Valid inference can be drawn from a random-effects model for repeated measures that are incomplet... more Valid inference can be drawn from a random-effects model for repeated measures that are incomplete if whether the data are missing or not, known as missingness, is independent of the missing data. Data that are missing completely at random or missing at random are two data types for which missingness is ignorable. Given ignorable missingness, statistical inference can proceed without addressing the source of the missing data in the model. If the missingness is not ignorable, however, recommendations are to fit multiple models that represent different plausible explanations of the missing data. A popular choice in methods for evaluating nonignorable missingness is a random-effects pattern-mixture model that extends a random-effects model to include one or more between-subjects variables that represent fixed patterns of missing data. Generally straightforward to implement, a fixed pattern-mixture model is one among several options for assessing nonignorable missingness, and when it is...
Latent curve models have become a popular approach to the analysis of longitudinal data. At the i... more Latent curve models have become a popular approach to the analysis of longitudinal data. At the individual level, the model expresses an individual’s response as a linear combination of what are called “basis functions” that are common to all members of a population and weights that may vary among individuals. This article uses differential calculus to define the basis functions of a latent curve model. This provides a meaningful interpretation of the unique and dynamic impact of each basis function on the individual-level response. Examples are provided to illustrate this sensitivity, as well as the sensitivity of the basis functions, to changes in the measure of time.
Mixed-effects models for repeated measures and longitudinal data include random coefficients that... more Mixed-effects models for repeated measures and longitudinal data include random coefficients that are unique to the individual, and thus permit subject-specific growth trajectories, as well as direct study of how the coefficients of a growth function vary as a function of covariates. Although applications of these models often assume homogeneity of the within-subject residual variance that characterizes within-person variation after accounting for systematic change and the variances of the random coefficients of a growth model that quantify individual differences in aspects of change, alternative covariance structures can be considered. These include allowing for serial correlations between the within-subject residuals to account for dependencies in data that remain after fitting a particular growth model or specifying the within-subject residual variance to be a function of covariates or a random subject effect to address between-subject heterogeneity due to unmeasured influences. ...
A mixed-effects location scale model allows researchers to study within- and between-person varia... more A mixed-effects location scale model allows researchers to study within- and between-person variation in repeated measures. Key components of the model include separate variance models to study predictors of the within-person variance, as well as predictors of the between-person variance of a random effect, such as a random intercept. In this paper, a latent variable mixed-effects location scale model is developed that combines a longitudinal common factor model and a mixed-effects location scale model to characterize within- and between-person variation in a common factor. The model is illustrated using daily reports of positive affect and daily stressors for a large sample of adult women.
There are massive literatures on initial attraction and established relationships. But few studie... more There are massive literatures on initial attraction and established relationships. But few studies capture early relationship development: the interstitial period in which people experience rising and falling romantic interest for partners who could—but often do not—become sexual or dating partners. In this study, 208 single participants reported on 1,065 potential romantic partners across 7,179 data points over 7 months. In stage 1, we used random forests (a type of machine learning) to estimate how well different classes of variables (e.g., individual differences vs. target-specific constructs) predicted participants’ romantic interest in these potential partners. We also tested (and found only modest support for) the perceiver × target moderation account of compatibility: the meta-theoretical perspective that some types of perceivers experience greater romantic interest for some types of targets. In stage 2, we used multilevel modeling to depict predictors retained by the random-forests models; robust (positive) main effects emerged for many variables, including sociosexuality, sex drive, perceptions of the partner’s positive attributes (e.g., attractive and exciting), attachment features (e.g., proximity seeking), and perceived interest. Finally, we found no support for ideal partner preference-matching effects on romantic interest. The discussion highlights the need for new models to explain the origin of romantic compatibility.
Objective:We examined whether two key emotion regulation strategies, cognitive reappraisal and ex... more Objective:We examined whether two key emotion regulation strategies, cognitive reappraisal and expressive suppression, moderated the relations between discrimination (i.e., foreigner objectification and general denigration) and adjustment.Methods:Participants were U.S. Latino/a and Asian-heritage college students (N = 1,279, 67% female, 72% U.S. born) from the Multi-Site University Study of Identity and Culture (MUSIC). Students completed online self-report surveys in 2009.Results:Multi-group path analysis demonstrated that a fully constrained model fit well for both Latino/a and Asian-heritage student data. The results showed that with increasing levels of denigration (but not foreigner objectification), the combination of lower cognitive reappraisal and higher expressive suppression was related to greater depressive symptoms, anxiety, and aggression.Conclusions:Our findings highlight the importance of examining multiple emotion regulation strategies simultaneously—considering what strategies are available to individuals and in what combination they are used—to understand how best to deal with negative emotions resulting from experiencing discrimination.
