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This paper reflects on some practical implications of the excellent treatment of sum scoring and classical test theory (CTT) by Sijtsma et al. (Psychometrika 89(1):84-117, 2024). I have no major disagreements about the content they present and found it to be an informative clarification of the properties and possible extensions of CTT. In this paper, I focus on whether sum scores-despite their mathematical justification-are positioned to improve psychometric practice in empirical studies in psychology, education, and adjacent areas. First, I summarize recent reviews of psychometric practice in empirical studies, subsequent calls for greater psychometric transparency and validity, and how sum scores may or may not be positioned to adhere to such calls. Second, I consider limitations of sum scores for prediction, especially in the presence of common features like ordinal or Likert response scales, multidimensional constructs, and moderated or heterogeneous associations. Third, I review previous research outlining potential limitations of using sum scores as outcomes in subsequent analyses where rank ordering is not always sufficient to successfully characterize group differences or change over time. Fourth, I cover potential challenges for providing validity evidence for whether sum scores represent a single construct, particularly if one wishes to maintain minimal CTT assumptions. I conclude with thoughts about whether sum scores-even if mathematically justified-are positioned to improve psychometric practice in empirical studies.
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There is high-level interest in diversifying workforces, which has led organizations-including the U.S. Armed Forces-to reevaluate recruiting and selection practices. The U.S. Coast Guard (USCG) has encountered particular difficulties in diversifying its workforce, and it relies mainly on the Armed Services Vocational Aptitude Battery (ASVAB) for assigning active-duty recruits to one of 19 specialized training schools. When recruits' scores fall below ASVAB entrance standards, the USCG sometimes offers admission waivers. Alternatively, recruits can retest until their ASVAB scores meet the entrance standard. Retesting has shown mixed results in the personnel selection literature, so our main interest is to determine whether retesting or waivers best support USCG recruits' training school outcomes, especially for recruits identifying as an underrepresented minority (URM). We use data from 16,624 USCG recruits entering between 2013 and 2021 and fit augmented inverse propensity weighted models to assess differences in training outcomes by pathway to admission while accounting for self-selection into pathways. Our analyses found (a) no difference in training outcomes between recruits who qualified from their initial scores and recruits who retested, (b) recruits who received waivers were less likely to complete training school on time and spent more time in remedial training when they failed training school compared to those who retested, and (c) improvement in training outcomes for retesting over waivers was larger for recruits identifying as an URM. Results suggest that retesting may be an effective strategy for workforce diversification and for improving outcomes among recruits identifying as an URM. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Militares , Humanos , Militares/educação , Instituições Acadêmicas , Grupos Minoritários , Seleção de Pessoal , Inquéritos e QuestionáriosRESUMO
Intensive longitudinal designs are increasingly popular, as are dynamic structural equation models (DSEM) to accommodate unique features of these designs. Many helpful resources on DSEM exist, though they focus on continuous outcomes while categorical outcomes are omitted, briefly mentioned, or considered as a straightforward extension. This viewpoint regarding categorical outcomes is not unwarranted for technical audiences, but there are non-trivial nuances in model building and interpretation with categorical outcomes that are not necessarily straightforward for empirical researchers. Furthermore, categorical outcomes are common given that binary behavioral indicators or Likert responses are frequently solicited as low-burden variables to discourage participant non-response. This tutorial paper is therefore dedicated to providing an accessible treatment of DSEM in Mplus exclusively for categorical outcomes. We cover the general probit model whereby the raw categorical responses are assumed to come from an underlying normal process. We cover probit DSEM and expound why existing treatments have considered categorical outcomes as a straightforward extension of the continuous case. Data from a motivating ecological momentary assessment study with a binary outcome are used to demonstrate an unconditional model, a model with disaggregated covariates, and a model for data with a time trend. We provide annotated Mplus code for these models and discuss interpretation of the results. We then discuss model specification and interpretation in the case of an ordinal outcome and provide an example to highlight differences between ordinal and binary outcomes. We conclude with a discussion of caveats and extensions.
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Modelos Estatísticos , HumanosRESUMO
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).
