Multilevel modeling in single-case studies with count and proportion data: A demonstration and evaluation.

The outcomes in single-case experimental designs (SCEDs) are often counts or proportions. In our study, we provided a colloquial illustration for a new class of generalized linear mixed models (GLMMs) to fit count and proportion data from SCEDs. We also addressed important aspects in the GLMM framework including overdispersion, estimation methods, statistical inferences, model selection methods by detecting overdispersion, and interpretations of regression coefficients. We then demonstrated the GLMMs with two empirical examples with count and proportion outcomes in SCEDs. In addition, we conducted simulation studies to examine the performance of GLMMs in terms of biases and coverage rates for the immediate treatment effect and treatment effect on the trend. We also examined the empirical Type I error rates of statistical tests. Finally, we provided recommendations about how to make sound statistical decisions to use GLMMs based on the findings from simulation studies. Our hope is that this article will provide SCED researchers with the basic information necessary to conduct appropriate statistical analysis of count and proportion data in their own research and outline the future agenda for methodologists to explore the full potential of GLMMs to analyze or meta-analyze SCED data. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Experimental elicitations of awe: a meta-analysis.

A meta-analytic review of studies that experimentally elicited awe and compared the emotion to other conditions (84; 487 effects; 17,801 participants) examined the degree to which experimentally elicited awe (1) affects outcomes relative to other positive emotions (2) affects experience, judgment, behaviour, and physiology, and (3) differs in its effects if the awe state was elicited through positive or threatening contexts. The efficacy of methods that have been used to experimentally elicit awe and the possibility of assessing changes in the state of the self with experimental awe elicitations were also examined. Meta-analyses with robust variance estimation revealed that awe affected outcomes compared to other positive emotions and control conditions; affected experience, judgment, and behaviour; and had similar effects if elicited through positive or threatening contexts. The ability to compare awe to negative emotion states and its effects on physiology was limited by a small number of available effects. Images, videos, autobiographical recall, and naturalistic exposure were effective in eliciting awe. Exploratory analyses suggested that some processes involved in changes in the self can be related to experimental awe elicitations. These findings suggest awe is a discrete emotion and identifies areas for future investigation.

Traumatic Life Experience and Pain Sensitization: Meta-analysis of Laboratory Findings.

OBJECTIVES: Psychological trauma often co-occurs with pain. This relationship has been explored using laboratory pain measures; however, findings have been mixed. Previous studies have limited operationalization of trauma (eg, posttraumatic stress disorder) or pain (eg, pain thresholds), which may contribute to conflicting results. Further, prior reviews likely underrepresent trauma experiences among people who are not receiving clinical care, limiting generalizability. MATERIALS AND METHODS: We systematically reviewed the existing literature on the relationship between psychological trauma (eg, car accidents, sexual assault, childhood abuse, neglect) and laboratory pain (ie, quantitative sensory testing measures of pain threshold, intensity, summation, modulation), using inclusive criteria. The direction of the relationship between psychological trauma and pain sensitivity was evaluated, and moderation by purported pain mechanism (ie, pain detection, suprathreshold pain, central sensitization, inhibition) was explored. RESULTS: Analyses were conducted using 48 studies that provided 147 effect sizes. A multivariate random-effects model with robust variance estimation resulted in a small but statistically significant overall effect size of g=0.24 (P=0.0002), reflecting a positive association between psychological trauma and enhanced laboratory pain sensitivity. Upon examination of mechanistic moderators, this relationship appears driven by effects on pain detection (g=0.28, P=0.002) and central sensitization (g=0.22, P=0.04). While effect sizes were similar across all moderators, effects on suprathreshold pain and inhibition were not statistically significant. DISCUSSION: Findings demonstrate an overall pattern of trauma-related pain enhancement and point to central sensitization as a key underlying mechanism.

Relations Between Adolescent Sensation Seeking and Risky Sexual Behaviors Across Sex, Race, and Age: A Meta-Analysis.

Sensation seeking (SS)-the seeking of novel and intense sensations or experiences and the willingness to take risks for the sake of such experiences-has been shown to be related to various risky sexual behaviors (RSBs) in areas such as multiple sexual partners, condom use, and sexual initiation. The aims of the current meta-analysis were to examine (1) how SS relates to specific RSBs in adolescents and (2) how the overall relationship between SS and RSB differs across sex, race, and age. Overall, a total of 40 studies met the inclusion criteria for our meta-analysis examining the relationship between SS and RSB, contributing 102 effect sizes. RSB variables included unprotected sex; multiple sexual partners; hazardous sexual activity; sexual initiation; virginity status; and history of sexually transmitted disease (STD) diagnosis. Moderating effects of sex, race, and age were also examined. The overall mean effect size of the correlational relationship between adolescent SS and RSB was statistically significant, as were the mean effect sizes of the relationships between SS and RSB subgroups, except for history of STD diagnosis. Race and age did not significantly moderate the overall relationship between SS and RSB; however, results indicated that SS and RSB relations were stronger in females compared to males. Our findings suggest that adolescents with elevations in SS tendencies tend to engage in more RSBs compared to their peers with lower levels of SS, increasing their risk of unplanned pregnancy and STD acquisition.

