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Ecological-Inference-Based Latent Growth Models: Modeling Changes of Alienation

Ecological-Inference-Based Latent Growth Models: Modeling Changes of Alienation Ecological-inference-based statistical methods employ aggregated (ecological) data to approximately infer individual-level structures of interests when individual-level data were not available. Under the same conceptual frames, we introduce the ecological-inference-based latent growth model (EI-LGM) to analyze cross-years latent trends of a general population when longitudinally collected data were not available. We showed both the substantive values and methodological feasibilities of EI-LGMs. Substantively, we analyze results from several Taiwan Social Change Surveys (TSCS) to show the cross-years latent trends using a subscale of alienation psychological characteristics. Not only the cross-years movements of measurement constructs of the scale were shown, the trends of latent factors were revealed as well. More importantly, these trends can be formally tested under the frameworks of EI-LGMs. Statistically, EI-LGMs were implemented under the weighted least square (WLS) approaches because of the dichotomous outcomes of the subscale. We demonstrate some of the estimation methods as well as some cautions of interpreting EI-LGMs using the estimated results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Ecological-Inference-Based Latent Growth Models: Modeling Changes of Alienation

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References (21)

Publisher
Springer Journals
Copyright
Copyright © 2005 by Springer
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
DOI
10.1007/s11135-004-1669-6
Publisher site
See Article on Publisher Site

Abstract

Ecological-inference-based statistical methods employ aggregated (ecological) data to approximately infer individual-level structures of interests when individual-level data were not available. Under the same conceptual frames, we introduce the ecological-inference-based latent growth model (EI-LGM) to analyze cross-years latent trends of a general population when longitudinally collected data were not available. We showed both the substantive values and methodological feasibilities of EI-LGMs. Substantively, we analyze results from several Taiwan Social Change Surveys (TSCS) to show the cross-years latent trends using a subscale of alienation psychological characteristics. Not only the cross-years movements of measurement constructs of the scale were shown, the trends of latent factors were revealed as well. More importantly, these trends can be formally tested under the frameworks of EI-LGMs. Statistically, EI-LGMs were implemented under the weighted least square (WLS) approaches because of the dichotomous outcomes of the subscale. We demonstrate some of the estimation methods as well as some cautions of interpreting EI-LGMs using the estimated results.

Journal

Quality & Quantity – Springer Journals

Published: Jul 30, 2004

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