The collection and widespread dissemination of panel data is one of the most important social sci... more The collection and widespread dissemination of panel data is one of the most important social science innovations of the past 40 years. During this period, social statisticians have struggled to develop models and methods that make ...
Journal of Educational and Behavioral Statistics, Sep 24, 2016
Daniel Mirman’s Growth Curve Analysis and Visualization Using R is one book in a series that aims... more Daniel Mirman’s Growth Curve Analysis and Visualization Using R is one book in a series that aims to cover the development and application of R, including applications within specific domains of study, the program’s use in studies of statistical methodology, and the development of R itself. This book in particular was developed as a practical guide to growth curve analysis of longitudinal data in the behavioral sciences, with a focus on applications in cognitive science, cognitive neuroscience, and psychology. Readers are required to have only minimal familiarity with R and no expertise in computer programming. In addition to base R, two add-on packages are necessary, namely, lme4 and ggplot2. Data examples rely on the R Datasets Package maintained by the R Development Core Team. The book includes eight chapters. Chapter 1 defines ‘‘time course data’’ as repeated measures observed for the same individuals either over time or according to some other continuous predictor, and introduces ggplot2 for graphing data in R. Guidance to transforming data files from wide to long format, a requirement of data for use with ggplot2, is given. A discussion of classic methods of analysis (e.g., repeated measures analysis of variance) that require ‘‘data binning’’ highlights key issues in studies of individual differences and some of the shortcomings of classic methods. Chapter 2 gives a conceptual overview of growth curve analysis and the basic theory of a growth curve model with a distinction made between the fixed and random effects of a model. Examples based on linear growth trends are given with extensive model formulas useful for new R users; formulas are later shortened by making use of default program settings and program operators. Likelihood ratio tests are introduced for testing differences in model fit between nested models, and plots of observed and fitted data are presented to visually evaluate model fit. Chapter 3 addresses nonlinear trends in longitudinal data. The chapter introduces higher order polynomial (e.g., quadratic, cubic) functions that are linear in their parameters and Journal of Educational and Behavioral Statistics 2016, Vol. 41, No. 6, pp. 650–652 DOI: 10.3102/1076998616646201 # 2016 AERA. http://jebs.aera.net
The life-history narratives of 10 Mexican American men with mobility limitations, age 55–77 years... more The life-history narratives of 10 Mexican American men with mobility limitations, age 55–77 years (mean = 63.8, SD = 5.8), were explored using a qualitatively driven, life-history mixed-methods study to understand perceptions of mobility limitations over the life course. Within that methodological and paradigmatic framework, conceptualizations of alterity and masculinity guided interpretation of data. Through an iterative, thematic analysis, we detail the way the men’s lives were influenced by growing familial responsibility with age. Quantitative data were integrated into themes of narrative inheritance, family, and masculinity. It was posited that masculinity with mobility limitations shaped and was shaped by ethnic identity and responsibility. This has implications for understanding the experience of Mexican American men over the life course.
Health Psychology has received numerous papers over the past several months on topics related to ... more Health Psychology has received numerous papers over the past several months on topics related to the COVID-19 pandemic. Many of them concern depression, anxiety, stress, or other forms of distress in the general population or in health care workers. We have received far fewer papers on COVID-related health behaviors and health communications-factors that have played central roles in the spread of the pandemic and that are major topics in health psychology. Our experience is consistent with the published scientific literature on the pandemic. A Medline search that we conducted in late September yielded over 23,000 English-language articles pertaining to COVID-19. Over 1,400 of them concerned topics that are within the scope of Health Psychology. As shown in Table 1, COVID-related mental disorders comprised the largest category. Many other studies concerned other forms of stress or emotional distress. At least 248 articles addressed the profound ethnic and racial disparities in COVID-19 infection and death rates and in access to health care that are accentuating longstanding health inequities; 22 (9%) of these articles addressed behavioral or psychosocial aspects of COVID-19 health disparities. Thus, the literature on the behavioral and psychosocial aspects of the pandemic has been dominated, so far at least, by research on stress or distress. Fewer reports have been published so far on critical COVID-related health behaviors, health communication, or health disparities. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Valid inference can be drawn from a random-effects model for repeated measures that are incomplet... more Valid inference can be drawn from a random-effects model for repeated measures that are incomplete if whether the data are missing or not, known as missingness, is independent of the missing data. Data that are missing completely at random or missing at random are two data types for which missingness is ignorable. Given ignorable missingness, statistical inference can proceed without addressing the source of the missing data in the model. If the missingness is not ignorable, however, recommendations are to fit multiple models that represent different plausible explanations of the missing data. A popular choice in methods for evaluating nonignorable missingness is a random-effects pattern-mixture model that extends a random-effects model to include one or more between-subjects variables that represent fixed patterns of missing data. Generally straightforward to implement, a fixed pattern-mixture model is one among several options for assessing nonignorable missingness, and when it is...
