scholarly journals Mixture Modeling for Lifespan Developmental Research

2019 ◽  
Author(s):  
Alexandre J.S. Morin ◽  
David Litalien
Keyword(s):  
Mixture Models ◽  
Structural Equation ◽  
Equation Modeling ◽  
Modeling Framework ◽  
Mixture Regression ◽  
Growth Mixture ◽  
Multiple Group ◽  
Latent Curve ◽  
Latent Curve Models ◽  
Over Time

As part of the Generalized Structural Equation Modeling framework, mixture models are person-centered analyses seeking to identify distinct subpopulations, or profiles, of participants differing quantitatively and qualitatively from one another on a configuration of indicators and/or relations among these indicators. Mixture models are typological (resulting in a classification system), probabilistic (each participant having a probability of membership into all profiles based on prototypical similarity), and exploratory (the optimal model is typically selected based on a comparison of alternative specifications) in nature, and can take different forms. Latent profile analyses seek to identify subpopulations of participants differing from one another on a configuration of indicators and can be extended to factor mixture analyses allowing for the incorporation of latent factors to the model. In contrast, mixture regression analyses seek to identify subpopulations of participants’ differing from one another in terms of relations among profile indicators. These analyses can be extended to the multiple-group and/or longitudinal analyses, allowing researchers to conduct tests of profile similarity across different samples of participants or time points, and latent transition analyses can be used to assess probabilities of profiles transition over time among a sample of participants (i.e., within person stability and change in profile membership). Finally, growth mixture analyses are built from latent curve models and seek to identify subpopulations of participants following quantitatively and qualitatively distinct trajectories over time. All of these models can accommodate covariates, used either as predictors, correlates, or outcomes, and can even be extended to tests of mediation and moderation.

One Size Fits All?

Methodology ◽  
2012 ◽  
Vol 8 (1) ◽  
pp. 23-38 ◽  
Author(s):  
Manuel C. Voelkle ◽  
Patrick E. McKnight
Keyword(s):  
Repeated Measures ◽  
Structural Equation ◽  
Type I Error ◽  
Equation Modeling ◽  
Type I ◽  
Finite Sample ◽  
Traditional Methods ◽  
Repeated Measures Anova ◽  
Special Cases ◽  
Latent Curve

The use of latent curve models (LCMs) has increased almost exponentially during the last decade. Oftentimes, researchers regard LCM as a “new” method to analyze change with little attention paid to the fact that the technique was originally introduced as an “alternative to standard repeated measures ANOVA and first-order auto-regressive methods” (Meredith & Tisak, 1990, p. 107). In the first part of the paper, this close relationship is reviewed, and it is demonstrated how “traditional” methods, such as the repeated measures ANOVA, and MANOVA, can be formulated as LCMs. Given that latent curve modeling is essentially a large-sample technique, compared to “traditional” finite-sample approaches, the second part of the paper addresses the question to what degree the more flexible LCMs can actually replace some of the older tests by means of a Monte-Carlo simulation. In addition, a structural equation modeling alternative to Mauchly’s (1940) test of sphericity is explored. Although “traditional” methods may be expressed as special cases of more general LCMs, we found the equivalence holds only asymptotically. For practical purposes, however, no approach always outperformed the other alternatives in terms of power and type I error, so the best method to be used depends on the situation. We provide detailed recommendations of when to use which method.

scholarly journals Dynamics of Multimorbidity Resilience and Health Outcomes Over Time in Community-Residing Older Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 890-891
Author(s):  
Yingzhi Xu ◽  
Zahra Rahmaty ◽  
Eleanor McConnell ◽  
Tingzhong (Michelle) Xue ◽  
Bada Kang ◽  
...  
Keyword(s):  
Older Adults ◽  
Health Outcomes ◽  
Physical Health ◽  
Social Life ◽  
Structural Equation ◽  
Equation Modeling ◽  
Psychological Resilience ◽  
Pain Level ◽  
Reciprocal Relationships ◽  
Over Time

Abstract Multimorbidity resilience may mitigate the adverse effects of multiple chronic diseases on older adults’ health. Wister et al.’s (2018) multimorbidity resilience index was developed and tested in a cross-sectional sample of older adults in Canada. Building on these findings, we examined the reciprocal relationships of resilience on outcomes to test these potentially mitigating effects in a community-based, U.S. sample of older adults over time. The study sample includes 1,054 older adults from waves 2 and 3 of the National Social Life, Health, and Aging Project (NSHAP) study (Waite et al 2020). Wister et al.’s (2018) index was mapped to NSHAP measures, and reciprocal relationships of multimorbidity resilience and health outcomes over a 5-year period was tested using structural equation modeling (SEM). Results indicated significant effects of multimorbidity resilience on self-rated physical health and pain. Interestingly, a better functional resilience at baseline conferred better self-rated physical health at follow-up, while better psychological resilience predicted lower pain level. By contrast, the influence of health outcomes on any domain of multimorbidity resilience was not detectable at all, supporting the direction of these associations from resilience to outcomes. The study systematically investigated the dynamic hypotheses between multimorbidity resilience and health outcomes. That is, whether they are determinants or consequences, or both. Our findings suggest multimorbidity resilience predicts subsequent 5-year change in health outcomes, especially self-rated physical health and pain level, but not vice versa, strengthening the evidence of the importance of resilience in the health of older adults.

