7563
R28 3.4448 0.0635 .
R29 3.2854 0.0699 .
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as uniform DIF items:
R2
R6
R7
R19
R20
Code
(fit5 <- difORD(Data, group, focal.name = 1, model = "adjacent", type = "nudif")
)
Output
Detection of non-uniformDifferential Item Functioning for
ordinal data using adjacent category logit regression model
Likelihood-ratio Chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
R1 0.7425 0.3889
R2 0.0196 0.8886
R3 0.0164 0.8980
R4 0.1165 0.7329
R5 1.0406 0.3077
R6 0.1104 0.7397
R7 4.7424 0.0294 *
R8 1.0083 0.3153
R9 0.0853 0.7702
R10 3.4049 0.0650 .
R11 0.4467 0.5039
R12 1.0224 0.3119
R13 0.4305 0.5117
R14 0.3198 0.5717
R15 0.9458 0.3308
R16 0.1404 0.7079
R17 0.2585 0.6112
R18 0.0017 0.9672
R19 2.2852 0.1306
R20 1.3898 0.2384
R21 0.5792 0.4466
R22 0.0468 0.8287
R23 1.7244 0.1891
R24 0.1989 0.6557
R25 0.6718 0.4124
R26 0.6833 0.4085
R27 0.1974 0.6568
R28 0.9431 0.3315
R29 0.2067 0.6494
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as non-uniform DIF items:
R7
Code
(fit6 <- difORD(Data, group, focal.name = 1, model = "adjacent", match = "score")
)
Output
Detection of both types of Differential Item Functioning
for ordinal data using adjacent category logit regression
model
Likelihood-ratio Chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
R1 1.2551 0.5339
R2 5.9526 0.0510 .
R3 0.0852 0.9583
R4 0.1258 0.9390
R5 1.0432 0.5936
R6 9.8619 0.0072 **
R7 9.9535 0.0069 **
R8 1.0119 0.6029
R9 2.8220 0.2439
R10 5.2412 0.0728 .
R11 2.5074 0.2855
R12 4.0344 0.1330
R13 1.6216 0.4445
R14 0.5069 0.7761
R15 1.6559 0.4370
R16 3.9444 0.1391
R17 1.7717 0.4124
R18 0.1236 0.9401
R19 9.1928 0.0101 *
R20 11.1244 0.0038 **
R21 3.0459 0.2181
R22 3.7980 0.1497
R23 2.7844 0.2485
R24 0.5137 0.7735
R25 1.0364 0.5956
R26 0.9524 0.6211
R27 0.2938 0.8634
R28 4.3879 0.1115
R29 3.4921 0.1745
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
R6
R7
R19
R20
Code
(fit7 <- difORD(Data, group, focal.name = 1, model = "cumulative"))
Output
Detection of both types of Differential Item Functioning
for ordinal data using cumulative logit regression model
Likelihood-ratio Chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
R1 0.5461 0.7611
R2 4.0133 0.1344
R3 0.4022 0.8178
R4 0.1243 0.9397
R5 0.1602 0.9230
R6 13.8917 0.0010 ***
R7 9.3795 0.0092 **
R8 1.2370 0.5388
R9 4.3732 0.1123
R10 6.1645 0.0459 *
R11 1.9460 0.3779
R12 3.8974 0.1425
R13 1.8335 0.3998
R14 0.7203 0.6976
R15 0.8203 0.6636
R16 3.4129 0.1815
R17 1.0467 0.5925
R18 0.9115 0.6340
R19 9.0748 0.0107 *
R20 10.6796 0.0048 **
R21 5.9576 0.0509 .
R22 3.7256 0.1552
R23 2.5638 0.2775
R24 0.2662 0.8754
R25 1.1370 0.5664
R26 0.7860 0.6750
R27 0.5704 0.7519
R28 2.9219 0.2320
R29 3.5553 0.1690
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
R6
R7
R10
R19
R20
testing paper code - R Journal 2020 - generated data
Code
summary(DataORD)
Output
Item1 Item2 Item3 Item4 Item5 group
0:488 0:376 0:417 0:530 0:556 Min. :0.0
1:229 1:237 1:331 1:226 1:253 1st Qu.:0.0
2:150 2:195 2:170 2:129 2:123 Median :0.5
3: 93 3:114 3: 71 3: 83 3: 47 Mean :0.5
4: 40 4: 78 4: 11 4: 32 4: 21 3rd Qu.:1.0
Max. :1.0
Code
(fit1 <- difORD(DataORD, group = "group", focal.name = 1, model = "cumulative"))
Output
Detection of both types of Differential Item Functioning
for ordinal data using cumulative logit regression model
Likelihood-ratio Chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 7.4263 0.0244 *
Item2 13.4267 0.0012 **
Item3 0.6805 0.7116
Item4 5.6662 0.0588 .
