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.

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.

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.

The MTurkification of Social and Personality Psychology.

The potential role of brief online studies in changing the types of research and theories likely to evolve is examined in the context of earlier changes in theory and methods in social and personality psychology, changes that favored low-difficulty, high-volume studies. An evolutionary metaphor suggests that the current publication environment of social and personality psychology is a highly competitive one, and that academic survival and reproduction processes (getting a job, tenure/promotion, grants, awards, good graduate students) can result in the extinction of important research domains. Tracking the prevalence of brief online studies, exemplified by studies using Amazon Mechanical Turk, in three top journals ( Journal of Personality and Social Psychology, Personality and Social Psychology Bulletin, Journal of Experimental Social Psychology) reveals a dramatic increase in their frequency and proportion. Implications, suggestions, and questions concerning this trend for the field and questions for its practitioners are discussed.

What meta-analyses reveal about the replicability of psychological research.

Can recent failures to replicate psychological research be explained by typical magnitudes of statistical power, bias or heterogeneity? A large survey of 12,065 estimated effect sizes from 200 meta-analyses and nearly 8,000 papers is used to assess these key dimensions of replicability. First, our survey finds that psychological research is, on average, afflicted with low statistical power. The median of median power across these 200 areas of research is about 36%, and only about 8% of studies have adequate power (using Cohen's 80% convention). Second, the median proportion of the observed variation among reported effect sizes attributed to heterogeneity is 74% (I2). Heterogeneity of this magnitude makes it unlikely that the typical psychological study can be closely replicated when replication is defined as study-level null hypothesis significance testing. Third, the good news is that we find only a small amount of average residual reporting bias, allaying some of the often-expressed concerns about the reach of publication bias and questionable research practices. Nonetheless, the low power and high heterogeneity that our survey finds fully explain recent difficulties to replicate highly regarded psychological studies and reveal challenges for scientific progress in psychology. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Application of the dyadic data analysis in behavioral medicine research: marital satisfaction and anxiety in infertile couples.

BACKGROUND: Dyadic data analysis (DDA) is increasingly being used to better understand, analyze and model intra- and inter-personal mechanisms of health in various types of dyads such as husband-wife, caregiver-patient, doctor-patient, and parent-child. A key strength of the DDA is its flexibility to take the nonindependence available in the dyads into account. In this article, we illustrate the value of using DDA to examine how anxiety is associated with marital satisfaction in infertile couples. METHODS: This cross-sectional study included 141 infertile couples from a referral infertility clinic in Tehran, Iran between February and May 2017. Anxiety and marital satisfaction were measured by the anxiety subscale of the Hospital Anxiety and Depression Scale and 10-Item ENRICH Marital Satisfaction Scale, respectively. We apply and compare tree different dyadic models to explore the effect of anxiety on marital satisfaction, including the Actor-Partner Interdependence Model (APIM), Mutual Influence Model (MIM), and Common Fate Model (CFM). RESULTS: This study demonstrated a practical application of the dyadic models. These dyadic models provide results that appear to give different interpretations of the data. The APIM analysis revealed that both men's and women's anxiety excreted an actor effect on their own marital satisfaction. In addition, women's anxiety exerted a significant partner effect on their husbands' marital satisfaction. In MIM analysis, in addition to significant actor effects of anxiety on marital satisfaction, women's reports of marital satisfaction significantly predicted men's marital satisfaction. The CFM analysis revealed that higher couple anxiety scores predicted lower couple marital satisfaction scores. CONCLUSION: In sum, the study highlights the usefulness of DDA to explore and test the phenomena with inherently dyadic nature. With regard to our empirical data, the findings confirmed that marital satisfaction was influenced by anxiety in infertile couples at both individual and dyadic level; thus, interventions to improve marital satisfaction should include both men and women. In addition, future studies should consider using DDA when dyadic data are available.

The Demand Curve Analyzer: Behavioral economic software for applied research.

