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.
Quality & Quantity – Springer Journals
Published: Jul 30, 2004
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