Using LinLog and FACETS to model item components in the LLTM

The current study investigates the performance of two Rasch measurement programs and their parameter estimations on the linear logistic test model (LLTM; Fischer, 1973). These two programs, LinLog (Whitely & Nieh, 1981) and FACETS (Linacre, 2002), are used to investigate within-item complexity factors in a spatial memory measure tool. LinLog uses conditional maximum likelihood to estimate person and item parameters and is an LLTM specific program. FACETS is usually reserved for the many-facet Rasch model (MFRM; Linacre, 1989), however in the case of specifically designed within-item solution processes, a multifaceted approach makes good sense. It is possible to consider each dimension within the item as a separate facet, just as if there were multiple raters for each item. Simulations of 500 and 1000 persons expand the original data set (114 persons) to better examine each estimation technique. LinLog and FACETS analyses show strikingly similar results in both the simulation and original data conditions, indicating that the FACETS program produces accurate LLTM parameter estimates.