Examining Population Heterogeneity in Finite Mixture Settings Using Latent Variable Modeling

Raykov, Tenko; Marcoulides, George A.; Chang, C. K.

doi:10.1080/10705511.2015.1103193

Cited by 27 publications

(28 citation statements)

References 9 publications

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“…Factor mixture modeling (FMM) is just such a tool to explore patterns of hitherto unobserved heterogeneity. Indeed, it has been used to investigate potential population heterogeneity (e.g., Lubke & Muthén, 2005; Raykov, Marcoulides, & Chang, 2016), and therefore, for our purposes, it can be used to investigate potential population heterogeneity in responses to positively and negatively worded items.…”

Section: Factor Mixture Modelingmentioning

confidence: 99%

Investigating Response Heterogeneity in the Context of Positively and Negatively Worded Items by Using Factor Mixture Modeling

Kam¹,

Fan

2018

Organizational Research Methods

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Factor mixture modeling was used to investigate potential response incongruity between positively and negatively worded items. Survey respondents ( N = 591) answered questions about job satisfaction and dissatisfaction. Results revealed two classes of respondent: a majority class, who generally do not have problems answering positively and negatively worded items, and a minority class, who have serious trouble with negatively worded items. With the exclusion of the minority class, job satisfaction and dissatisfaction were found to be essentially unidimensional rather than bidimensional as previous research had suggested. These results challenge previous findings regarding the bidimensionality of job satisfaction and cast doubt on the widespread research practice of assuming population homogeneity in survey responses. A detailed flowchart illustrating the analytic procedure and an Mplus syntax program are provided so that researchers can conduct similar investigations on constructs of interest.

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Section: Factor Mixture Modelingmentioning

confidence: 99%

Investigating Response Heterogeneity in the Context of Positively and Negatively Worded Items by Using Factor Mixture Modeling

Kam¹,

Fan

2018

Organizational Research Methods

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“…Therefore, we would like to raise caution that the proposed method—like all conventional, single-class statistical and measurement approaches—may be misleading when applied to samples from populations with substantial unobserved heterogeneity if the latter is not accounted for (cf. Raykov et al, 2016; Raykov et al, 2019). Relatedly, we presumed also no clustering effects in a studied population, but it may be suggested that minor such effects may be accommodated by the robust ML method especially with normality or under up to limited violations of it.…”

Section: Resultsmentioning

confidence: 99%

“…., X k can be the k subtests in a test battery, k testlets within an overall test, or k subscales of a psychometric scale. We advance also the frequent assumption that the instrument is utilized with (an appropriate sample from) a studied single-level, single-class population, that is, a population with no clustering effects and consisting of a single as opposed to multiple latent classes (e.g., Raykov et al, 2016; see also the “Conclusion” section).…”

Section: Notation and Assumptionsmentioning

confidence: 99%

Examining Multidimensional Measuring Instruments for Proximity to Unidimensional Structure Using Latent Variable Modeling

Raykov

Bluemke

2020

Educational and Psychological Measurement

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A widely applicable procedure of examining proximity to unidimensionality for multicomponent measuring instruments with multidimensional structure is discussed. The method is developed within the framework of latent variable modeling and allows one to point and interval estimate an explained variance proportion-based index that may be considered a measure of proximity to unidimensional structure. The approach is readily utilized in educational, behavioral, and social research when it is of interest to evaluate whether a more general structure scale, test, or measuring instrument could be treated as being associated with an approximately unidimensional latent structure for some empirical purposes.

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“…Subgroups of respondents with distinct types of job (dis)satisfaction were identified using finite mixture modelling (see Hallquist & Wright, ; Morin et al, ). Following Raykov, Marcoulides, and Chang (), the number of latent classes were identified by modelling the five Bruggemann job (dis)satisfaction items as latent factors and comparing the fit of different mixture models with one to nine classes. Across the different classes, strict measurement invariance of the latent factors was enforced.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, the factor variances and covariances were constrained across classes. In this way, the identification of distinct satisfaction types was based on the means of the five items (see Raykov et al, ). The number of subgroups (i.e.…”

Section: Methodsmentioning

confidence: 99%

Exploring Different Forms of Job (Dis)Satisfaction and Their Relationship with Well‐Being, Motivation and Performance

et al. 2017

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Job satisfaction is often treated as a one-dimensional construct. In contrast, Bruggemann (1974) postulated six distinct forms of (dis)satisfaction: four types of satisfaction (progressive, stabilised, resigned, pseudo) and two types of dissatisfaction (constructive, fixated). Despite her theorys practical relevance, few researchers have explored its assumptions or applications. The current study aimed to characterise a German-speaking employee sample (n 5 892) according to Bruggemanns theory using mixture modelling. We investigated stability of the (dis)satisfaction forms over a five-month period, as well as their relationship with well-being, motivation and (self-reported) performance. We found latent clusters corresponding to most Bruggemann types, though no distinction between progressive and stabilised satisfaction was possible. While cluster membership varied over time, some clusters (e.g. resigned satisfaction) were more stable than others (e.g. constructive dissatisfaction). Overall satisfaction level explained 25-51 per cent variance in well-being and motivation, and 13-16 per cent variance in performance. Including forms of satisfaction improved cross-sectional prediction by 2-6 per cent explained variance. Results suggest that unfavourable consequences of job dissatisfaction may be limited to fixated-not constructive-dissatisfaction, though no consistent longitudinal effects emerged. We argue that exploring qualitative differences in job satisfaction promotes a more nuanced and potentially useful understanding of the relationship between satisfaction and work outcomes.

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Examining Population Heterogeneity in Finite Mixture Settings Using Latent Variable Modeling

Cited by 27 publications

References 9 publications

Investigating Response Heterogeneity in the Context of Positively and Negatively Worded Items by Using Factor Mixture Modeling

Investigating Response Heterogeneity in the Context of Positively and Negatively Worded Items by Using Factor Mixture Modeling

Examining Multidimensional Measuring Instruments for Proximity to Unidimensional Structure Using Latent Variable Modeling

Exploring Different Forms of Job (Dis)Satisfaction and Their Relationship with Well‐Being, Motivation and Performance

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