An Exact Bayesian Model for Meta-Analysis of the Standardized Mean Difference with Its Simultaneous Credible Intervals.

While Bayesian methodology is increasingly favored in behavioral research for its clear probabilistic inference and model structure, its widespread acceptance as a standard meta-analysis approach remains limited. Although some conventional Bayesian hierarchical models are frequently used for analysis, their performance has not been thoroughly examined. This study evaluates two commonly used Bayesian models for meta-analysis of standardized mean difference and identifies significant issues with these models. In response, we introduce a new Bayesian model equipped with novel features that address existing model concerns and a broader limitation of the current Bayesian meta-analysis. Furthermore, we introduce a simple computational approach to construct simultaneous credible intervals for the summary effect and between-study heterogeneity, based on their joint posterior samples. This fully captures the joint uncertainty in these parameters, a task that is challenging or impractical with frequentist models. Through simulation studies rooted in a joint Bayesian/frequentist paradigm, we compare our model's performance against existing ones under conditions that mirror realistic research scenarios. The results reveal that our new model outperforms others and shows enhanced statistical properties. We also demonstrate the practicality of our models using real-world examples, highlighting how our approach strengthens the robustness of inferences regarding the summary effect.

Critical mass: The rise of a touchscreen technology community for rodent cognitive testing.

The rise in the number of users and institutions utilizing the rodent touchscreen technology for cognitive testing over the past decade has prompted the need for knowledge mobilization and community building. To address the needs of the growing touchscreen community, the first international touchscreen symposium was hosted at Western University. Attendees from around the world attended talks from expert neuroscientists using touchscreens to examine a vast array of questions regarding cognition and the nervous system. In addition to the symposium, a subset of attendees was invited to partake in a hands-on training course where they received touchscreen training covering both hardware and software components. Beyond the two touchscreen events, virtual platforms have been developed to further support touchscreen users: (a) Mousebytes.ca, which includes a data repository of rodent touchscreen tasks, and (b) Touchscreencognition.org, an online community with numerous training and community resources, perhaps most notably a forum where members can ask and answer questions. The advantages of the rodent touchscreen technology for cognitive neuroscience research has allowed neuroscientists from diverse backgrounds to test specific cognitive processes using well-validated and standardized apparatus, contributing to its rise in popularity and its relevance to modern neuroscience research. The commitment of the touchscreen community to data, task development and information sharing not only ensures an expansive future of the use of rodent touchscreen technology but additionally, quality research that will increase translation from preclinical studies to clinical successes.

The Role of Gender in Adolescent Dating Abuse: An Interpretive Meta-Synthesis of the Qualitative Literature.

The role of gender has received considerable attention in the academic literature on intimate partner violence (IPV). The Grand Challenges for Social Work take a gender-neutral approach, without regard to the influence of gender on adolescent development and dating relationships. This positioning is inconsistent with gender mainstreaming approaches that have been integrated into international framings of IPV. The purpose of this article is to conduct a qualitative interpretive meta-synthesis to investigate how gender is represented in research on adolescent dating abuse across qualitative literature (N = 17 articles). Results underscore that gender influences the impact of abuse, with female adolescents more likely to be fearful in relationships, at higher risk for damage to their social standing, and more likely to be blamed for the abuse. Gender-specific attitudes affect perceptions of the seriousness of abuse, antecedents of abuse, and rationales for perpetrating violence. Findings across the studies indicate that adolescents have internalized gender scripts. Therefore, strategies to prevent dating abuse need to be cognizant of the socializing role of gender and the myriad ways it influences adolescents' lived experiences. Therefore, the American Academy of Social Work and Social Welfare should consider revising the language of the existing challenges to mainstream gender.

Racial Inequality in Psychological Research: Trends of the Past and Recommendations for the Future.

Race plays an important role in how people think, develop, and behave. In the current article, we queried more than 26,000 empirical articles published between 1974 and 2018 in top-tier cognitive, developmental, and social psychology journals to document how often psychological research acknowledges this reality and to examine whether people who edit, write, and participate in the research are systematically connected. We note several findings. First, across the past five decades, psychological publications that highlight race have been rare, and although they have increased in developmental and social psychology, they have remained virtually nonexistent in cognitive psychology. Second, most publications have been edited by White editors, under which there have been significantly fewer publications that highlight race. Third, many of the publications that highlight race have been written by White authors who employed significantly fewer participants of color. In many cases, we document variation as a function of area and decade. We argue that systemic inequality exists within psychological research and that systemic changes are needed to ensure that psychological research benefits from diversity in editing, writing, and participation. To this end, and in the spirit of the field's recent emphasis on metascience, we offer recommendations for journals and authors.

New Recommendations on the Use of R-Squared Differences in Multilevel Model Comparisons.

When comparing multilevel models (MLMs) differing in fixed and/or random effects, researchers have had continuing interest in using R-squared differences to communicate effect size and importance of included terms. However, there has been longstanding confusion regarding which R-squared difference measures should be used for which kind of MLM comparisons. Furthermore, several limitations of recent studies on R-squared differences in MLM have led to misleading or incomplete recommendations for practice. These limitations include computing measures that are by definition incapable of detecting a particular type of added term, considering only a subset of the broader class of available R-squared difference measures, and incorrectly defining what a given R-squared difference measure quantifies. The purpose of this paper is to elucidate and resolve these issues. To do so, we define a more general set of total, within-cluster, and between-cluster R-squared difference measures than previously considered in MLM comparisons and give researchers concrete step-by-step procedures for identifying which measure is relevant to which model comparison. We supply simulated and analytic demonstrations of limitations of previous MLM studies on R-squared differences and show how application of our step-by-step procedures and general set of measures overcomes each. Additionally, we provide and illustrate graphical tools and software allowing researchers to automatically compute and visualize our set of measures in an integrated manner. We conclude with recommendations, as well as extensions involving (a) how our framework relates to and can be used to obtain pseudo-R-squareds, and (b) how our framework can accommodate both simultaneous and hierarchical model-building approaches.