Latent curve models have become a popular approach to the analysis of longitudinal data. At the i... more Latent curve models have become a popular approach to the analysis of longitudinal data. At the individual level, the model expresses an individual’s response as a linear combination of what are called “basis functions” that are common to all members of a population and weights that may vary among individuals. This article uses differential calculus to define the basis functions of a latent curve model. This provides a meaningful interpretation of the unique and dynamic impact of each basis function on the individual-level response. Examples are provided to illustrate this sensitivity, as well as the sensitivity of the basis functions, to changes in the measure of time.
Mixed-effects models for repeated measures and longitudinal data include random coefficients that... more Mixed-effects models for repeated measures and longitudinal data include random coefficients that are unique to the individual, and thus permit subject-specific growth trajectories, as well as direct study of how the coefficients of a growth function vary as a function of covariates. Although applications of these models often assume homogeneity of the within-subject residual variance that characterizes within-person variation after accounting for systematic change and the variances of the random coefficients of a growth model that quantify individual differences in aspects of change, alternative covariance structures can be considered. These include allowing for serial correlations between the within-subject residuals to account for dependencies in data that remain after fitting a particular growth model or specifying the within-subject residual variance to be a function of covariates or a random subject effect to address between-subject heterogeneity due to unmeasured influences. ...
A mixed-effects location scale model allows researchers to study within- and between-person varia... more A mixed-effects location scale model allows researchers to study within- and between-person variation in repeated measures. Key components of the model include separate variance models to study predictors of the within-person variance, as well as predictors of the between-person variance of a random effect, such as a random intercept. In this paper, a latent variable mixed-effects location scale model is developed that combines a longitudinal common factor model and a mixed-effects location scale model to characterize within- and between-person variation in a common factor. The model is illustrated using daily reports of positive affect and daily stressors for a large sample of adult women.
There are massive literatures on initial attraction and established relationships. But few studie... more There are massive literatures on initial attraction and established relationships. But few studies capture early relationship development: the interstitial period in which people experience rising and falling romantic interest for partners who could—but often do not—become sexual or dating partners. In this study, 208 single participants reported on 1,065 potential romantic partners across 7,179 data points over 7 months. In stage 1, we used random forests (a type of machine learning) to estimate how well different classes of variables (e.g., individual differences vs. target-specific constructs) predicted participants’ romantic interest in these potential partners. We also tested (and found only modest support for) the perceiver × target moderation account of compatibility: the meta-theoretical perspective that some types of perceivers experience greater romantic interest for some types of targets. In stage 2, we used multilevel modeling to depict predictors retained by the random-forests models; robust (positive) main effects emerged for many variables, including sociosexuality, sex drive, perceptions of the partner’s positive attributes (e.g., attractive and exciting), attachment features (e.g., proximity seeking), and perceived interest. Finally, we found no support for ideal partner preference-matching effects on romantic interest. The discussion highlights the need for new models to explain the origin of romantic compatibility.
Objective:We examined whether two key emotion regulation strategies, cognitive reappraisal and ex... more Objective:We examined whether two key emotion regulation strategies, cognitive reappraisal and expressive suppression, moderated the relations between discrimination (i.e., foreigner objectification and general denigration) and adjustment.Methods:Participants were U.S. Latino/a and Asian-heritage college students (N = 1,279, 67% female, 72% U.S. born) from the Multi-Site University Study of Identity and Culture (MUSIC). Students completed online self-report surveys in 2009.Results:Multi-group path analysis demonstrated that a fully constrained model fit well for both Latino/a and Asian-heritage student data. The results showed that with increasing levels of denigration (but not foreigner objectification), the combination of lower cognitive reappraisal and higher expressive suppression was related to greater depressive symptoms, anxiety, and aggression.Conclusions:Our findings highlight the importance of examining multiple emotion regulation strategies simultaneously—considering what strategies are available to individuals and in what combination they are used—to understand how best to deal with negative emotions resulting from experiencing discrimination.
