Richard J. McNally

Researchers have increasingly adopted complex methodological approaches to investigate the co-development of symptoms over longer time frames, such as months and years. Panel studies assess a large group of individuals at multiple time points over an extended period. Various analytical approaches exist for examining the co-development of variables in panel data, yet a comprehensive overview is lacking. This study provides a detailed review and application of four key analytical approaches for examining cross-lagged associations in panel data. The four approaches include two Structural Equation Models (SEM): the Cross- Lagged Panel Model (CLPM) and the Random-Intercept Cross-Lagged Panel Model (RI- CLPM), along with two network model adaptations: Cross-Lagged Panel Network analysis (CLPN) and Panel Graphical Vector Autoregression Model (GVAR). We describe each method’s distinct characteristics, advantages, and limitations. To illustrate these, we applied these models to a panel dataset of adolescents and young adults (NSPN 2400 cohort study), examining the relationships between impulsivity and symptoms of depression and anxiety. Results showed varying temporal associations, highlighting the importance of model selection based on research objectives and data characteristics. A simulation study demonstrated that models separating within- and between-person effects (panel GVAR, RI- CLPM) reproduced true within-person temporal effects more accurately. Our review highlights the value of using multiple approaches in multiverse analyses to assess the sensitivity of findings to different analytical methods. Ultimately, the choice of analytical method greatly influences how dynamic cross-lagged processes in developmental psychopathology are interpreted, affecting the development and refinement of relevant clinical theories.

Exposure to potentially morally injurious events (PMIE) poses a threat to one’s moral beliefs that can lead to prolonged and impairing mental health outcomes related to moral injury (MI). In a large German-speaking sample ( N = 364, 48.9% female) of high-risk populations (legal, health care, military, security, social sector, press), we administered the Moral Injury Outcome Scale (MIOS) and Moral Injury and Distress Scale (MIDS) to investigate the frequency of PMIE exposure and MI outcome and distress. About three-quarters of the sample endorsed having experienced a PMIE, while 11.5% and 5.5.% screened positive for clinically meaningful MI on the MIDS and MIOS, respectively. Both the MIOS and the MIDS demonstrated good to excellent internal consistency. The CFA provided further evidence for their factorial validity; correlations supported aspects of convergent and discriminant validity. Exposure to PMIE and MI is a highly prevalent phenomenon across different occupational fields in Germany. The German versions of both scales seem suitable for assessing MI outcome and distress.

People differ in their belief that speech can cause lasting psychological harm. We present the ten-item Words Can Harm Scale (WCHS) as a valid and reliable measure of this belief. Items assess attitudes about harmful speech (e.g., "Vulnerable people should not be exposed to certain kinds of speech, as this might harm them") and written words (e.g., "I could be left emotionally scarred by something I read"). In a representative sample of U.S. adults (N = 956), the WCHS demonstrated strong internal consistency (α = .92) and robust two-week test-retest reliability (r = .80). People higher in the belief that words can harm tended to be younger, female, non-White, and politically liberal. People with higher WCHS scores rated themselves as higher in intellectual humility, empathy, moral grandstanding, and the belief in the importance of silencing others. They were also more likely to support political correctness and endorse trigger warnings and safe spaces. People who believed that words can harm had worse mental health: they reported being more anxious and depressed, less resilient, and having more difficulties in emotion regulation. The WCHS is a reliable tool for measuring beliefs about the harmfulness of words—a divisive issue within modern cultural discourse.

Trigger warnings (TWs) aim to alert individuals to potentially distressing content. Most studies found no beneficial effects; some have even reported adverse effects. However, due to their increasing use, there is a large heterogeneity in wording and design across contexts. Inspired by expectation research, this study examined the impact of different TW formulations on negative affect, expectations, symptom experiences (i.e. intrusions), and perceived respect and autonomy. Using the trauma film paradigm, N = 143 healthy participants were randomly assigned to one of three groups: (1) TW with content and emotional reaction details, (2) TW without details, or (3) no TW. There were no group differences in negative affect. Between the TW conditions, participants who received the less specific TW reported lower coping expectations, more intrusions, and greater distress over three days. The wording of a TW seems to have differential effects. Providing both content information and expected emotional reactions appears to be more beneficial.

