Recovering unimodal latent patterns of change by unfolding analysis: Application to smoking cessation
Recovering unimodal latent patterns of change by unfolding analysis: Application to smoking cessation
复制标题
通过展开分析恢复单峰潜在变化模式：在戒烟中的应用
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
Yvonnick Noël
中科院分区：
文献类型：
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作者：
Yvonnick Noël
本文提出了一种新的变化模型，该模型基于这样的假设：随着吸烟者走向有效的变化，变化的认知和行为过程基本上遵循倒u型变化模式：每个过程首先被越来越多地使用，达到最大值，然后减少。有人认为，这种数据模型可以通过专门为这些情况设计的展开模型来适当地处理。本文提出了一个基于概率推理的变化展开模型的理论基础，该模型首先由D. Andrich和G. Luo（1993）提出。对140名法国吸烟者对C. C. DiClemente和J. O. Prochaska（1985）的变化过程问卷的反应进行了说说性分析，得出了一个非常令人满意的单维解决方案，其中项目的位置与以前在变化阶段传统中的纵向研究一致，吸烟者的位置似乎是实际戒烟的良好预测因子。已经提出了许多用于测量态度和认知能力的潜在特质模型（Andrich, 1978b; Bock, 1972; Lord, 1952; Masters, 1982; Muraki, 1992; Samejima, 1969），这些模型扩展并完善了最初由Rasch（1960）在心理测量传统中提出的概率反应模型，该模型可以追溯到Thurstone（1927, 1928）的开创性作品。已经提出将这些模型从结构特征测量扩展到纵向数据分析（Fischer, 1989; Fischer & Parzer, 1991）。在结构方程建模传统中，潜在增长模型也可用于分析变化（Duncan, Duncan, & Stoolmiller, 1994; Raykov, 1994）。然而，这些方法要么假设一个累积的潜在进化过程，要么需要重复测量来估计（潜在的非线性）增长函数。
A new model of change is proposed, based on the assumption that cognitive and behavioral processes of change basically follow inverse-U-shaped patterns of variation as smokers move toward effective change: Each process is first increasingly used, up to a maximum value, and then decreases. It is argued that such a model of data is properly dealt with by unfolding models specially designed for those cases. A theoretical foundation for an unfolding model of change is proposed, based on probabilistic reasoning first developed by D. Andrich and G. Luo (1993). An illustrative analysis on responses of 140 French smokers to C. C. DiClemente and J. O. Prochaska's (1985) Processes of Change Questionnaire is presented, which yields a very satisfactory unidimensional solution, along which items' locations are in convergence with previous longitudinal studies in the stage-of-change tradition and smokers' locations appear to be a good predictor of actual quitting. Numerous latent trait models for the measurement of attitudes and cognitive abilities have been proposed (Andrich, 1978b; Bock, 1972; Lord, 1952; Masters, 1982; Muraki, 1992; Samejima, 1969) that have expanded and sophisticated probabilistic response models initially proposed by Rasch (1960) in a tradition of psychological measurement that could be traced to Thurstone's (1927, 1928) seminal works. Extensions in the use of these models from structural traits measurement to longitudinal data analysis have been proposed (Fischer, 1989; Fischer & Parzer, 1991). In the structural equation modeling tradition, latent growth models are also available for the analysis of change (Duncan, Duncan, & Stoolmiller, 1994; Raykov, 1994). Those approaches, however, either assume a cumulative latent evolutionary process or require that repeated measures be available to estimate the (potentially nonlinear) growth function.
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