Preferences of Academic Researches and Pattern of Utilization of Behavior Laboratory Science for Preclinical Researches - a Cross Sectional Study.

INTRODUCTION: Several studies confirmed the relation between mortality, behavioral and social factors and emphasized the importance of behavioral and social science to public health practice. AIM: This study aimed to determine the preferences of the researchers who utilize the behavioral sciences laboratory at the Preclinical Research Unit and define the patter of laboratory utilization in order to maximize the benefits gained from it. METHODS: This cross sectional study conducted at the KFMRC, KAU, Jeddah, Saudi Arabia in 2018 on the researchers who visited the behavior research laboratory between October 2018 and December 2018. A structured self-administered questionnaire was utilized to collect the demographic data and preferences of the participants and the pattern of utilization of the behavior science laboratory. The response rate was 100%. The Data were analyzed using the Statistical Package of Social Sciences (SPSS) version 21. RESULTS: About 47% of the participants were working at the faculty of medicine (FOM) and about 47% were assistant professor. About 53 had previously conducted researches in behaviors science field. The majority of the participants were interested in memory field (about 57%) followed by the social field (20%). The least attractive field were the nutritional and anxiety (1.4%). The percent of non-medical researchers who had no interest in co-ordination field was significantly higher (p=0.041) compared to the medical/paramedical specialists. CONCLUSION: This study shed the light on the relative reduced interest in behavior researches among the academic researchers. There is need for more orientation programs and campaigns to raise the awareness of the importance of behaviors researches laboratories and researches.

Mapping online hate: A scientometric analysis on research trends and hotspots in research on online hate.

Internet and social media participation open doors to a plethora of positive opportunities for the general public. However, in addition to these positive aspects, digital technology also provides an effective medium for spreading hateful content in the form of cyberbullying, bigotry, hateful ideologies, and harassment of individuals and groups. This research aims to investigate the growing body of online hate research (OHR) by mapping general research indices, prevalent themes of research, research hotspots, and influential stakeholders such as organizations and contributing regions. For this, we use scientometric techniques and collect research papers from the Web of Science core database published through March 2019. We apply a predefined search strategy to retrieve peer-reviewed OHR and analyze the data using CiteSpace software by identifying influential papers, themes of research, and collaborating institutions. Our results show that higher-income countries contribute most to OHR, with Western countries accounting for most of the publications, funded by North American and European funding agencies. We also observed increased research activity post-2005, starting from more than 50 publications to more than 550 in 2018. This applies to a number of publications as well as citations. The hotbeds of OHR focus on cyberbullying, social media platforms, co-morbid mental disorders, and profiling of aggressors and victims. Moreover, we identified four main clusters of OHR: (1) Cyberbullying, (2) Sexual solicitation and intimate partner violence, (3) Deep learning and automation, and (4) Extremist and online hate groups, which highlight the cross-disciplinary and multifaceted nature of OHR as a field of research. The research has implications for researchers and policymakers engaged in OHR and its associated problems for individuals and society.

A GEE-type approach to untangle structural and random zeros in predictors.

Count outcomes with excessive zeros are common in behavioral and social studies, and zero-inflated count models such as zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) can be applied when such zero-inflated count data are used as response variable. However, when the zero-inflated count data are used as predictors, ignoring the difference of structural and random zeros can result in biased estimates. In this paper, a generalized estimating equation (GEE)-type mixture model is proposed to jointly model the response of interest and the zero-inflated count predictors. Simulation studies show that the proposed method performs well for practical settings and is more robust for model misspecification than the likelihood-based approach. A case study is also provided for illustration.

Analysis of single-case experimental count data using the linear mixed effects model: A simulation study.

When (meta-)analyzing single-case experimental design (SCED) studies by means of hierarchical or multilevel modeling, applied researchers almost exclusively rely on the linear mixed model (LMM). This type of model assumes that the residuals are normally distributed. However, very often SCED studies consider outcomes of a discrete rather than a continuous nature, like counts, percentages or rates. In those cases the normality assumption does not hold. The LMM can be extended into a generalized linear mixed model (GLMM), which can account for the discrete nature of SCED count data. In this simulation study, we look at the effects of misspecifying an LMM for SCED count data simulated according to a GLMM. We compare the performance of a misspecified LMM and of a GLMM in terms of goodness of fit, fixed effect parameter recovery, type I error rate, and power. Because the LMM and the GLMM do not estimate identical fixed effects, we provide a transformation to compare the fixed effect parameter recovery. The results show that, compared to the GLMM, the LMM has worse performance in terms of goodness of fit and power. Performance in terms of fixed effect parameter recovery is equally good for both models, and in terms of type I error rate the LMM performs better than the GLMM. Finally, we provide some guidelines for applied researchers about aspects to consider when using an LMM for analyzing SCED count data.