Optimally generate policy-based evidence before scaling.

Social scientists have increasingly turned to the experimental method to understand human behaviour. One critical issue that makes solving social problems difficult is scaling up the idea from a small group to a larger group in more diverse situations. The urgency of scaling policies impacts us every day, whether it is protecting the health and safety of a community or enhancing the opportunities of future generations. Yet, a common result is that, when we scale up ideas, most experience a 'voltage drop'-that is, on scaling, the cost-benefit profile depreciates considerably. Here I argue that, to reduce voltage drops, we must optimally generate policy-based evidence. Optimality requires answering two crucial questions: what information should be generated and in what sequence. The economics underlying the science of scaling provides insights into these questions, which are in some cases at odds with conventional approaches. For example, there are important situations in which I advocate flipping the traditional social science research model to an approach that, from the beginning, produces the type of policy-based evidence that the science of scaling demands. To do so, I propose augmenting efficacy trials by including relevant tests of scale in the original discovery process, which forces the scientist to naturally start with a recognition of the big picture: what information do I need to have scaling confidence?

Mainstreaming Gender: An Examination of Feminist Methodology in Social Work Research.

Despite gender being central to any given social issue and the profession of social work's commitment to social justice, gender and gender inequality remain tangential to mainstream social work goals as partially indicated by the Grand Challenges for Social Work initiative led by the American Academy of Social Work and Social Welfare. Feminist methodologists prioritize the investigation of gender inequality by elevating the voices of oppressed groups, particularly women, using qualitative and mixed-methods studies, focusing on structural social change, and emphasizing the need for intersectional inquiry. Feminist and other critical methodologies frame structural inequality as central to the examination of all social issues and research questions. This study investigates the extent to which gender and gender inequality are investigated in mainstream social work research. Specifically, drawing on 404 research articles from three mainstream social work journals, this research relies on content analysis to demonstrate the dearth of studies examining gender and gender inequality in mainstream social work research. This work also presents opportunities for social workers to position gender as central to understanding persisting structural inequalities of the 21st century and work toward a more equitable social order.

Molecular (moment-to-moment) and molar (aggregate) analyses of behavior.

Ten meanings or usages of the terms molecular and molar analyses are based on (1) numbers of responses, (2) durations of activities, (3) levels, (4) scales, (5) contiguity versus correlation, (6) behavioral standards, (7) function with or without structure, (8) local versus global phenomena, and (9) control by shaping of sequential moment-to-moment behavior. These usages reveal divisive viewpoints along with ambiguities in the Law of Effect, the definition of an operant, response strength, response probability, random behavior, time allocation, shaping, controlled versus uncontrolled operants, and roles for ordinary language. Usage 10 is less divisive and combines, and in that sense unifies, molecular behavior, defined as shaped moment-to-moment sequential behaving, and molar, defined as averages of aggregates of those shaped responses. It combines shaping, that establishes and changes operant behaviors, and strengthening that changes the amounts of those shaped behaviors. I conclude that general behavioral theory will combine strengthening with such methods as parametric, hybrid, or nonparametric shaping, and will use computational methods to simulate moment-to-moment behavior streams from which any aggregates of theoretical interest may be computed. Such a synthesis may not require different levels, scales, or new scientific paradigms.

National Institutes of Health social and behavioral research in response to the SARS-CoV2 Pandemic.

The COVID-19 pandemic has been mitigated primarily using social and behavioral intervention strategies, and these strategies have social and economic impacts, as well as potential downstream health impacts that require further study. Digital and community-based interventions are being increasingly relied upon to address these health impacts and bridge the gap in health care access despite insufficient research of these interventions as a replacement for, not an adjunct to, in-person clinical care. As SARS-CoV-2 testing expands, research on encouraging uptake and appropriate interpretation of these test results is needed. All of these issues are disproportionately impacting underserved, vulnerable, and health disparities populations. This commentary describes the various initiatives of the National Institutes of Health to address these social, behavioral, economic, and health disparities impacts of the pandemic, the findings from which can improve our response to the current pandemic and prepare us better for future infectious disease outbreaks.

Bayesian Extended Redundancy Analysis: A Bayesian Approach to Component-based Regression with Dimension Reduction.