Item5 2.7916 0.2476
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
Item1
Item2
Code
(fit2 <- difORD(DataORD, group = 6, focal.name = 1, model = "adjacent"))
Output
Detection of both types of Differential Item Functioning
for ordinal data using adjacent category logit regression
model
Likelihood-ratio Chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 8.9024 0.0117 *
Item2 12.9198 0.0016 **
Item3 1.0313 0.5971
Item4 4.3545 0.1134
Item5 2.3809 0.3041
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
Item1
Item2
Code
coef(fit2)[[1]]
Output
b1 b2 b3 b4 a bDIF1
estimate 0.01290771 0.6026391 1.499871 2.500063 1.776348 -0.04192627
CI2.5 -0.11300539 0.4593669 1.316037 2.223942 1.553720 -0.15544582
CI97.5 0.13882081 0.7459112 1.683704 2.776184 1.998976 0.07159328
bDIF2 bDIF3 bDIF4 aDIF
estimate -0.12056646 -0.24021154 -0.3735864 0.27332175
CI2.5 -0.22781499 -0.39912272 -0.6281280 0.04802373
CI97.5 -0.01331792 -0.08130036 -0.1190447 0.49861976
Code
coef(fit2, IRTpars = FALSE)[[1]]
Output
(Intercept):1 (Intercept):2 (Intercept):3 (Intercept):4 x
estimate -0.02292859 -1.0704966 -2.664292 -4.440982 1.776348
CI2.5 -0.24583456 -1.3476911 -3.079675 -5.059781 1.553720
CI97.5 0.19997739 -0.7933022 -2.248909 -3.822182 1.998976
group x:group
estimate 0.08240705 0.27332175
CI2.5 -0.13183983 0.04802373
CI97.5 0.29665392 0.49861976
testing paper code - R Journal 2020 - LearningToLearn
Code
summary(LtL6_change_ord[, 1:4])
Output
track Item6A_changes Item6B_changes Item6C_changes
BS:391 0: 33 0: 33 0: 36
AS:391 1:318 1:335 1:494
2:431 2:414 2:252
Code
(fitex5 <- difORD(Data = LtL6_change_ord, group = "track", focal.name = "AS",
model = "adjacent", match = zscore6))
Output
Detection of both types of Differential Item Functioning
for ordinal data using adjacent category logit regression
model
Likelihood-ratio Chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item6A_changes 5.5961 0.0609 .
Item6B_changes 7.8798 0.0194 *
Item6C_changes 1.1242 0.5700
Item6D_changes 7.7136 0.0211 *
Item6E_changes 8.6452 0.0133 *
Item6F_changes 5.6488 0.0593 .
Item6G_changes 0.8469 0.6548
Item6H_changes 0.6567 0.7201
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
Item6B_changes
Item6D_changes
Item6E_changes
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difORD - examples at help page
Code
(fit1 <- difORD(Data, group, focal.name = 1, model = "adjacent"))
Output
Detection of both types of Differential Item Functioning
for ordinal data using adjacent category logit regression
model
Likelihood-ratio Chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
R1 1.2551 0.5339
R2 5.9526 0.0510 .
R3 0.0852 0.9583
R4 0.1258 0.9390
R5 1.0432 0.5936
R6 9.8619 0.0072 **
R7 9.9535 0.0069 **
R8 1.0119 0.6029
R9 2.8220 0.2439
R10 5.2412 0.0728 .
