應用成長混合模式剖析臺灣青少年憂鬱發展軌跡的異質性：六步驟策略性模式發展機制研究

Application of Growth Mixture Model to Heterogeneous Trajectories of Depressive Moods of Adolescents: A Six-Step Strategic Model Development Mechanism

10.3966/181665042013120904005

王郁琮(Yu-Chung Lawrence Wang)

成長混合模式 ； 青少年憂鬱發展軌跡 ； GMM模式建構 ； growth mixture model ； developmental trajectory of depression of adolescence ； GMM model development mechanism

教育研究與發展期刊

9卷4期（2013 / 12 / 31）

119 - 147

繁體中文

新興成長混合模式（Growth Mixture Model，簡稱GMM）針對可能存在的潛在異質次群體，進行多元發展軌跡估計，故比起傳統潛在成長曲線模式（Latent Growth Curve Model）基於同質性假設而僅以單一軌跡進行整體成長歷程描述，顯得更加詳盡但模式結構也更複雜。當研究者進行GMM分析卻缺乏一套策略性建構機制時，極易造成過度依賴資料探索，並遭致模式無法收斂的窘境。本研究旨在發展一套步驟明確的GMM標準化建構策略，做為實徵應用分析研究的參考準則；並以臺灣青少年研究從國一至高三所蒐集之六波段憂鬱症狀實徵資料進行示範分析。研究結果顯示，作者所發展的六步驟GMM建構機制，除了兼顧理論驗證與分類實質意義，並可有效地提升模式收斂。實徵資料分析結果發現，臺灣青少年從國一到高三的憂鬱發展軌跡可以分為三種類型，包括：持續低孤獨鬱卒感的「合群快樂型」（82.3%）、先低後高的「晚發憂鬱型」（7.7%）、以及先高轉低的「早發憂鬱型」（9.9%）。GMM是目前少數提供具有統計模式基礎的縱貫軌跡分類，針對如何客觀區分發展軌跡次群體，本文的GMM策略發展機制具重大實用意涵。

The recently developed Growth Mixture Mode (GMM) provides multiple trajectories to account for the heterogeneity of population, and is therefore more comprehensive than Latent Growth Curve Model (LGCM) that uses a single trajectory to describe development of all subjects, based on its homogeneity assumption. Nonetheless, without a strategic model development mechanism, researchers often encounter convergence problem with GMM, due to model complexity and flexibility. The aim of this study was to fulfill this deficiency by establishing a standardized step-by-step model development procedure. Results from empirical data showed that the six-step procedure improved the likelihood of model convergence significantly. Results from empirical analyses concluded three classes of developmental trajectory of depression among Taiwanese adolescents including stably low depression, named ＂cheerful＂ (82.3%); ＂start low end high＂, named ＂late onset depression＂(7.7%); and ＂start high end low＂, named ＂early onset depression＂ (9.9%). GMM is a promising method with model-based longitudinal classification, and the mechanism proposed makes a significant contribution to GMM application.

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