Sarah Depaoli

Improving transparency and replication in Bayesian statistics: The WAMBS-Checklist.

Bayesian statistical methods are slowly creeping into all fields of science and are becoming ever more popular in applied research. Although it is very attractive to use Bayesian statistics, our personal experience has led us to believe that naively applying Bayesian methods can be dangerous for at least 3 main reasons: the potential influence of priors, misinterpretation of Bayesian features and results, and improper reporting of Bayesian results. To deal with these 3 points of potential danger, we have developed a succinct checklist: the WAMBS-checklist (When to worry and how to Avoid the Misuse of Bayesian Statistics). The purpose of the questionnaire is to describe 10 main points that should be thoroughly checked when applying Bayesian analysis. We provide an account of “when to worry” for each of these issues related to: (a) issues to check before estimating the model, (b) issues to check after estimating the model but before interpreting results, (c) understanding the influence of priors, and (d) actions to take after interpreting results. To accompany these key points of concern, we will present diagnostic tools that can be used in conjunction with the development and assessment of a Bayesian model. We also include examples of how to interpret results when “problems” in estimation arise, as well as syntax and instructions for implementation. Our aim is to stress the importance of openness and transparency of all aspects of Bayesian estimation, and it is our hope that the WAMBS questionnaire can aid in this process.

A systematic review of Bayesian articles in psychology: The last 25 years.

Although the statistical tools most often used by researchers in the field of psychology over the last 25 years are based on frequentist statistics, it is often claimed that the alternative Bayesian approach to statistics is gaining in popularity. In the current article, we investigated this claim by performing the very first systematic review of Bayesian psychological articles published between 1990 and 2015 (n = 1,579). We aim to provide a thorough presentation of the role Bayesian statistics plays in psychology. This historical assessment allows us to identify trends and see how Bayesian methods have been integrated into psychological research in the context of different statistical frameworks (e.g., hypothesis testing, cognitive models, IRT, SEM, etc.). We also describe take-home messages and provide “big-picture” recommendations to the field as Bayesian statistics becomes more popular. Our review indicated that Bayesian statistics is used in a variety of contexts across subfields of psychology and related disciplines. There are many different reasons why one might choose to use Bayes (e.g., the use of priors, estimating otherwise intractable models, modeling uncertainty, etc.). We found in this review that the use of Bayes has increased and broadened in the sense that this methodology can be used in a flexible manner to tackle many different forms of questions. We hope this presentation opens the door for a larger discussion regarding the current state of Bayesian statistics, as well as future trends.

A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes

Multilevel Structural equation models are most often estimated from a frequentist framework via maximum likelihood. However, as shown in this article, frequentist results are not always accurate. Alternatively, one can apply a Bayesian approach using Markov chain Monte Carlo estimation methods. This simulation study compared estimation quality using Bayesian and frequentist approaches in the context of a multilevel latent covariate model. Continuous and dichotomous variables were examined because it is not yet known how different types of outcomes—most notably categorical—affect parameter recovery in this modeling context. Within the Bayesian estimation framework, the impact of diffuse, weakly informative, and informative prior distributions were compared. Findings indicated that Bayesian estimation may be used to overcome convergence problems and improve parameter estimate bias. Results highlight the differences in estimation quality between dichotomous and continuous variable models and the importance of prior distribution choice for cluster-level random effects.

Iteration of Partially Specified Target Matrices: Applications in Exploratory and Bayesian Confirmatory Factor Analysis.

We describe and evaluate a factor rotation algorithm, iterated target rotation (ITR). Whereas target rotation (Browne, 2001) requires a user to specify a target matrix a priori based on theory or prior research, ITR begins with a standard analytic factor rotation (i.e., an empirically informed target) followed by an iterative search procedure to update the target matrix. In Study 1, Monte Carlo simulations were conducted to evaluate the performance of ITR relative to analytic rotations from the Crawford-Ferguson family with population factor structures varying in complexity. Simulation results: (a) suggested that ITR analyses will be particularly useful when evaluating data with complex structures (i.e., multiple cross-loadings) and (b) showed that the rotation method used to define an initial target matrix did not materially affect the accuracy of the various ITRs. In Study 2, we: (a) demonstrated the application of ITR as a way to determine empirically informed priors in a Bayesian confirmatory factor analysis (BCFA; Muthén & Asparouhov, 2012) of a rater-report alexithymia measure (Haviland, Warren, & Riggs, 2000) and (b) highlighted some of the challenges when specifying empirically based priors and assessing item and overall model fit.

The Impact of Inaccurate “Informative” Priors for Growth Parameters in Bayesian Growth Mixture Modeling

Within Bayesian estimation, prior distributions are placed on model parameters and these distributions can take on many different levels of informativeness. Although much of the research conducted within this estimation framework uses what are called diffuse (or noninformative) priors, there are certain models and modeling circumstances where it is more optimal to use what are referred to as informative priors. This study focuses on the latter situation and examines the effects of inaccurate informative priors on the growth parameters within the context of growth mixture modeling. Overall, results indicated that growth mixture modeling is relatively robust to the use of inaccurate mean hyperparameters for the growth parameters, as long as the variance hyperparameters are somewhat large.

