The Lazy Bootstrap. A Fast Resampling Method for Evaluating Latent Class Model Fit

The latent class model is a powerful unsupervised clustering algorithm for categorical data. Many statistics exist to test the fit of the latent class model. However, traditional methods to evaluate those fit statistics are not always useful. Asymptotic distributions are not always known, and empirical reference distributions can be very time consuming to obtain. In this paper we propose a fast resampling scheme with which any type of model fit can be assessed. We illustrate it here on the latent class model, but the methodology can be applied in any situation. The principle behind the lazy bootstrap method is to specify a statistic which captures the characteristics of the data that a model should capture correctly. If those characteristics in the observed data and in model-generated data are very different we can assume that the model could not have produced the observed data. With this method we achieve the flexibility of tests from the Bayesian framework, while only needing maximum likelihood estimates. We provide a step-wise algorithm with which the fit of a model can be assessed based on the characteristics we as researcher find important. In a Monte Carlo study we show that the method has very low type I errors, for all illustrated statistics. Power to reject a model depended largely on the type of statistic that was used and on sample size. We applied the method to an empirical data set on clinical subgroups with risk of Myocardial infarction and compared the results directly to the parametric bootstrap. The results of our method were highly similar to those obtained by the parametric bootstrap, while the required computations differed three orders of magnitude in favour of our method.Comment: This is an adaptation of chapter of a PhD dissertation available at https://pure.uvt.nl/portal/files/19030880/Kollenburg_Computer_13_11_2017.pd

Structural Equation Modeling for Description, Prediction, and Causation

Structural equation modeling (``SEM'' for short) is a widely applicable statistical analysis framework that is popular among psychological scientists (and related disciplines). Valid use of this technique requires researchers to clearly distinguish the type of research they are interested in. Scientific research can roughly be divided into descriptive, predictive, and causal research, and each type of research has different implications for the analysis strategy. One of the problems, however, is that this distinction is often implicit in psychological research. Therefore, it can be unclear whether research results actually answer the research question. In Chapters 2 through 5, I collaborate with applied researchers on both descriptive and predictive research projects, and I describe extensions of one specific popular longitudinal SEM model. Moreover, many alternative analytical techniques have been in disciplines such as epidemiology and biostatistics for causal research. These methods are still largely unknown among psychological researchers. In Chapters 6 and 7, I therefore compare popular SEM models with analytical techniques from biostatistics for longitudinal and observational causal research: which method performs better and under what conditions? Furthermore, I introduce psychological researchers to these alternative causal methods, and discuss if they can be applied in the context of psychological research

Power Analysis for the Random Intercept Cross-Lagged Panel Model Using the powRICLPM R-Package

The random intercept cross-lagged panel model (RI-CLPM) is a popular model among psychologists for studying reciprocal effects in longitudinal panel data. Although various texts and software packages have been published concerning power analyses for structural equation models (SEM) generally, none have proposed a power analysis strategy that is tailored to the particularities of the RI-CLPM. This can be problematic because mismatches between the power analysis design, the model, and reality, can negatively impact the validity of the recommended sample size and number of repeated measures. As power analyses play an increasingly important role in the preparation phase of research projects, an RI-CLPM-specific strategy for the design of a power analysis is detailed, and implemented in the R-package powRICLPM. This paper focuses on the (basic) bivariate RI-CLPM, and extensions to include constraints over time, measurement error (leading to the stable trait autoregressive trait state model), non-normal data, and bounded estimation

Inlet Sluices for Flood Control Areas with Controlled Reduced Tide in the Scheldt Estuary: an Overview

Weirs and Gate

Behavioral and neural responses to social rejection: Individual differences in developmental trajectories across childhood and adolescence

Dealing with social rejection is challenging, especially during childhood when behavioral and neural responses to social rejection are still developing. In the current longitudinal study, we used a Bayesian multilevel growth curve model to describe individual differences in the development of behavioral and neural responses to social rejection in a large sample (n > 500). We found a peak in aggression following negative feedback (compared to neutral feedback) during late childhood, as well as individual differences during this developmental phase, possibly suggesting a sensitive window for dealing with social rejection across late childhood. Moreover, we found evidence for individual differences in the linear development of neural responses to social rejection in our three brain regions of interest: The anterior insula, the medial prefrontal cortex, and the dorsolateral prefrontal cortex. In addition to providing insights in the individual trajectories of dealing with social rejection during childhood, this study also makes a meaningful methodological contribution: Our statistical analysis strategy (and online supplementary information) can be used as an example on how to take into account the many complexities of developmental neuroimaging datasets, while still enabling researchers to answer interesting questions about individual-level relationships

Oral vitamins C and E as additional treatment in patients with acute anterior uveitis

AIM: To investigate the effect of additional oral vitamins C and E on acute anterior uveitis. METHODS: A placebo controlled double masked study on the effect of vitamin C 500 mg in combination wit