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Messages - gsbeglia

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Thanks Dan. The console version of Conquest is not running to completion. After running for several hours, it stopped while computing empirical errors.

I have a few questions and observations about the code you sent:

1) The GUI is mostly working for the code you sent as long as I run each model separately. When I run both models together, the three dimensional model runs fine and then after the itn file is produced for the one dimensional model, I get the error that "Matrices not the same dimension - can't add".

2) I'm confused as to the use of 0 in the score code. A 0 indicates missing data in my data set, which is why I left it out of my code statement. Must 0 be included in the code statement of a partial credit model? Should I change the missing data to something else instead? When i try to run this model with missing data scored as 9, the wle file is incorrect (raw total scores are very off) and the program crashes before generating the three dimensional shw file.

3) Also, I noticed that your three-dimensional model output says "At termination the solution was not the best attained solution. The reported results are for the earlier better solution. Rerunning this analysis using the current estimates as initial values is strongly advised." Did the model converge? Must I re-run the analysis in order to use the item and person scores in parametric statistics? How do I modify the code to do this?

Questions and Answers / Re: equivalent analyses for Winsteps and Conquest
« on: January 08, 2018, 04:39:23 PM »
Hi Dan,

I attached the two outputs for my dif analysis on race. The second output appears to not have converged well. Is it appropriate to subtract the item*race estimates from one another for each race and item and use the resulting number to determine the magnitude of dif? We have done something like this in Winsteps but I am not sure if the estimate values are equivalent.

Relatedly, it is possible that the data are three dimensional. How would I run a dif for three dimensional data? Would I run dif on each dimension separately?


title dif_race Partial credit model for DIF analysis for Race;

datafile pre_test_121917.dat;
format sex 12 race 13 responses 14-37;

labels <<labels_sex_and_race.lab;

model item+race+item*race+race*item*step;

estimate !fit=no,stderr=quick;
show !table=1:2 >> sample_race_dif_output.shw;
plot expected!gins=1:2, overlay=yes, legend=yes;


datafile pre_test_121917.dat;
format sex 12 race 13 responses 14-37;

labels << labels_sex_and_race.lab;

model item+race+item*race+item*step;

estimate !fit=no,stderr=empirical;
show !table=1:2 >> sample_race_dif_output.shw;
plot expected!gins=1:2, overlay=yes, legend=yes;

Hi Dan,

Thank you for the reply. I am using Conquest version 4.14.2 and I am running the GUI version. I have not tried running at the console but am running the code on it now to see if this fixes the problem.

When I ran the model requesting only the shw file (not the wle or itn file), I was able to get an output that looks similar to the one you sent and has a similar final deviance (105017.74117). However, the WLE person separation reliability is unavailable in my output even though it is available in your output of the same model.

Something that seems strange to me about the outcome of the three dimensional model is that the final deviance is much higher than for the one-dimensional model using the same data and the code below. I thought shouldn't happen because models with more parameters will always have better fit.

Title pre_test_121917 1D;
datafile pre_test_121917.dat;
format pid 1-11 responses 14-37;
codes 1,2,3,4,5;
Labels << labels_ISEA.lab;
Model item + item*step;
Estimate ;
itanal                          >> pre_121917_D1.itn;
show cases !estimate=wle        >> ipre_121917_D1.wle;
show !estimate=latent           >> pre_121917_D1.shw;

Questions and Answers / equivalent analyses for Winsteps and Conquest
« on: January 05, 2018, 08:19:16 PM »
My lab is in the process of trying to move from Winsteps to Conquest and we need to make sure we are running equivalent analyses in the two programs. The first question I have is about the ICC figures, which have different x axis labels and look somewhat different in Winsteps and Conquest (I am unable to attach a document with ICC figures because of their size but the probabilities for selecting each answer option are somewhat different for the two programs). I want to determine if both programs have the same axes and can be interpreted the same way. The  x-axis in Conquest is "latent trait (logits)" and in Winsteps the x-axis is "person measure minus item measure".

The second question I have is about determining dif. Using these item*gender estimates in ConQuest, I would like to apply the same cutoffs for determining the presence of dif that we have historically used for Winsteps outputs. In a Winsteps dif analysis, there are estimates given for each gender that we then subtract from one another to determine the magnitude of dif between males and females. Are these Winsteps estimates equivalent to the item*gender estimate that ConQuest generates? If so, can I subtract the item*gender estimates in ConQuest to determine the magnitude of dif?


Questions and Answers / Three dimensional partial credit model won't run
« on: January 05, 2018, 04:14:20 AM »
Hi all. I'm trying to run a three dimensional between item partial credit model for a 24 item instrument on ConQuest4. I was able to run the one dimensional model but when I try to run the three dimensional model, it runs for several hours, generates the itn and wle files and then ConQuest crashes. This three dimensional model worked fine on our old version of ConQuest (version 1). The syntax is below and the data set is attached. Any help would be much appreciated.


datafile pre_test_121917.dat;
format pid 1-11 responses 14-37;
codes 1,2,3,4,5;
set constraints=cases, warnings=no;
score (1,2,3,4,5) (1,2,3,4,5) ()           ()          !items(1-8);
score (1,2,3,4,5)   ()        (1,2,3,4,5)  ()          !items(9-16);
score (1,2,3,4,5) ()         ()            (1,2,3,4,5) !items(17-24);
Model item + item*step;
export parameters            >> pre_121917_D3.prm;
export reg_coefficients      >> pre_121917_D3.reg;
export covariance            >> ipre_121917_D3.cov;
Estimate !method=montecarlo, nodes=2000, converge = .005;
itanal                       >> pre_121917_D3.itn;
show cases !estimate=wle     >> pre_121917_D3.wle;
show !estimate=latent        >> pre_121917_D3.shw;
equivalence wle           >> pre_equivalence_121917_D3.wle;
plot icc;

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