The GRoLTS-Checklist: Guidelines for Reporting on Latent Trajectory Studies

Estimating models within the mixture model framework, like latent growth mixture modeling (LGMM) or latent class growth analysis (LCGA), involves making various decisions throughout the estimation process. This has led to a wide variety in how results of latent trajectory analysis are reported. To overcome this issue, using a 4-round Delphi study, we developed Guidelines for Reporting on Latent Trajectory Studies (GRoLTS). The purpose of GRoLTS is to present criteria that should be included when reporting the results of latent trajectory analysis across research fields. We have gone through a systematic process to identify key components that, according to a panel of experts, are necessary when reporting results for trajectory studies. We applied GRoLTS to 38 papers where LGMM or LCGA was used to study trajectories of posttraumatic stress after a traumatic event.

Using subscales when scoring the Cushing's quality of life questionnaire

CONTEXT Patients in long-term remission of Cushings syndrome (CS) commonly report impaired quality of life (QoL). The CushingQoL questionnaire is a disease-specific QoL questionnaire for patients diagnosed with CS. The developers of the CushingQoL recommend using a global (total) score to assess QoL. However, the global score does not capture all aspects of QoL as outlined by the World Health Organization (WHO). OBJECTIVE The aim of the study was to compare the performance of different scoring options to determine the optimal method for the CushingQoL. DESIGN AND PATIENTS Patients in remission from CS (n=341) were recruited from the Cushings Syndrome Research Foundations email listserv and Facebook page, and asked to complete the CushingQoL and a short demographics survey. RESULTS Using an exploratory analysis, adequate model fit was obtained for the global score, as well as a 2-subscale (psychosocial issues and physical problems) scoring solution. Confirmatory methods were performed to identify the optimal scoring solution. Both the global score and the 2-subscale scoring solution showed adequate model fit. However, a χ(2) difference test indicated that the 2-subscale scoring solution was a significantly better fit than the global score (P<0.05). CONCLUSION If doctors or researchers would like to tease apart physical and psychosocial issues, the 2-subscale scoring solution would be recommended, since this solution showed to be optimal in scoring the CushingQoL. Regardless of the scoring solution used, the CushingQoL has proven to be a valuable resource for assessing health-related QoL in patients with CS.

An Introduction to Bayesian Statistics in Health Psychology

ABSTRACT The aim of the current article is to provide a brief introduction to Bayesian statistics within the field of health psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation models, latent growth curve (and mixture) models, and hierarchical linear models. Likewise, Bayesian methods can be used with small sample sizes since they do not rely on large sample theory. In this article, we discuss several important components of Bayesian statistics as they relate to health-based inquiries. We discuss the incorporation and impact of prior knowledge into the estimation process and the different components of the analysis that should be reported in an article. We present an example implementing Bayesian estimation in the context of blood pressure changes after participants experienced an acute stressor. We conclude with final thoughts on the implementation of Bayesian statistics in health psychology, including suggestions for reviewing Bayesian manuscripts and grant proposals. We have also included an extensive amount of online supplementary material to complement the content presented here, including Bayesian examples using many different software programmes and an extensive sensitivity analysis examining the impact of priors.

Just Another Gibbs Sampler (JAGS): Flexible Software for MCMC Implementation.

A review of the software Just Another Gibbs Sampler (JAGS) is provided. We cover aspects related to history and development and the elements a user needs to know to get started with the program, including (a) definition of the data, (b) definition of the model, (c) compilation of the model, and (d) initialization of the model. An example using a latent class model with large-scale education data is provided to illustrate how easily JAGS can be implemented in R. We also cover details surrounding the many programs implementing JAGS. We conclude with a discussion of the newest features and upcoming developments. JAGS is constantly evolving and is developing into a flexible, user-friendly program with many benefits for Bayesian inference.

Gender role orientation is associated with health-related quality of life differently among African-American, Hispanic, and White youth

AbstractPurpose This study examined the association between gender role orientation (GRO) and health-related quality of life (HRQOL) in youth, and how this relationship may differ between males and females as well as among African-American, White, and Hispanic individuals. GRO has been reported to influence serious health outcomes including cancer, heart disease, mental illness, and mortality rates. However, few studies have examined the link between GRO and health outcomes for children, even though gender identity is formed in childhood.MethodsData were examined from 4824 participants in the Healthy Passages™ project, a population-based survey of fifth-grade children in three US metropolitan areas. Children reported their own HRQOL using the PedsQL and degree of female, male, and androgynous GRO using the Children’s Sex Role Inventory.ResultsBased on structural equations analysis, male GRO was positively associated with HRQOL for all racial/ethnic groups, regardless of sex, whereas female GRO was associated with better HRQOL for Hispanic and White females and poorer HRQOL for Hispanic males. Androgynous GRO was associated with better HRQOL among Hispanic and White females, but not males nor African-Americans of either sex.ConclusionsRacial/ethnic differences emerged for female and androgynous, but not male, GROs. Hispanic males are the only group for which GRO (female) was associated with poorer HRQOL. Future research should find ways to help youth overcome negative effects on health from gender beliefs and behavior patterns with sensitivity to racial/ethnic membership.