A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research

By R. van de Schoot, David L. Kaplan, Jaap J. A. Denissen, Jens B. Asendorpf, Franz J. Neyer, M. V. van Aken. Child Development, 2013

741 citations recorded by the source corpus.

Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First, the ingredients underlying Bayesian methods are introduced using a simplified example. Thereafter, the advantages and pitfalls of the specification of prior knowledge are discussed. To illustrate Bayesian methods explained in this study, in a second example a series of studies that examine the theoretical framework of dynamic interactionism are considered. In the Discussion the advantages and disadvantages of using Bayesian statistics are reviewed, and guidelines on how to report on Bayesian statistics are provided.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b32ce0fd2e25f90c6baf79382a58ef913efbfd06

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Ten Frequently Asked Questions About Latent Class Analysis (2018, neutral): “Bayesian estimation methods are gaining popularity in the social sciences (van de Schoot et al., 2014) and can be used as an alternative to ML to estimate LCA models.”
Effects of Mindfulness-Acceptance-Commitment (MAC) on Sport-Specific Dispositional Mindfulness, Emotion Regulation, and Self-Rated Athletic Performance in a Multiple-Sport Population: an RCT Study (2019, result): “Using different prior specifications can potentially lead to different results; hence, we followed recommendations in the literature (e.g., Van de Schoot et al. 2014) and performed a sensitivity analysis.”
On Using Bayesian Methods to Address Small Sample Problems (2016, methodology): “…that MCMCmethods are better equipped to model data with small samples because MCMC does not rely on asymptotics in the same way that frequentist methods do (Depaoli & van de Schoot, 2015; Dunson, 2001; Kadane, 2015; Lee & Song, 2004; Muthén & Asparouhov, 2012; van de Schoot et al., 2014).”
Modeling Clustered Data with Very Few Clusters (2016, methodology): “For further expository details onMCMC estimation, readers are referred to Kruschke, Aguinis, and Joo (2012), van de Schoot et al. (2014), or Zyphur, Oswald, and Rupp (2015).”
Multilevel Mediation With Small Samples: A Cautionary Note on the Multilevel Structural Equation Modeling Framework (2017, methodology): “For readers unfamiliar with Bayesian methods, readable introductions can be found in Kruschke, Aguinis, and Joo (2012), van de Schoot et al. (2014), and Zyphur and Oswald (2015). simple fix for small sample problems (much of this work pertains to growth models and might not have much traction for…”
Small Sample Size Solutions (2020, methodology): “In order to incorporate the teacher’s knowledge in our estimate of Ti, we can make use of the Bayesian toolbox (see Chapter 1, Miočević, Levy, and Van de Schoot; as well as Kaplan & Depaoli, 2013; Kruschke, 2014; How to improve the estimation 113 Van de Schoot et al., 2014).”
Bayesian alternatives to null-hypothesis significance testing for repeated-measures designs (2016, background): “Another important alternative that is gaining traction is the Bayesian approach to model comparison and estimation (e.g., Kruschke, 2011, 2013; Rouder, Morey, Speckman, & Province, 2012; Schoot et al., 2014 and Wagenmakers, 2007).”
A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes (2015, methodology): “For more information on Bayesian analysis, we refer the reader to Kaplan and Depaoli (2013), Rupp, Dey, and Zumbo (2004), and van de Schoot et al. (2014) for a simple introduction; and Gelman, Carlin, Stern, & Rubin, (2004) for a more technical treatment of the topic.”
Reducing bias due to systematic attrition in longitudinal studies: The benefits of multiple imputation (2014, methodology): “Alternatively Bayesian estimation can be used that is however outside of the scope of this article (see van de Schoot et al., 2014, for a gentle introduction).”