The association between school exclusion,delinquency and subtypes of cyber- and F2F-victimizations: Identifying and predicting risk profiles and subtypes using latent class analysis

This purpose of this paper is to identify risk profiles of youth who are victimized by on- and offline harassment and to explore the consequences of victimization on school outcomes. Latent class analysis is used to explore the overlap and co-occurrence of different clusters of victims and to examine the relationship between class membership and school exclusion and delinquency. Participants were a random sample of youth between the ages of 12 and 18 selected for inclusion to participate in the 2011 National Crime Victimization Survey: School Supplement. The latent class analysis resulted in four categories of victims: approximately 3.1% of students were highly victimized by both bullying and cyberbullying behaviors; 11.6% of youth were classified as being victims of relational bullying, verbal bullying and cyberbullying; a third class of students were victims of relational bullying, verbal bullying and physical bullying but were not cyberbullied (8%); the fourth and final class, characteristic of the majority of students (77.3%), was comprised of non-victims. The inclusion of covariates to the latent class model indicated that gender, grade and race were significant predictors of at least one of the four victim classes. School delinquency measures were included as distal outcomes to test for both overall and pairwise associations between classes. With one exception, the results were indicative of a significant relationship between school delinquency and the victim subtypes. Implications for these findings are discussed.     

Performance of the Grade of Membership Model Under a Variety of Sample Sizes,Group Size Ratios,and Differential Group Response Probabilities for Dichotomous Indicators

Social scientists are frequently interested in identifying latent subgroups within the population, based on a set of observed variables. One of the more common tools for this purpose is latent class analysis (LCA), which models a scenario involving k finite and mutually exclusive classes within the population. An alternative approach to this problem is presented by the grade of membership (GoM) model, in which individuals are assumed to have partial membership in multiple population subgroups. In this respect, it differs from the hard groupings associated with LCA. The current Monte Carlo simulation study extended on prior work on the GoM by investigating its ability to recover underlying subgroups in the population for a variety of sample sizes, latent group size ratios, and differing group response profiles. In addition, this study compared the performance of GoM with that of LCA. Results demonstrated that when the underlying process conforms to the GoM model form, the GoM approach yielded more accurate classification results than did LCA. In addition, it was found that the GoM modeling paradigm yielded accurate results for samples as small as 200, even when latent subgroups were very unequal in size. Implications for practice were discussed.     

Patterns of victimization: A person-centered approach to physical and sexual violence in a representative sample of Danish youth

Professionals and policy makers have only recently begun to recognize the extent to which different types of victimization are interconnected. To enhance our knowledge of the co-occurrence of physical and sexual violence across childhood and adolescence/early adulthood, the present study investigated distinct typologies of victimization in a sample of adolescents and young adults and explored the relationship between victimization typologies and gender, age, and mental health. Data from the Danish 2013 National Health Survey (“How are you?”) were used. Latent class analysis (i.e., a person-centered approach) was used to identify typologies of physical and sexual victimization in a representative sample of 3812 adolescents and young adults aged 16–24 years. Five distinct victimization typologies emerged: a normative, non-victimization group (76%), a physical victimization in adolescence/early adulthood group (13%), a physical revictimization group (5%), a poly-victimization group (4%), and a physical victimization in childhood group (2%). Physical revictimization, poly-victimization, and physical victimization in childhood were highly associated with poor mental health status. Using a latent variable modeling approach, we identified meaningful subgroups of the victimized population. Two subgroups (i.e., physical revictimization and poly-victimization), which comprised a third of all individuals in the victimization subgroups, had a high probability of exposure to multiple traumatic events and poor mental health. These study findings underscore the need to promote interventions addressing the co-occurrence of physical and sexual victimization and the persistence of victimization over time.     