Using Bayesian Meta-Regression to Advance Prevention Science Research: an Introduction and Empirical Illustration.

Driven by the movement of evidence-based practices, Bayesian statistical methods have become increasingly popular. This paper introduces a Bayesian approach to meta-regression, focusing on the use and implementation in prevention science research. We first compare Bayesian meta-analysis and meta-regression to a frequentist approach. Thereafter, we illustrate Bayesian methods in meta-regression, highlighting advantages, providing detailed interpretation, and presenting results. The example is completed using several R packages. We also provide annotated R code for readers as a foundation for their own research.

Estimation and statistical inferences of variance components in the analysis of single-case experimental design using multilevel modeling.

Multilevel models (MLMs) can be used to examine treatment heterogeneity in single-case experimental designs (SCEDs). With small sample sizes, common issues for estimating between-case variance components in MLMs include nonpositive definite matrix, biased estimates, misspecification of covariance structures, and invalid Wald tests for variance components with bounded distributions. To address these issues, unconstrained optimization, model selection procedure based on parametric bootstrap, and restricted likelihood ratio test (RLRT)-based procedure are introduced. Using simulation studies, we compared the performance of two types of optimization methods (constrained vs. unconstrained) when the covariance structures are correctly specified or misspecified. We also examined the performance of a model selection procedure to obtain the optimal covariance structure. The results showed that the unconstrained optimization can avoid nonpositive definite issues to a great extent without a compromise in model convergence. The misspecification of covariance structures would cause biased estimates, especially with small between case variance components. However, the model selection procedure was found to attenuate the magnitude of bias. A practical guideline was generated for empirical researchers in SCEDs, providing conditions under which trustworthy point and interval estimates can be obtained for between-case variance components in MLMs, as well as the conditions under which the RLRT-based procedure can produce acceptable empirical type I error rate and power.

A penalization approach to random-effects meta-analysis.

Systematic reviews and meta-analyses are principal tools to synthesize evidence from multiple independent sources in many research fields. The assessment of heterogeneity among collected studies is a critical step when performing a meta-analysis, given its influence on model selection and conclusions about treatment effects. A common-effect (CE) model is conventionally used when the studies are deemed homogeneous, while a random-effects (RE) model is used for heterogeneous studies. However, both models have limitations. For example, the CE model produces excessively conservative confidence intervals with low coverage probabilities when the collected studies have heterogeneous treatment effects. The RE model, on the other hand, assigns higher weights to small studies compared to the CE model. In the presence of small-study effects or publication bias, the over-weighted small studies from a RE model can lead to substantially biased overall treatment effect estimates. In addition, outlying studies may exaggerate between-study heterogeneity. This article introduces penalization methods as a compromise between the CE and RE models. The proposed methods are motivated by the penalized likelihood approach, which is widely used in the current literature to control model complexity and reduce variances of parameter estimates. We compare the existing and proposed methods with simulated data and several case studies to illustrate the benefits of the penalization methods.

Bayesian Methods for Meta-Analyses of Binary Outcomes: Implementations, Examples, and Impact of Priors.

Bayesian methods are an important set of tools for performing meta-analyses. They avoid some potentially unrealistic assumptions that are required by conventional frequentist methods. More importantly, meta-analysts can incorporate prior information from many sources, including experts' opinions and prior meta-analyses. Nevertheless, Bayesian methods are used less frequently than conventional frequentist methods, primarily because of the need for nontrivial statistical coding, while frequentist approaches can be implemented via many user-friendly software packages. This article aims at providing a practical review of implementations for Bayesian meta-analyses with various prior distributions. We present Bayesian methods for meta-analyses with the focus on odds ratio for binary outcomes. We summarize various commonly used prior distribution choices for the between-studies heterogeneity variance, a critical parameter in meta-analyses. They include the inverse-gamma, uniform, and half-normal distributions, as well as evidence-based informative log-normal priors. Five real-world examples are presented to illustrate their performance. We provide all of the statistical code for future use by practitioners. Under certain circumstances, Bayesian methods can produce markedly different results from those by frequentist methods, including a change in decision on statistical significance. When data information is limited, the choice of priors may have a large impact on meta-analytic results, in which case sensitivity analyses are recommended. Moreover, the algorithm for implementing Bayesian analyses may not converge for extremely sparse data; caution is needed in interpreting respective results. As such, convergence should be routinely examined. When select statistical assumptions that are made by conventional frequentist methods are violated, Bayesian methods provide a reliable alternative to perform a meta-analysis.

