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ICPSR IRT Workshop Handout #1 1

Obtaining Item Response Model Estimates with Mplus Handout #1: Introduction to Mplus/The One‐ and Two‐ Parameter Logistic Models

ICPSR Workshop

To demonstrate how Item Response Models can be estimated using Mplus, input syntax and output are included in this document.

Analysis Data Set:

Twenty Item Test of Fraction Subtraction o Open ended response type; Items scored Correct (X=1) or Incorrect (X=0)

536 Examinees – middle school students circa 1990

Fraction Subtraction Test Items:

1.

2.

3.

4. 3 2

5. 4 3

6.

7. 3 2

8.

9. 3 2

10. 4 2

11. 4 2

12.

13. 3 2

14. 3 3

15. 2

16. 4 1

17. 7

18. 4 2

19. 7 1

20. 4 1

Lab Example #1: Page 1 of 10

D:\Teaching\2011\ICPSR IRT\Lab Activities\01 Fraction S...\Mplus Example #1- 1PL Model.inp

!Mplus Example #1: 1PL Model and Introduction to Mplus!Briefly Annotated Syntax!----Comments appear in green (use exclamation point)!----Line width is a maximum of 90 characters only

TITLE:!Title section contains title of analysis that appears!on top of output

ICPSR IRT Workshop -- Mplus Example #1;IRT Model Analysis with the 1PL Model;

DATA:FILE = fsdata.csv; !indictes data are in fsdata.csv (comma delimited format)

!data are stored with one person per row,!one item per column

VARIABLE:NAMES = X1-X20; !define the names of variables here;CATEGORICAL = X1-X20; !indicate that the variables are categorical (0/1)

ANALYSIS:ESTIMATOR = ML; !set the estimator to (marginal) maximum likelihoodPROCESSORS = 8; !use multiple processors (if available)

MODEL:THETA by X1* X2-X20 (1); !define latent variable (theta) and items measuring it

!the X1* overrides the identification constraint from Mplus!the (1) sets all parameters equal for the 1PL

THETA@1; !fixes the variance of theta to 1[THETA@0]; !fixes the mean of theta to 0

PLOT:TYPE = PLOT1 PLOT2 PLOT3; !indicates we wish to have IRT graphics created

SAVEDATA:SAVE = FSCORES; !saves latent trait estimatesFILE = IRT1PL_person.dat; !puts latent trait estimates into file named IRT1PL_person.d

at

OUTPUT:TECH1 TECH5 TECH8 TECH10; !displays model estimation and fit information

`

Page: 1Lab Example #1: Page 2 of 10

d:\teaching\2011\icpsr irt\lab activities\01 fraction s...\mplus example #1- 1pl model.out

UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES

X1Category 1 0.468 251.000Category 2 0.532 285.000



















X20Category 1 0.608 326.000



Category 2 0.392 210.000

THE MODEL ESTIMATION TERMINATED NORMALLY

THE CHI-SQUARE TEST IS NOT COMPUTED BECAUSE THE FREQUENCY TABLE FOR THELATENT CLASS INDICATOR MODEL PART IS TOO LARGE.

MODEL FIT INFORMATION

Number of Free Parameters 21

Loglikelihood

H0 Value -4797.178

Information Criteria

Akaike (AIC) 9636.355Bayesian (BIC) 9726.322Sample-Size Adjusted BIC 9659.661

(n* = (n + 2) / 24)

MODEL RESULTS

Two-TailedEstimate S.E. Est./S.E. P-Value

THETA BYX1 2.339 0.090 25.865 0.000X2 2.339 0.090 25.865 0.000X3 2.339 0.090 25.865 0.000X4 2.339 0.090 25.865 0.000X5 2.339 0.090 25.865 0.000X6 2.339 0.090 25.865 0.000X7 2.339 0.090 25.865 0.000X8 2.339 0.090 25.865 0.000X9 2.339 0.090 25.865 0.000X10 2.339 0.090 25.865 0.000X11 2.339 0.090 25.865 0.000X12 2.339 0.090 25.865 0.000X13 2.339 0.090 25.865 0.000X14 2.339 0.090 25.865 0.000X15 2.339 0.090 25.865 0.000X16 2.339 0.090 25.865 0.000X17 2.339 0.090 25.865 0.000X18 2.339 0.090 25.865 0.000X19 2.339 0.090 25.865 0.000X20 2.339 0.090 25.865 0.000

