LATENT CLASS ANALYSIS: A GENERAL SOLUTION AND AN EMPIRICAL EVALUATION

An application of Measurement error evaluation using latent class analysis

‎Latent class analysis (LCA) is a method of evaluating non sampling errors‎, ‎especially measurement error in categorical data‎. ‎Biemer (2011) introduced four latent class modeling approaches‎: ‎probability model parameterization‎, ‎log linear model‎, ‎modified path model‎, ‎and graphical model using path diagrams‎. ‎These models are interchangeable‎. ‎Latent class probability models express l...

Data envelopment analysis in service quality evaluation: an empirical study

Service quality is often conceptualized as the comparison between service expectations and the actual performance perceptions. It enhances customer satisfaction, decreases customer defection, and promotes customer loyalty. Substantial literature has examined the concept of service quality, its dimensions, and measurement methods. We introduce the perceived service quality index (PSQI) as a sing...

Latent Growth Modeling and Latent Class Analysis

Geared towards capturing change, longitudinal research is able to provide insight into a variety of phenomena of interest to IS researchers, especially IT adoption and use. However, its potential is constrained by the data analysis methods typically used. In this paper, I introduce an advanced technique – Latent Curve Modeling – and demonstrate how this technique supports longitudinal data anal...

Latent Trait Latent Class Analysis of an Eysenck Personality Questionnaire

In this paper two scales of a personality questionnaire, extraversion and neuroticism, are analyzed using a family of latent trait latent class models. The items are responded to in one of three categories: “yes”, “?”, or “no”. The results show that one single measurement model does not suffice in describing the data. More specifically, the meaning of the “?” response category is not invariant ...

Latent Class Cluster Analysis