The use of finite mixture modelling (FMM) is becoming increasingly popular for the analysis of longitudinal repeated measures data. FMMs assist in identifying latent classes following similar paths of temporal development. This paper aims to address the confusion experienced by practitioners new to these methods by introducing the various available techniques, which includes an overview of their interrelatedness and applicability. Our focus will be on the commonly used model-based approaches which comprise latent class growth analysis (LCGA), group-based trajectory models (GBTM), and growth mixture modelling (GMM). We discuss criteria for model selection, highlight often encountered challenges and unresolved issues in model fitting, showcase model availability in software, and illustrate a model selection strategy using an applied example.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Reliability of measurement instruments providing quantitative outcomes is usually assessed by an intraclass correlation coefficient. When participants are repeatedly measured by a single rater or device, or, are each rated by a different group of raters, the intraclass correlation coefficient is based on a one-way analysis of variance model. When planning a reliability study, it is essential to determine the number of participants and measurements per participant (i.e. number of raters or number of repeated measurements). Three different sample size determination approaches under the one-way analysis of variance model were identified in the literature, all based on a confidence interval for the intraclass correlation coefficient. Although eight different confidence interval methods can be identified, Wald confidence interval with Fisher’s large sample variance approximation remains most commonly used despite its well-known poor statistical properties. Therefore, a first objective of this work is comparing the statistical properties of all identified confidence interval methods—including those overlooked in previous studies. A second objective is developing a general procedure to determine the sample size using all approaches since a closed-form formula is not always available. This procedure is implemented in an R Shiny app. Finally, we provide advice for choosing an appropriate sample size determination method when planning a reliability study.

The I-Change Model for explaining motivational and behavioral change postulates that an awareness phase precedes the motivation phase of a person, and that effects of pre-motivational factors on behavior are partially mediated by motivational factors. This study tests this assumption with regard to physical activity. Observational longitudinal survey study (baseline, three months, six months) amongst Dutch adults (N = 2434). Structural equation modelling was used to investigate whether the influence of (1) knowledge, (2) cognizance, (3) cues, and (4) risk perception separately on intention and physical activity were mediated by motivational factors (i.e. attitudes, self-efficacy and social influence). Subsequently, a comprehensive model including all pre-motivational factors was estimated to test the same assumption for all pre-motivational factors simultaneously. The results indicate that the associations of cognizance, risk perception and cues with behavior were fully mediated by motivational factors when tested separately. When tested simultaneously only the effect of cognizance remained. Cognizance was most strongly associated with positive attitudes β = .13, p < .01, self-efficacy β = .13, p < .01, and intention β = .14, p < .01. No direct link with behavior was found. The results suggest that pre-motivational factors are important to form a motivation; however, they do not directly influence behavior. The inclusion of factors such as risk perception and cognizance would help to get a better understanding of motivation formation and behavior.