Item Response Theory/Rasch models in SPSS Statistics

The basic idea of IRT models is that there is an underlying trait - a skill or knowledge level or an attitude, for example, that is reflected in the response to the test or survey items. The probability of getting each item correct (or agreeing to it, in an attitude survey) is a function of the item's difficulty and the 'amount' of the underlying trait in the respondent. In building the test, you hope to find items with difficulty levels across the range of ability in your target population of testees. In 2-parameter models, the slope of the IRT function (discrimination) is the second parameter to estimate. 3-parameter models include a 'guessing' parameter, usually for multiple-choice items. Rasch models are 1-parameter models, but they are also based on a different philosophy of test analysis and construction than higher-parameter IRT models.

SPSS does not have any built-in procedures for IRT models (also known as latent trait models). As of this writing, much IRT research is still conducted with specialized IRT software, such as that produced by Scientific Software, Assessment Systems Corp. and Winsteps (for Rasch models), for example. An enhancement request has been filed with SPSS Development. However, the SPSS Community at IBM developerWorks provides a set of Extension commands that can be installed into the SPSS Statistics directory to extend the capabilities of the program. These extension commands require the programmability plug-ins for R and/or Python. there is a Rasch extension command available for SPSS versions 17 and above for users that have installed the SPSS Programmability module and the R and Python plugins. Click the "Download materials for IBM SPSS Statistics" link in the SPSS Community site to learn more about the programmability plug-ins and the extension commands.

As of this writing, there are three extension commands that apply IRT-based analyses. The commands and descriptions from the extensions page are:

1. SPSSINC RASCH - installed from the SPSSINC_RASCH.zip link in the extensions page.
R Extension Command for Rasch Models
Version: 1.1.0, Minimum Statistics Version: 17.0.0, Author: AR, IBM SPSS
As of Statistics Version 19, this package is included in the R Essentials.
This package provides a procedure and a dialog box interface for estimation of Rasch models.
It requires R, the R Plug-Ins and the R ltm package.

2. STATS IRM - installed from the STATS_IRM.zip link in the extensions page.
The STATS IRM command fits three-parameter item response models using the tpm function from the R ltm package. It is assumed that the values of the item variables are 0,1. By default, the procedure produces estimates of the model coefficients, and you can request optional output such as the item fit statistics, plots of the factor scores, and item characteristic curves, and save person-fit statistics to a new dataset.

3. STATS GRM - installed from the STATS_GRM.zip link in the extensions page.
This package fits the Graded Response model for ordinal polytomous data via an IRT approach. It requires R, the R plugin, and the R package ltm. Installation instructions are in the readme file.

All three of the above extension commands require at least version 17.0 of SPSS Statistics. The "Essentials and Plugins area of the "Downloads for IBM SPSS Statistics" page will help you install the programmability tools and the correct versions of Python and R for your version of SPSS Statistics.