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PURPOSE: To examine patterns in adolescent and young adult tobacco use, comparing Latinx foreign-born children and children of foreign-born parents (i.e., children of immigrants(COI)) to Latinx US-born children of US-born parents (i.e., children of nonimmigrants,(CONI)) and to CONI White youth who grew up in small and rural towns. METHODS: Data were from youth who lived in control communities that participated in a community-randomized trial of the Communities That Care prevention system. We compared Latinx CONI (n = 154) with Latinx COI (n = 316) and with non-Latinx White CONI (n = 918). We examined tobacco use in adolescence (any adolescent use, early onset, and chronic use) and young adulthood (any past-year tobacco use, any daily smoking, any nicotine dependence symptoms) with mixed-effects logistic regressions. RESULTS: In adolescence, Latinx CONI had a higher prevalence of any and chronic tobacco use relative to Latinx COI, and of any and early onset tobacco use relative to non-Latinx White CONI. In young adulthood, Latinx CONI were more likely to report tobacco use in the past year, any symptoms of nicotine dependence, and daily smoking relative to Latinx COI; and more likely to report daily smoking relative to non-Latinx White CONI. Generation differences in young adult tobacco use were explained by chronic tobacco use in adolescence. DISCUSSION: The study suggests targeting chronic tobacco use in adolescence to prevent disparities in tobacco outcomes among Latinx young adults from rural communities.
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Tabagismo , Adulto Jovem , Criança , Humanos , Adolescente , Adulto , Tabagismo/epidemiologia , População Rural , Uso de Tabaco/epidemiologia , Fumar/epidemiologia , Fumar TabacoRESUMO
To evaluate the fit of a confirmatory factor analysis model, researchers often rely on fit indices such as SRMR, RMSEA, and CFI. These indices are frequently compared to benchmark values of .08, .06, and .96, respectively, established by Hu and Bentler (Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55). However, these indices are affected by model characteristics and their sensitivity to misfit can change across models. Decisions about model fit can therefore be improved by tailoring cutoffs to each model. The methodological literature has proposed methods for deriving customized cutoffs, although it can require knowledge of linear algebra and Monte Carlo simulation. Given that many empirical researchers do not have training in these technical areas, empirical studies largely continue to rely on fixed benchmarks even though they are known to generalize poorly and can be poor arbiters of fit. To address this, this paper introduces the R package, dynamic, to make computation of dynamic fit index cutoffs (which are tailored to the user's model) more accessible to empirical researchers. dynamic heavily automatizes this process and only requires a lavaan object to automatically conduct several custom Monte Carlo simulations and output fit index cutoffs designed to be sensitive to misfit with the user's model characteristics.
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Modelos Estatísticos , Simulação por Computador , Análise de Classes Latentes , Análise Fatorial , Método de Monte CarloRESUMO
Factor analysis is often used to model scales created to measure latent constructs, and internal structure validity evidence is commonly assessed with indices like RMSEA, and CFI. These indices are essentially effect size measures and definitive benchmarks regarding which values connote reasonable fit have been elusive. Simulations from the 1990s suggesting possible benchmark values are among the most highly cited methodological papers across any discipline. However, simulations have suggested that fixed benchmarks do not generalize well - fit indices are systematically impacted by characteristics like the number of items and the magnitude of the loadings, so fixed benchmarks can confound misfit with model characteristics. Alternative frameworks for creating customized, model-specific benchmarks have recently been proposed to circumvent these issues but they have not been systematically evaluated. Motivated by two empirical applications where different methods yield inconsistent conclusions, two simulation studies are performed to assess the ability of three different approaches to correctly classify models that are correct or misspecified across different conditions. Results show that dynamic fit indices and equivalence testing both improved upon the traditional Hu & Bentler benchmarks and dynamic fit indices appeared to be least confounded with model characteristics in the conditions studied.
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Simulação por Computador , Análise FatorialRESUMO
OBJECTIVES: This uncontrolled pilot study examined the feasibility, acceptability, and preliminary HIV and psychological health effects of iTHRIVE 365, a multicomponent intervention designed by and for Black same gender loving men (SGLM) to promote: health knowledge and motivation, Black SGLM social support, affirming health care, and housing and other economic resources. DESIGN METHODS: We conducted a 14-day daily diary study with 32 Black SGLM living with HIV connected to THRIVE SS in Atlanta, GA. Daily surveys assessed intervention engagement, antiretroviral medication (ART) use, depressive symptoms, anxiety symptoms, and emotion regulation difficulties. App paradata (ie, process data detailing app usage) assessed amount of intervention engagement via page access. Participants began receiving access to the intervention on day 7. After the 14-day daily diary period, participants responded to follow-up items on the user-friendliness, usefulness, helpfulness, and whether they would recommend iTHRIVE 365 to others. Chi-square analyses examined associations between intervention engagement and ART use, and dynamic structural equation modelling assessed longitudinal associations from intervention engagement to next-day psychological health. This intervention trial is registered on ClinicalTrials.gov (NCT05376397). RESULTS: On average, participants engaged with iTHRIVE 365 over once every other day and accessed intervention pages 4.65 times per day. Among participants who engaged with the intervention, 78% reported it was helpful to extremely helpful, 83% reported it was moderately to extremely useful, and 88% reported it was user-friendly and they would recommend it to others. On intervention engagement days, participants had higher odds of ART use, χ 2 (1) = 4.09, P = 0.04, than intervention nonengagement days. On days after intervention engagement, participants showed non-null decreases in depressive symptoms (τ = -0.14; 95% CI : = [-0.23, -0.05]) and emotion regulation difficulties (τ = -0.16; 95% CI : = [-0.24, -0.02]). CONCLUSIONS: Findings suggest iTHRIVE 365 is feasible, acceptable, and positively affects daily ART use, depressive symptoms, and emotion regulation difficulties.
