A Monte Carlo evaluation of growth mixture modeling / Tiffany M. SHADER in Development and Psychopathology, 34-4 (October 2022)  

Public ISBD [article]  inDevelopment and Psychopathology > 34-4 (October 2022) . - p.1604-1617 Titre : A Monte Carlo evaluation of growth mixture modeling Type de document : texte imprimé Auteurs : Tiffany M. SHADER, Auteur ; Theodore P. BEAUCHAINE, Auteur Article en page(s) : p.1604-1617 Langues : Anglais (eng) Mots-clés : Computer Simulation  Humans  Models, Statistical  Monte Carlo Method  Monte Carlo  growth mixture modeling  latent classes  longitudinal  simulation Index. décimale : PER Périodiques Résumé : Growth mixture modeling (GMM) and its variants, which group individuals based on similar longitudinal growth trajectories, are quite popular in developmental and clinical science. However, research addressing the validity of GMM-identified latent subgroupings is limited. This Monte Carlo simulation tests the efficiency of GMM in identifying known subgroups (k = 1-4) across various combinations of distributional characteristics, including skew, kurtosis, sample size, intercept effect size, patterns of growth (none, linear, quadratic, exponential), and proportions of observations within each group. In total, 1,955 combinations of distributional parameters were examined, each with 1,000 replications (1,955,000 simulations). Using standard fit indices, GMM often identified the wrong number of groups. When one group was simulated with varying skew and kurtosis, GMM often identified multiple groups. When two groups were simulated, GMM performed well only when one group had steep growth (whether linear, quadratic, or exponential). When three to four groups were simulated, GMM was effective primarily when intercept effect sizes and sample sizes were large, an uncommon state of affairs in real-world applications. When conditions were less ideal, GMM often underestimated the correct number of groups when the true number was between two and four. Results suggest caution in interpreting GMM results, which sometimes get reified in the literature. En ligne : http://dx.doi.org/10.1017/s0954579420002230 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=489 [article] A Monte Carlo evaluation of growth mixture modeling [texte imprimé] / Tiffany M. SHADER, Auteur ; Theodore P. BEAUCHAINE, Auteur . - p.1604-1617. Langues : Anglais (eng) in Development and Psychopathology > 34-4 (October 2022) . - p.1604-1617 Mots-clés : Computer Simulation  Humans  Models, Statistical  Monte Carlo Method  Monte Carlo  growth mixture modeling  latent classes  longitudinal  simulation Index. décimale : PER Périodiques Résumé : Growth mixture modeling (GMM) and its variants, which group individuals based on similar longitudinal growth trajectories, are quite popular in developmental and clinical science. However, research addressing the validity of GMM-identified latent subgroupings is limited. This Monte Carlo simulation tests the efficiency of GMM in identifying known subgroups (k = 1-4) across various combinations of distributional characteristics, including skew, kurtosis, sample size, intercept effect size, patterns of growth (none, linear, quadratic, exponential), and proportions of observations within each group. In total, 1,955 combinations of distributional parameters were examined, each with 1,000 replications (1,955,000 simulations). Using standard fit indices, GMM often identified the wrong number of groups. When one group was simulated with varying skew and kurtosis, GMM often identified multiple groups. When two groups were simulated, GMM performed well only when one group had steep growth (whether linear, quadratic, or exponential). When three to four groups were simulated, GMM was effective primarily when intercept effect sizes and sample sizes were large, an uncommon state of affairs in real-world applications. When conditions were less ideal, GMM often underestimated the correct number of groups when the true number was between two and four. Results suggest caution in interpreting GMM results, which sometimes get reified in the literature. En ligne : http://dx.doi.org/10.1017/s0954579420002230 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=489

Quantifying respiratory sinus arrhythmia: Effects of misspecifying breathing frequencies across development / Tiffany M. SHADER in Development and Psychopathology, 30-1 (February 2018)  

