Testing Process Factor Analysis Models Using the Parametric Bootstrap

被引：7

作者：

Zhang, Guangjian ^{[1
]}

机构：

[1] Univ Notre Dame, Notre Dame, IN 46556 USA

来源：

MULTIVARIATE BEHAVIORAL RESEARCH | 2018年 / 53卷 / 02期

关键词：

Dynamic factor analysis; time series analysis; parametric bootstrap; intensive longitudinal data; model testing; MULTIVARIATE TIME-SERIES; DYNAMIC FACTOR-ANALYSIS;

D O I：

10.1080/00273171.2017.1415123

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

Process factor analysis (PFA) is a latent variable model for intensive longitudinal data. It combines P-technique factor analysis and time series analysis. The goodness-of-fit test in PFA is currently unavailable. In the paper, we propose a parametric bootstrap method for assessing model fit in PFA. We illustrate the test with an empirical data set in which 22 participants rated their effects everyday over a period of 90 days. We also explore Type I error and power of the parametric bootstrap test with simulated data.

引用

页码：219 / 230

页数：12

共 31 条

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