The collection and widespread dissemination of panel data is one of the most important social sci... more The collection and widespread dissemination of panel data is one of the most important social science innovations of the past 40 years. During this period, social statisticians have struggled to develop models and methods that make ...
Journal of Educational and Behavioral Statistics, Sep 24, 2016
Daniel Mirman’s Growth Curve Analysis and Visualization Using R is one book in a series that aims... more Daniel Mirman’s Growth Curve Analysis and Visualization Using R is one book in a series that aims to cover the development and application of R, including applications within specific domains of study, the program’s use in studies of statistical methodology, and the development of R itself. This book in particular was developed as a practical guide to growth curve analysis of longitudinal data in the behavioral sciences, with a focus on applications in cognitive science, cognitive neuroscience, and psychology. Readers are required to have only minimal familiarity with R and no expertise in computer programming. In addition to base R, two add-on packages are necessary, namely, lme4 and ggplot2. Data examples rely on the R Datasets Package maintained by the R Development Core Team. The book includes eight chapters. Chapter 1 defines ‘‘time course data’’ as repeated measures observed for the same individuals either over time or according to some other continuous predictor, and introduces ggplot2 for graphing data in R. Guidance to transforming data files from wide to long format, a requirement of data for use with ggplot2, is given. A discussion of classic methods of analysis (e.g., repeated measures analysis of variance) that require ‘‘data binning’’ highlights key issues in studies of individual differences and some of the shortcomings of classic methods. Chapter 2 gives a conceptual overview of growth curve analysis and the basic theory of a growth curve model with a distinction made between the fixed and random effects of a model. Examples based on linear growth trends are given with extensive model formulas useful for new R users; formulas are later shortened by making use of default program settings and program operators. Likelihood ratio tests are introduced for testing differences in model fit between nested models, and plots of observed and fitted data are presented to visually evaluate model fit. Chapter 3 addresses nonlinear trends in longitudinal data. The chapter introduces higher order polynomial (e.g., quadratic, cubic) functions that are linear in their parameters and Journal of Educational and Behavioral Statistics 2016, Vol. 41, No. 6, pp. 650–652 DOI: 10.3102/1076998616646201 # 2016 AERA. http://jebs.aera.net
The life-history narratives of 10 Mexican American men with mobility limitations, age 55–77 years... more The life-history narratives of 10 Mexican American men with mobility limitations, age 55–77 years (mean = 63.8, SD = 5.8), were explored using a qualitatively driven, life-history mixed-methods study to understand perceptions of mobility limitations over the life course. Within that methodological and paradigmatic framework, conceptualizations of alterity and masculinity guided interpretation of data. Through an iterative, thematic analysis, we detail the way the men’s lives were influenced by growing familial responsibility with age. Quantitative data were integrated into themes of narrative inheritance, family, and masculinity. It was posited that masculinity with mobility limitations shaped and was shaped by ethnic identity and responsibility. This has implications for understanding the experience of Mexican American men over the life course.
Health Psychology has received numerous papers over the past several months on topics related to ... more Health Psychology has received numerous papers over the past several months on topics related to the COVID-19 pandemic. Many of them concern depression, anxiety, stress, or other forms of distress in the general population or in health care workers. We have received far fewer papers on COVID-related health behaviors and health communications-factors that have played central roles in the spread of the pandemic and that are major topics in health psychology. Our experience is consistent with the published scientific literature on the pandemic. A Medline search that we conducted in late September yielded over 23,000 English-language articles pertaining to COVID-19. Over 1,400 of them concerned topics that are within the scope of Health Psychology. As shown in Table 1, COVID-related mental disorders comprised the largest category. Many other studies concerned other forms of stress or emotional distress. At least 248 articles addressed the profound ethnic and racial disparities in COVID-19 infection and death rates and in access to health care that are accentuating longstanding health inequities; 22 (9%) of these articles addressed behavioral or psychosocial aspects of COVID-19 health disparities. Thus, the literature on the behavioral and psychosocial aspects of the pandemic has been dominated, so far at least, by research on stress or distress. Fewer reports have been published so far on critical COVID-related health behaviors, health communication, or health disparities. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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