An alternative approach to frequentist meta-analysis: A demonstration of Bayesian meta-analysis in adolescent development research.

INTRODUCTION: Past decades have seen a surge of applied and methodological research on meta-analysis. One methodological advancement that has gained significant traction is a Bayesian approach to meta-analysis. METHODS: We present a non-technical introduction to Bayesian meta-analysis. This introduction re-analyzes data from a meta-analysis concerning the impact of media literacy interventions on attitudes and intentions related to risky health behaviors using a Bayesian approach. One data relate media literacy interventions to media literacy skills, and another relates media literacy interventions to attitudes and behavioral intentions towards risky health behaviors. In these examples we focus on how to conduct unconditional models via graphical and quantitative results. Further, we demonstrate how to conduct subgroup analyses using risk behavior type (drinking, sexual, or smoking). RESULTS: We demonstrated how several meta-analytical quantities could be computed and interpreted in a Bayesian framework. This was done both graphically (plot of the marginal posterior distributions) and quantitatively (e.g., central tendency measures, highest posterior density intervals). Results also showed how analyzing effect sizes at the risk-behavior level could affect several interpretations. CONCLUSIONS: We emphasize that in no way are Bayesian methods "superior" to frequentist methods, nor that frequentist methods should be abandoned. Instead, the two approaches should be viewed as familial, each with advantages and disadvantages, but strive at a common purpose. We hope for increased use of Bayesian meta-analyses, and Bayesian methodology at large, in adolescence research. Last, all R code is provided for readers to use as a foundation for their own research.

Assessing the relationship between youth religiosity and their alcohol use: A meta-analysis from 2008 to 2018.

OBJECTIVE: Underage alcohol use, and associated deleterious consequences, persists as a serious public health issue. In particular, early initiation of alcohol use increases risk for the development of alcohol use disorders later on in life. Religiosity - a multidimensional construct, encompassing personal beliefs, commitments, practices, and public behaviors - has demonstrated a strong protective effect on alcohol consumption; as one's religiosity increases their alcohol use behaviors decrease. This meta-analysis includes research spanning years 2008-2018, and specifically examines whether measuring religiosity via a single dimension, as compared to multiple dimensions, impacts the association between alcohol use and religiosity. METHOD: A systematic electronic database search spanning three databases using relevant key terms was conducted. Overall, 16 studies were deemed appropriate for subsequent analyses. Effect sizes were calculated, homogeneity of effect sizes was assessed, overall weighted effects were computed, and moderator analyses were conducted to examine the effects of study-level characteristics on the variability of effect sizes. RESULTS: Religiosity demonstrated a statistically significant protective effect on adolescent alcohol use (Z = -0.21, p < .001). Measurement of religiosity (i.e., unidimensional versus multidimensional) explained a statistically significant amount of effect-size heterogeneity (Qb(1) = 7.38, p = .007). Thus, religiosity measure dimensionality had a significant effect on the protective effect of youth religiosity on alcohol use. CONCLUSION: Results highlight the protective effect of youth religiosity on alcohol use. To further understand the scope of this protective association, future research would benefit from exploring the multidimensional nature of religiosity and the associations between varying conceptualizations of religiosity and adolescent alcohol use outcomes.

A group-specific prior distribution for effect-size heterogeneity in meta-analysis.

While both methodological and applied work on Bayesian meta-analysis have flourished, Bayesian modeling of differences between groups of studies remains scarce in meta-analyses in psychology, education, and the social sciences. On rare occasions when Bayesian approaches have been used, non-informative prior distributions have been chosen. However, more informative prior distributions have recently garnered popularity. We propose a group-specific weakly informative prior distribution for the between-studies standard-deviation parameter in meta-analysis. The proposed prior distribution incorporates a frequentist estimate of the between-studies standard deviation as the noncentrality parameter in a folded noncentral t distribution. This prior distribution is then separately modeled for each subgroup of studies, as determined by a categorical factor. Use of the new prior distribution is shown in two extensive examples based on a published meta-analysis on psychological interventions aimed at increasing optimism. We compare the folded noncentral t prior distribution to several non-informative prior distributions. We conclude with discussion, limitations, and avenues for further development of Bayesian meta-analysis in psychology and the social sciences.