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Cited by 1,481

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

A general approach to representing multifaceted personality constructs: Application to state self‐esteem

Bagozzi, Richard P., Heatherton, Todd F.·1994·Structural Equation Modeling: A Multidisciplinary Journal

Abstract

This article proposes a framework for representing personality constructs at four levels of abstraction. The total aggregation model is the composite formed by the sum of scores on all items in a scale. The partial aggregation model treats separate dimensions of a personality construct as indicators of a single latent variable, with each dimension being an aggregation of items. The partial disaggregation model represents each dimension as a separate latent variable, either freely correlated with the other dimensions or loading on one or more than one higher order factor; the measures of the dimensions are multiple indicators formed as aggregates of subsets of items. The total disaggregation model also represents each dimension as a separate latent variable but, unlike the partial disaggregation model, uses each item in the scale as an indicator of its respective factor. Illustrations of the models are provided on the State Self‐Esteem Scale—including tests of psychometric properties, invariance, and generalizability.

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Crossref · 10.1080/10705519409539961

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Metadata

Authors
Bagozzi, R. P., & Heatherton, T. F.
Venue
Structural Equation Modeling: A Multidisciplinary Journal
Published
1994-01
Volume
1
Issue
1
Pages
35-67
ISSN
  • 1070-5511 (ISSN-L)
  • 1532-8007
OpenAlex
W2083675669
Cited by
1,481
References
8
FWCI
19.52
Type
journal article
Language
en
Open access
closed

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.