Free and open-source software for applying models of operant demand called the Demand Curve Analyzer (DCA) was developed and systematically evaluated for use in research. The software was constructed to streamline the use of recommended screening measures, prepare suitable scaling parameters, fit one of several models of operant demand, and provide publication-quality figures. The DCA allows users to easily import price and consumption data into spreadsheet-based controls and to perform statistical modeling with the aid of a graphical user interface. The results from computer simulations and reanalyses of published study data indicated that the DCA provides results consistent with commercially available software that has been traditionally used to apply these analyses (i.e., GraphPadTM Prism). Further, the DCA provides additional functionality that other statistical packages do not include. Practical issues and future directions related to the determination of scaling parameter k, screening for nonsystematic data, and the incorporation of more advanced behavioral economic methods are also discussed.

The Aarhus statement on cancer diagnostic research: turning recommendations into new survey instruments.

BACKGROUND: Over recent years there has been a growth in cancer early diagnosis (ED) research, which requires valid measurement of routes to diagnosis and diagnostic intervals. The Aarhus Statement, published in 2012, provided methodological guidance to generate valid data on these key pre-diagnostic measures. However, there is still a wide variety of measuring instruments of varying quality in published research. In this paper we test comprehension of self-completion ED questionnaire items, based on Aarhus Statement guidance, and seek input from patients, GPs and ED researchers to refine these questions. METHODS: We used personal interviews and consensus approaches to generate draft ED questionnaire items, then a combination of focus groups and telephone interviews to test comprehension and obtain feedback. A framework analysis approach was used, to identify themes and potential refinements to the items. RESULTS: We found that many of the questionnaire items still prompted uncertainty in respondents, in both routes to diagnosis and diagnostic interval measurement. Uncertainty was greatest in the context of multiple or vague symptoms, and potentially ambiguous time-points (such as 'date of referral'). CONCLUSIONS: There are limits on the validity of self-completion questionnaire responses, and refinements to the wording of questions may not be able to completely overcome these limitations. It's important that ED researchers use the best identifiable measuring instruments, but accommodate inevitable uncertainty in the interpretation of their results. Every effort should be made to increase clarity of questions and responses, and use of two or more data sources should be considered.

Testing moderator hypotheses in meta-analytic structural equation modeling using subgroup analysis.

Meta-analytic structural equation modeling (MASEM) is a statistical technique to pool correlation matrices and test structural equation models on the pooled correlation matrix. In Stage 1 of MASEM, correlation matrices from independent studies are combined to obtain a pooled correlation matrix, using fixed- or random-effects analysis. In Stage 2, a structural model is fitted to the pooled correlation matrix. Researchers applying MASEM may have hypotheses about how certain model parameters will differ across subgroups of studies. These moderator hypotheses are often addressed using suboptimal methods. The aim of the current article is to provide guidance and examples on how to test hypotheses about group differences in specific model parameters in MASEM. We illustrate the procedure using both fixed- and random-effects subgroup analysis with two real datasets. In addition, we present a small simulation study to evaluate the effect of the number of studies per subgroup on convergence problems. All data and the R-scripts for the examples are provided online.

Participation in HIV Behavioral Research: Unanticipated Benefits and Burdens.

HIV behavioral research has provided an invaluable knowledge base for effective approaches to behavioral challenges along the HIV care cascade. Little attention has been paid to tracking unanticipated effects of research participation, whether negative or positive. We used qualitative methods to elicit impressions of unanticipated effects of participation in behavioral research. An instrument was developed and piloted to assess positive (emotional gains, practical gains, HIV prevention knowledge and skills gains) and negative (emotional stress, discomfort with research) unanticipated effects. Participants (N = 25) from five projects, including men who have sex with men, adults who use substances, and youth, reported multiple positive unanticipated effects (sexual and drug risk reduction, goal setting, improvements in self-esteem and mood, relationship gains, health care behavior gains, knowledge and introspection gains) and rare unanticipated negative effects. Developing a systematic tool of unanticipated positive and negative effects of participation in behavioral research is a crucial next step.

Retrieving relevant factors with exploratory SEM and principal-covariate regression: A comparison.