MeansTHETA 0.000 0.000 999.000 999.000

ThresholdsX1$1 -0.253 0.158 -1.599 0.110X2$1 -0.600 0.159 -3.778 0.000X3$1 -0.123 0.158 -0.779 0.436



X4$1 -0.224 0.158 -1.417 0.157X5$1 -0.658 0.159 -4.141 0.000X6$1 -2.456 0.174 -14.126 0.000X7$1 0.779 0.160 4.879 0.000X8$1 -2.012 0.168 -12.000 0.000X9$1 -1.159 0.161 -7.211 0.000X10$1 0.913 0.160 5.700 0.000X11$1 0.194 0.158 1.226 0.220X12$1 -1.846 0.166 -11.127 0.000X13$1 1.641 0.165 9.966 0.000X14$1 -1.781 0.165 -10.775 0.000X15$1 0.368 0.159 2.321 0.020X16$1 -1.557 0.163 -9.536 0.000X17$1 0.514 0.159 3.234 0.001X18$1 0.238 0.158 1.500 0.134X19$1 1.513 0.164 9.245 0.000X20$1 0.839 0.160 5.244 0.000

VariancesTHETA 1.000 0.000 999.000 999.000

IRT PARAMETERIZATION IN TWO-PARAMETER LOGISTIC METRICWHERE THE LOGIT IS 1.7*DISCRIMINATION*(THETA - DIFFICULTY)

Item Discriminations


MeansTHETA 0.000 0.000 0.000 1.000

Item DifficultiesX1$1 -0.108 0.068 -1.597 0.110X2$1 -0.256 0.068 -3.758 0.000X3$1 -0.053 0.068 -0.779 0.436X4$1 -0.096 0.068 -1.416 0.157X5$1 -0.281 0.068 -4.113 0.000X6$1 -1.050 0.081 -13.003 0.000X7$1 0.333 0.069 4.820 0.000X8$1 -0.860 0.076 -11.291 0.000X9$1 -0.495 0.070 -7.054 0.000X10$1 0.390 0.070 5.609 0.000



X11$1 0.083 0.068 1.225 0.221X12$1 -0.789 0.075 -10.557 0.000X13$1 0.702 0.074 9.530 0.000X14$1 -0.761 0.074 -10.256 0.000X15$1 0.157 0.068 2.314 0.021X16$1 -0.666 0.073 -9.174 0.000X17$1 0.220 0.068 3.216 0.001X18$1 0.102 0.068 1.497 0.134X19$1 0.647 0.073 8.891 0.000X20$1 0.358 0.069 5.172 0.000

VariancesTHETA 1.000 0.000 0.000 1.000


D:\Teaching\2011\ICPSR IRT\Lab Activities\01 Fraction S...\Mplus Example #2- 2PL Model.inp

!Mplus Example #1: 2PL Model and Introduction to Mplus!Briefly Annotated Syntax!----Comments appear in green (use exclamation point)!----Line width is a maximum of 90 characters only

TITLE:!Title section contains title of analysis that appears!on top of output

ICPSR IRT Workshop -- Mplus Example #1;IRT Model Analysis with the 2PL Model;

DATA:FILE = fsdata.csv; !indictes data are in fsdata.csv (comma delimited format)

!data are stored with one person per row,!one item per column

VARIABLE:NAMES = X1-X20; !define the names of variables here;CATEGORICAL = X1-X20; !indicate that the variables are categorical (0/1)


MODEL:THETA by X1* X2-X20; !define latent variable (theta) and items measuring it

!the X1* overrides the identification constraint from Mplus!all parameters are estimated in the 2PL

THETA@1; !fixes the variance of theta to 1[THETA@0]; !fixes the mean of theta to 0


SAVEDATA:SAVE = FSCORES; !saves latent trait estimatesFILE = IRT2PL_person.dat; !puts latent trait estimates into a

! file named IRT2PL_person.dat





THE CHI-SQUARE TEST IS NOT COMPUTED BECAUSE THE FREQUENCY TABLE FOR THELATENT CLASS INDICATOR MODEL PART IS TOO LARGE.