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Infecções por HIV , Humanos , Masculino , Antirretrovirais/uso terapêutico , Estudos de Viabilidade , Infecções por HIV/tratamento farmacológico , Motivação , Projetos PilotoRESUMO
Growth mixture models (GMMs) are a popular method to identify latent classes of growth trajectories. One shortcoming of GMMs is nonconvergence, which often leads researchers to apply covariance equality constraints to simplify estimation, though this may be a dubious assumption. Alternative model specifications have been proposed to reduce nonconvergence without imposing covariance equality constraints. These methods perform well when the correct number of classes is known, but research has not yet examined their use when the number of classes is unknown. Given the importance of selecting the number of classes, more information about class enumeration performance is crucial to assess the potential utility of these methods. We conducted an extensive simulation to explore class enumeration and classification accuracy of model specifications that are more robust to nonconvergence. Results show that the typical approach of applying covariance equality constraints performs quite poorly. Instead, we recommended covariance pattern GMMs because they (a) had the highest convergence rates, (b) were most likely to identify the correct number of classes, and (c) had the highest classification accuracy in many conditions, even with modest sample sizes. An analysis of empirical posttraumatic stress disorder (PTSD) data is provided to show that the typical four-class solution found in many empirical PTSD studies may be an artifact of the covariance equality constraint method that has permeated this literature. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Transtornos de Estresse Pós-Traumáticos , Humanos , Simulação por Computador , Tamanho da Amostra , Pesquisa EmpíricaRESUMO
Growth mixture models (GMMs) are applied to intervention studies with repeated measures to explore heterogeneity in the intervention effect. However, traditional GMMs are known to be difficult to estimate, especially at sample sizes common in single-center interventions. Common strategies to coerce GMMs to converge involve post hoc adjustments to the model, particularly constraining covariance parameters to equality across classes. Methodological studies have shown that although convergence is improved with post hoc adjustments, they embed additional tenuous assumptions into the model that can adversely impact key aspects of the model such as number of classes extracted and the estimated growth trajectories in each class. To facilitate convergence without post hoc adjustments, this paper reviews the recent literature on covariance pattern mixture models, which approach GMMs from a marginal modeling tradition rather than the random effect modeling tradition used by traditional GMMs. We discuss how the marginal modeling tradition can avoid complexities in estimation encountered by GMMs that feature random effects, and we use data from a lifestyle intervention for increasing insulin sensitivity (a risk factor for type 2 diabetes) among 90 Latino adolescents with obesity to demonstrate our point. Specifically, GMMs featuring random effects-even with post hoc adjustments-fail to converge due to estimation errors, whereas covariance pattern mixture models following the marginal model tradition encounter no issues with estimation while maintaining the ability to answer all the research questions.
