A model is proposed that combines the theoretical
strength of the Rasch model with the heuristic
power of latent class analysis. It assumes that the
Rasch model holds for all persons within a latent
class, but it allows for different sets of item
parameters between the latent classes. An estimation
algorithm is outlined that gives conditional
maximum likelihood estimates of item parameters
for each class. No a priori assumption about the
item order in the latent classes or the class sizes is
required. Application of the model is illustrated,
both for simulated data and for real data. Index
terms: conditional likelihood, EM algorithm, latent
class analysis, Rasch model.
Rost, Jurgen. (1990). Rasch models in latent classes: An integration of two approaches to item analysis. Applied Psychological Measurement, 14, 271-282. doi:10.1177/014662169001400305
Rasch models in latent classes: An integration of two approaches to item analysis.
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