Patterns of childhood maltreatment and intimate partner violence,emotion dysregulation,and mental health symptoms among lesbian,gay, and bisexual emerging adults: A three-step latent class approach

BackgroundChildhood abuse and neglect (CAN) and intimate partner violence victimization (IPV) is prevalent among lesbian, gay, and bisexual individuals (LGB). Identification of distinct patterns of childhood and adult victimization, including technology-mediated and face-to-face IPV, and their cumulative relations to mental/behavioral health challenges, among LGB people is needed to facilitate identification of at-risk individuals.ObjectiveUsing latent class analysis, we first sought to identify patterns of lifetime interpersonal victimization, primarily five types of CAN and IPV in LGB emerging adults. Second, we examined if LGB-status and race/ethnicity predicted class-membership; third, we assessed differences between the latent classes on emotion dysregulation, depressive and anxiety symptoms, and alcohol use.ParticipantsParticipants were 288 LGB adults between 18–29 years (M = 25.35, SD = 2.76; 41.7% gay/lesbian) recruited via Amazon MTurk.Methods and resultsThe 3-step LCA identified five-latent classes: high victimization, childhood emotional abuse and neglect, cybervictimization, adult face-to-face IPV, and lower victimization. People of color (including Hispanics) were more likely to be in the high victimization class, and bisexual individuals, especially bisexual women, in the childhood emotional abuse and neglect class. High victimization and childhood emotional abuse and neglect classes had elevated emotion dysregulation levels and depression and anxiety symptoms, and the high victimization class reported the highest levels of alcohol use.ConclusionFindings suggest a detrimental effect of cumulative interpersonal victimization on emotion dysregulation and the mental/behavioral health of LGB emerging adults, with bisexuals and LGB-people of color at heightened risk of cumulative victimization and of related mental/behavioral health challenges.     

The Impact and Interpretation of Modeling Residual Noninvariance in Growth Mixture Models

Current practices for growth mixture modeling emphasize the importance of the proper parameterization and number of classes, but the impact of these decisions on latent class composition and the substantive implications has not been thoroughly addressed. Using measures of behavior from 575 middle school students, we compared the results of several multilevel growth mixture models. Results indicated a dramatic shift in class assignment as the models allowed class-varying parameters, with different substantive interpretations and resulting typologies. This research suggests that using variability as a criterion for class differences in a behavior typology can dramatically impact latent class membership. This study describes decisions and results from testing for noninvariance, with particular emphasis on how decisions about the nature of within-person variance can affect resulting subgroups and model parameters.     

Trajectory subgroups of perceived emotional support from teachers: Associations with change in mastery climate and intentions to quit upper secondary school

The aims of this three-wave longitudinal study were to identify and describe trajectories of perceived emotional support from teachers and investigate whether these trajectories were related to the development of intentions to quit upper secondary school via change in perceived mastery climate. Among 1379 Norwegian upper secondary school students, three trajectory subgroups were identified: stable high (84.9%), decreasing (7.8%), and low increasing (7.3%). The subgroups differed in levels of achievement ambition and academic self-concept. Further, a parallel process latent growth curve model revealed essential associations with change in intentions to quit school. Specifically, students with high probabilities of membership in the decreasing emotional support subgroup appeared to be at particular risk, perceiving a decrease in mastery climate that was related to a worrying development of intentions to quit school. The results are discussed considering the importance of a sustained supportive learning environment for late adolescents.     

What are the Conditions Associated with Subscore Added Value Noninvariance? Implications for Improving Subscore Interpretation Fairness

Subscore added value analyses assume invariance across test taking populations; however, this assumption may be untenable in practice as differential subdomain relationships may be present among subgroups. The purpose of this simulation study was to understand the conditions associated with subscore added value noninvariance when manipulating: (a) subdomain test length, (b) differences in subgroup mean ability, and (c) subgroup differences in intersubdomain correlations. Results demonstrated that subscore added value was noninvariant for 24–100% of replications (depending on subdomain test length) when the subgroup difference in intersubdomain correlation was equal to .30. To examine if this condition was met in practice, applied invariance analyses of three operational testing programs were conducted. Across these datasets, noninvariant subscore added value was present for some subdomains across sex and ethnic subgroups. Overall, these results indicate that subscore added value noninvariance is largely driven by differential intersubdomain correlations among subgroups, which may be present in some operational testing programs.     

