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.https://deepblue.lib.umich.edu/bitstream/2027.42/136925/1/A Systematic Review of Bayesian Articles in Psychology The Last 25 Years.pdfDescription of A Systematic Review of Bayesian Articles in Psychology The Last 25 Years.pdf : Main Articl

Bayesian statistics and modelling

Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. This Primer describes the stages involved in Bayesian analysis, from specifying the prior and data models to deriving inference, model checking and refinement. We discuss the importance of prior and posterior predictive checking, selecting a proper technique for sampling from a posterior distribution, variational inference and variable selection. Examples of successful applications of Bayesian analysis across various research fields are provided, including in social sciences, ecology, genetics, medicine and more. We propose strategies for reproducibility and reporting standards, outlining an updated WAMBS (when to Worry and how to Avoid the Misuse of Bayesian Statistics) checklist. Finally, we outline the impact of Bayesian analysis on artificial intelligence, a major goal in the next decade

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

PurposeThis 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

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

Assessing Differences in How the CushingQoL Is Interpreted Across Countries: Comparing Patients From the U.S. and the Netherlands.

Background: Cultural factors influence how individuals define, evaluate, and approach their quality of life (QoL). The CushingQoL is a widely used disease-specific questionnaire to assess QoL in patients with Cushing's syndrome. However, there is no information about potential cross-country differences in the way patients interpret the items on the CushingQoL. Thus, the current study examined if the CushingQoL is interpreted in the same way across nationalities. Methods: Patients from the U.S. (n = 260) and the Netherlands (n = 103) were asked to fill out the CushingQoL and a short demographics survey. Measurement invariance testing was utilized to explore whether or not the patient samples from the U.S. and the Netherlands interpreted items on the CushingQoL in the same way. Results: A two-subscale scoring approach was used for the CushingQoL. Model fit was good for the U.S. sample (e.g., CFI = 0.983; TLI = 0.979), as well as the Dutch sample (e.g., CFI = 0.971; TLI = 0.964). Invariance testing revealed that three of the 12 items on the CushingQoL were interpreted differently across the groups. These items are all related to psychosocial issues (e.g., irritable mood and worrying about one's health). Items assessing physical aspects of QoL did not vary across the U.S. and Dutch samples. Conclusions: Interpreting results from the CushingQoL requires careful consideration of country of residence, as this appears to impact the interpretation of the questionnaire

Latent Growth Curve Models for Biomarkers of the Stress Response

Objective: The stress response is a dynamic process that can be characterized by predictable biochemical and psychological changes. Biomarkers of the stress response are typically measured over time and require statistical methods that can model change over time. One flexible method of evaluating change over time is the latent growth curve model (LGCM). However, stress researchers seldom use the LGCM when studying biomarkers, despite their benefits. Stress researchers may be unaware of how these methods can be useful. Therefore, the purpose of this paper is to provide an overview of LGCMs in the context of stress research. We specifically highlight the unique benefits of using these approaches.Methods: Hypothetical examples are used to describe four forms of the LGCM.Results: The following four specifications of the LGCM are described: basic LGCM, latent growth mixture model, piecewise LGCM, and LGCM for two parallel processes. The specifications of the LGCM are discussed in the context of the Trier Social Stress Test. Beyond the discussion of the four models, we present issues of modeling nonlinear patterns of change, assessing model fit, and linking specific research questions regarding biomarker research using different statistical models.Conclusions: The final sections of the paper discuss statistical software packages and more advanced modeling capabilities of LGCMs. The online Appendix contains example code with annotation from two statistical programs for the LCGM