Loglikelihood

H0 Value -4640.159



(n* = (n + 2) / 24)

MODEL RESULTS



MeansTHETA 0.000 0.000 999.000 999.000

ThresholdsX1$1 -0.159 0.167 -0.955 0.340X2$1 -0.633 0.213 -2.971 0.003X3$1 -0.014 0.178 -0.080 0.936X4$1 -0.133 0.126 -1.048 0.294X5$1 -0.431 0.115 -3.732 0.000X6$1 -2.790 0.304 -9.180 0.000X7$1 1.015 0.205 4.950 0.000



X8$1 -1.430 0.143 -10.016 0.000X9$1 -0.704 0.107 -6.598 0.000X10$1 1.337 0.244 5.488 0.000X11$1 0.413 0.206 2.007 0.045X12$1 -1.726 0.196 -8.819 0.000X13$1 2.109 0.289 7.301 0.000X14$1 -1.890 0.228 -8.300 0.000X15$1 0.597 0.202 2.956 0.003X16$1 -1.446 0.183 -7.878 0.000X17$1 0.928 0.242 3.834 0.000X18$1 0.367 0.176 2.086 0.037X19$1 2.559 0.390 6.557 0.000X20$1 1.412 0.272 5.197 0.000

VariancesTHETA 1.000 0.000 999.000 999.000




MeansTHETA 0.000 0.000 0.000 1.000

Item DifficultiesX1$1 -0.063 0.066 -0.950 0.342X2$1 -0.185 0.062 -2.998 0.003X3$1 -0.005 0.064 -0.080 0.936X4$1 -0.083 0.080 -1.043 0.297X5$1 -0.345 0.095 -3.613 0.000X6$1 -0.998 0.082 -12.230 0.000X7$1 0.348 0.063 5.476 0.000X8$1 -1.107 0.128 -8.660 0.000X9$1 -0.811 0.147 -5.506 0.000X10$1 0.394 0.061 6.423 0.000X11$1 0.125 0.061 2.056 0.040X12$1 -0.784 0.082 -9.538 0.000X13$1 0.658 0.066 10.011 0.000X14$1 -0.710 0.075 -9.529 0.000



X15$1 0.191 0.062 3.092 0.002X16$1 -0.647 0.078 -8.310 0.000X17$1 0.248 0.059 4.171 0.000X18$1 0.138 0.065 2.136 0.033X19$1 0.596 0.060 9.877 0.000X20$1 0.366 0.059 6.150 0.000

VariancesTHETA 1.000 0.000 0.000 1.000



Handout #2: Polytomous Item Response Models in Mplus

To demonstrate how polytomous Item Response Models can be estimated using Mplus, input syntax and output are included in this document.

Analysis Data Set:

12 item South Oaks Gambling Screen o 3 items with multiple response options; 9 binary (yes/no) items

1192 College Students; 112 Experienced Gamblers

South Oaks Gambling Screen Items:

4. When you gamble, how often do you go back another day to win back money you lost? (never; occasionally; most of the time; every time)

5. Have you ever claimed to be winning money gambling but weren't really? In fact, you lost? (score: number of yes responses)

6. Do you feel you have a problem with betting money or gambling? (score: number of yes responses)

7. Do you ever gamble more than you intend to? (yes/no)

8. Have people criticized your betting or told you that you had a gambling problem, regardless of whether or not you thought it was true? (yes/no)

9. Have you ever felt guilty about the way you gamble or what happens when you gamble? (yes/no)

10. Have you ever felt like you would like to stop betting money or gambling but didn't think you could? (yes/no)

11. Have you ever hidden betting slips, lottery tickets, gambling money, IOU's or other signs of betting or gambling from your spouse/partner, children or other important people in your life? (yes/no)