Adaptive Behavior,Social Adjustment,and Academic Achievement of Regular and Special Education Children

The purpose of the present study was to compare the adaptive behavior, social adjustment and academic achievement of educable mentally retarded, learning disabled, behavior disordered, and regular class children. A sample of 120 children, aged 7 through 9 years, was selected from class rolls on the basis of group membership, race and sex. Significant differences were found in adaptive behavior, social adjustment, and academic achievement among the four groups; none of the instruments, however, was able to differentiate among all four categories of children. Adaptive behavior and academic achievement differentiated EMR and regular class children from the other two groups, while social adjustment appeared to be the most effective variable in differentiating BD children from the other three categories. Findings of this study support the notion that measures of these abilities provide information of use to teachers, psychologists, and placement committees in their attempts to provide the most appropriate and meaningful education for students with special needs.     

Extending poly-victimization theory: Differential effects of adolescents’ experiences of victimization on substance use disorder diagnoses upon treatment entry

BackgroundAlthough victimization is a known contributor to the development of substance use disorders, no research has simultaneously examined how characteristics of victimization experienced over time, such as the type of abuse, the presence of poly-victimization, closeness to perpetrator(s), life threat or fear, and negative social reactions to disclosing victimization, cluster into profiles that predict substance use disorders.ObjectiveThe aim of the current study is to assess how profiles of victimization and trauma characteristics are associated with substance use disorders and assess potential gender differences.Participants and SettingParticipants were 20,092 adolescents entering substance use treatment.MethodsWe used latent class and multi-group latent class analysis to extract classes of victimization and associated characteristics. Emergent classes were used to predicted substance use disorder status at treatment intake.ResultsFive classes were extracted: poly-victimization + high harmful trauma characteristics, sexual abuse + negative social reaction and perceived life threat, emotional abuse + trusted perpetrator, physical abuse and low all. Similar classes were found for the multi-group model. In both the overall and female-specific models, the poly-victimization + high harmful trauma characteristics class was more severe than all other classes in terms of opioid use disorder, tobacco use disorder, and dual diagnosis. Other class differences were found across gender.ConclusionsAdolescents entering treatment can be distinguished by their profiles of victimization experiences and associated characteristics, and these profiles evidence different associations with substance use disorder diagnoses. Results point to a need for more nuanced assessment of victimization experiences and gender-specific interventions.     

Patterns of adversity and pathways to health among White,Black, and Latinx youth

Research has demonstrated the negative impact of Adverse Childhood Experiences (ACEs) on long-term trajectories of mental and physical health. Yet existing literature on this topic is limited in its understanding of outcomes among youth samples, optimal measurement items and methods, and differences in adverse experiences across race/ethnicity. The current study used a person-centered approach to measure ACEs and their impact on youth health outcomes across three different racial/ethnic groups from a large national database. Patterns of exposure to adverse experiences among Black, Latinx, and White youth (N = 30,668, ages 12–17) were determined empirically using latent class analysis (LCA). Significant differences in class membership by demographic indicators (age, household income, sex) and concurrent health outcomes were identified. Different models emerged for Black (2 classes), Latinx (3 classes), and White youth (3 classes). Older and lower-income youth were more likely to have experienced adversities, but there were no differences in adversity likelihood by sex. Additionally, racial/ethnic minority youth were at greater risk of experiencing higher levels of adversity, poverty, and poor health when compared to their White counterparts. Rather than occuring in meaningful clusters, adverse experiences among youth reflected a cumulative risk model such that classes were defined by the overall intensity of adverse experiences (i.e., low, moderate, high). Findings provide greater knowledge regarding the relationship between ACEs and health and future research directions to inform more targeted and culturally-appropriate screening, prevention, and intervention programs.     

