Latent class analysis is a clustering method that is nowadays widely used in social science research. Researchers applying latent class analysis will typically not only construct a typology based on a set of observed variables but also investigate how the encountered clusters are related to other, external variables. Although it is possible to incorporate such external variables into the latent class model itself, researchers usually prefer using a three-step approach. This is the approach wherein after establishing the latent class model for clustering (step 1), one obtains predictions for the class membership scores (step 2) and subsequently uses these predicted scores to assess the relationship between class membership and other variables (step 3). Bolck, Croon, and Hagenaars (2004) showed that this approach leads to severely downward-biased estimates of the strength of the relationships studied in step 3. These authors and later also Vermunt (2010) developed Downloaded from methods to correct for this bias. In the current study, we extended these correction methods to situations where class membership is not predicted but used as an explanatory variable in the third step, a situation widely encountered in social science applications. A simulation study tested the performance of the proposed correction methods, and their practical use was illustrated with real data examples. The results showed that also when the latent class variable is used as a predictor of external variables, the uncorrected three-step approach leads to severely biased estimates. The proposed correction methods perform well under conditions encountered in practice.

BackgroundThe patient assessment of chronic illness care (PACIC) is a promising instrument to evaluate the chronic care experiences of patients, yet additional validation is needed to improve its usefulness.Methods A total of 1941 patients with diabetes completed the questionnaire. Reliability coefficients and factor analyses were used to psychometrically test the PACIC and PACIC+ (i.e. PACIC extended with six additional multidisciplinary team functioning items to improve content validity). Intra-class correlations were computed to identify the extent to which variation in scores can be attributed to GP practices.Results The PACIC and PACIC+ showed a good psychometric quality (Cronbach’s alpha’s >0.9). Explorative factor analyses showed inconclusive results. Confirmative factor analysis showed that none of the factor structures had an acceptable fit (RMSEA>0.10). In addition, 5.1 to 5.4% of the total variation was identified at the GP practice level.ConclusionThe PACIC and PACIC+ are reliable instruments to measure the chronic care management experiences of patients. The PACIC+ is preferred because it also includes multidisciplinary coordination and cooperation—one of the central pillars of chronic care management—with good psychometric quality. Previously identified subscales should be used with caution. Both PACIC instruments are useful in identifying GP practice variation.

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Latent class (LC) analysis is used to construct empirical evidence on the existence of latent subgroups based on the associations among a set of observed discrete variables. One of the tests used to infer about the number of underlying subgroups is the bootstrap likelihood ratio test (BLRT). Although power analysis is rarely conducted for this test, it is important to identify, clarify, and specify the design issues that influence the statistical inference on the number of latent classes based on the BLRT. This paper proposes a computationally efficient 'short-cut' method to evaluate the power of the BLRT, as well as presents a procedure to determine a required sample size to attain a specific power level. Results of our numerical study showed that this short-cut method yields reliable estimates of the power of the BLRT. The numerical study also showed that the sample size required to achieve a specified power level depends on various factors of which the class separation plays a dominant role. In some situations, a sample size of 200 may be enough, while in others 2000 or more subjects are required to achieve the required power.

Latent class (LC) analysis is used by social, behavioral, and medical science researchers among others as a tool for clustering (or unsupervised classification) with categorical response variables, for analyzing the agreement between multiple raters, for evaluating the sensitivity and specificity of diagnostic tests in the absence of a gold standard, and for modeling heterogeneity in developmental trajectories. Despite the increased popularity of LC analysis, little is known about statistical power and required sample size in LC modeling. This paper shows how to perform power and sample size computations in LC models using Wald tests for the parameters describing association between the categorical latent variable and the response variables. Moreover, the design factors affecting the statistical power of these Wald tests are studied. More specifically, we show how design factors which are specific for LC analysis, such as the number of classes, the class proportions, and the number of response variables, affect the information matrix. The proposed power computation approach is illustrated using realistic scenarios for the design factors. A simulation study conducted to assess the performance of the proposed power analysis procedure shows that it performs well in all situations one may encounter in practice.