Daniel McNeish

Many research designs in behavioral sciences feature repeated measures where multiple datapoints are collected from the same individuals across multiple timepoints. Traditionally, repeated measures...

Smartphones and wearable devices have altered the landscape of longitudinal data analysis by permitting more frequent data collection, in participants’ natural settings, with less reliance on parti...

Purpose: Moral distress is a pervasive challenge in nursing and has been associated with burnout, job dissatisfaction, and turnover. Although Magnet Recognition has been associated with more favorable nursing practice environments and nurse outcomes, little is known about the association between Magnet status and nurses' experiences of moral distress or the role of leadership responsiveness in this association. This study examined whether management responsiveness to patient care concerns accounted for part of the association between hospital Magnet status and nurse moral distress. Design: This was a cross‐sectional observational study. Methods: Data from the 2024 Penn Nurses4All Survey and the 2023 American Hospital Association Annual Survey were linked. The analytic sample included 18,678 direct care nurses from 1829 hospitals across 10 US states. The dependent variable was nurse moral distress, the independent variable was hospital Magnet status, and the mediator was nurse‐reported management responsiveness to patient care concerns. A 2‐1‐1 multilevel mediation model using multilevel structural equation modeling was estimated to distinguish within‐ and between‐hospital associations while accounting for the clustering of nurses within hospitals. Models adjusted for nurse‐ and hospital‐level characteristics. Results: Nurses in Magnet hospitals reported lower levels of moral distress and more favorable assessments of management responsiveness than nurses in non‐Magnet hospitals. At the between‐hospital level, Magnet status was associated with higher management responsiveness (unstandardized coefficient b = 0.18, 95% CI = 0.15 to 0.21, p < 0.001), and higher management responsiveness was associated with lower moral distress ( b = −0.31, 95% CI = −0.39 to −0.23, p < 0.001). Greater management responsiveness was also associated with lower moral distress at the within‐hospital level ( b = −0.34, 95% CI = −0.35 to −0.33, p < 0.001). The direct between‐hospital association between Magnet status and moral distress remained statistically significant after accounting for management responsiveness ( b = −0.05, 95% CI = −0.08 to −0.01, p = 0.011). The between‐hospital indirect association between Magnet status and moral distress involving management responsiveness was statistically significant ( b = −0.06, Monte Carlo 95% CI = −0.08 to −0.04, p < 0.001). Similar findings were observed using administrative responsiveness to nurse concerns. Conclusions: Nurses working in Magnet hospitals reported lower levels of moral distress and more favorable assessments of management responsiveness than nurses working in non‐Magnet hospitals. Management responsiveness statistically accounted for part of the observed between‐hospital association between Magnet status and moral distress. Clinical Relevance: Leadership responsiveness to nurses' patient care concerns may represent an important and potentially modifiable organizational factor associated with moral distress. Organizational strategies to strengthen management responsiveness and nurses' ability to raise patient care concerns warrant further evaluation as approaches to supporting nurse well‐being.

Intensive longitudinal data (ILD) are commonly used to study within-person processes, and psychometric methods for computing within-person reliability with ILD have recently appeared in the methodological literature. The dense, frequent nature of data collection in ILD often leads to copious amounts of missing data-recent reviews find that 30%-40% missing data rates are typical. However, given the nascent state of psychometrics for ILD, the intersection of psychometrics and missing ILD has yet to be explored despite the pervasiveness of missing ILD in empirical studies. In addition, given that ILD are frequently used to study sensitive topics such as mental health and substance use, there is a heightened risk of missing not at random (MNAR) data. That is, the reason data are missing is associated with what the value would have been (e.g., depression responses are missing when a person is momentarily too depressed to respond). The goal of this article is to (a) clarify how missing values affect estimates of within-person reliability with ILD and (b) to extend one recently proposed method (the measurement error autoregressive model) with a Diggle-Kenward selection process to evaluate sensitivity to a potential MNAR mechanism. Simulations in the article find that the accuracy of within-person reliability estimates from standard approaches can deteriorate when missing data are present, but the proposed model can better recover population within-person reliability-even with a large amount of MNAR data-under the conditions and missingness mechanisms studied. Limitations and extensions to other methods for computing within-person reliability are also discussed. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

