Publication rates from biomedical and behavioral and social science R01s funded by the National Institutes of Health.

Prior research has shown a serious lack of research transparency resulting from the failure to publish study results in a timely manner. The National Institutes of Health (NIH) has increased its use of publication rate and time to publication as metrics for grant productivity. In this study, we analyze the publications associated with all R01 and U01 grants funded from 2008 through 2014, providing sufficient time for these grants to publish their findings, and identify predictors of time to publication based on a number of variables, including if a grant was coded as a behavioral and social sciences research (BSSR) grant or not. Overall, 2.4% of the 27,016 R01 and U01 grants did not have a publication associated with the grant within 60 months of the project start date, and this rate of zero publications was higher for BSSR grants (4.6%) than for non-BSSR grants (1.9%). Mean time in months to first publication was 15.2 months, longer for BSSR grants (22.4 months) than non-BSSR grants (13.6 months). Survival curves showed a more rapid reduction of risk to publish from non-BSSR vs BSSR grants. Cox regression models showed that human research (vs. animal, neither, or both) and clinical trials research (vs. not) are the strongest predictors of time to publication and failure to publish, but even after accounting for these and other predictors, BSSR grants continued to show longer times to first publication and greater risk of no publications than non-BSSR grants. These findings indicate that even with liberal criteria for publication (any publication associated with a grant), a small percentage of R01 and U01 grantees fail to publish in a timely manner, and that a number of factors, including human research, clinical trial research, child research, not being an early stage investigator, and conducting behavioral and social sciences research increase the risk of time to first publication.

Specification issues in nonlinear SEM: The moderation that wasn't.

BACKGROUND: Analysis of interaction or moderation effects between latent variables is a common requirement in the social sciences. However, when predictors are correlated, interaction and quadratic effects become more alike, making them difficult to distinguish. As a result, when data are drawn from a quadratic population model and the analysis model specifies interactions only, misleading results may be obtained. METHOD: This article addresses the consequences of different types of specification error in nonlinear structural equation models using a Monte Carlo study. RESULTS: Results show that fitting a model with interactions when quadratic effects are present in the population will almost certainly lead to erroneous detection of moderation effects, and that the same is true in the opposite scenario. Simultaneous estimation of interactions and quadratic effects yields correct results. CONCLUSIONS: Simultaneous estimation of interaction and quadratic effects prevents detection of spurious or misleading nonlinear effects. Results are discussed and recommendations are offered to applied researchers.

Health Sciences cultural safety education in Australia, Canada, New Zealand, and the United States: a literature review.

OBJECTIVES: To review the research literature on cultural safety education within post-secondary health science programs. METHODS: We conducted health and social science database searches from 1996-2016, using combined keywords: cultural competence or safety; teaching or curriculum; universities, polytechnics or professional programs; and Aboriginal or Indigenous. In dyads, authors selected, and reviewed studies independently followed by discussion and consensus to identify thematic linkages of major findings. RESULTS: A total of 1583 abstracts and 122 full-text articles were reviewed with 40 selected for final inclusion. Publications from Australia, Canada, New Zealand and the United States described curriculum development and delivery. A variety of evaluation approaches were used including anecdotal reports, focus groups, interviews, course evaluations, reflective journals, pre-post surveys, critical reflective papers, and exam questions. Duration and depth of curricular exposure ranged from one day to integration across a six-year program.  Changes in student knowledge, attitude, self-confidence, and behaviour when working with Indigenous populations were reported. Cultural safety education and application to practice were shown to be linked to improved relationships, healthier outcomes, and increased number of Indigenous people entering health education programs and graduates interested in working in diverse communities. CONCLUSIONS: This review provides a summary of multidisciplinary didactic and experiential instructional approaches to cultural safety education and the impact on students, educators and Indigenous people.  Institutional support, strategic planning and cultural safety curriculum policy within post-secondary settings and community engagement are imperative for positive student experiences, advocacy, and actions toward health equity and improved health for Indigenous people and communities.

Modelling partially cross-classified multilevel data.

This article proposes an approach to modelling partially cross-classified multilevel data where some of the level-1 observations are nested in one random factor and some are cross-classified by two random factors. Comparisons between a proposed approach to two other commonly used approaches which treat the partially cross-classified data as either fully nested or fully cross-classified are completed with a simulation study. Results show that the proposed approach demonstrates desirable performance in terms of parameter estimates and statistical inferences. Both the fully nested model and the fully cross-classified model suffer from biased estimates of some variance components and statistical inferences of some fixed effects. Results also indicate that the proposed model is robust against cluster size imbalance.

Parkinson's disease and occupation: differences in associations by case identification method suggest referral bias.

METHODS: We used a population-based sample of 403 Parkinson's disease cases and 405 controls to examine risks by occupation. Results were compared to a previous clinic-based analysis. RESULTS: With censoring of jobs held within 10 years of diagnosis, the following had significantly or strongly increased risks: social science, law and library jobs (OR = 1.8); farming and horticulture jobs (OR = 2.0); gas station jobs (OR = 2.6); and welders (OR = 3.0). The following had significantly decreased risks: management and administration jobs (OR = 0.70); and other health care jobs (OR = 0.44). CONCLUSIONS: These results were consistent with other findings for social science and farming occupations. Risks for teaching, medicine and health occupations were not elevated, unlike our previous clinic-based study. This underscores the value of population-based over clinic-based samples. Occupational studies may be particularly susceptible to referral bias because social networks may spread preferentially via jobs.

Bayesian analysis of mixtures in structural equation models with non-ignorable missing data.