12. Have you ever argued with people you love over how you handle money? (yes/no)

13. (if yes to 12) Have money arguments ever centered on your gambling? (yes/no)

14. Have you ever borrowed from someone and not paid them back as a result of your gambling? (yes/no)

15. Have you ever lost time from work (or school) due to money or gambling? (yes/no)


D:\Teaching\2011\ICPSR IRT\Lab Activities\02 SOGS Examples\alldata_sogs_r.inp

TITLE:South Oaks Gambling Screen ItemsData from 1192 College Students/112 Gamblers9 Binary Items (yes/no): SOGS7-SOGS151 4-choice item SOGS4: Recoded binary SOGS4r2 3-choice items SOGS5 & SOGS6: Recoded binary SOGS5r & SOGS6r========================================2PL Analysis (so recoded items used)========================================

DATA:FILE = alldata_sogsr.csv;

VARIABLE:NAMES = SOGS4-SOGS15 SOGS4r SOGS5r SOGS6r student ID;USEVARIABLES = SOGS4r SOGS5r SOGS6r SOGS7-SOGS15; !use only binary itemsIDVARIABLE = ID; !label subjectcsCATEGORICAL = SOGS4r SOGS5r SOGS6r SOGS7-SOGS15; !categorical option for allMISSING = ALL(99); !missing data = 99 (MCAR)


MODEL:

THETA by SOGS4r* SOGS5r SOGS6r SOGS7-SOGS15;

THETA@1;[THETA@0];


SAVEDATA:SAVE = FSCORES; !saves latent trait estimatesFILE = alldata_sogsr_person2PL.dat;



d:\teaching\2011\icpsr irt\lab activities\02 sogs examples\alldata_sogs_r.out

UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES

SOGS4RCategory 1 0.906 1182.000Category 2 0.094 122.000



SOGS7Category 1 0.764 996.000Category 2 0.236 307.000












Loglikelihood

H0 Value -3297.402



(n* = (n + 2) / 24)

Chi-Square Test of Model Fit for the Binary and Ordered Categorical(Ordinal) Outcomes**



Pearson Chi-Square

Value 2012.921Degrees of Freedom 4054P-Value 1.0000

Likelihood Ratio Chi-Square


** Of the 10248 cells in the latent class indicator table, 17were deleted in the calculation of chi-square due to extreme values.

Chi-Square Test for MCAR under the Unrestricted Latent Class Indicator Model

Pearson Chi-Square




MODEL RESULTS


THETA BYSOGS4R 0.452 0.125 3.622 0.000SOGS5R 1.837 0.183 10.037 0.000SOGS6R 4.321 0.589 7.332 0.000SOGS7 2.747 0.302 9.097 0.000SOGS8 3.403 0.388 8.777 0.000SOGS9 2.553 0.275 9.299 0.000SOGS10 4.519 0.691 6.544 0.000SOGS11 4.064 0.615 6.606 0.000SOGS12 0.775 0.104 7.425 0.000SOGS13 5.701 1.144 4.983 0.000SOGS14 2.247 0.316 7.117 0.000SOGS15 2.570 0.326 7.887 0.000

MeansTHETA 0.000 0.000 999.000 999.000

ThresholdsSOGS4R$1 2.352 0.106 22.130 0.000SOGS5R$1 3.119 0.201 15.530 0.000SOGS6R$1 7.561 0.899 8.406 0.000SOGS7$1 2.348 0.220 10.695 0.000SOGS8$1 5.409 0.508 10.638 0.000SOGS9$1 4.539 0.359 12.640 0.000SOGS10$1 8.567 1.148 7.461 0.000SOGS11$1 8.086 1.039 7.784 0.000SOGS12$1 1.603 0.087 18.341 0.000



