Many Classes, Restricted Measurement (MACREM) Models for Improved Measurement of Activities of Daily Living

Scientists use latent class (LC) models to identify subgroups in heterogeneous data. LC models reduce an item set to a latent variable and estimate measurement error. Researchers typically use unrestricted LC models, which have many measurement estimates, yet scientific interest primarily concerns the classes. We present highly restricted LC measurement models as an alternate method of operationalization. MACREM (Many Classes, Restricted Measurement) models have a larger number of latent classes than a typical unrestricted model, but many fewer measurement estimates. Goals of this approach include producing more interpretable classes and better measurement error estimates. Parameter constraints accomplish this structuring. We present unrestricted and MACREM model results using data on activities of daily living (ADL) from a national survey ( = 3485). We compare a four class unrestricted model with a 14 class MACREM model. The four class unrestricted model approximates a dimension of functional limitation. The 14 class model includes unordered classes at lower levels of limitation, but ordered classes at higher levels of limitation. In contrast to the four class model, all measurement error rates are reasonably small in the 14 class model. The four class model fits the data better, but the 14 class model is more parsimonious (43 vs. 25 parameters). Three covariates reveal specific associations with MACREM classes. In multinomial logistic regression models with a no limitation class as the reference class, past 12 month diabetes only distinguishes low limitation classes that include cutting one's own toenails as a limitation. It does not distinguish low limitation classes characterized by other common limitations. Past 12 month asthma and current disability status perform similarly, but for heavy housework and walking limitation classes, respectively. These limitation specific covariate associations are not apparent in the unrestricted model analyses. Identifying such connections could provide useful information to advance theory and intervention efforts.