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Diabetes Mellitus Tipo 2 , Humanos , Diabetes Mellitus Tipo 2/prevenção & controle , Fatores de Risco , Obesidade , Projetos de Pesquisa , Estilo de VidaRESUMO
Model fit assessment is a central component of evaluating confirmatory factor analysis models and the validity of psychological assessments. Fit indices remain popular and researchers often judge fit with fixed cutoffs derived by Hu and Bentler (1999). Despite their overwhelming popularity, methodological studies have cautioned against fixed cutoffs, noting that the meaning of fit indices varies based on a complex interaction of model characteristics like factor reliability, number of items, and number of factors. Criticism of fixed cutoffs stems primarily from the fact that they were derived from one specific confirmatory factor analysis model and lack generalizability. To address this, we propose a simulation-based method called dynamic fit index cutoffs such that derivation of cutoffs is adaptively tailored to the specific model and data characteristics being evaluated. Unlike previously proposed simulation-based techniques, our method removes existing barriers to implementation by providing an open-source, Web based Shiny software application that automates the entire process so that users neither need to manually write any software code nor be knowledgeable about foundations of Monte Carlo simulation. Additionally, we extend fit index cutoff derivations to include sets of cutoffs for multiple levels of misspecification. In doing so, fit indices can more closely resemble their originally intended purpose as effect sizes quantifying misfit rather than improperly functioning as ad hoc hypothesis tests. We also provide an approach specifically designed for the nuances of 1-factor models, which have received surprisingly little attention in the literature despite frequent substantive interests in unidimensionality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Software , Humanos , Reprodutibilidade dos Testes , Simulação por Computador , Análise Fatorial , Método de Monte CarloRESUMO
Individual differences in the timing of developmental processes are often of interest in longitudinal studies, yet common statistical approaches to modeling change cannot directly estimate the timing of when change occurs. The time-to-criterion framework was recently developed to incorporate the timing of a prespecified criterion value; however, this framework has difficulty accommodating contexts where the criterion value differs across people or when the criterion value is not known a priori, such as when the interest is in individual differences in when change starts or stops. This article combines aspects of reparameterized quadratic models and multiphase models to provide information on the timing of change. We first consider the more common situation of modeling decelerating change to an offset point, defined as the point in time at which change ceases. For increasing trajectories, the offset occurs when the criterion attains its maximum ("inverted J-shaped" trajectories). For decreasing trajectories, offset instead occurs at the minimum. Our model allows for individual differences in both the timing of offset and ultimate level of the outcome. The same model, reparameterized slightly, captures accelerating change from a point of onset ("J-shaped" trajectories). We then extend the framework to accommodate "S-shaped" curves where both the onset and offset of change are within the observation window. We provide demonstrations that span neuroscience, educational psychology, developmental psychology, and cognitive science, illustrating the applicability of the modeling framework to a variety of research questions about individual differences in the timing of change. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Individualidade , Psicologia Educacional , Humanos , Fatores de Tempo , Estudos LongitudinaisRESUMO
Commentary in Widaman and Revelle (2022) argued that sum scoring is justified as long as unidimensionality holds because sum score reliability is defined. My response begins with a review of the literature supporting the perspective we adopted in the original article. I then conduct simulation studies to assess the psychometric properties of sum scores created using Widaman and Revelle's justification relative to scores created by the weighted factor score approach in the original article. In my simulations, I generate data where sum and factor scores are correlated at 0.96 or 0.98 because high factor-sum score correlations are often used to support the contention that sum and factor scores have interchangeable psychometric properties. I explore (a) correlations between estimated scores and true scores, (b) classification accuracy of sum and factor scores, and (c) reliability of sum and factor scores. Results show that factor scores have (a) higher correlations with true scores (Δ = 0.02-0.04), (b) higher sensitivity (Δ = 4-8 percentage points), and (c) higher reliability (Δ = 0.04-0.07). Factor score performance metrics also have less sampling variability in most conditions. Psychometric properties of sum scores-even when highly correlated with factor scores-remain less desirable than those of factor scores. Additional considerations like models with multiple factors and measurement invariance are also discussed. Essentially, even if accepting Widaman and Revelle's justification for sum scoring, it is uncertain whether researchers generally would want to sum score after fitting a factor analysis unless sum and factor scores correlate at (and not merely close to) 1.00.
