Over the past four decades, psychometric meta-analysis (PMA) has emerged a key way that psychological disciplines build cumulative scientific knowledge. Despite the importance and popularity of PMA, software implementing the method has tended to be closed-source, inflexible, limited in terms of the psychometric corrections available, cumbersome to use for complex analyses, and/or costly. To overcome these limitations, we created the psychmeta R package: a free, open-source, comprehensive program for PMA.

This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team’s workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings.

Background: Due to an increased risk of sexually transmitted infections (STIs), gay, bisexual and other men who have sex with men (MSM) have been recommended to receive vaccinations against human papillomavirus, meningitis C and hepatitis A/B. This review aimed to compare the rates of vaccine acceptability, uptake and completion, and to identify determinants of vaccine outcomes specific to MSM to inform a theoretical framework.Methods: In January 2020 four databases were explored to identify vaccination behaviours and associated factors among MSM. A narrative systematic review and meta-analysis were performed. Data were synthesised for theoretical modelling. Results: Seventy-eight studies, mostly from the USA, were included. The average vaccine acceptability was 63% (median=72%, range: 30%-97%), vaccine uptake 45% (median=42%, range: 5%-100%) and vaccine completion 47% (median=45%, range: 12%-89%). Six categories of factors associated with vaccination acceptability, uptake and completion were conceptualised:Individual (e.g., demographic and psychosocial); Interpersonal (e.g., peer education); Healthcare provider (e.g., vaccine recommendation); Organisational and practice setting (e.g., routine collection of patient sexual orientation information that is integrated into a clinical decision support system); Community environment (e.g., targeted health promotion campaigns); and National, state and local policy environment (e.g., public health guidelines targeting MSM). Conclusion: Despite overall high levels of acceptability, uptake and completion rates were below targets predicted by cost-effectiveness modelling across all recommended vaccines. These parameters may need to be adjusted for more precise estimations of cost-effectiveness. Addressing the multiple levels of determinants, as outlined in our theoretical framework, will help guide interventions to increase vaccine completion among MSM.

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Abstract. Research interest in personality dynamics over time is rapidly growing. Passive personality assessment via mobile sensors offers an intriguing new approach for measuring a wide variety of personality dynamics. In this paper, we address the possibility of integrating sensor-based assessments to enhance personality dynamics research. We consider a variety of research designs that can incorporate sensor-based measures and address pitfalls and limitations in terms of psychometrics and practical implementation. We also consider analytic challenges related to data quality and model evaluation that researchers must address when applying machine learning methods to translate sensor data into composite personality assessments.