Expressions of Emotion in Intergroup Apologies and Forgiveness: The moderating role of percieved perpetrator morality

Apologies for intergroup harm have become increasingly common. Despite this, the evidence for the effectiveness of intergroup apologies in promoting forgiveness is mixed. One reason for the mixed findings across studies may be that victim groups attempt to infer the emotions perpetrators are experiencing. The emotions perpetrators express may play an important role in communicating the perpetrator group's motivation for apologizing. Three studies investigated how expressions of emotions in an intergroup apology influenced forgiveness of the perpetrator group. The perceived morality of the perpetrator group emerged as an important moderator of the relationship between the emotion expressed in an apology and forgiveness. When the emotion expressed in an apology is inconsistent with the perceived morality of perpetrators, forgiveness decreased. For example, when perpetrators were moral and expressed guilt (consistency between an emotion and morality), forgiveness was higher than when perpetrators were immoral and expressed guilt (inconsistency between emotion and morality). Implications for research and policy concerning when intergroup apologies can promote reconciliation are discussed

Planned Missing Data Designs & Small Sample Size: How Small is Too Small?

Utilizing planned missing data (PMD) designs (ex. 3-form surveys) enables researchers to ask participants fewer questions during the data collection process. An important question, however, is just how few participants are needed to effectively employ planned missing data designs in research studies. This paper explores this question by using simulated three-form planned missing data to assess analytic model convergence, parameter estimate bias, standard error bias, mean squared error (MSE), and relative efficiency (RE).Three models were examined: a one-time point, cross-sectional model with 3 constructs; a two-time point model with 3 constructs at each time point; and a three-time point, mediation model with 3 constructs over three time points. Both full-information maximum likelihood (FIML) and multiple imputation (MI) were used to handle the missing data. Models were found to meet convergence rate and acceptable bias criteria with FIML at smaller sample sizes than with MI

Beyond the Lines: Exploring the Impact of Adverse Childhood Experiences on NCAA Student-Athlete Health

Emerging research has highlighted a link between adverse childhood experiences (ACEs) and various health concerns experienced by NCAA student-athletes. Building on prior work (Kaier, Cromer, Davis, & Strunk, 2015), we hypothesized that ACEs would significantly predict student-athletes’ biopsychosocial (BPS) health and that spirituality would serve as a protective factor against the effect of ACEs on BPS health outcomes. Division I, II, and III NCAA student-athletes (N = 477) representing 20 sports across 53 universities completed an online quantitative survey (k = 133) that assessed for ACEs, injury/physical health concerns, anxiety, depression, stress, social support, substance use, and spirituality. Nearly two-thirds (64.5%) of student-athletes endorsed at least one ACE. Structural Equation Models (SEMs) yielded significant positive relationships between ACEs and anxiety, depression, perceived stress, injury/health problems, and substance use, and a negative relationship with social support while controlling for sex, race, school, and division. Additionally, spirituality had a significant negative effect on anxiety, depression, perceived stress, injury/health problems, and substance use, and a positive effect on social support. SEM moderation analyses indicated that spirituality only moderated the relationship between ACEs and substance use. Specifically, at average and high levels of spirituality, the relationship between ACEs and substance use was stronger. Clinical implications, study limitations, and future research directions are discussed

Methods for handling missing data for multiple-item questionnaires

Missing data is a common problem, especially in the social and behavioral sciences. Modern missing data methods are underutilized in the industrial/organizational psychology and human resource management literature. This topic has gained increasing attention due to technological advancements in statistical software, although recommendations for handling missing data and default options in software packages often use outdated, suboptimal methods for missing data. Resulting analyses tend to be biased, underpowered, or both. Best practice recommends for the handling of missing data includes the use of multiple imputation (MI) methods, in which missing values are filled in multiple times with predicted values, analyzed, and combined to produce one overall dataset. However this method is often ignored in favor of more convenient methods. For industrial/organizational psychologists, missing data is particularly problematic on multiple-item questionnaires, such as the Counterproductive Work Behavior Checklist (CWB-C). This commonly occurs when participants choose not to respond to a certain item or items on a questionnaire. Person mean imputation is one of the most common methods used to handle missing data on multiple-item questionnaires. This method involves filling in missing data with an average score from the other items for a single dimension and participant. However, it makes strong assumptions about the missing data mechanism and the underlying factor structure of a measure and should be avoided, particularly if data has a high rate of non-response. MI does not make the same assumptions as person mean imputation and may be a superior method when items are missing from a multiple-item questionnaire. Therefore, this study aims to provide recommendations via Monte Carlo simulations with data that is both missing at random (MAR) and missing completely at random (MCAR) for the use of MI methods or person mean imputation when investigating correlations among scale scores for multi-item questionnaires such as the CWB-C

