In longitudinal studies involving multiple latent variables, researchers often seek to predict how iterations of latent variables measured at early time points predict iterations measured at later time points. Cross-lagged panel modeling, a form of structural equation modeling, is a useful way to conceptualize and test these relationships. However, prior to making causal claims, researchers must first ensure that the measured constructs are indeed equivalent between time points. To do this, they test for measurement invariance, constructing and comparing a series of increasingly strict and parsimonious models, each making more constraints across time than the last. This comparison process, though challenging, is an important prerequisite to interpretation of results. Fortunately, testing for measurement invariance in cross-lagged panel models has become easier in recent years, thanks to the wide availability of R and its packages. This paper serves as a tutorial in testing for measurement invariance using the lavaan package. Using real data from an openly available study on perfectionism and drinking problems, we provide a step-by-step guide of how to test for longitudinal measurement invariance and interpret the results. Original data source with materials: https://osf.io/gduy4/. Project website with data/syntax for the tutorial: https://osf.io/hwkem/.
Much research on moral judgment is centered on moral dilemmas in which deontological perspectives (i.e., emphasizing rules, individual rights and duties) are in conflict with utilitarian judgements (i.e., following the greater good defined through consequences). A central finding of this field Greene et al. showed that psychological and situational factors (e.g., the intent of the agent, or physical contact between the agent and the victim) play an important role in people’s use of deontological versus utilitarian considerations when making moral decisions. As their study was conducted with US samples, our knowledge is limited concerning the universality of this effect, in general, and the impact of culture on the situational and psychological factors of moral judgments, in particular. Here, we empirically test the universality of deontological and utilitarian judgments by replicating Greene et al.’s experiments on a large (N = X,XXX) and diverse (WEIRD and non-WEIRD) sample across the world to explore the influence of culture on moral judgment. The relevance of this exploration to a broad range of policy-making problems is discussed.
This study examines reactions to a recent evolutionary psychology article that uses self-report data to claim that same-sex attraction in women evolved because men find it a desirable quality in a mate. Our study explores a novel perspective on the article by interviewing 29 women with attraction to women in Halifax, Nova Scotia. Participants read a news article about the original study, then the original article, and were then asked about their thoughts and feelings. Our research questions were as follows: 1) What cognitive and emotional reactions to the article do women with attraction to women experience? 2) Why did this article generate a lot of online criticism, from the perspective of women who are attracted to women? 3) Do papers like this cause women with attraction to women to change their opinions about psychological journals and/or psychological research? Using thematic analysis, we found seven themes: Negative emotion, identity threat, failure to address sociocultural explanations, male/hetero-centric, differences in paradigms, sensationalized news article, and contextualizing. The proliferation of articles like these represents one of many small stressors women attracted to women deal with as a minority population, and this stress can have important implications for health.
Background: Excessive perioperative blood loss is a concern for patients undergoing high risk procedures; thus, tranexamic acid (TXA) is administered at the beginning of surgery to minimize blood loss. Previous studies have had mixed results. The aim of this retrospective chart review is to determine whether low or high dose TXA minimizes blood transfusions and post-operative adverse events.Methods: Retrospective data was obtained on patients undergoing elective, major cardiovascular surgery performed by the same surgeon. Participants were separated into low and high dose TXA groups based on total amount given intra-operatively. Negative binomial regression and t-tests were used to compare primary outcomes, including total blood components transfused and their individual components. Secondary post-operative events (stroke, mortality, MI, seizure, PE, renal failure, and DVT) were rare and compared observationally. Results:The high TXA group received more units of RBCs (2.05 vs. 1.21, p < 0.05), FFP (1.06 vs. 0.66, p < 0.05) and total units transfused (4.40 vs. 2.81, p < 0.05), compared to the low group. There was no significant difference between groups regarding platelet (0.69 vs. 0.46, p = 0.11), cryoprecipitate (0.38 vs. 0.31, p = 0.50) and factor concentrate administration. Secondary events were rare, though 6 patients (7.1%) in the high TXA group had post-operative seizures compared to 2 (2.4%) in the low dose. Conclusion:Higher doses of TXA were associated with increased transfusions among cardiac surgery patients and increased seizures post-operatively. This suggests using a lower dose of TXA could minimize blood transfusion in the perioperative setting and minimize post-operative seizures.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.