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DIF

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Winsteps: MFQ & DIF

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Sample

• 2500 “boys” and 2500 “girls”• All roughly 14 years old

• Data collected from ALSPAC hands-on clinic• Short-form (13-item) MFQ

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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

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Assessment of non-uniform gender DIF using PCM in Winsteps

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Non-uniform DIF plots

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Case 1

Item fits model well

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Case 2

Item fits model poorly

- PCM (Rasch) modelassumes equal slopesfor all items –

but performance of item similar across genders

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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

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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

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Assessment of gender DIF using GRM MIMIC model in Mplus

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Simple unidimensional trait model

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F

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Effect of gender on trait

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Gender

F

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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;

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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

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Uniform DIFDirect effect of gender on item

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Gender

F

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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;

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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

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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

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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

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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’

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Non-uniform DIFInteraction effect of gender*F on item

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Gender

F

Model must be fitted in a multiple group set up constraining parameters across (gender) groups