Behavioral researchers often linearly regress a criterion on multiple predictors, aiming to gain insight into the relations between the criterion and predictors. Obtaining this insight from the ordinary least squares (OLS) regression solution may be troublesome, because OLS regression weights show only the effect of a predictor on top of the effects of other predictors. Moreover, when the number of predictors grows larger, it becomes likely that the predictors will be highly collinear, which makes the regression weights' estimates unstable (i.e., the "bouncing beta" problem). Among other procedures, dimension-reduction-based methods have been proposed for dealing with these problems. These methods yield insight into the data by reducing the predictors to a smaller number of summarizing variables and regressing the criterion on these summarizing variables. Two promising methods are principal-covariate regression (PCovR) and exploratory structural equation modeling (ESEM). Both simultaneously optimize reduction and prediction, but they are based on different frameworks. The resulting solutions have not yet been compared; it is thus unclear what the strengths and weaknesses are of both methods. In this article, we focus on the extents to which PCovR and ESEM are able to extract the factors that truly underlie the predictor scores and can predict a single criterion. The results of two simulation studies showed that for a typical behavioral dataset, ESEM (using the BIC for model selection) in this regard is successful more often than PCovR. Yet, in 93% of the datasets PCovR performed equally well, and in the case of 48 predictors, 100 observations, and large differences in the strengths of the factors, PCovR even outperformed ESEM.

On the ambiguity of interaction and nonlinear main effects in a regime of dependent covariates.

The analysis of large experimental datasets frequently reveals significant interactions that are difficult to interpret within the theoretical framework guiding the research. Some of these interactions actually arise from the presence of unspecified nonlinear main effects and statistically dependent covariates in the statistical model. Importantly, such nonlinear main effects may be compatible (or, at least, not incompatible) with the current theoretical framework. In the present literature, this issue has only been studied in terms of correlated (linearly dependent) covariates. Here we generalize to nonlinear main effects (i.e., main effects of arbitrary shape) and dependent covariates. We propose a novel nonparametric method to test for ambiguous interactions where present parametric methods fail. We illustrate the method with a set of simulations and with reanalyses (a) of effects of parental education on their children's educational expectations and (b) of effects of word properties on fixation locations during reading of natural sentences, specifically of effects of length and morphological complexity of the word to be fixated next. The resolution of such ambiguities facilitates theoretical progress.

Case conceptualization research in cognitive behavior therapy: A state of the science review.

OBJECTIVE: Prominent models of cognitive behavior therapy (CBT) assert that case conceptualization is crucial for tailoring interventions to adequately address the needs of the individual client. We aimed to review the research on case conceptualization in CBT. METHOD: We conducted a systematic search of PsychINFO, MEDLINE, Psychology and Behavioral Science Collection, and CINAHL databases to February 2016. RESULTS: A total of 24 studies that met inclusion criteria were identified. It was notable that studies (a) focused on the assessment function of case conceptualization, (b) employed diverse methodologies, and, overall, (c) there remains a paucity of studies examining the in-session process of using case conceptualization or examining relations with outcome. CONCLUSION: Results from the existing studies suggest that experienced therapists can reliably construct some elements of case conceptualizations, but importance for the efficacy of case conceptualization in CBT has yet to be demonstrated. Research that involves direct observation of therapist competence in case conceptualization as a predictor of CBT outcomes is recommended as a focus for future hypothesis testing.

Analyzing individual growth with clustered longitudinal data: A comparison between model-based and design-based multilevel approaches.

To prevent biased estimates of intraindividual growth and interindividual variability when working with clustered longitudinal data (e.g., repeated measures nested within students; students nested within schools), individual dependency should be considered. A Monte Carlo study was conducted to examine to what extent two model-based approaches (multilevel latent growth curve model - MLGCM, and maximum model - MM) and one design-based approach (design-based latent growth curve model - D-LGCM) could produce unbiased and efficient parameter estimates of intraindividual growth and interindividual variability given clustered longitudinal data. The solutions of a single-level latent growth curve model (SLGCM) were also provided to demonstrate the consequences of ignoring individual dependency. Design factors considered in the present simulation study were as follows: number of clusters (NC = 10, 30, 50, 100, 150, 200, and 500) and cluster size (CS = 5, 10, and 20). According to our results, when intraindividual growth is of interest, researchers are free to implement MLGCM, MM, or D-LGCM. With regard to interindividual variability, MLGCM and MM were capable of producing accurate parameter estimates and SEs. However, when D-LGCM and SLGCM were applied, parameter estimates of interindividual variability were not comprised exclusively of the variability in individual (e.g., students) growth but instead were the combined variability of individual and cluster (e.g., school) growth, which cannot be interpreted. The take-home message is that D-LGCM does not qualify as an alternative approach to analyzing clustered longitudinal data if interindividual variability is of interest.