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Latent Trait Models under IRT
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Applying scaling factor d (1.7) to apply the logistic item response model #16

Closed state-o-flux closed 3 years ago

state-o-flux commented 3 years ago

Hi Dr.Rizopoulos,

I'm wondering if there is any way to apply the scaling factor d to the item response models in ltm. Specifically I'm working with gpcm as I have dichotomous and polytomous items.

drizopoulos commented 3 years ago

It is not clear what you exactly mean.

—— Dimitris Rizopoulos Professor of Biostatistics Erasmus University Medical Center The Netherlands

From: state-o-flux notifications@github.com Sent: Sunday, January 10, 2021 6:05:24 PM To: drizopoulos/ltm ltm@noreply.github.com Cc: Subscribed subscribed@noreply.github.com Subject: [drizopoulos/ltm] Applying scaling factor d (1.7) to apply the logistic item response model (#16)

Hi Dr.Rizopoulos,

I'm wondering if there is any way to apply the scaling factor d to the item response models in ltm. Specifically I'm working with gpcm as I have dichotomous and polytomous items.

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state-o-flux commented 3 years ago

I've read that IRT models can scale their discrimination parameter by a factor of 1.7 in order to estimate the logistic item response model (as opposed to the normal ogive item response model). I'm wondering if there is a way to apply this to gpcm. I've noticed that the optim and nlminb functions both have parameters that can scale their optimization routines and wonder if it's possible to implement into the model writ large.

drizopoulos commented 3 years ago

No, unfortunately, this will not be possible in gpcm().

From: state-o-flux notifications@github.com Sent: Tuesday, January 12, 2021 12:50 AM To: drizopoulos/ltm ltm@noreply.github.com Cc: D. Rizopoulos d.rizopoulos@erasmusmc.nl; Comment comment@noreply.github.com Subject: Re: [drizopoulos/ltm] Applying scaling factor d (1.7) to apply the logistic item response model (#16)

I've read that IRT models can scale their discrimination parameter by a factor of 1.7 in order to estimate the logistic item response model (as opposed to the normal ogive item response model). I'm wondering if there is a way to apply this to gpcm. I've noticed that the optim and nlminb functions both have parameters that can scale their optimization routines and wonder if it's possible to implement into the model writ large.

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