Structural equation models (SEMs) have become widely used to determine the interrelationships between latent and observed variables in social, psychological, and behavioural sciences. As heterogeneous data are very common in practical research in these fields, the analysis of mixture models has received a lot of attention in the literature. An important issue in the analysis of mixture SEMs is the presence of missing data, in particular of data missing with a non-ignorable mechanism. However, only a limited amount of work has been done in analysing mixture SEMs with non-ignorable missing data. The main objective of this paper is to develop a Bayesian approach for analysing mixture SEMs with an unknown number of components and non-ignorable missing data. A simulation study shows that Bayesian estimates obtained by the proposed Markov chain Monte Carlo methods are accurate and the Bayes factor computed via a path sampling procedure is useful for identifying the correct number of components, selecting an appropriate missingness mechanism, and investigating various effects of latent variables in the mixture SEMs. A real data set on a study of job satisfaction is used to demonstrate the methodology.

Bayesian analysis for finite mixture in non-recursive non-linear structural equation models.

This paper considers finite mixtures of structural equation models with non-linear effects of exogenous latent variables and non-recursive relations among endogenous latent variables. A Bayesian approach is developed to analyse this kind of model. In order to cope with the label switching problem, the permutation sampler is used to choose an appropriate identification constraint. Furthermore, a hybrid Markov chain Monte Carlo method that combines the Gibbs sampler, Metropolis-Hastings algorithm, and Langevin-Hastings algorithm is implemented to produce the Bayesian outputs. Finally, the proposed approach is illustrated by a simulation study and a real example.

Clickstream data yields high-resolution maps of science.

BACKGROUND: Intricate maps of science have been created from citation data to visualize the structure of scientific activity. However, most scientific publications are now accessed online. Scholarly web portals record detailed log data at a scale that exceeds the number of all existing citations combined. Such log data is recorded immediately upon publication and keeps track of the sequences of user requests (clickstreams) that are issued by a variety of users across many different domains. Given these advantages of log datasets over citation data, we investigate whether they can produce high-resolution, more current maps of science. METHODOLOGY: Over the course of 2007 and 2008, we collected nearly 1 billion user interactions recorded by the scholarly web portals of some of the most significant publishers, aggregators and institutional consortia. The resulting reference data set covers a significant part of world-wide use of scholarly web portals in 2006, and provides a balanced coverage of the humanities, social sciences, and natural sciences. A journal clickstream model, i.e. a first-order Markov chain, was extracted from the sequences of user interactions in the logs. The clickstream model was validated by comparing it to the Getty Research Institute's Architecture and Art Thesaurus. The resulting model was visualized as a journal network that outlines the relationships between various scientific domains and clarifies the connection of the social sciences and humanities to the natural sciences. CONCLUSIONS: Maps of science resulting from large-scale clickstream data provide a detailed, contemporary view of scientific activity and correct the underrepresentation of the social sciences and humanities that is commonly found in citation data.

Reconsidering formative measurement.

The relationship between observable responses and the latent constructs they are purported to measure has received considerable attention recently, with particular focus on what has become known as formative measurement. This alternative to reflective measurement in the area of theory-testing research is examined in the context of the potential for interpretational confounding and a construct's ability to function as a point variable within a larger model. Although these issues have been addressed in the traditional reflective measurement context, the authors suggest that they are particularly relevant in evaluating formative measurement models. On the basis of this analysis, the authors conclude that formative measurement is not an equally attractive alternative to reflective measurement and that whenever possible, in developing new measures or choosing among alternative existing measures, researchers should opt for reflective measurement. In addition, the authors provide guidelines for researchers dealing with existing formative measures.

Participation of African social scientists in malaria control: identifying enabling and constraining factors.

OBJECTIVE: To examine the enabling and constraining factors that influence African social scientists involvement in malaria control. METHODS: Convenience and snowball sampling was used to identify participants. Data collection was conducted in two phases: a mailed survey was followed by in-depth phone interviews with selected individuals chosen from the survey. FINDINGS: Most participants did not necessarily seek malaria as a career path. Having a mentor who provided research and training opportunities, and developing strong technical skills in malaria control and grant or proposal writing facilitated career opportunities in malaria. A paucity of jobs and funding and inadequate technical skills in malaria limited the type and number of opportunities available to social scientists in malaria control. CONCLUSION: Understanding the factors that influence job satisfaction, recruitment and retention in malaria control is necessary for better integration of social scientists into malaria control. However, given the wide array of skills that social scientists have and the variety of deadly diseases competing for attention in Sub Saharan Africa, it might be more cost effective to employ social scientists to work broadly on issues common to communicable diseases in general rather than solely on malaria.

The Nash equilibrium: a perspective.

In 1950, John Nash contributed a remarkable one-page PNAS article that defined and characterized a notion of equilibrium for n- person games. This notion, now called the "Nash equilibrium," has been widely applied and adapted in economics and other behavioral sciences. Indeed, game theory, with the Nash equilibrium as its centerpiece, is becoming the most prominent unifying theory of social science. In this perspective, we summarize the historical context and subsequent impact of Nash's contribution.

The effect of missing data on estimating a respondent's location using ratings data.

In social science research there are a number of instruments that utilize a rating scale such as a Likert response scale. For a number of reasons a respondent's response vector may not contain responses to each item. This study investigated the effect on a respondent's location estimate when a respondent is presented an item, has ample time to answer the item, but decides to not respond to the item. For these situations different strategies have been developed for handling missing data. In this study, four different approaches for handling missing data were investigated for their capability to mitigate against the effect of omitted responses on person location estimation. These methods included ignoring the omitted response, selecting the "midpoint" response category, hot-decking, and a Likelihood-based approach. A Monte Carlo study was performed and the effect of different levels of omissions on the simulees' location estimates was determined. Results showed that the hot-decking procedure performed the best of methods examined. Implications for practitioners were discussed.