Probing Latent Interactions Estimated with a Residual Centering Approach

Understanding latent interactions is an important need for the structural equation modeler. Plotting and probing latent interactions, however, has not been well defined. We describe methods for plotting and probing two- and three-way latent interactions fit with a variety of approaches (LMS/QML, residual centering, double mean centering). The methods are demonstrated through a small simulation and examples based on existing data

The From Survivor to Thriver Program: RCT of an Online Therapist-Facilitated Program for Rape-Related PTSD.

This study evaluated the efficacy of the From Survivor to Thriver program, an interactive, online therapist-facilitated cognitive-behavioral program for rape-related PTSD. Eighty-seven college women with rape-related PTSD were randomized to complete the interactive program (n = 46) or a psycho-educational self-help website (n = 41). Both programs led to large reductions in interview-assessed PTSD at post-treatment (interactive d = 2.22, psycho-educational d = 1.10), which were maintained at three month follow-up. Both also led to medium- to large-sized reductions in self-reported depressive and general anxiety symptoms. Follow-up analyses supported that the therapist-facilitated interactive program led to superior outcomes among those with higher pre-treatment PTSD whereas the psycho-educational self-help website led to superior outcomes for individuals with lower pre-treatment PTSD. Future research should examine the efficacy and effectiveness of online interventions for rape-related PTSD including whether treatment intensity matching could be utilized to maximize outcomes and therapist resource efficiency

Methods of identifying delirium: A research protocol

Delirium is an acute disorder affecting up to 80% of intensive care unit (ICU) patients. It is associated with a 10‐fold increase in cognitive impairment, triples the rate of in‐hospital mortality, and costs $164 billion annually. Delirium acutely affects attention and global cognitive function with fluctuating symptoms caused by underlying organic etiologies. Early detection is crucial because the longer a patient experiences delirium the worse it becomes and the harder it is to treat. Currently, identification is through intermittent clinical assessment using standardized tools, like the Confusion Assessment Method for ICU. Such tools work well in clinical research but do not translate well into clinical practice because they are subjective, intermittent and have low sensitivity. As such, healthcare providers using these tools fail to recognize delirium symptoms as much as 80% of the time. Delirium‐related biochemical derangement leads to electrical changes in electroencephalographic (EEG) patterns followed by behavioral signs and symptoms. However, continuous EEG monitoring is not feasible due to cost and need for skilled interpretation. Studies using limited‐lead EEG show large differences between patients with and without delirium while discriminating delirium from other causes. The Ceribell is a limited‐lead device that analyzes EEG. If it is capable of detecting delirium, it would provide an objective physiological monitor to identify delirium before symptom onset. This pilot study was designed to explore relationships between Ceribell and delirium status. Completion of this study will provide a foundation for further research regarding delirium status using the Ceribell data

Power to Detect What? Considerations for Planning and Evaluating Sample Size

In the wake of the replication crisis, social and personality psychologists have increased attention to power analysis and the adequacy of sample sizes. In this paper, we analyze current controversies in this area, including choosing effect sizes, why and whether power analyses should be conducted on already-collected data, how to mitigate negative effects of sample size criteria on specific kinds of research, and which power criterion to use. For novel research questions, we advocate that researchers base sample sizes on effects that are likely to be cost-effective for other people to implement (in applied settings) or to study (in basic research settings), given the limitations of interest-based minimums or field-wide effect sizes. We discuss two alternatives to power analysis, precision analysis and sequential analysis, and end with recommendations for improving the practices of researchers, reviewers, and journal editors in social-personality psychology