Stephen G. West’s research while affiliated with Eastern Michigan University and other places

Early racial disparities in COVID-19 vaccination rates have been attributed primarily to personal vaccine attitudes and behavior. Little attention has been paid to the possibility that inequitable vaccine distribution may have contributed to racial disparities in vaccine uptake when supplies were most scarce. We test the hypothesis that scarce vaccines were distributed inequitably using the shipping addresses of 385,930 COVID-19 vaccine doses distributed in the first 17 weeks of Pennsylvania’s Phase 1 rollout (December 14, 2020 through April 12, 2021). All shipments we analyze were allocated via the Federal Retail Pharmacy Program, a public-private partnership coordinated by the Centers for Disease Control and Prevention. Overall, White people had an average of 81.4% more retail pharmacy program doses shipped to their neighborhoods than did Black people. Regression models reveal that weekly vaccine allocations determined by pharmacy chains—rather than initial shipment and administration site decisions requiring state and federal approval—drove these effects. All findings remained consistent after controlling for neighborhood differences in income, population density, insurance coverage, number of pharmacies, and other social determinants of health. Our findings suggest that the private distribution of scarce public resources should be assessed for racial impact, regulated as public resources, and monitored continuously.

Coping Power (CP) is a preventive intervention that focuses on reducing child externalizing problems. Although it is typically delivered in a group format (GCP), individually-delivered CP (ICP) has produced greater mean reductions in externalizing problems. However, standard analysis of randomized trials loses individual-level information regarding which youth improve, fail to improve, or get worse, whereas clinically significant change (CSC) metrics capture information on individual change. The present study is a secondary analysis of an ICP/GCP trial (N=360) that examines differences in CSC-based individual-level inferences on externalizing. A novel method for assessing CSC under measurement error-corrected multilevel modeling was used, overcoming three limitations of traditional CSC methods: a) restriction to two timepoints, b) use of total scores, and c) assumption of constant reliability across time and participants. Because of concerns about Type II errors with all CSC methods, an individual-level effect size metric for CSC was also developed. Based on individualized Cohen’s d estimates, individual-level improvements in externalizing from 4th through 11th grades of d ≥ .5 were significantly greater in ICP (73%) versus GCP (45%). Further, GCP saw significantly higher percentages of youth with worsening of externalizing, underscoring concerns about diminished effects for GCP. Half of the sample had improvement that was not statistically significant but exceeded d ≥ .5, highlighting the susceptibility to Type II errors of CSCs results based on statistical significance. An examination of ICP/GCP differences under advanced CSC analysis gives more nuanced information than conventional RCT analysis and greater precision in estimating individual-level outcomes than standard CSC methodologies.

Psychologists leverage longitudinal designs to examine the causal effects of a focal predictor (i.e., treatment or exposure) over time. But causal inference of naturally observed time-varying treatments is complicated by treatment-dependent confounding in which earlier treatments affect confounders of later treatments. In this tutorial article, we introduce psychologists to an established solution to this problem from the causal inference literature: the parametric g-computation formula. We explain why the g-formula is effective at handling treatment-dependent confounding. We demonstrate that the parametric g-formula is conceptually intuitive, easy to implement, and well-suited for psychological research. We first clarify that the parametric g-formula essentially utilizes a series of statistical models to estimate the joint distribution of all post-treatment variables. These statistical models can be readily specified as standard multiple linear regression functions. We leverage this insight to implement the parametric g-formula using lavaan, a widely adopted R package for structural equation modeling. Moreover, we describe how the parametric g-formula may be used to estimate a marginal structural model whose causal parameters parsimoniously encode time-varying treatment effects. We hope this accessible introduction to the parametric g-formula will equip psychologists with an analytic tool to address their causal inquiries using longitudinal data.

Dispositional communication competencies can be assessed in (a) a generic form that does not include any reference to a particular medium of interaction or in (b) a communication medium-specific version. To date, little is known about the specific media that individuals use as a reference and the weights they assign to them when responding to generic communication items – an important research gap because the use of diverse communication media has risen considerably during the COVID-19 pandemic. Based on media theories, two hypotheses were derived: Generic ratings contain a “hidden” face-to-face (FtF) communication framing that is dominant in the cognitive processing (media naturalness perspective) versus media are equally weighted in the mental aggregate of respondents (adaptation perspective). According to a preregistered study plan, generic and medium-specific communication items were assessed to investigate these hypotheses (referencing FtF, videoconferencing, chat, and e-mail interaction contexts). Training (n = 200) and test (n = 389) datasets were analyzed using latent variable modeling. Results indicated that generic ratings have a strong hidden FtF framing. These hidden framings impact the predictive power of the competencies to explain communication criteria (i.e. communication satisfaction). Exploratory analyses indicated that individual differences in media experience may affect the framings.