Trigger warnings and safe space notifications are common in higher education. Although researchers have evaluated these practices as mental health tools, little attention has been paid to the interpersonal signals they send. In this experiment conducted in fall 2024, we examined how trigger warnings and safe space notifications shape students' perceptions of instructors and the classroom environment. We randomly assigned 738 American undergraduate students to view videos of instructors delivering a brief lecture on trauma, preceded by the instructor providing a trigger warning, a safe space notification, both, or neither. Participants rated the instructor's epistemic trustworthiness, concern for student well-being, political orientation, and Left-Wing Authoritarianism scale, as well as their own feelings of psychological safety and willingness to discuss controversial topics in the classroom. Analyses using Bayes Factors provided substantial evidence that trigger warnings had no overall impact on students' perceptions. In contrast, safe space notifications increased students' feelings of psychological safety and willingness to discuss controversial topics. Safe spaces also increased perceptions of instructors as caring and trustworthy but signaled that instructors were liberal and left-wing authoritarian, including the subscale measuring support for top-down censorship. Implications for the use of trigger warnings and safe spaces in educational contexts are discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

In individuals with dissociative identity disorder (DID), a key question is whether individuals in one identity state can access memories formed while being in another identity state. Beker and colleagues (2024) addressed this through a systematic review and four meta-analyses. While the review and two meta-analyses suggested memory transfer between identities, the other two meta-analyses appeared to support inter-identity amnesia. We argue that four systematic errors may explain the conflicting conclusions, including the lack of a control group, inappropriate hypotheses to establish whether amnesia is present, inconsistencies and calculation errors, and problematic reasoning regarding case data versus group-level statistics. In our meta-analyses we corrected for these errors and the results are consistent with the narrative results: both support memory transfer between identities, despite the self-reported inter-identity amnesia in individuals with DID.

Introduction: We tested the “coddling hypothesis” that students’ exposure to campus safetyism — efforts to support student well-being by preventing emotional discomfort (e.g., trigger warnings; safe spaces; deplatforming controversial speakers) — is associated with decreases in their psychological resilience in an observational design. Methods: 107 first-year university students reported their exposure to campus safetyism at four weekly intervals. We tested the relationship between safetyism exposure and various indicators of resilience at endpoint assessment, controlling for baseline resilience. We also assessed students’ own perceived need for safetyism practices.Results: Perceived need for safetyism was positively related to female gender and the belief that words can harm, and negatively related to self-reported resilience. Analysis using Bayes Factors suggested relative evidence for the null hypothesis that self-reported safetyism exposure was unrelated to changes in psychological resilience.Discussion: Exposure to safetyism practices was not associated with changes in student resilience. Implications for higher education are discussed.Keywords: safetyism, resilience, college, mental health

A plethora of studies have pointed to startling worldwide rates of people reporting anxiety related to climate change. However, uncertainties persist regarding the psychological processes that contribute to climate anxiety and how this anxiety can lead to adaptive behaviors (i.e., pro-environmental behaviors) and maladaptive consequences (i.e., impairments in daily functioning). In this study, we took inspiration from decades of basic research on anxiety that emphasizes that the way individuals cope with the unknown and do not tolerate uncertainty can hinder adaptive behaviors and lead to functional impairments. Therefore, we investigated the relationships among intolerance of uncertainty, the experience of climate change, as well as climate anxiety and it (mal)adaptive outcomes. To achieve this, we constructed two network models: a graphical Gaussian model (GGM) to explore the network's structure, potential cluster of variables, and influential nodes; and a directed acyclic graph (DAG) to examine the probabilistic dependencies among variables from data from an international unselected sample (n = 728). Our results indicate that both intolerance of uncertainty and the experience of climate change appear as driving forces of the entire network structure. We position this study within innovative computational network approaches to create data-driven models of climate anxiety.