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Dynamic Structural Equation Modeling With Floor Effects
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DOI：10.1037/met0000720.png)
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Abstract
En 中文
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.
Keywords:
intensive longitudinal data
two-part modeling
contemporaneous effects
smoking urge
negative affect
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7.8
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1.3K
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2.1W
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