Public ISBD [article]  inDevelopment and Psychopathology > 30-1 (February 2018) . - p.351-366 Titre : Quantifying respiratory sinus arrhythmia: Effects of misspecifying breathing frequencies across development Type de document : texte imprimé Auteurs : Tiffany M. SHADER, Auteur ; Lisa GATZKE-KOPP, Auteur ; Sheila E. CROWELL, Auteur ; M. Jamila REID, Auteur ; Julian F. THAYER, Auteur ; Michael W. VASEY, Auteur ; Carolyn WEBSTER-STRATTON, Auteur ; Ziv BELL, Auteur ; Theodore P. BEAUCHAINE, Auteur Article en page(s) : p.351-366 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Low resting respiratory sinus arrhythmia (RSA), and to a lesser extent excessive RSA reactivity to emotion evocation, are observed in many psychiatric disorders characterized by emotion dysregulation, including syndromes spanning the internalizing and externalizing spectra, and other conditions such as nonsuicidal self-injury. Nevertheless, some inconsistencies exist. For example, null outcomes in studies of RSA–emotion dysregulation relations are sometimes observed among younger participants. Such findings may derive from use of age inappropriate frequency bands in calculating RSA. We combine data from five published samples (N = 559) spanning ages 4 to 17 years, and reanalyze RSA data using age-appropriate respiratory frequencies. Misspecifying respiratory frequencies results in overestimates of resting RSA and underestimates of RSA reactivity, particularly among young children. Underestimates of developmental shifts in RSA and RSA reactivity from preschool to adolescence were also observed. Although correlational analyses revealed weak negative associations between resting RSA and aggression, those with clinical levels of externalizing exhibited lower resting RSA than their peers. No associations between RSA reactivity and externalizing were observed. Results confirm that age-corrected frequency bands should be used when estimating RSA, and that literature-wide overestimates of resting RSA, underestimates of RSA reactivity, and underestimates of developmental shifts in RSA and RSA reactivity may exist. En ligne : https://doi.org/10.1017/S0954579417000669 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=336 [article] Quantifying respiratory sinus arrhythmia: Effects of misspecifying breathing frequencies across development [texte imprimé] / Tiffany M. SHADER, Auteur ; Lisa GATZKE-KOPP, Auteur ; Sheila E. CROWELL, Auteur ; M. Jamila REID, Auteur ; Julian F. THAYER, Auteur ; Michael W. VASEY, Auteur ; Carolyn WEBSTER-STRATTON, Auteur ; Ziv BELL, Auteur ; Theodore P. BEAUCHAINE, Auteur . - p.351-366. Langues : Anglais (eng) in Development and Psychopathology > 30-1 (February 2018) . - p.351-366 Index. décimale : PER Périodiques Résumé : Low resting respiratory sinus arrhythmia (RSA), and to a lesser extent excessive RSA reactivity to emotion evocation, are observed in many psychiatric disorders characterized by emotion dysregulation, including syndromes spanning the internalizing and externalizing spectra, and other conditions such as nonsuicidal self-injury. Nevertheless, some inconsistencies exist. For example, null outcomes in studies of RSA–emotion dysregulation relations are sometimes observed among younger participants. Such findings may derive from use of age inappropriate frequency bands in calculating RSA. We combine data from five published samples (N = 559) spanning ages 4 to 17 years, and reanalyze RSA data using age-appropriate respiratory frequencies. Misspecifying respiratory frequencies results in overestimates of resting RSA and underestimates of RSA reactivity, particularly among young children. Underestimates of developmental shifts in RSA and RSA reactivity from preschool to adolescence were also observed. Although correlational analyses revealed weak negative associations between resting RSA and aggression, those with clinical levels of externalizing exhibited lower resting RSA than their peers. No associations between RSA reactivity and externalizing were observed. Results confirm that age-corrected frequency bands should be used when estimating RSA, and that literature-wide overestimates of resting RSA, underestimates of RSA reactivity, and underestimates of developmental shifts in RSA and RSA reactivity may exist. En ligne : https://doi.org/10.1017/S0954579417000669 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=336

Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research / Nathaniel HAINES in Development and Psychopathology, 31-3 (August 2019)  

Public ISBD [article]  inDevelopment and Psychopathology > 31-3 (August 2019) . - p.871-886 Titre : Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research Type de document : texte imprimé Auteurs : Nathaniel HAINES, Auteur ; Ziv BELL, Auteur ; Sheila E. CROWELL, Auteur ; Hunter HAHN, Auteur ; Dana KAMARA, Auteur ; Heather MCDONOUGH-CAPLAN, Auteur ; Tiffany SHADER, Auteur ; Theodore P. BEAUCHAINE, Auteur Article en page(s) : p.871-886 Langues : Anglais (eng) Mots-clés : arousal  emotion dysregulation  facial expression  negative valence system  positive valence system Index. décimale : PER Périodiques Résumé : As early as infancy, caregivers’ facial expressions shape children's behaviors, help them regulate their emotions, and encourage or dissuade their interpersonal agency. In childhood and adolescence, proficiencies in producing and decoding facial expressions promote social competence, whereas deficiencies characterize several forms of psychopathology. To date, however, studying facial expressions has been hampered by the labor-intensive, time-consuming nature of human coding. We describe a partial solution: automated facial expression coding (AFEC), which combines computer vision and machine learning to code facial expressions in real time. Although AFEC cannot capture the full complexity of human emotion, it codes positive affect, negative affect, and arousal—core Research Domain Criteria constructs—as accurately as humans, and it characterizes emotion dysregulation with greater specificity than other objective measures such as autonomic responding. We provide an example in which we use AFEC to evaluate emotion dynamics in mother–daughter dyads engaged in conflict. Among other findings, AFEC (a) shows convergent validity with a validated human coding scheme, (b) distinguishes among risk groups, and (c) detects developmental increases in positive dyadic affect correspondence as teen daughters age. Although more research is needed to realize the full potential of AFEC, findings demonstrate its current utility in research on emotion dysregulation. En ligne : http://dx.doi.org/10.1017/S0954579419000312 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=403 [article] Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research [texte imprimé] / Nathaniel HAINES, Auteur ; Ziv BELL, Auteur ; Sheila E. CROWELL, Auteur ; Hunter HAHN, Auteur ; Dana KAMARA, Auteur ; Heather MCDONOUGH-CAPLAN, Auteur ; Tiffany SHADER, Auteur ; Theodore P. BEAUCHAINE, Auteur . - p.871-886. Langues : Anglais (eng) in Development and Psychopathology > 31-3 (August 2019) . - p.871-886 Mots-clés : arousal  emotion dysregulation  facial expression  negative valence system  positive valence system Index. décimale : PER Périodiques Résumé : As early as infancy, caregivers’ facial expressions shape children's behaviors, help them regulate their emotions, and encourage or dissuade their interpersonal agency. In childhood and adolescence, proficiencies in producing and decoding facial expressions promote social competence, whereas deficiencies characterize several forms of psychopathology. To date, however, studying facial expressions has been hampered by the labor-intensive, time-consuming nature of human coding. We describe a partial solution: automated facial expression coding (AFEC), which combines computer vision and machine learning to code facial expressions in real time. Although AFEC cannot capture the full complexity of human emotion, it codes positive affect, negative affect, and arousal—core Research Domain Criteria constructs—as accurately as humans, and it characterizes emotion dysregulation with greater specificity than other objective measures such as autonomic responding. We provide an example in which we use AFEC to evaluate emotion dynamics in mother–daughter dyads engaged in conflict. Among other findings, AFEC (a) shows convergent validity with a validated human coding scheme, (b) distinguishes among risk groups, and (c) detects developmental increases in positive dyadic affect correspondence as teen daughters age. Although more research is needed to realize the full potential of AFEC, findings demonstrate its current utility in research on emotion dysregulation. En ligne : http://dx.doi.org/10.1017/S0954579419000312 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=403

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