SOGS13$1 11.072 2.037 5.435 0.000SOGS14$1 5.474 0.493 11.105 0.000SOGS15$1 5.552 0.500 11.107 0.000

VariancesTHETA 1.000 0.000 999.000 999.000



THETA BYSOGS4R 0.266 0.073 3.622 0.000SOGS5R 1.081 0.108 10.037 0.000SOGS6R 2.542 0.347 7.332 0.000SOGS7 1.616 0.178 9.097 0.000SOGS8 2.002 0.228 8.777 0.000SOGS9 1.501 0.161 9.299 0.000SOGS10 2.658 0.406 6.544 0.000SOGS11 2.390 0.362 6.606 0.000SOGS12 0.456 0.061 7.425 0.000SOGS13 3.354 0.673 4.983 0.000SOGS14 1.322 0.186 7.117 0.000SOGS15 1.512 0.192 7.887 0.000

MeansTHETA 0.000 0.000 0.000 1.000

Item DifficultiesSOGS4R$1 5.200 1.356 3.836 0.000SOGS5R$1 1.698 0.105 16.235 0.000SOGS6R$1 1.750 0.073 23.947 0.000SOGS7$1 0.855 0.054 15.970 0.000SOGS8$1 1.590 0.072 22.210 0.000SOGS9$1 1.778 0.092 19.279 0.000SOGS10$1 1.896 0.080 23.645 0.000SOGS11$1 1.990 0.090 22.133 0.000SOGS12$1 2.067 0.245 8.425 0.000SOGS13$1 1.942 0.080 24.144 0.000SOGS14$1 2.436 0.171 14.252 0.000SOGS15$1 2.161 0.124 17.392 0.000

VariancesTHETA 1.000 0.000 0.000 1.000


D:\Teaching\2011\ICPSR IRT\Lab Activities\02 SOGS Examples\alldata_sogs_grm.inp

TITLE: Body Dysmorphic Disorder ItemsDATA: FILE IS alldata_sogsr.csv;VARIABLE:

NAMES = SOGS4-SOGS15 SOGS4r SOGS5r SOGS6r student ID;USEVARIABLES = SOGS4-SOGS15;IDVARIABLE = ID;CATEGORICAL = SOGS4-SOGS15;MISSING = ALL(99);


MODEL:THETA by SOGS4* SOGS5-SOGS15;

THETA@1;[THETA@0];


SAVEDATA:SAVE = FSCORES; !saves latent trait estimatesFILE = alldata_sogsr_personGRM.dat;



d:\teaching\2011\icpsr irt\lab activities\02 sogs examples\alldata_sogs_grm.out




Loglikelihood

H0 Value -3931.198



(n* = (n + 2) / 24)


Pearson Chi-Square






Pearson Chi-Square




MODEL RESULTS


THETA BYSOGS4 0.146 0.091 1.605 0.108SOGS5 1.818 0.177 10.249 0.000


d:\teaching\2011\icpsr irt\lab activities\02 sogs examples\alldata_sogs_grm.out

SOGS6 3.969 0.489 8.111 0.000SOGS7 2.755 0.304 9.052 0.000SOGS8 3.413 0.390 8.751 0.000SOGS9 2.582 0.279 9.259 0.000SOGS10 4.548 0.696 6.535 0.000SOGS11 4.187 0.645 6.491 0.000SOGS12 0.774 0.105 7.396 0.000SOGS13 6.045 1.285 4.706 0.000SOGS14 2.270 0.320 7.095 0.000SOGS15 2.603 0.331 7.872 0.000

MeansTHETA 0.000 0.000 999.000 999.000

ThresholdsSOGS4$1 -2.066 0.088 -23.436 0.000SOGS4$2 2.279 0.096 23.799 0.000SOGS4$3 3.275 0.148 22.181 0.000SOGS5$1 3.105 0.197 15.737 0.000SOGS5$2 5.810 0.344 16.899 0.000SOGS6$1 7.097 0.757 9.372 0.000SOGS6$2 8.642 0.857 10.084 0.000SOGS7$1 2.355 0.221 10.636 0.000SOGS8$1 5.423 0.512 10.590 0.000SOGS9$1 4.574 0.365 12.523 0.000SOGS10$1 8.603 1.155 7.446 0.000SOGS11$1 8.282 1.090 7.598 0.000SOGS12$1 1.602 0.087 18.338 0.000SOGS13$1 11.649 2.286 5.096 0.000SOGS14$1 5.503 0.499 11.028 0.000SOGS15$1 5.596 0.507 11.030 0.000