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Psicometria , Humanos , Reprodutibilidade dos Testes , Análise Fatorial , Inquéritos e QuestionáriosRESUMO
Use of Bayesian methods has proliferated in recent years as technological and software developments have made Bayesian methods more approachable for researchers working with empirical data. Connected with the increased usage of Bayesian methods in empirical studies is a corresponding increase in recommendations and best practices for Bayesian methods. However, given the extensive scope of Bayes, theorem, there are various compelling perspectives one could adopt for its application. This paper first describes five different perspectives, including examples of different methodologies that are aligned within these perspectives. We then discuss how the different perspectives can have implications for modeling and reporting practices, such that approaches and recommendations that are perfectly reasonable under one perspective might be unreasonable when viewed from another perspective. The ultimate goal is to show the heterogeneity of defensible practices in Bayesian methods and to foster a greater appreciation for the variety of orientations that exist. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Análise de Dados , Humanos , Teorema de Bayes , Pesquisa EmpíricaRESUMO
BACKGROUND: The aim of this study was to identify distinct trajectories of BMI growth from 2 to 7.5 years and examine their associations with markers of cardiometabolic risk at age 7.5 years among a sample of low-income Mexican American children. METHODS: This longitudinal cohort study recruited 322 mother-child dyads to participate prenatally and at child age 2, 3, 4.5, 6, and 7.5 years. Child height/weight, waist circumference, and blood pressure were assessed at each time point. Blood was collected from child at 7.5 years. RESULTS: Covarying for birthweight, three BMI trajectories were identified: Low-Stable BMI (73% of the sample), High-Stable BMI (5.6% of the sample), and Increasing BMI over time (21.4% of the sample). The High-Stable and Increasing BMI classes had higher waist circumference and systolic blood pressure and lower HDL-c than the Low-Stable BMI class (ps < 0.05). Among children with BMIs below the 85th percentile, 16% had three or more cardiometabolic risk indicators. CONCLUSIONS: BMI classes were consistent with existing literature. For youth, standard medical practice is to examine cardiometabolic risk indicators when BMI is high; however, this practice would miss 16% of youth in our sample who exhibit cardiometabolic risk but do not screen in based on BMI. IMPACT: Research indicates Mexican American youth are at risk for cardiometabolic dysregulation relative to other ethnic groups, yet there is a paucity of longitudinal research. An Increasing BMI and a High-Stable BMI class were associated with larger waist circumference, higher systolic blood pressure, and lower HDL cholesterol than the Low-Stable BMI class. BMI trajectories in childhood predict cardiometabolic risk indicators. As the sole screener for deciding when to test cardiometabolic indicators, BMI alone will miss some children exhibiting cardiometabolic dysregulation.
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Doenças Cardiovasculares , Americanos Mexicanos , Criança , Humanos , Índice de Massa Corporal , Doenças Cardiovasculares/diagnóstico , Doenças Cardiovasculares/epidemiologia , Estudos Longitudinais , Fatores de Risco , Circunferência da Cintura/fisiologia , Pré-EscolarRESUMO
Assessing whether a multiple-item scale can be represented with a one-factor model is a frequent interest in behavioral research. Often, this is done in a factor analysis framework with approximate fit indices like RMSEA, CFI, or SRMR. These fit indices are continuous measures, so values indicating acceptable fit are up to interpretation. Cutoffs suggested by Hu and Bentler (1999) are a common guideline used in empirical research. However, these cutoffs were derived with intent to detect omitted cross-loadings or omitted factor covariances in multifactor models. These types of misspecifications cannot exist in one-factor models, so the appropriateness of using these guidelines in one-factor models is uncertain. This paper uses a simulation study to address whether traditional fit index cutoffs are sensitive to the types of misspecifications common in one-factor models. The results showed that traditional cutoffs have very poor sensitivity to misspecification in one-factor models and that the traditional cutoffs generalize poorly to one-factor contexts. As an alternative, we investigate the accuracy and stability of the recently introduced dynamic fit cutoff approach for creating fit index cutoffs for one-factor models. Simulation results indicated excellent performance of dynamic fit index cutoffs to classify correct or misspecified one-factor models and that dynamic fit index cutoffs are a promising approach for more accurate assessment of model fit in one-factor contexts.
