Date post: | 04-Jan-2016 |
Category: |
Documents |
Upload: | aldous-russell |
View: | 215 times |
Download: | 2 times |
1
DIF
2
Winsteps: MFQ & DIF
3
Sample
• 2500 “boys” and 2500 “girls”• All roughly 14 years old
• Data collected from ALSPAC hands-on clinic• Short-form (13-item) MFQ
4
odd / even items
• [01] I felt miserable or unhappy• [02] I didn't enjoy anything at all• [03] I felt so tired I just sat around and did nothing• [04] I was very restless• [05] I felt I was no good any more• [06] I cried a lot• [07] I found it hard to think properly or concentrate• [08] I hated myself• [09] I was a bad person• [10] I felt lonely• [11] I thought nobody really loved me• [12] I thought I could never been as good as other kids• [13] I did everything wrong
5
Assessment of non-uniform gender DIF using PCM in Winsteps
6
Non-uniform DIF plots
7
Case 1
Item fits model well
8
Case 2
Item fits model poorly
- PCM (Rasch) modelassumes equal slopesfor all items –
but performance of item similar across genders
9
Case 3
Item fits model wellat population levelbut evidence ofdifferent functioningacross genders
For a given trait level, boys are less likely to endorse the item (crying) than girls are
ICCs appear to diverge
10
Case 4
Item fits model wellat population levelbut evidence ofdifferent functioningacross genders
For a given trait level, boys are more likely to endorse the item (being as good as other kids) than girls are
ICCs appear parallel
11
Assessment of gender DIF using GRM MIMIC model in Mplus
12
Simple unidimensional trait model
i1 i3 i6 i8 i10 i12
F
13
Effect of gender on trait
i1 i3 i6 i8 i10 i12
Gender
F
14
Model for gender main-effect on trait
Data: File is "C:\work\courses\summer_school\MFQ\mplus\mfq_14yr_5000.dta.dat" ;Variable: Names are sex ID ta01_012 ta02_012 ta03_012 ta04_012 ta05_012 ta06_012 ta07_012 ta08_012 ta09_012 ta10_012 ta11_012 ta12_012 ta13_012 ta01_001 ta01_011 ta02_001 ta02_011 ta03_001 ta03_011 ta04_001 ta04_011 ta05_001 ta05_011 ta06_001 ta06_011 ta07_001 ta07_011 ta08_001 ta08_011 ta09_001 ta09_011 ta10_001 ta10_011 ta11_001 ta11_011 ta12_001 ta12_011 ta13_001 ta13_011; Missing are all (-9999) ;
usevariables = ta01_012 ta03_012 ta06_012 ta08_012 ta10_012 ta12_012 sex; categorical = ta01_012 ta03_012 ta06_012 ta08_012 ta10_012 ta12_012;
Analysis: estimator = MLR ; link = probit;
model: F by ta01_012* ta03_012 ta06_012 ta08_012 ta10_012 ta12_012; F@1; F on sex;
15
Estimate S.E. Est./S.E. P-Value
F BY TA01_012 1.284 0.058 22.017 0.000 TA03_012 0.556 0.029 19.162 0.000 TA06_012 1.176 0.066 17.863 0.000 TA08_012 1.851 0.159 11.679 0.000 TA10_012 1.264 0.064 19.696 0.000 TA12_012 1.119 0.064 17.375 0.000
F ON SEX 0.260 0.036 7.146 0.000
Thresholds TA01_012$1 0.859 0.084 10.251 0.000 TA01_012$2 3.326 0.130 25.528 0.000 TA03_012$1 0.934 0.042 22.414 0.000 TA03_012$2 2.493 0.061 41.130 0.000 TA06_012$1 2.671 0.123 21.697 0.000 TA06_012$2 4.372 0.175 25.009 0.000 TA08_012$1 3.925 0.280 14.017 0.000 TA08_012$2 6.158 0.414 14.863 0.000 TA10_012$1 2.151 0.106 20.379 0.000 TA10_012$2 4.284 0.158 27.168 0.000 TA12_012$1 2.116 0.096 21.993 0.000 TA12_012$2 3.676 0.134 27.519 0.000
Residual Variances F 1.000 0.000 999.000 999.000
16
Uniform DIFDirect effect of gender on item
i1 i3 i6 i8 i10 i12
Gender
F
17
Model for uniform DIF
model:
F by ta01_012* ta03_012 ta06_012 ta08_012 ta10_012 ta12_012;
F@1;
F on sex;
! ta01_012 on sex;
! ta03_012 on sex;
ta06_012 on sex;
! ta08_012 on sex;
! ta10_012 on sex;
! ta12_012 on sex;
18
Estimate S.E. Est./S.E. P-Value
F BY TA01_012 1.283 0.058 22.007 0.000 TA03_012 0.556 0.029 19.150 0.000 TA06_012 1.165 0.066 17.646 0.000 TA08_012 1.872 0.162 11.587 0.000 TA10_012 1.265 0.064 19.791 0.000 TA12_012 1.134 0.065 17.364 0.000
F ON SEX 0.233 0.037 6.374 0.000
TA06_012 ON SEX 0.365 0.073 5.005 0.000
Thresholds TA01_012$1 0.808 0.084 9.618 0.000 TA01_012$2 3.272 0.130 25.203 0.000 TA03_012$1 0.912 0.042 21.960 0.000 TA03_012$2 2.471 0.060 40.973 0.000 TA06_012$1 3.191 0.164 19.495 0.000 TA06_012$2 4.910 0.205 23.977 0.000 TA08_012$1 3.884 0.282 13.766 0.000 TA08_012$2 6.136 0.419 14.634 0.000 TA10_012$1 2.100 0.104 20.115 0.000 TA10_012$2 4.229 0.156 27.148 0.000 TA12_012$1 2.087 0.097 21.581 0.000 TA12_012$2 3.658 0.135 27.182 0.000
Residual Variances F 1.000 0.000 999.000 999.000
19
6 DIFferent models
Two-Tailed Estimate S.E. Est./S.E. P-Value
TA01_012 ON SEX 0.095 0.052 1.801 0.072
TA03_012 ON SEX 0.038 0.041 0.917 0.359
TA06_012 ON SEX 0.365 0.073 5.005 0.000
TA08_012 ON SEX -0.161 0.099 -1.635 0.102
TA10_012 ON SEX 0.019 0.060 0.308 0.758
TA12_012 ON SEX -0.362 0.062 -5.887 0.000
20
Uniform gender-DIF for item 6
Male ICC’s are shifted to the right
For a given trait level (depressive symptoms) boys are less likely than girls to endorse crying item
21
Uniform gender-DIF for item 12
Female ICC’s are shifted to the right
For a given trait level (depressive symptoms) boys are more likely than girls to endorse the item ‘being as good as other kids’
22
Non-uniform DIFInteraction effect of gender*F on item
i1 i3 i6 i8 i10 i12
Gender
F
Model must be fitted in a multiple group set up constraining parameters across (gender) groups