Paranormal Experience Profiles and Their Association With Variations in Executive Functions: A Latent Profile Analysis

This study investigated relationships between inter-class variations in paranormal experience and executive functions. A sample of 516 adults completed self-report measures assessing personal encounter-based paranormal occurrences (i.e., Experience, Practitioner Visiting, and Ability), executive functions (i.e., General Executive Function, Working and Everyday Memory, and Decision Making) together with Emotion Regulation and Belief in the Paranormal. Paranormal belief served as a measure of convergent validity for experience-based phenomena. Latent profile analysis (LPA) combined experience-based indices into four classes based on sample subpopulation scores. Multivariate analysis of variance (MANOVA) then examined interclass differences. Results revealed that breadth of paranormal experience was associated with higher levels of executive functioning difficulties for General Executive Function, Working Memory, Decision Making, and Belief in the Paranormal. On the Everyday Memory Questionnaire, scores differed on Attention Tracking (focus loss) and Factor 3 (visual reconstruction), but not Retrieval (distinct memory failure). In the case of the Emotion Regulation Scale, class scores varied on Expressive Suppression (control), however, no difference was evident on Cognitive Reappraisal (reframing). Overall, inter-class comparisons identified subtle differences in executive functions related to experience. Since the present study was exploratory, sampled only a limited subset of executive functions, and used subjective, self-report measures, further research is necessary to confirm these outcomes. This should employ objective tests and include a broader range of executive functions.

Heterogeneity of Prevalence of Social Media Addiction Across Multiple Classification Schemes: Latent Profile Analysis

Background As social media is a major channel of interpersonal communication in the digital age, social media addiction has emerged as a novel mental health issue that has raised considerable concerns among researchers, health professionals, policy makers, mass media, and the general public. Objective The aim of this study is to examine the prevalence of social media addiction derived from 4 major classification schemes (strict monothetic, strict polythetic, monothetic, and polythetic), with latent profiles embedded in the empirical data adopted as the benchmark for comparison. The extent of matching between the classification of each scheme and the actual data pattern was evaluated using sensitivity and specificity analyses. The associations between social media addiction and 2 comorbid mental health conditions—depression and anxiety—were investigated. Methods A cross-sectional web-based survey was conducted, and the replicability of findings was assessed in 2 independent samples comprising 573 adults from the United Kingdom (261/573, 45.6% men; mean age 43.62 years, SD 12.24 years) and 474 adults from the United States (224/474, 47.4% men; mean age 44.67 years, SD 12.99 years). The demographic characteristics of both samples were similar to those of their respective populations. Results The prevalence estimates of social media addiction varied across the classification schemes, ranging from 1% to 15% for the UK sample and 0% to 11% for the US sample. The latent profile analysis identified 3 latent groups for both samples: low-risk, at-risk, and high-risk. The sensitivity, specificity, and negative predictive values were high (83%-100%) for all classification schemes, except for the relatively lower sensitivity (73%-74%) for the polythetic scheme. However, the polythetic scheme had high positive predictive values (88%-94%), whereas such values were low (2%-43%) for the other 3 classification schemes. The group membership yielded by the polythetic scheme was largely consistent (95%-96%) with that of the benchmark. Conclusions Among the classification schemes, the polythetic scheme is more well-balanced across all 4 indices.

Latent Profiles of Writing-related Skills, Knowledge, and Motivation for Elementary Students and Their Relations to Writing Performance across Multiple Genres

Our goal in this study is to expand the limited research on writer profiles using the advantageous model-based approach of latent profile analysis and independent tasks to evaluate aspects of individual knowledge, motivation, and cognitive processes that align with Hayes’ (1996) writing framework, which has received empirical support. We address three research questions. First, what latent profiles are observed for late elementary writers using measures aligned with an empirically validated model of writing? Second, do student sociodemographic characteristics—namely grade, gender, English learner status, and special education status—influence latent profile membership? Third, how does student performance on narrative, opinion, and informative writing tasks, determined by quality of writing, vary by latent profiles? A five-profile model had the best fit statistics and classified student writers as Globally Weak, At Risk, Average Motivated, Average Unmotivated, and Globally Proficient. Overall, fifth graders, female students, students without disabilities, and native English speakers had greater odds of being in the Globally Proficient group of writers. For all three genres, other latent profiles were significantly inversely related to the average quality of papers written by students who were classified as Globally Proficient; however, the Globally Weak and At Risk writers were not significantly different in their writing quality, and the Average Motivated and Average Unmotivated writers did not significantly differ from each other with respect to quality. These findings indicate upper elementary students exhibit distinct patterns of writing-related strengths and weaknesses that necessitate comprehensive yet differentiated instruction to address skills, knowledge, and motivation to yield desirable outcomes.

Use of latent profile analysis and k-means clustering to identify student anxiety profiles

Background Anxiety disorders are often the first presentation of psychopathology in youth and are considered the most common psychiatric disorders in children and adolescents. This study aimed to identify distinct student anxiety profiles to develop targeted interventions. Methods A cross-sectional study was conducted with 9738 students in Yingshan County. Background characteristics were collected and Mental Health Test (MHT) were completed. Latent profile analysis (LPA) was applied to define student anxiety profiles, and then the analysis was repeated using k-means clustering. Results LPA yielded 3 profiles: the low-risk, mild-risk and high-risk groups, which comprised 29.5, 38.1 and 32.4% of the sample, respectively. Repeating the analysis using k-means clustering resulted in similar groupings. Most students in a particular k-means cluster were primarily in a single LPA-derived student profile. The multinomial ordinal logistic regression results showed that the high-risk group was more likely to be female, junior, and introverted, to live in a town, to have lower or average academic performance, to have heavy or average academic pressure, and to be in schools that have never or occasionally have organized mental health education activities. Conclusions The findings suggest that students with anxiety symptoms may be categorized into distinct profiles that are amenable to varying strategies for coordinated interventions.

Identifying Distinct Patterns of Social Media Usage During the Transition into College as Predictors of First-Year Alcohol Use and Consequences: A Latent Profile Analysis

Social media (SM) users are a combination of several behaviors across platforms. Patterns of SM use across platforms may be a better indicator of risky drinking than individual behaviors or sets of behaviors examined previously. This longitudinal study addressed this gap in the literature using latent profile analysis (LPA) to identify subpopulations of SM users during the college transition (N=319). Indicators included in the LPA were general SM (checking, time spent, and posting to Instagram/Facebook/Snapchat; Finstagram ownership) and alcohol-related posting (alcohol, partying, and marijuana content) behaviors. LPA results revealed three SM user subpopulations at baseline: low general use with low alcohol-related posting (LGU+LAP), and high general use with low alcohol-related posting (HGU+LAP) or high alcohol-related posting (HGU+HAP). Baseline drinking, injunctive norms, and alcohol beliefs were associated with greater odds of HGU+HAP membership. Prospective analyses revealed that HGU+HAP was associated with greater alcohol use and consequences relative to HGU+LAP and LGU+LAP. Results suggest that there are distinct patterns of SM use during the college transition associated with risky drinking that can inform interventions combating SM-related alcohol risks. These findings also illustrate the importance of analyzing multiple SM user behaviors across multiple platforms simultaneously in future studies.