R11 2.5074 0.2855
R12 4.0344 0.1330
R13 1.6216 0.4445
R14 0.5069 0.7761
R15 1.6559 0.4370
R16 3.9444 0.1391
R17 1.7717 0.4124
R18 0.1236 0.9401
R19 9.1928 0.0101 *
R20 11.1244 0.0038 **
R21 3.0459 0.2181
R22 3.7980 0.1497
R23 2.7844 0.2485
R24 0.5137 0.7735
R25 1.0364 0.5956
R26 0.9524 0.6211
R27 0.2938 0.8634
R28 4.3879 0.1115
R29 3.4921 0.1745
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
R6
R7
R19
R20
Code
AIC(fit1)
Output
[1] 868.7506 828.2355 762.8070 1172.1709 834.1942 1066.3088 1327.1609
[8] 1222.1158 1355.7572 698.8149 1342.9936 1431.2480 1320.1761 1413.2859
[15] 966.0284 1322.9039 511.0202 1571.5938 705.2756 912.1476 1287.1975
[22] 1051.6305 1422.2094 1246.3794 1972.2709 1450.7519 1090.9833 1380.3714
[29] 900.9954
Code
BIC(fit1)
Output
[1] 891.9565 851.4414 786.0129 1195.3768 857.4002 1098.7970 1359.6492
[8] 1245.3217 1378.9631 722.0208 1366.1995 1454.4539 1343.3820 1436.4918
[15] 989.2343 1346.1098 534.2261 1594.7997 737.7639 944.6358 1310.4034
[22] 1074.8364 1445.4153 1269.5853 1995.4768 1473.9578 1114.1892 1403.5774
[29] 924.2013
Code
logLik(fit1)
Output
[1] -429.3753 -409.1178 -376.4035 -581.0855 -412.0971 -526.1544 -656.5804
[8] -606.0579 -672.8786 -344.4075 -666.4968 -710.6240 -655.0880 -701.6429
[15] -478.0142 -656.4519 -250.5101 -780.7969 -345.6378 -449.0738 -638.5987
[22] -520.8152 -706.1047 -618.1897 -981.1354 -720.3759 -540.4916 -685.1857
[29] -445.4977
Code
AIC(fit1, item = 1)
Output
[1] 868.7506
Code
BIC(fit1, item = 1)
Output
[1] 891.9565
Code
logLik(fit1, item = 1)
Output
'log Lik.' -429.3753 (df=5)
Code
(fit2 <- difORD(Data, group, focal.name = 1, model = "adjacent",
p.adjust.method = "BH"))
Output
Detection of both types of Differential Item Functioning
for ordinal data using adjacent category logit regression
model
Likelihood-ratio Chi-square statistics
Item purification was not applied
Multiple comparisons made with Benjamini-Hochberg adjustment of p-values
Chisq-value P-value Adj. P-value
R1 1.2551 0.5339 0.7832
R2 5.9526 0.0510 0.2957
R3 0.0852 0.9583 0.9583
R4 0.1258 0.9390 0.9583
R5 1.0432 0.5936 0.7832
R6 9.8619 0.0072 0.0698 .
R7 9.9535 0.0069 0.0698 .
R8 1.0119 0.6029 0.7832
R9 2.8220 0.2439 0.5148
R10 5.2412 0.0728 0.3517
R11 2.5074 0.2855 0.5519
R12 4.0344 0.1330 0.4342
R13 1.6216 0.4445 0.7161
R14 0.5069 0.7761 0.9003
R15 1.6559 0.4370 0.7161
R16 3.9444 0.1391 0.4342
R17 1.7717 0.4124 0.7161
R18 0.1236 0.9401 0.9583
R19 9.1928 0.0101 0.0731 .
R20 11.1244 0.0038 0.0698 .
R21 3.0459 0.2181 0.5148
R22 3.7980 0.1497 0.4342
R23 2.7844 0.2485 0.5148
R24 0.5137 0.7735 0.9003
R25 1.0364 0.5956 0.7832
R26 0.9524 0.6211 0.7832
R27 0.2938 0.8634 0.9583
R28 4.3879 0.1115 0.4342
R29 3.4921 0.1745 0.4600
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
None of items is detected as DIF
Code
(fit3 <- difORD(Data, group, focal.name = 1, model = "adjacent", purify = TRUE))
Output
Detection of both types of Differential Item Functioning
for ordinal data using adjacent category logit regression
model
Likelihood-ratio Chi-square statistics
Item purification was applied with 2 iterations
No p-value adjustment for multiple comparisons
Chisq-value P-value
R1 1.6404 0.4403
R2 6.7384 0.0344 *
R3 0.0320 0.9841
R4 0.0008 0.9996
R5 0.8556 0.6520
R6 7.8654 0.0196 *
R7 9.9131 0.0070 **
R8 0.7217 0.6971
R9 2.7545 0.2523
R10 4.2581 0.1190
R11 2.8458 0.2410
R12 3.7298 0.1549
R13 1.2787 0.5276
R14 0.4712 0.7901
R15 1.9018 0.3864
R16 4.1030 0.1285
R17 2.2937 0.3176
R18 0.1617 0.9223
R19 7.4481 0.0241 *
R20 11.2324 0.0036 **
R21 2.7952 0.2472
R22 4.2428 0.1199
R23 2.1986 0.3331
R24 0.7738 0.6792
R25 0.5600 0.7558
R26 0.6024 0.7399
R27 0.7387 0.6912
R28 4.2304 0.1206
R29 3.1896 0.2030
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
R2
R6
R7
R19
R20
Code
(fit4 <- difORD(Data, group, focal.name = 1, model = "adjacent", type = "udif"))
Output
Detection of uniform Differential Item Functioning for
ordinal data using adjacent category logit regression model
Likelihood-ratio Chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
R1 0.5126 0.4740
R2 5.9329 0.0149 *
R3 0.0688 0.7932
R4 0.0093 0.9231
R5 0.0026 0.9592
R6 9.7515 0.0018 **
R7 5.2111 0.0224 *
R8 0.0037 0.9518
R9 2.7367 0.0981 .