VariancesTHETA 1.000 0.000 999.000 999.000


D:\Teaching\2011\ICPSR IRT\Lab Activities\02 SOGS Examples\alldata_sogs_nrm.inp

TITLE:South Oaks Gambling Screen ItemsData from 1192 College Students/112 Gamblers9 Binary Items (yes/no): SOGS7-SOGS151 4-choice item SOGS4: Recoded binary SOGS4r2 3-choice items SOGS5 & SOGS6: Recoded binary SOGS5r & SOGS6r========================================Nominal Response Model Analysis (so original items used)========================================

DATA:FILE = alldata_sogsr.csv;

VARIABLE:NAMES = SOGS4-SOGS15 SOGS4r SOGS5r SOGS6r student ID;USEVARIABLES = SOGS4-SOGS15;IDVARIABLE = ID;NOMINAL = SOGS4-SOGS6; !indicate that these variables need the NRMCATEGORICAL = SOGS7-SOGS15; !the rest use the 2PLMISSING = ALL(99);


MODEL:

THETA by SOGS4#1* SOGS4#2 SOGS4#3SOGS5#1 SOGS5#2SOGS6#1 SOGS6#2 SOGS7-SOGS15;

THETA@1;[THETA@0];


SAVEDATA:SAVE = FSCORES; !saves latent trait estimatesFILE = alldata_sogsr_personNRM.dat;



d:\teaching\2011\icpsr irt\lab activities\02 sogs examples\alldata_sogs_nrm.out




Loglikelihood

H0 Value -3917.319



(n* = (n + 2) / 24)


Pearson Chi-Square






Pearson Chi-Square




MODEL RESULTS


THETA BYSOGS4#1 0.283 0.238 1.191 0.234SOGS4#2 0.118 0.214 0.553 0.581SOGS4#3 0.904 0.260 3.477 0.001


d:\teaching\2011\icpsr irt\lab activities\02 sogs examples\alldata_sogs_nrm.out

SOGS5#1 -2.102 0.412 -5.101 0.000SOGS5#2 -0.307 0.402 -0.763 0.445SOGS6#1 -4.351 0.707 -6.157 0.000SOGS6#2 -0.174 0.604 -0.287 0.774SOGS7 2.761 0.304 9.074 0.000SOGS8 3.376 0.383 8.815 0.000SOGS9 2.547 0.273 9.321 0.000SOGS10 4.493 0.685 6.560 0.000SOGS11 4.038 0.609 6.627 0.000SOGS12 0.783 0.104 7.512 0.000SOGS13 5.645 1.131 4.990 0.000SOGS14 2.233 0.313 7.131 0.000SOGS15 2.550 0.323 7.905 0.000

MeansTHETA 0.000 0.000 999.000 999.000

InterceptsSOGS4#1 1.135 0.170 6.666 0.000SOGS4#2 3.083 0.152 20.296 0.000SOGS4#3 0.192 0.216 0.892 0.372SOGS5#1 5.661 0.592 9.560 0.000SOGS5#2 2.462 0.602 4.089 0.000SOGS6#1 8.494 1.161 7.317 0.000SOGS6#2 0.569 1.144 0.497 0.619

ThresholdsSOGS7$1 2.366 0.222 10.661 0.000SOGS8$1 5.393 0.504 10.695 0.000SOGS9$1 4.546 0.359 12.658 0.000SOGS10$1 8.559 1.145 7.472 0.000SOGS11$1 8.075 1.034 7.806 0.000SOGS12$1 1.609 0.088 18.318 0.000SOGS13$1 11.016 2.024 5.443 0.000SOGS14$1 5.469 0.491 11.136 0.000SOGS15$1 5.542 0.497 11.143 0.000

VariancesTHETA 1.000 0.000 999.000 999.000



Handout #3: Test Construction with Item Response Models

To demonstrate how test construction functions with Item Response Models we will use the Gambling Research Instrument.

Analysis Data Set:

41 item Gambling Research Instrument o All 41 items use 6‐point Likert Scale

1192 College Students; 112 Experienced Gamblers


Gambling 20

APPENDIX A

NOTE: Items in Bold are kept in instrument for analysis and the criterion number is listed in parentheses at the end of each item.