Relationship between single digit addition strategies and working memory reflects general reasoning sophistication

With age, children employ increasingly more sophisticated strategies to solve single-digit addition (SDA) number fact problems. Changes in SDA strategy-use are correlated with age-related changes in cognitive measures (e.g., working-memory capacity, WM); however, this correlation does not explain the basis of SDA strategy-use or change per se. Does strategy-use reflect differences in generally reasoning abilities or are they uniquely tied to SDA abilities? Answers to these questions would help clarify the nature of SDA abilities specifically and early math abilities more generally. To investigate these issues, we assessed 144 5- to 9-year-olds' SDA strategies, visuospatial working memory (VSWM), verbal WM, non-verbal IQ, basic RT, and visuospatial reasoning abilities. We focused on these abilities since recent research shows visuospatial processes support early math development. We used latent profile/class analysis to classify children's SDA strategies and visuospatial reasoning abilities, which yielded four ability subgroups in both cases, only partially related to age. Findings show SDA strategy subgroup and general reasoning subgroup memberships were related. More sophisticated subgroups had better WM, but did not differ in IQ and RT. Findings suggest SDA reasoning reflect general reasoning abilities. Implications for math skills acquisition and intervention are discussed.     

Co-developmental trajectories of psychological need satisfactions at school: Relations to mental health and academic functioning in Chinese elementary school students

This study explored the co-developmental trajectories of autonomy, competence and relatedness need satisfactions at school and their relations to mental health and academic functioning in Chinese elementary school students. An accelerated longitudinal design was used with a sample of three cohorts (grade 3, grade 4, and grade 5) (N = 1070, 45.8% female; M_age = 9.44, SD = 0.97) on four occasions at 6-month intervals. Parallel process latent class growth models revealed five heterogeneous patterns (i.e., Congruent-moderate; Congruent-high; Congruent-low; Congruent-decreasing; Low-autonomy, High-competence and relatedness), all of which significantly associated with time-varying mental health and academic functioning indicators. The findings highlight the importance of subgroup differences and possible cultural considerations in understanding the progression of psychological need satisfactions and the need for universal screening and dynamic monitoring of students’ psychological need satisfactions at school and implementing more sophisticated interventions tailored to the unique characteristics of the relevant subgroups to promote optimal mental health and learning.     

USING DEMOGRAPHIC SUBGROUP AND DUMMY VARIABLE EQUATIONS TO PREDICT COLLEGE FRESHMAN GRADE AVERAGE

The purpose of this study was to determine whether adjustments for the differential prediction observed among sex, racial/ethnic, or age subgroups in one freshman class at a college could be used to improve prediction accuracy for these subgroups in future freshman classes. For older students, dummy variable and separate subgroup prediction equations were found, on cross-validation, to be more accurate than the total group equations. For sex subgroups, dummy variable and separate subgroup equations were only moderately effective in improving prediction accuracy. For racial/ethnic subgroups, they were more often than not less accurate, on cross-validation, than total group equations. Among all three kinds of demographic subgroupings, shifts over time in colleges' mean grade averages were found to be a more important source of prediction bias than differential prediction.     

Subtypes, severity, and structural stability of peer victimization: what does latent class analysis say?

This study uses latent class analysis (LCA) to empirically identify victimization groups during middle school. Approximately 2,000 urban, public middle school students (mean age in sixth grade = 11.57) reported on their peer victimization during the Fall and Spring semesters of their sixth, seventh, and eighth grades. Independent LCA analyses at each semester yielded 3 victim classes based on victimization degree rather than type (e.g., physical vs. relational). The most victimized class always represented the smallest proportion of the sample, decreasing from 20% in sixth grade to 6% by the end of eighth grade. This victimized class also always reported feeling less safe at school concurrently and more depressed than others 1 semester later, illustrating the validity of the LCA approach.     

The effects of college students’ perfectionism on career stress and indecision: self-esteem and coping styles as moderating variables

This study identified the subgroups (latent classes) of Korean college students according to the influence of perfectionism on career stress and indecision, and explored the effects of sub-factors of perfectionism on career stress and indecision for each subgroup. Also, the study examined how individual self-esteem and stress coping styles affect the subgroup classification. Data from 476 South Korean college students were analyzed via mixture regression and logistic regression. Four latent classes were identified. In class 1, career indecision increased as self-oriented perfectionism increased, and career stress increased as socially prescribed perfectionism increased. In class 2, career stress increased as self-oriented perfectionism increased, whereas both career stress and career indecision decreased as others-oriented perfectionism increased. In class 3, both career stress and career indecision increased as others-oriented perfectionism increased. In class 4, career stress and career indecision decreased as others-oriented perfectionism increased, while career stress increased as socially prescribed perfectionism increased. In differentiating the classes, self-esteem and coping styles were analyzed as predictor variables. The results indicated that self-esteem helped to distinguish class 1 from class 2, and class 1 from class 3. Avoidance-oriented coping style could distinguish class 1 from class 3. Career counselors would benefit by noting that the influence of each sub-trait of perfectionism on career-related issues may vary by latent class, and that self-esteem and coping styles may moderate the effects of perfectionism on career-related issues.