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Pesquisa Comportamental , Modelos Estatísticos , Humanos , Simulação por Computador , Análise Fatorial , Pesquisa EmpíricaRESUMO
Scale validation is vital to psychological research because it ensures that scores from measurement scales represent the intended construct. Fit indices are commonly used to provide quantitative evidence that a proposed factor structure is plausible. However, there is a mismatch between guidelines for evaluating fit of the factor models and the data that most researchers have. Namely, fit guidelines are based on the simulations that assume item responses are collected on a continuous scale whereas most researchers collect discrete responses such as with a Likert-type scale. In this article, we show that common guidelines derived from assuming continuous responses (e.g., root-mean-square error of approximation < 0.06, comparative fit index > 0.95) do not generalize to factor models applied to discrete responses. Specifically, discrete responses provide less information than continuous responses, so less information about misfit is passed to fit indices. Traditional guidelines, therefore, end up being too lenient and lose their ability to identify that a model may have a poor fit. We provide one possible solution by extending the recently developed dynamic fit index framework to accommodate discrete responses common in psychology. We conduct a simulation study to provide evidence that the proposed method consistently distinguishes between well-fitting and poorly fitting models. Results showed that our proposed cutoffs maintained at least 90% sensitivity to misspecification across studied conditions, whereas traditional cutoffs were highly inconsistent and frequently exhibited sensitivity below 50%. The proposed method is included in the dynamic R package and as a web-based Shiny application to make it easily accessible to psychologists. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Análise Fatorial , Modelos PsicológicosRESUMO
Much of the existing longitudinal mediation literature focuses on panel data where relatively few repeated measures are collected over a relatively broad timespan. However, technological advances in data collection (e.g., smartphones, wearables) have led to a proliferation of short duration, densely collected longitudinal data in behavioral research. These intensive longitudinal data differ in structure and focus relative to traditionally collected panel data. As a result, existing methodological resources do not necessarily extend to nuances present in the recent influx of intensive longitudinal data and designs. In this tutorial, we first cover potential limitations of traditional longitudinal mediation models to accommodate unique characteristics of intensive longitudinal data. Then, we discuss how recently developed dynamic structural equation models (DSEMs) may be well-suited for mediation modeling with intensive longitudinal data and can overcome some of the limitations associated with traditional approaches. We describe four increasingly complex intensive longitudinal mediation models: (a) stationary models where the indirect effect is constant over time and people, (b) person-specific models where the indirect effect varies across people, (c) dynamic models where the indirect effect varies across time, and (d) cross-classified models where the indirect effect varies across both time and people. We apply each model to a running example featuring a mobile health intervention designed to improve health behavior of individuals with binge eating disorder. In each example, we provide annotated Mplus code and interpretation of the output to guide empirical researchers through mediation modeling with this increasingly popular type of longitudinal data. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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OBJECTIVE: The relationship between smoking and adolescents' peer relationships is complex, with studies showing increased risk of smoking for adolescents of both very high and very low social position. A key question is whether the impact of social position on smoking depends on an adolescent's level of coping motives (i.e., their desire to use smoking to mitigate negative affect). METHOD: We assessed how social position predicts nicotine dependence in a longitudinal sample (N = 3,717; 44.8% male; mean age = 13.41 years) of adolescent lifetime smokers measured between 6th and 12th grades. Using both social network analysis and multilevel modeling, we assessed this question at the between-person and within-person level, hypothesizing that within-person decreases in social position would lead to increased risk of nicotine dependence among those with high levels of coping motives. RESULTS: In contrast to our hypotheses, only interactions with the between-person measures of social position were found, with a slight negative relationship at low levels of coping motives. In addition, the main effect of coping motives was considerably stronger than that of social position at the between-person level, and social position had no significant within-person main effect on nicotine dependence risk. CONCLUSIONS: These results suggest that adolescents with higher overall levels of social position among their peers may have slightly decreased risk for nicotine dependence, but only when coping motives are low. Counter to expectations, higher levels of nicotine dependence risk were not linked to fluctuations in social position.
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Abandono do Hábito de Fumar , Tabagismo , Adolescente , Feminino , Humanos , Masculino , Motivação , Grupo Associado , Fumar/epidemiologia , Abandono do Hábito de Fumar/métodos , Tabagismo/epidemiologiaRESUMO
A large body of literature suggests that parent-child separation predicts child maladjustment. However, further advancement in methodology is needed to account for heterogeneity in types of separation. Additionally, given a lack of research examining different types of separation as predictors of offspring substance use, further research into this topic is warranted. The present study tested the relation between parent-child separation and young-adult substance use disorder (SUD), capturing heterogeneity in these effects based on group differences and measurement of separation. In a sample of 427 young adults from a larger longitudinal study oversampled for parental alcohol use disorder (AUD), effects of number and type of separations on SUD diagnosis were tested. Further, we explored whether these associations were moderated by gender, ethnicity, or parental AUD. Two underlying types of separation were found: parental health-related separation (i.e., parental death, hospitalization) and nonhealth-related separation (i.e., divorce, arrest). A higher sum of separations and greater nonhealth-related separation predicted higher odds of SUD. Greater health-related separation predicted lower odds of SUD. However, these effects were qualified by interactions with ethnicity and parental AUD. Although the vast majority of studies measure cumulative parent-child separation with sum scores, the present study demonstrates that measuring underlying "types" of cumulative separation also reveals important effects. Moreover, childhood separation is a significant risk factor for SUD. Future research on separation should implement methods to capture separation types and further account for potential effects of selection into separation types. (PsycInfo Database Record (c) 2022 APA, all rights reserved).