Extended redundancy analysis (ERA) combines linear regression with dimension reduction to explore the directional relationships between multiple sets of predictors and outcome variables in a parsimonious manner. It aims to extract a component from each set of predictors in such a way that it accounts for the maximum variance of outcome variables. In this article, we extend ERA into the Bayesian framework, called Bayesian ERA (BERA). The advantages of BERA are threefold. First, BERA enables to make statistical inferences based on samples drawn from the joint posterior distribution of parameters obtained from a Markov chain Monte Carlo algorithm. As such, it does not necessitate any resampling method, which is on the other hand required for (frequentist's) ordinary ERA to test the statistical significance of parameter estimates. Second, it formally incorporates relevant information obtained from previous research into analyses by specifying informative power prior distributions. Third, BERA handles missing data by implementing multiple imputation using a Markov Chain Monte Carlo algorithm, avoiding the potential bias of parameter estimates due to missing data. We assess the performance of BERA through simulation studies and apply BERA to real data regarding academic achievement.

A Viable Alternative When Propensity Scores Fail: Evaluation of Inverse Propensity Weighting and Sequential G-Estimation in a Two-Wave Mediation Model.

Two methods from the potential outcomes framework - inverse propensity weighting (IPW) and sequential G-estimation - were evaluated and compared to linear regression for estimating the mediated effect in a two-wave design with a randomized intervention and continuous mediator and outcome. Baseline measures of the mediator and outcome can be considered confounders of the follow-up mediator - outcome relation for which adjustment is necessary to eliminate bias. To adjust for baseline measures of the mediator and outcome, IPW uses stabilized inverse propensity weights whereas sequential G-estimation uses regression adjustment. Theoretical differences between the models are described, and Monte Carlo simulations compared the performance of linear regression; IPW without weight truncation; IPW with weights truncated at the 1st/99th, 5th/95th, and 10th/90th percentiles; and sequential G-estimation. Sequential G-estimation performed similarly to linear regression, but IPW provided a biased estimate of the mediated effect, lower power, lower confidence interval coverage, and higher mean squared error. Simulation results show that IPW failed to fully adjust the follow-up mediator - outcome relation for confounding due to the baseline measures. We then compared the mediated effect estimates using data from a randomized experiment evaluating a steroid prevention program for high school athletes. Implications and future directions are discussed.

Modeling of self-report behavior data using the generalized covariates in a uniform and shifted binomial mixture model: An empirical example and Monte Carlo simulation.

Social scientists routinely collect data using questionnaires and surveys. Items on these instruments frequently involve scales with multiple ordered options that respondents use to report intensity of feelings or behaviors. Given their popularity, a variety of statistical models have been developed for analyzing data collected using these items. A model that has been recently described for working with ordinal items is the covariates in a uniform and shifted binomial mixture (CUB). The CUB model characterizes responses to ordinal items as a function of two parameters: (a) response feeling (or intensity), and (b) response uncertainty. This model has been extended to include a third parameter measuring likelihood of respondents selecting a socially desirable or safe response, known as the shelter option. This model has been primarily used to investigate items measuring political opinions or product preferences. However, the CUB with a shelter parameter and covariates generalized covariates in a uniform and shifted ninomial mixture model (GeCUB) seems particularly well suited for characterizing self-reported behaviors, particularly those that are not considered positive (i.e., substance abuse). The purpose of this study is to apply this extension of the CUB to the modeling of self-reported substance use behavior by teenagers. Results from the GeCUB model estimation revealed that subjects used the "no use" response as a shelter option at relatively high rates for marijuana use but not for cigarettes or alcohol. In addition, females reported less use and less certainty in their responses than did males. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Evaluating Supplemental Samples in Longitudinal Research: Replacement and Refreshment Approaches.

Despite the wide application of longitudinal studies, they are often plagued by missing data and attrition. The majority of methodological approaches focus on participant retention or modern missing data analysis procedures. This paper, however, takes a new approach by examining how researchers may supplement the sample with additional participants. First, refreshment samples use the same selection criteria as the initial study. Second, replacement samples identify auxiliary variables that may help explain patterns of missingness and select new participants based on those characteristics. A simulation study compares these two strategies for a linear growth model with five measurement occasions. Overall, the results suggest that refreshment samples lead to less relative bias, greater relative efficiency, and more acceptable coverage rates than replacement samples or not supplementing the missing participants in any way. Refreshment samples also have high statistical power. The comparative strengths of the refreshment approach are further illustrated through a real data example. These findings have implications for assessing change over time when researching at-risk samples with high levels of permanent attrition.

A Diagnostic Procedure for Detecting Outliers in Linear State-Space Models.

