Open source software improves the reproducibility of scientific research. Because existing open source tools often do not offer dedicated support for longitudinal data collection on phones and computers, we built formr, a study framework that enables researchers to conduct both simple surveys and more intricate studies. With automated email and text message reminders according to any schedule, longitudinal and experience sampling studies become easy to implement. By integrating a web-based API for the statistical programming language R via OpenCPU, formr allows researchers to use a familiar programming language to enable complex features. These can range from adaptive testing to graphical and interactive feedback, to integration with non-survey data sources such as self-trackers or online social network data. Here, we showcase three studies created in formr: a study of couples with dyadic feedback; a longitudinal study over months including social networks, peer, and partner ratings; and a diary study with daily invitations by text message and email and extensive feedback on intraindividual patterns.

People differ in their willingness to take risks. Recent work found that revealed preference tasks (e.g., laboratory lotteries)—a dominant class of measures—are outperformed by survey-based stated preferences, which are more stable and predict real-world risk taking across different domains. How can stated preferences, often criticised as inconsequential “cheap talk,” be more valid and predictive than controlled, incentivized lotteries? In our multimethod study, over 3,000 respondents from population samples answered a single widely used and predictive risk-preference question. Respondents then explained the reasoning behind their answer. They tended to recount diagnostic behaviours and experiences, focusing on voluntary, consequential acts and experiences from which they seemed to infer their risk preference. We found that third-party readers of respondents’ brief memories and explanations reached similar inferences about respondents’ preferences, indicating the intersubjective validity of this information. Our results help unpack the self perception behind stated risk preferences that permits people to draw upon their own understanding of what constitutes diagnostic behaviours and experiences, as revealed in high-stakes situations in the real world.

Previous research reported ovulatory changes in women’s appearance, mate preferences, extra- and in-pair sexual desire and behaviour, but has been criticised for small sample sizes, inappropriate designs, and undisclosed flexibility in analyses. In the present study, we sought to address these criticisms by preregistering our hypotheses and analysis plan and by collecting a large diary sample. We gathered over 26 thousand usable online self-reports in a diary format from 1043 women, of which 421 were naturally cycling. We inferred the fertile period from menstrual onset reports. We used hormonal contraceptive users as a quasi-control group, as they experience menstruation, but not ovulation. We probed our results for robustness to different approaches (including different fertility estimates, different exclusion criteria, adjusting for potential confounds, moderation by methodological factors). We found robust evidence supporting previously reported ovulatory increases in extra-pair desire and behaviour, in-pair desire, and self-perceived desirability, as well as no unexpected associations. Yet, we did not find predicted effects on partner mate retention behaviour, clothing choices, or narcissism. Contrary to some of the earlier literature, partners’ sexual attractiveness did not moderate the cycle shifts. Taken together, the replicability of the existing literature on ovulatory changes was mixed. We conclude with simulation-based recommendations for reading the past literature and for designing future large-scale preregistered within-subject studies to understand ovulatory cycle changes and the effects of hormonal contraception. Interindividual differences in the size of ovulatory changes emerge as an important area for further study.

Path models to test claims about mediation and moderation are a staple in psychology. But applied researchers sometimes do not understand the underlying causal inference problems and thus endorse conclusions that rest on unrealistic assumptions. In this article, we aim to provide a clear explanation for the limited conditions under which standard procedures for mediation and moderation analysis can succeed. We discuss why reversing arrows or comparing model fit indices cannot tell us which model is the right one, and how tests of conditional independence can at least tell us where our model goes wrong. Causal modeling practices in psychology are far from optimal but may be kept alive by domain norms which demand that every article makes some novel claim about processes and boundary conditions. We end with a vision for a different research culture in which causal inference is pursued in a much slower, more deliberate and collaborative manner.

Data from two studies were used to estimate the reliability of facial EMG when used to index facial mimicry (Study 1) or affective reactions to pictorial stimuli (Study 2). Results for individual muscle sites varied between muscles and depending on data treatment. For difference scores, acceptable internal consistencies were found only for corrugator supercilii, and test-retest reliabilities were low. For contrast measures describing patterns of reactions to stimuli, such as high zygomaticus major combined with low corrugator supercilii, acceptable internal consistencies were found for facial reactions to smiling faces and positive affective reactions to affiliative images (Study 2). Facial reactions to negative emotions (Study 1) and facial reactions to power and somewhat less to achievement imagery (Study 2) showed unsatisfactory internal consistencies. For contrast measures, good temporal stability over 24 months (Study 1) and 15 months (Study 2), respectively, was obtained. In Study 1, the effect of method factors such as mode of presentation was more reliable than the emotion effect. Overall, people's facial reactions to affective stimuli seem to be influenced by a variety of factors other than the emotion-eliciting element per se, which resulted in biased internal consistency estimates. However, the influence of these factors in turn seemed to be stable over time.