     

Out-of-school learning assistance in adolescence

This study inquired into the structure and predictors of out-of-school learning assistance among adolescents. It used longitudinal data gathered from up to 4936 Polish lower-secondary school students. The analysis of the assistance structure, performed with latent class analysis, showed that about 50% of students belonged to the latent class with no assistance. All other students belonged to one of four latent classes, representing types of assistance. These latent classes were: frequent non-paid assistance in all subjects, moderate non-paid assistance in all subjects, non-paid assistance in selected subjects, paid and non-paid assistance in mathematics. The analysis of predictors of assistance provision showed that 12 out of 14 hypothesised factors affected membership probability (MP) in at least one latent class. A lower level of past school achievement raised MP in all the classes. Students’ gender, intelligence, intellectual helplessness, learning disability, educational aspirations, family affluence, level of parental education, and school location predicted MP in selected classes.     

Sociocultural Differences in Examinees' Attitudes Toward Scholastic Ability Exams

The major aim of the present study was threefold: (a) to compare the test attitudes and perceptions o f examinees of varying sociocultural group membership toward verbal and nonverbal standardized ability tests; (b) to determine the degree of covariation between test attitudes and test scores; and (c) to delineate the properties and potential applications of a test attitude or feedback inventory specifically designed to assess examinees' perceptions of key situational variables in the test context. The feedback inventory was administered to a sample of 259 seventh grade students in Israel immediately following standardized group scholastic ability testing procedures. On the whole, few meaningful group differences in test attitudes were observed by social class, ethnicity, or sex. However, a nonverbal test was generally rated more favorably than a verbal test, among varying sociocultural and sex subgroups. Considered together, test attitude scales share a meaningful proportion o f variance with the test score on both verbal and nonverbal tests. However, in view o f the negligible ethnic and social class differences in test attitudes and the nonsignificant interaction between test attitudes and background variables, the data provide little support for the situational bias claim     

Performance of Factor Mixture Models as a Function of Model Size,Covariate Effects,and Class-Specific Parameters

Abstract

Factor mixture models are designed for the analysis of multivariate data obtained from a population consisting of distinct latent classes. A common factor model is assumed to hold within each of the latent classes. Factor mixture modeling involves obtaining estimates of the model parameters, and may also be used to assign subjects to their most likely latent class. This simulation study investigates aspects of model performance such as parameter coverage and correct class membership assignment and focuses on covariate effects, model size, and class-specific versus class-invariant parameters. When fitting true models, parameter coverage is good for most parameters even for the smallest class separation investigated in this study (0.5 SD between 2 classes). The same holds for convergence rates. Correct class assignment is unsatisfactory for the small class separation without covariates, but improves dramatically with increasing separation, covariate effects, or both. Model performance is not influenced by the differences in model size investigated here. Class-specific parameters may improve some aspects of model performance but negatively affect other aspects.     

Latent Class Modeling with A Time-To-Event Distal Outcome: A Comparison of One,Two and Three-Step Approaches

Latent class methods can be used to identify unobserved subgroups which differ in their observed data. Researchers are often interested in outcomes for the identified subgroups and in some disciplines time-to-event outcome measures are common, e.g., overall survival in oncology. In this study Monte Carlo simulation is used to evaluate the empirical properties of latent class effect estimates on a time-to-event distal outcome using one, two and three-step approaches. Both standard and inclusive bias-corrected three-step approaches are considered. One-step latent class effect estimates are shown to be superior to the evaluated alternatives. Both the two-step approach and a standard three-step approach, where subjects are partially assigned to latent classes, produced unbiased estimates with nominal confidence interval coverage when latent classes were well separated, but not otherwise.