Outliers can be more problematic in longitudinal data than in independent observations due to the correlated nature of such data. It is common practice to discard outliers as they are typically regarded as a nuisance or an aberration in the data. However, outliers can also convey meaningful information concerning potential model misspecification, and ways to modify and improve the model. Moreover, outliers that occur among the latent variables (innovative outliers) have distinct characteristics compared to those impacting the observed variables (additive outliers), and are best evaluated with different test statistics and detection procedures. We demonstrate and evaluate the performance of an outlier detection approach for multi-subject state-space models in a Monte Carlo simulation study, with corresponding adaptations to improve power and reduce false detection rates. Furthermore, we demonstrate the empirical utility of the proposed approach using data from an ecological momentary assessment study of emotion regulation together with an open-source software implementation of the procedures.

Indirect Effects in Sequential Mediation Models: Evaluating Methods for Hypothesis Testing and Confidence Interval Formation.

Complex mediation models, such as a two-mediator sequential model, have become more prevalent in the literature. To test an indirect effect in a two-mediator model, we conducted a large-scale Monte Carlo simulation study of the Type I error, statistical power, and confidence interval coverage rates of 10 frequentist and Bayesian confidence/credible intervals (CIs) for normally and nonnormally distributed data. The simulation included never-studied methods and conditions (e.g., Bayesian CI with flat and weakly informative prior methods, two model-based bootstrap methods, and two nonnormality conditions) as well as understudied methods (e.g., profile-likelihood, Monte Carlo with maximum likelihood standard error [MC-ML] and robust standard error [MC-Robust]). The popular BC bootstrap showed inflated Type I error rates and CI under-coverage. We recommend different methods depending on the purpose of the analysis. For testing the null hypothesis of no mediation, we recommend MC-ML, profile-likelihood, and two Bayesian methods. To report a CI, if data has a multivariate normal distribution, we recommend MC-ML, profile-likelihood, and the two Bayesian methods; otherwise, for multivariate nonnormal data we recommend the percentile bootstrap. We argue that the best method for testing hypotheses is not necessarily the best method for CI construction, which is consistent with the findings we present.

The Research Centers in Minority Institutions (RCMI) Translational Research Network: Building and Sustaining Capacity for Multi-Site Basic Biomedical, Clinical and Behavioral Research.

The Research Centers in Minority Institutions (RCMI) program was established by the US Congress to support the development of biomedical research infrastructure at minority-serving institutions granting doctoral degrees in the health professions or in a health-related science. RCMI institutions also conduct research on diseases that disproportionately affect racial and ethnic minorities (ie, African Americans/Blacks, American Indians and Alaska Natives, Hispanics, Native Hawaiians and Other Pacific Islanders), those of low socioeconomic status, and rural persons. Quantitative metrics, including the numbers of doctoral science degrees granted to underrepresented students, NIH peer-reviewed research funding, peer-reviewed publications, and numbers of racial and ethnic minorities participating in sponsored research, demonstrate that RCMI grantee institutions have made substantial progress toward the intent of the Congressional legislation, as well as the NIH/NIMHD-linked goals of addressing workforce diversity and health disparities. Despite this progress, nationally, many challenges remain, including persistent disparities in research and career development awards to minority investigators. The continuing underrepresentation of minority investigators in NIH-sponsored research across multiple disease areas is of concern, in the face of unrelenting national health inequities. With the collaborative network support by the RCMI Translational Research Network (RTRN), the RCMI community is uniquely positioned to address these challenges through its community engagement and strategic partnerships with non-RCMI institutions. Funding agencies can play an important role by incentivizing such collaborations, and incorporating metrics for research funding that address underrepresented populations, workforce diversity and health equity.

Application of the Within- and Between-Series Estimators to Non-normal Multiple-Baseline Data: Maximum Likelihood and Bayesian Approaches.

In single-case research, multiple-baseline (MB) design provides the opportunity to estimate the treatment effect based on not only within-series comparisons of treatment phase to baseline phase observations, but also time-specific between-series comparisons of observations from those that have started treatment to those that are still in the baseline. For analyzing MB studies, two types of linear mixed modeling methods have been proposed: the within- and between-series models. In principle, those models were developed based on normality assumptions, however, normality may not always be found in practical settings. Therefore, this study aimed to investigate the robustness of the within- and between-series models when data were non-normal. A Monte Carlo study was conducted with four statistical approaches. The approaches were defined by the crossing of two analytic decisions: (a) whether to use a within- or between-series estimate of effect and (b) whether to use restricted maximum likelihood or Markov chain Monte Carlo estimations. The results showed the treatment effect estimates of the four approaches had minimal bias, that within-series estimates were more precise than between-series estimates, and that confidence interval coverage was frequently acceptable, but varied across conditions and methods of estimation. Applications and implications were discussed based on the findings.