Psychiatric epidemiologists, developmental psychopathologists, prevention scientists, and treatment researchers have long speculated that treating child anxiety disorders could prevent alcohol and other drug use disorders in young adulthood. A primary challenge in examining long-term effects of anxiety disorder treatment from randomized controlled trials is that all participants receive an immediate or delayed study-related treatment prior to long-term follow-up assessment. Thus, if a long-term follow-up is conducted, a comparison condition no longer exists within the trial. Quasi-experimental designs (QEDs) pairing such clinical samples with comparable untreated epidemiological samples offer a method of addressing this challenge. Selection bias, often a concern in QEDs, can be mitigated by propensity score weighting. A second challenge may arise because the clinical and epidemiological studies may not have used identical measures, necessitating Integrative Data Analysis (IDA) for measure harmonization and scale score estimation. The present study uses a combination of propensity score weighting, zero-inflated mixture moderated nonlinear factor analysis (ZIM-MNLFA), and potential outcomes mediation in a child anxiety treatment QED/IDA (n = 396). Under propensity score–weighted potential outcomes mediation, CBT led to reductions in substance use disorder severity, the effects of which were mediated by reductions in anxiety severity in young adulthood. Sensitivity analyses highlighted the importance of attending to multiple types of bias. This study illustrates how hybrid QED/IDAs can be used in secondary prevention contexts for improved measurement and causal inference, particularly when control participants in clinical trials receive study-related treatment prior to long-term assessment.

A large body of research has examined the link between personality and face-to-face (FtF) communication knowledge, skills, abilities, and other characteristics (KSAOs). With the rise of digital media, text-based computer-mediated (CM) communication KSAOs have gained increasing attention. We conducted two studies to investigate how personality relates to KSAOs in the different contexts of FtF and CM communication. Contrasting perspectives hypothesize that the results in the FtF and CM contexts would be very similar or distinctly different. In Study 1 (n = 454), an online panel study, the Big Five personality dimensions were assessed and their relationships to FtF and CM communication KSAOs were investigated. Structural equation models and relative weight regression analyses showed that these personality dimensions, mostly extraversion and neuroticism, explained more variance in FtF as compared to CM communication KSAOs. Study 2 (n = 173), conducted in a laboratory context, showed similar results compared to Study 1. In addition, when the Big Five personality dimensions were assessed with a CM frame of reference, more variance was explained in CM than in FtF communication KSAOs. These results point to the importance of considering context effects in communication and in personality research: FtF and CM communication KSAOs need to be differentiated. If not properly contextualized, the relevance of personality and communication competencies in predicting criteria may be underestimated due to contextual mismatches.

To determine the extent to which secure attachment moderates the effects of previous child abuse history on the intermediate variables (putative mediators) of emotion dysregulation and coping, which, in turn, influence adult behavioral health and mental health problems. Black women (N = 440, Mage = 20.33, SD = 1.88) were selected from the baseline data collection of a large, randomized trial. Study participants had consumed alcohol, had had unprotected sex in the last 90 days, and either reported abuse prior to age 18 or no lifetime history of abuse. Women completed measures of sociodemographics, abuse history, attachment security, coping, emotion dysregulation, psychological functioning, risky sexual behavior, and substance use problems. At low attachment security, the conditional indirect effects of childhood abuse through the intermediate variable, coping, were statistically significant for all dependent variables except proportion condom use and perceived stress. At high attachment security, none of the conditional indirect effects through coping achieved statistical significance. High attachment security also mitigated the conditional indirect effects of childhood abuse through the intermediate variable, emotion dysregulation, reducing the magnitude of the relationship with trait anger, depression, marijuana problems, and perceived stress by about 50%. These results demonstrate the potential mitigating effects of secure attachment on the relationship between childhood abuse history and select behavioral and mental health problems through the intermediate variables of coping and emotional dysregulation.

It is often claimed that only experiments can support strong causal inferences and therefore they should be privileged in the behavioral sciences. We disagree. Overvaluing experiments results in their overuse both by researchers and decision makers and in an underappreciation of their shortcomings. Neglect of other methods often follows. Experiments can suggest whether X causes Y in a specific experimental setting; however, they often fail to elucidate either the mechanisms responsible for an effect or the strength of an effect in everyday natural settings. In this article, we consider two overarching issues. First, experiments have important limitations. We highlight problems with external, construct, statistical-conclusion, and internal validity; replicability; and conceptual issues associated with simple X causes Y thinking. Second, quasi-experimental and nonexperimental methods are absolutely essential. As well as themselves estimating causal effects, these other methods can provide information and understanding that goes beyond that provided by experiments. A research program progresses best when experiments are not treated as privileged but instead are combined with these other methods.