GAMBLING RESEARCH INSTRUMENT (PART I) Please read the statements below and indicate how much you agree or disagree with each one. Write a number from the following rating scale beside each statement.

1 2 3 4 5 6 Strongly Disagree

Disagree Slightly Disagree

Slightly Agree

Agree Strongly Agree

1.____ I would like to cut back on my gambling. (3)

2.____ There are few things I would rather do than gamble. (1)

3.____ If I lost a lot of money gambling one day, I would be more likely to want to play again the

following day. (6)

4.____ I enjoy talking with my family and friends about my past gambling experiences. (7R)

5.____ I find it necessary to gamble with larger amounts of money (than when I first gambled) for

gambling to be exciting. (2)

6.____ I have gone to great lengths to obtain money for gambling. (8)

7.____ I feel “high” when I gamble. (5)

8.____ I worry that I am spending too much money gambling. (3)

9.____ I feel restless when I try to cut down or stop gambling. (4)

10.____ It bothers me when I have no money to gamble. (1)

11.____ I gamble to take my mind off my worries. (5)

12.____ When I lose money gambling, it is a long time before I gamble again. (6R)

13.____ I find it difficult to stop gambling. (3)

14.____ I am drawn more by the thrill of gambling than by the money I could win. (2)

15.____ I am private about my gambling experiences. (7)

16.____ I am ashamed of the things I’ve done to obtain money for gambling. (8)

17.____ Gambling helps me to feel less anxious. (5)

18.____ My family, coworkers, or others who are close to me disapprove of my gambling. (9)

19.____ I would like to stop gambling. (3)

20.____ When gambling, I have an amount of money in mind that I am willing to lose, and I stop if

I reach that point. (6R) R indicates a reverse item

21.____ It is hard to get my mind off gambling. (1)

22.____ Gambling has hurt my financial situation. (10)

23.____ I gamble to improve my mood. (5)

24.____ I worry that I am spending too much time gambling. (3)


Gambling 21

GAMBLING RESEARCH INSTRUMENT (PART II) For the previous set of questions, you were asked to indicate how much you agreed or disagreed with each statement. For the set of items below, please indicate how often the following things occur or have occurred during the past 12 months. Write a number from the following rating scale beside each statement.

1 2 3 4 5 6 Never Rarely Occasionally Sometimes Often Very

Frequently

1.____ I have gambled with money that I intended to spend on something else. (10)

2.____ I think about gambling. (1)

3.____ I make larger bets than I did when I first started gambling. (2)

4.____ I have gotten into trouble over things I have done to finance my gambling. (8)

5.____ I have attempted to cut back on my gambling. (3)

6.____ I have arguments with others about my gambling. (9)

7.____ I become irritable when I am unable to gamble. (4)

8.____ After losing money, I gamble again to win it back. (6)

9.____ I have gotten into financial trouble because of gambling. (10)

10.____ I think about ways to get money for gambling. (1)

11.____ I have lied to family or friends about my gambling. (7)

12.____ I have been unsuccessful in past attempts to control my gambling. (3)

13.____ I have later been sorry about things I have done to obtain money for gambling. (8)

14.____ I have missed work, class, or other appointments because of gambling. (9)

15.____ I have spent more money gambling than I intended to. (3, 6)

16.____ I have borrowed money from others for gambling. (10)

17.____ I think about my past gambling experiences. (1)



Handout #4: Equating

To demonstrate equating with Item Response Models (DCMs) can be estimated using Mplus, we will combine scales: the SOGS and the GRI

Analysis Data Sets

12 item South Oaks Gambling Screen o 3 items with multiple response options; 9 binary (yes/no) items

41 item Gambling Research Instrument o All 41 items use 6‐point Likert Scale


Documents

ICPSR IRT Workshop Handout - Jonathan Templin's Website · ICPSR IRT Workshop Handout #1 1 ... Category 2 0.310 166.000 X20 ... IRT PARAMETERIZATION IN TWO-PARAMETER LOGISTIC METRIC