scholarly journals Is Sport Sponsorship Global? Evidence from the United States, the United Kingdom, and India

2016 ◽  
Vol 8 (3) ◽  
pp. 43 ◽  
Author(s):  
Noni Zaharia ◽  
Kurt C. Mayer Jr. ◽  
Eric Hungenberg ◽  
Dianna Gray ◽  
David Stotlar
Keyword(s):  
United States ◽  
United Kingdom ◽  
Structural Equation ◽  
The United States ◽  
Purchase Intentions ◽  
Cultural Dimensions ◽  
Equation Modeling ◽  
Sport Sponsorship ◽  
Multiple Group ◽  
The United Kingdom

<p>This study sought to develop and test a cross-national sport sponsorship model. Sponsorship and Hofstede’s cultural dimensions theories were utilized for the theoretical framework for this study. A survey was conducted with 522 Chelsea FC soccer club’s fans from the United States, the United Kingdom, and India in the area of sponsorship through a jersey sponsorship. Single and multiple-group confirmatory factor analysis and structural equation modeling were used to analyze the global sport sponsorship model. The results acknowledged the measurement and structural invariance of a global model for five sport sponsorship outcomes (i.e., sponsorship awareness, sponsorship fit, attitude toward the sponsor, gratitude, and purchase intentions), controlling for age, gender, education, household income and the household’s decision maker. The statistical analyses indicated that structural relationships among the analyzed sponsorship outcomes were invariant among all three countries. The effect of sponsorship fit predicted the presence of purchase intentions, while the attitude toward the sponsor was the strongest predictor of purchase intentions.</p>

scholarly journals Person perception across group boundaries: a dynamic model of perception across race and gender lines

2021 ◽  
Author(s):  
Sally Yan-Jun Xie
Keyword(s):  
Face Perception ◽  
Impression Formation ◽  
Structural Equation ◽  
Person Perception ◽  
Two Dimensions ◽  
Equation Modeling ◽  
Modeling Framework ◽  
Race And Gender ◽  
And Gender ◽  
Trait Impressions

People form impressions of others from their faces, inferring character traits (e.g., friendly) along two broad, influential dimensions: Warmth and Competence. Although these two dimensions are presumed to be independent, research has yet to examine the generalizability of this model to cross-group impressions, despite extant evidence that Warmth and Competence are not independent for outgroup targets. This thesis explores this possibility by testing models of person perception for own-group and other-group perceptions, implementing confirmatory factor analysis in a structural equation modeling framework, and analyzing the underlying trait space using representational similarity analysis. I fit 402,473 ratings of 873 unique faces from 5,040 participants on 14 trait impressions to own-group and other-group models, exploring whether perceptions across race and gender are more unidimensional. Results indicate that current models of face perception fit poorly and are not universal as presumed: the space of trait impressions varies depending on targets’ race and gender. Keywords: person perception, impression formation, face perception, intergroup processes, social cognition

scholarly journals Longitudinal Relationships Between Subjective Cognitive Decline and Objective Memory: Depressive Symptoms Mediate Between-Person Associations

2021 ◽  
pp. 1-14
Author(s):  
Nikki L. Hill ◽  
Sakshi Bhargava ◽  
Emily Bratlee-Whitaker ◽  
Jennifer R. Turner ◽  
Monique J. Brown ◽  
...  
Keyword(s):  
Older Adults ◽  
Depressive Symptoms ◽  
Cognitive Decline ◽  
Structural Equation ◽  
Disease Risk ◽  
Well Being ◽  
Equation Modeling ◽  
Subjective Cognitive Decline ◽  
Future Research ◽  
Over Time

Background: Subjective cognitive decline (SCD) may be an early indicator of cognitive impairment, but depressive symptoms can confound this relationship. Associations may be influenced by differences between individuals (i.e., between-persons) or how each individual changes in their experiences over time (i.e., within-persons). Objective: We examined depressive symptoms as a mediator of the between- and within-person associations of SCD and objective memory in older adults. Methods: Coordinated analyses were conducted across four datasets drawn from large longitudinal studies. Samples (range: n = 1,889 to n = 15,841) included participants 65 years of age or older with no dementia at baseline. We used multilevel structural equation modeling to examine the mediation of SCD and objective memory through depressive symptoms, as well as direct relationships among SCD, objective memory, and depressive symptoms. Results: Older adults who were more likely to report SCD had lower objective memory on average (between-person associations), and depressive symptoms partially mediated this relationship in three of four datasets. However, changes in depressive symptoms did not mediate the relationship between reports of SCD and declines in objective memory in three of four datasets (within-person associations). Conclusion: Individual differences in depressive symptoms, and not changes in an individual’s depressive symptoms over time, partially explain the link between SCD and objective memory. Older adults with SCD and depressive symptoms may be at greater risk for poor cognitive outcomes. Future research should explore how perceived changes in memory affect other aspects of psychological well-being, and how these relationships influence cognitive decline and Alzheimer’s disease risk.

Sign in / Sign up

Export Citation Format

Share Document