R10 1.8363 0.1754
R11 2.0607 0.1511
R12 3.0119 0.0827 .
R13 1.1911 0.2751
R14 0.1871 0.6654
R15 0.7101 0.3994
R16 3.8040 0.0511 .
R17 1.5132 0.2186
R18 0.1219 0.7270
R19 6.9075 0.0086 **
R20 9.7346 0.0018 **
R21 2.4667 0.1163
R22 3.7512 0.0528 .
R23 1.0600 0.3032
R24 0.3149 0.5747
R25 0.3647 0.5459
R26 0.2691 0.6039
R27 0.0963 0.7563
R28 3.4448 0.0635 .
R29 3.2854 0.0699 .
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as uniform DIF items:
R2
R6
R7
R19
R20
Code
(fit5 <- difORD(Data, group, focal.name = 1, model = "adjacent", type = "nudif")
)
Output
Detection of non-uniformDifferential Item Functioning for
ordinal data using adjacent category logit regression model
Likelihood-ratio Chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
R1 0.7425 0.3889
R2 0.0196 0.8886
R3 0.0164 0.8980
R4 0.1165 0.7329
R5 1.0406 0.3077
R6 0.1104 0.7397
R7 4.7424 0.0294 *
R8 1.0083 0.3153
R9 0.0853 0.7702
R10 3.4049 0.0650 .
R11 0.4467 0.5039
R12 1.0224 0.3119
R13 0.4305 0.5117
R14 0.3198 0.5717
R15 0.9458 0.3308
R16 0.1404 0.7079
R17 0.2585 0.6112
R18 0.0017 0.9672
R19 2.2852 0.1306
R20 1.3898 0.2384
R21 0.5792 0.4466
R22 0.0468 0.8287
R23 1.7244 0.1891
R24 0.1989 0.6557
R25 0.6718 0.4124
R26 0.6833 0.4085
R27 0.1974 0.6568
R28 0.9431 0.3315
R29 0.2067 0.6494
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as non-uniform DIF items:
R7
Code
(fit6 <- difORD(Data, group, focal.name = 1, model = "adjacent", match = "score")
)
Output
Detection of both types of Differential Item Functioning
for ordinal data using adjacent category logit regression
model
Likelihood-ratio Chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
R1 1.2551 0.5339
R2 5.9526 0.0510 .
R3 0.0852 0.9583
R4 0.1258 0.9390
R5 1.0432 0.5936
R6 9.8619 0.0072 **
R7 9.9535 0.0069 **
R8 1.0119 0.6029
R9 2.8220 0.2439
R10 5.2412 0.0728 .
R11 2.5074 0.2855
R12 4.0344 0.1330
R13 1.6216 0.4445
R14 0.5069 0.7761
R15 1.6559 0.4370
R16 3.9444 0.1391
R17 1.7717 0.4124
R18 0.1236 0.9401
R19 9.1928 0.0101 *
R20 11.1244 0.0038 **
R21 3.0459 0.2181
R22 3.7980 0.1497
R23 2.7844 0.2485
R24 0.5137 0.7735
R25 1.0364 0.5956
R26 0.9524 0.6211
R27 0.2938 0.8634
R28 4.3879 0.1115
R29 3.4921 0.1745
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
R6
R7
R19
R20
Code
(fit7 <- difORD(Data, group, focal.name = 1, model = "cumulative"))
Output
Detection of both types of Differential Item Functioning
for ordinal data using cumulative logit regression model
Likelihood-ratio Chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
R1 0.5461 0.7611
R2 4.0133 0.1344
R3 0.4022 0.8178
R4 0.1243 0.9397
R5 0.1602 0.9230
R6 13.8917 0.0010 ***
R7 9.3795 0.0092 **
R8 1.2370 0.5388
R9 4.3732 0.1123
R10 6.1645 0.0459 *
R11 1.9460 0.3779
R12 3.8974 0.1425
R13 1.8335 0.3998
R14 0.7203 0.6976
R15 0.8203 0.6636
R16 3.4129 0.1815
R17 1.0467 0.5925
R18 0.9115 0.6340
R19 9.0748 0.0107 *
R20 10.6796 0.0048 **
R21 5.9576 0.0509 .
