Mplus vs Multilog in 2PL IRT

Mplus-Multilog-2PL

This is an example result of IRT analysis using MPLUS and Multilog.   DATASET 1. MPLUS Syntax & Output lsat-mplus.dat | lsat-multilog.MLG 2. MULTILOG  Syntax & Output lsat-multilog.dat | lsatmplus.inp MPLUS MPLUS SYNTAX TITLE:  Analisis Teori Respons Butir MPLUS (GRM)  DATA:   FILE IS lsat-mplus.dat; VARIABLE:   NAMES ARE a01-a05 freq;             FREQWEIGHT IS freq;             MISSING ARE ALL (-9);             USEVARIABLE ARE a01-a05;             CATEGORICAL ARE a01-a05; ANALYSIS:   ESTIMATOR IS ML;             LINK IS LOGIT;  MODEL:   F1 BY a01-a05*; !F1 BY a01@1 a02-a05;     [a01$1-a05$1*] [F1@0]; F1@1; OUTPUT:     STAND; PLOT:   TYPE IS PLOT1; ! Perintah Plot untuk deskriptif         TYPE IS PLOT2; ! Perintah Plot untuk kurva IRT         TYPE IS PLOT3; ! Perintah Plot untuk nilai theta   MPLUS OUTPUT. MODEL RESULTS                                                     Two-Tailed                     Estimate       S.E.  Est./S.E.    P-Value  F1       BY     A01                0.825      0.258      3.198      0.001     A02                0.722      0.186      3.872      0.000     A03                0.891      0.233      3.826      0.000     A04                0.688      0.185      3.718      0.000     A05                0.659      0.210      3.133      0.002  Means     F1                 0.000      0.000    999.000    999.000  Thresholds     A01$1             -2.773      0.206    -13.482      0.000     A02$1             -0.990      0.090    -11.005      0.000     A03$1             -0.249      0.076     -3.266      0.001     A04$1             -1.285      0.099    -12.977      0.000     A05$1             -2.054      0.136    -15.148      0.000  Variances     F1                 1.000      0.000    999.000    999.000 IRT PARAMETERIZATION IN TWO-PARAMETER LOGISTIC METRIC WHERE THE LOGIT IS 1.7*DISCRIMINATION*(THETA - DIFFICULTY)  Item Discriminations  F1       BY     A01                0.486      0.152      3.198      0.001     A02                0.425      0.110      3.872      0.000     A03                0.524      0.137      3.826      0.000     A04                0.405      0.109      3.718      0.000     A05                0.387      0.124      3.133      0.002  Means     F1                 0.000      0.000      0.000      1.000  Item Difficulties     A01$1             -3.360      0.867     -3.875      0.000     A02$1             -1.371      0.308     -4.455      0.000     A03$1             -0.280      0.100     -2.807      0.005     A04$1             -1.868      0.435     -4.296      0.000     A05$1             -3.119      0.868     -3.595      0.000  Variances     F1                 1.000      0.000      0.000      1.000 MULTILOG Syntax Analysis MML PARAMETER ESTIMATION FOR THE 2PL MODEL, LSAT DATA >PROBLEM RANDOM, PATTERN, NITEMS=5, NGROUPS=1, NPATTERNS=32, DATA='lsat-multilog.DAT'; >TEST ALL, L2; >SAVE; >END; 2 01 11111 N (4X,5A1,F4.0) OUTPUT ANALYSIS ITEM 1: 2 GRADED CATEGORIES P(#) ESTIMATE (S.E.) A 1 0.82 (0.18) B( 1) 2 -3.36 (0.62) @THETA: INFORMATION: (Theta values increase in steps of 0.2) -3.0 - -1.6 0.166 0.161 0.154 0.146 0.136 0.126 0.115 0.104 -1.4 - 0.0 0.094 0.084 0.074 0.065 0.057 0.050 0.043 0.038 0.2 - 1.6 0.032 0.028 0.024 0.021 0.018 0.015 0.013 0.011 1.8 - 3.0 0.009 0.008 0.007 0.006 0.005 0.004 0.004 OBSERVED AND EXPECTED COUNTS/PROPORTIONS IN CATEGORY(K): 1 2 OBS. FREQ. 76 924 OBS. PROP. 0.0760 0.9240 EXP. PROP. 0.0760 0.9240 ITEM 2: 2 GRADED CATEGORIES P(#) ESTIMATE (S.E.) A 3 0.72 (0.11) B( 1) 4 -1.37 (0.21) @THETA: INFORMATION: (Theta values increase in steps of 0.2) -3.0 - -1.6 0.094 0.101 0.108 0.115 0.120 0.125 0.128 0.130 -1.4 - 0.0 0.131 0.131 0.129 0.126 0.122 0.116 0.110 0.104 0.2 - 1.6 0.097 0.089 0.082 0.075 0.068 0.061 0.055 0.049 1.8 - 3.0 0.044 0.039 0.034 0.030 0.027 0.023 0.020 OBSERVED AND EXPECTED COUNTS/PROPORTIONS IN CATEGORY(K): 1 2 OBS. FREQ. 291 709 OBS. PROP. 0.2910 0.7090 EXP. PROP. 0.2910 0.7090     COMPARISON   Multilog MPLUS a b a b 0.824 -3.363 0.825 -2.773 0.725 -1.367 0.722 -0.99 0.885 -0.281 0.891 -0.249 0.690 -1.862 0.688 -1.285 0.660 -3.113 0.659 -2.054

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