Decomposing Group Differences of Latent Means of Ordered Categorical Variables within a Genetic Factor Model

Cho, Seung Bin; Wood, Phillip K.; Heath, Andrew C.

doi:10.1007/s10519-008-9237-9

Cited by 10 publications

(6 citation statements)

References 39 publications

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“…In addition, as with all latent response variable models, the number of logically identifiable classes in analysis is limited by the number of variables and categories used. Specifically, changes in elevation or in variability in the latent response variable can be modeled, but not both (Millsap & Yun-Tein, 2004; Cho et al . 2009).…”

Section: Discussionmentioning

confidence: 99%

Towards the characterization and validation of alcohol use disorder subtypes: integrating consumption and symptom data

et al. 2013

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Background There is evidence that measures of alcohol consumption, dependence and abuse are valid indicators of qualitatively different subtypes of alcohol involvement yet also fall along a continuum. The present study attempts to resolve the extent to which variations in alcohol involvement reflect a difference in kind versus a difference in degree. Method Data were taken from the 2001–2002 National Epidemiologic Survey of Alcohol and Related Conditions. The sample (51% male; 72% white/non-Hispanic) included respondents reporting past 12-month drinking at both waves (wave 1: n=33644; wave 2: n=25186). We compared factor mixture models (FMMs), a hybrid of common factor analysis (FA) and latent class analysis (LCA), against FA and LCA models using past 12-month alcohol use disorder (AUD) criteria and five indicators of alcohol consumption reflecting frequency and heaviness of drinking. Results Model comparison revealed that the best-fitting model at wave 1 was a one-factor four-class FMM, with classes primarily varying across dependence and consumption indices. The model was replicated using wave 2 data, and validated against AUD and dependence diagnoses. Class stability from waves 1 to 2 was moderate, with greatest agreement for the infrequent drinking class. Within-class associations in the underlying latent factor also revealed modest agreement over time. Conclusions There is evidence that alcohol involvement can be considered both categorical and continuous, with responses reduced to four patterns that quantitatively vary along a single dimension. Nosologists may consider hybrid approaches involving groups that vary in pattern of consumption and dependence symptomatology as well as variation of severity within group.

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Section: Discussionmentioning

confidence: 99%

Towards the characterization and validation of alcohol use disorder subtypes: integrating consumption and symptom data

et al. 2013

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“…The robust weighted least squares (WLSMV) method using a diagonal weight matrix and robust standard errors and a mean- and variance adjusted χ2 test statistic ( Muthén, 1998 ; Muthén and Asparouhov, 2002 ; Muthén and Muthén, 2012b ) was used to estimate parameters. The WLSMV is a robust estimator which does not assume normally distributed data ( Brown, 2014 ) and seems to work well under a variety of conditions if sample size is 200 or better ( Flora and Curran, 2004 ; Rhemtulla et al, 2012 ).…”

Section: Methodsmentioning

confidence: 99%

Dimensions of Anxiety, Age, and Gender: Assessing Dimensionality and Measurement Invariance of the State-Trait for Cognitive and Somatic Anxiety (STICSA) in an Italian Sample

et al. 2018

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The State–Trait Inventory for Cognitive and Somatic Anxiety (STICSA) is a widely used measure of state and trait anxiety that permits a specific assessment of cognitive and somatic anxiety. Previous research provided inconsistent findings about its factor structure in non-clinical samples (e.g., hierarchical or bi-factor structure). To date, no psychometric validation of the Italian version of the STICSA has been conducted. Our study aimed to determine the psychometric functioning of the Italian version of the STICSA, including its dimensionality, gender and age measurement equivalence, and convergent/divergent validity in a large sample of community-dwelling participants (N = 2,938; 55.9% female). Through confirmatory factor analysis, the multidimensional structure of both State and Trait STICSA scales, with each including Cognitive and Somatic dimensions, was supported. Factor structure invariance was tested and established at configural, metric, and scalar levels for males and females. Additionally, full factorial measurement invariance was supported for the State scale across young, middle age, and old adult groups whereas the Trait scale was partially invariant across age groups. The STICSA also showed good convergent validity with concurrent anxiety measures (State-Trait Anxiety Inventory and Beck Anxiety Inventory), and satisfactory internal discriminant validity with two depression measures (Teate Depression Inventory and Beck Depression Inventory-II). Results provided support for the multidimensionality of the STICSA, as well as the generalizability of the State and Trait scales as independent measures of Cognitive and Somatic symptomatology across gender in the general population. Implications for research and personality and clinical assessment are discussed.

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“…Much new work in structural equation modeling involves extensions to binary and ordered categorical data using latent response variables (e.g. Cho, Wood, & Heath, 2009;Millsap & Yun-Tein, 2004). In general, heuristically similar models to the FCSI model advanced by Meredith and Tisak (1990) are specifiable under these extensions, but are the object of future research.…”

Section: Future Directionsmentioning

confidence: 99%

Developmental models for children's temperament: alternatives to chronometric polynomial curves

Wood¹

2011

Inf. Child Develop.

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Partridge and Lerner (2007), in a secondary analysis of the New York Longitudinal Study, employed a chronometric polynomial growth curve model to argue that the developmental course of difficult temperament follows a non-linear trajectory over the first 5 years of life. The free curve slope intercept (FCSI) growth curve model of Meredith and Tisak (1990) is presented as a preferable conceptual alternative because it contains a number of currently popular statistical models, including repeated measures multivariate analysis of variance, factor mean, linear growth, linear factor analysis, and hierarchical linear models as special cases. As such, researchers can compare the fit of each of these models relative to the FCSI model, and, at times, to each other. The present paper conducts a re-analysis of the data, and establishes that fit of the FCSI model is arguably better than other statistical alternatives. The FCSI model is also used as the basis for identifying subgroups of individuals with their qualitatively distinct growth patterns within a growth mixture modeling framework. Copyright r 2010 John Wiley & Sons, Ltd.Key words: temperament; longitudinal data; growth curves; structural equation modeling; growth mixture models; MANOVA Partridge and Lerner (2007) considered whether difficult temperament is relatively stable or undergoes significant change over the course of early childhood. In a longitudinal study of annual assessments across years 1-5, they explored whether inter-individual differences in difficult temperament appeared largely stable or whether individual differences in change occurred during this time. Given that fluctuation in temperament scores occurs across repeated measurements, their article examined whether observed changes in scores over

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Decomposing Group Differences of Latent Means of Ordered Categorical Variables within a Genetic Factor Model

Cited by 10 publications

References 39 publications

Towards the characterization and validation of alcohol use disorder subtypes: integrating consumption and symptom data

Towards the characterization and validation of alcohol use disorder subtypes: integrating consumption and symptom data

Dimensions of Anxiety, Age, and Gender: Assessing Dimensionality and Measurement Invariance of the State-Trait for Cognitive and Somatic Anxiety (STICSA) in an Italian Sample

Developmental models for children's temperament: alternatives to chronometric polynomial curves

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