R22 3.7256 0.1552
R23 2.5638 0.2775
R24 0.2662 0.8754
R25 1.1370 0.5664
R26 0.7860 0.6750
R27 0.5704 0.7519
R28 2.9219 0.2320
R29 3.5553 0.1690
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
R6
R7
R10
R19
R20
testing paper code - R Journal 2020 - generated data
Code
summary(DataORD)
Output
Item1 Item2 Item3 Item4 Item5 group
0:488 0:376 0:417 0:530 0:556 Min. :0.0
1:229 1:237 1:331 1:226 1:253 1st Qu.:0.0
2:150 2:195 2:170 2:129 2:123 Median :0.5
3: 93 3:114 3: 71 3: 83 3: 47 Mean :0.5
4: 40 4: 78 4: 11 4: 32 4: 21 3rd Qu.:1.0
Max. :1.0
Code
(fit1 <- difORD(DataORD, group = "group", focal.name = 1, model = "cumulative"))
Output
Detection of both types of Differential Item Functioning
for ordinal data using cumulative logit regression model
Likelihood-ratio Chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 7.4263 0.0244 *
Item2 13.4267 0.0012 **
Item3 0.6805 0.7116
Item4 5.6662 0.0588 .
Item5 2.7916 0.2476
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
Item1
Item2
Code
(fit2 <- difORD(DataORD, group = 6, focal.name = 1, model = "adjacent"))
Output
Detection of both types of Differential Item Functioning
for ordinal data using adjacent category logit regression
model
Likelihood-ratio Chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 8.9024 0.0117 *
Item2 12.9198 0.0016 **
Item3 1.0313 0.5971
Item4 4.3545 0.1134
Item5 2.3809 0.3041
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
Item1
Item2
Code
coef(fit2)[[1]]
Output
b1 b2 b3 b4 a bDIF1
estimate 0.01290771 0.6026391 1.499871 2.500063 1.776348 -0.04192627
CI2.5 -0.11300539 0.4593669 1.316037 2.223942 1.553720 -0.15544582
CI97.5 0.13882081 0.7459112 1.683704 2.776184 1.998976 0.07159328
bDIF2 bDIF3 bDIF4 aDIF
estimate -0.12056646 -0.24021154 -0.3735864 0.27332175
CI2.5 -0.22781499 -0.39912272 -0.6281280 0.04802373
CI97.5 -0.01331792 -0.08130036 -0.1190447 0.49861976
Code
coef(fit2, IRTpars = FALSE)[[1]]
Output
(Intercept):1 (Intercept):2 (Intercept):3 (Intercept):4 x
estimate -0.02292859 -1.0704966 -2.664292 -4.440982 1.776348
CI2.5 -0.24583456 -1.3476911 -3.079675 -5.059781 1.553720
CI97.5 0.19997739 -0.7933022 -2.248909 -3.822182 1.998976
group x:group
estimate 0.08240705 0.27332175
CI2.5 -0.13183983 0.04802373
CI97.5 0.29665392 0.49861976
testing paper code - R Journal 2020 - LearningToLearn
Code
summary(LtL6_change_ord[, 1:4])
Output
track Item6A_changes Item6B_changes Item6C_changes
BS:391 0: 33 0: 33 0: 36
AS:391 1:318 1:335 1:494
2:431 2:414 2:252
Code
(fitex5 <- difORD(Data = LtL6_change_ord, group = "track", focal.name = "AS",
model = "adjacent", match = zscore6))
Output
Detection of both types of Differential Item Functioning
for ordinal data using adjacent category logit regression
model
Likelihood-ratio Chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item6A_changes 5.5961 0.0609 .
Item6B_changes 7.8798 0.0194 *
Item6C_changes 1.1242 0.5700
Item6D_changes 7.7136 0.0211 *
Item6E_changes 8.6452 0.0133 *
Item6F_changes 5.6488 0.0593 .
Item6G_changes 0.8469 0.6548
Item6H_changes 0.6567 0.7201
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DIF items:
Item6B_changes
Item6D_changes
Item6E_changes
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