Moving psychological assessment out of the controlled laboratory setting: Practical challenges.

Behavioral assessment using smart devices affords novel methods, notably remote self-administration by the individuals themselves. However, this new approach requires navigating complex legal and technical terrain. Given the limited empirical data that currently exists, we provide and discuss anecdotes of the methodological, technical, legal, and cultural issues associated with an implementation in both U.S. and European settings of a mobile software application for regular psychological monitoring purposes. The tasks required participants to listen, watch, speak, and touch to interact with the smart device, thus assessing cognition, motor skill, and language. Four major findings merit mention: First, moving assessment out of the hands of a trained investigator necessitates excellent usability engineering, such that the tool is easily usable by the participant and the resulting data relevant to the investigator. Second, remote assessment requires that the data are transferred safely back to the investigator, and that risk of compromising participant confidentiality is minimized. Third, frequent data collection over long periods of time is associated with a possibility that participants may choose to withdraw consent for participation thus requiring data retraction. Fourth, data collection and analysis across international borders creates new challenges and new opportunities because of important cultural and language issues that may inform the underlying behavioral constructs of interest. In conclusion, the new technological frameworks provide unprecedented opportunities for remote self-administered behavioral assessments but will be most productive in multidisciplinary teams to ensure the highest level of user satisfaction and data quality, and to guarantee the highest level of data protection. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The accuracy of dominance analysis as a metric to assess relative importance: The joint impact of sampling error variance and measurement unreliability.

Dominance analysis (DA) has been established as a useful tool for practitioners and researchers to identify the relative importance of predictors in a linear regression. This article examines the joint impact of two common and pervasive artifacts-sampling error variance and measurement unreliability-on the accuracy of DA. We present Monte Carlo simulations that detail the decrease in the accuracy of DA in the presence of these artifacts, highlighting the practical extent of the inferential mistakes that can be made. Then, we detail and provide a user-friendly program in R (R Core Team, 2017) for estimating the effects of sampling error variance and unreliability on DA. Finally, by way of a detailed example, we provide specific recommendations for how researchers and practitioners should more appropriately interpret and report results of DA. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Validation of a brief behavioral economic assessment of demand among cigarette smokers.

Basic and clinical addiction research use demand measures and analysis extensively to characterize drug use motivations. Hence, obtaining an accurate and brief measurement of demand that can be easily utilized in different settings is highly valued. In the current study, 2 versions of a breakpoint measure, designed to capture cigarette demand, were investigated in 119 smokers who were recruited from an online crowdsourcing platform. The first version determines the maximum price a smoker is willing to pay for one cigarette received right now when paid out of pocket, and the second determines the maximum price when paid using a hypothetical $100 gift card received for free. The breakpoint measures were administered along with the Cigarette Purchase Task (CPT), Fagerström Test for Cigarette Dependence (FTCD), and The Questionnaire of Smoking Urges (QSU-brief). Both single-item breakpoint versions were significantly correlated with CPT-derived demand measures loaded on the persistence factor (i.e., elasticity of demand, breakpoint, Pmax, and Omax), but not with those loaded on the amplitude factor (i.e., intensity of demand). In addition, both single-item measures were associated with metrics of tobacco dependence (e.g., FTCD, QSU) with effect sizes that are similar to the ones found between CPT-derived breakpoint and those same metrics. These findings suggest that the single-item breakpoint measure is a viable method for measuring demand that may provide a useful and efficient tool to capture crucial and distinct aspects of smoking. In addition, the breakpoint measures may help increase the utility of behavioral demand measures in novel research and clinical settings. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The (doubtful) role of financial reward in obedience to authority.

The Milgram experiments are among the most well-known and important in the history of psychology. Since first published, there have been countless discussions held on the subject of what factors induce people to exhibit extreme obedience towards authority. One such potential factor, not yet explored empirically, is the receipt in advance of financial gratification by a study participant. In our experiment we compare obedience among participants in classic Milgram paradigm conditions with obedience in a situation where the participant does not receive financial gratification in advance. The results did not show that obedience differed in the two comparable situations. In conditions where the participants were not given money up front, however, it was necessary to employ more verbal prompts in order to induce obedience.