Muthén Bengt, Asparouhov Tihomir, Shiffman Saul
Mplus.
Department of Psychology, University of Pittsburgh.
Psychol Methods. 2025 Jan 6. doi: 10.1037/met0000720.
Intensive longitudinal data analysis, commonly used in psychological studies, often concerns outcomes that have strong floor effects, that is, a large percentage at its lowest value. Ignoring a strong floor effect, using regular analysis with modeling assumptions suitable for a continuous-normal outcome, is likely to give misleading results. This article suggests that two-part modeling may provide a solution. It can avoid potential biasing effects due to ignoring the floor effect. It can also provide a more detailed description of the relationships between the outcome and covariates allowing different covariate effects for being at the floor or not and the value above the floor. A smoking cessation example is analyzed to demonstrate available analysis techniques. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
密集纵向数据分析常用于心理学研究，它常常涉及具有强烈下限效应的结果，即很大比例处于最低值。忽略强烈的下限效应，使用适用于连续正态结果的建模假设进行常规分析，很可能会得出误导性结果。本文表明，两部分建模可能提供一种解决方案。它可以避免因忽略下限效应而产生的潜在偏差效应。它还可以更详细地描述结果与协变量之间的关系，允许对处于下限与否以及下限之上的值有不同的协变量效应。通过一个戒烟示例进行分析，以展示可用的分析技术。（PsycInfo数据库记录（c）2025美国心理学会，保留所有权利）