A primary objective of intensive longitudinal studies is to investigate within-person dynamics. In this context, item heterogeneity plays a critical role, as within-person processes may vary across items within a scale. A common example is the assessment of momentary affect using adjective lists (e.g., sad, angry, anxious, stressed), where each item captures different facets of positive or negative affect, providing unique and non-interchangeable information. However, standard practices often overlook item heterogeneity by aggregating item scores or assuming a single within-person factor in dynamic structural equation models. This simplification does not permit a fine-grained analysis of within-person dynamics and compromises cross-study comparability when item pools differ across studies. In this article, we reanalyze five large-scale intensive longitudinal datasets assessing momentary affect to illustrate how item heterogeneity can be explicitly modeled. We introduce a flexible modeling approach that accommodates item-specific and person-specific dynamics while improving psychometric comparability across studies, based on residual dynamic structural equation modeling with reference items. We compare this method to conventional modeling strategies and provide practical guidance for addressing item heterogeneity in the analysis of intensive longitudinal data.

The prevalence of intensive longitudinal data (ILD) in behavioral science studies has surged as technological advances like smartphones, smartwatches, and wearable devices have facilitated data col...

Many intensive longitudinal studies are interested in topics that are not always amenable to direct physical measurement and instead are often theorized as

Psychological data are often clustered within organizational units, which violates the independence assumption in standard regression models. Clustered errors, multilevel models, and fixed-effects models all address this issue, but in different ways. Disciplinary preferences for approaching clustered data are strong, which can restrict questions researchers ask because certain approaches are better equipped to handle particular types of questions. Resources comparing approaches to facilitate broader understanding of clustered data approaches exist for economists, political scientists, and biostatisticians. These existing resources use concepts and terminology consistent with statistical training in other disciplines, so this article provides a resource using language and principles familiar to psychologists. The article starts by walking through the origin and importance of the independence assumption to motivate the problem and emergence of different solutions in different fields. Then, information on clustered errors, multilevel models, and fixed-effect models is provided, including (a) how each approach addresses independence violations, (b) research questions ideally suited for each approach, and (c) example analyses highlighting advantages and disadvantages. The article then discusses how these approaches are not mutually exclusive but instead can be blended together to create tailor-made models that flexibly accommodate idiosyncrasies in research questions and are robust to nuances of a particular data set. The broader theme is that there is no one-size-fits-all approach to clustered data. The research question-not disciplinary preferences-should inform the statistical approach. Wider appreciation of the landscape of clustered data approaches can expand the questions researchers ask and improve the theoretical foundation of statistical models. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Measurement invariance is a common interest in behavioral research to ensure that scores are comparable across groups or time. Evidence for measurement inv

Background: The Evidence-Based Practice Attitude Scale (EBPAS) is a widely used measurement tool to assess mental health providers’ attitudes toward adopting research-based interventions. To date, this scale has not been used or validated in an interdisciplinary sample of mental health professionals in Latin America. This study investigated the factor structure, psychometric properties, cross-cultural validity, and model fit of the EBPAS in a sample of Spanish-speaking and Latino social workers, counselors, and psychologists. Methods: A culturally and linguistically tailored version of the 15-item EBPAS scale was administered to a sample of Puerto Rican mental health professionals (N = 222) working across various settings, including schools, healthcare clinics, and community organizations. The EBPAS’s scores were derived from four distinct constructs involving willingness to adopt EBPs (i.e., requirements, openness to innovation, appeal, and divergence from research). A Confirmatory Factor Analysis (CFA) examined the psychometric properties of the EBPAS scale. Several first and second-order factor models were specified. A global and approximate fit examination of the measurement model and composite reliability estimation for each subscale was conducted. RStudio version 4.3.1 software was used for the CFA. Results: The CFA supported a first-order factor model. Most subscales showed strong reliability coefficients ranging from 0.83 to 0.91, except for the divergence subscale, which showed a coefficient of 0.77 . After allowing for covariance between two items in the appeal dimension, the correlated factor model demonstrated a satisfactory fit to the data, although some misspecification was observed. Conclusions: The tailored EBPAS-15 demonstrated adequate psychometric properties in this Latinx sample of mental health professionals, suggesting that its factor structure and reliability may be useful in a Spanish-speaking and Caribbean sample of mental health professionals working across a variety of settings and contexts. Findings contribute to the scant literature on culturally and linguistically validated measures examining attitudes toward EBPs in Latin America.