ISSN 1671-3710
CN 11-4766/R

心理科学进展 ›› 2022, Vol. 30 ›› Issue (8): 1734-1746.doi: 10.3724/SP.J.1042.2022.01734

• 国内心理统计方法研究热点回顾 • 上一篇    下一篇


刘源1,2, 都弘彦1, 方杰3, 温忠麟4()   

  1. 1西南大学心理学部
    2认知与人格教育部重点实验室, 重庆 400715
    3广东财经大学新发展研究院/应用心理学系, 广州 510320
    4华南师范大学心理学院/心理应用研究中心, 广州 510631
  • 收稿日期:2021-09-24 出版日期:2022-08-15 发布日期:2022-06-23
  • 通讯作者: 温忠麟
  • 基金资助:

Methodology study and model development for analyzing longitudinal data in China’s mainland

LIU Yuan1,2, DU Hongyan1, FANG Jie3, WEN Zhonglin4()   

  1. 1School of Psychology, Southwest University
    2Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
    3Institute of New Development & Department of Applied Psychology, Guangdong University of Finance & Economics, Guangzhou 510320, China
    4Center for Studies of Psychological Application & School of Psychology, South China Normal University, Guangzhou 510631, China
  • Received:2021-09-24 Online:2022-08-15 Published:2022-06-23
  • Contact: WEN Zhonglin


追踪研究因其可以得到比横断研究更有说服力的变量关系论证, 在心理学等科学中具有重要地位。梳理国内以心理学为主的相关领域中追踪数据分析方法研究的发表现状、主要解决的研究问题和模型发展。追踪研究可以进行均值差异比较、分析多变量相互影响、描述总体发展趋势及差异和探究心理动态变化过程。近20年的研究热点和发展思路也集中在上述研究问题当中, 特别是总体发展趋势及差异、多变量相互影响、总体发展趋势与多变量相互影响的融合、追踪研究设计、缺失数据等议题上。最后, 比较国内外研究的差异, 并结合交叉学科对国内追踪研究未来发展做出展望。

关键词: 追踪研究, 相互影响, 增长趋势, 动态变化


Longitudinal research could systematically capture the change of the target variable and thus is more convincing than cross-sectional research. It is popular in the fields of social sciences such as psychology, management, statistics, sociology, etc. The present study reviews the methodology study and model development for analyzing longitudinal data in China’s mainland. We aim to retrospect the methods used, the main research questions, and the popular research domains in longitudinal models.

The target publications ranged from 1st Jan. 2001 to 31st Dec. 2020 in CNKI core collections in the relative domains, and finally, 75 articles met our selecting criterion. Results also indicated that the research topic widely includes latent growth model, multilevel modeling, autoregression, cross-lagged model, missing data, etc. Among these research topics, latent growth model ranked as the first. Typically, the latent growth model and experience sampling method were favored in the field of psychology.

There are mainly four research questions retrieved from the publications. The first research question is to compare the mean difference, which is less popular. The second research question is to examine the reciprocal relationship between variables. It often uses the cross-lag model and the causal model to reveal the autoregressive and cross-lagged relationships within and between variables. The third research question is to depict growth trajectory with individual differences. It uses the latent growth model (LGM) and multilevel model (MLM) as the main methods to show a growth trajectory from the between-person perspective, as well as the individual difference included. The last one is to explore the dynamic changes. This research question does not focus on the general tendency of change but on the fluctuation between different time points. It usually uses autoregression with its extensions, MLM, time-varying effect model, and some newly developed models such as the dynamic structural equation model.

The recent 20 years' publication broadens the domains of longitudinal models, such as the extension of the shape and pattern of growth, the combination of latent class analysis leading to growth mixture model and latent transition analysis. The causal effect, longitudinal mediation and moderation models are also introduced to reveal the relationship between variables. Meanwhile, models depicting growth trajectory with individual differences combines with models examining reciprocal relationships, thus they were extended and integrated to random intercept cross-lagged model, latent variable autoregressive latent trajectory, as well as general cross lagged model. Furthermore, research design becomes more complex; the intensive longitudinal data was introduced and thus the models were according developed, such as MLM, time-varying effect model, dynamic structural equation model, group iterative multiple model estimation, and so forth. Particularly, missing data issue is also hot discussed in the field.

To summarize, methodology study for analyzing longitudinal data in China’s mainland has made fruitful development on the above topics and are in an advanced position all over the world. However, when comparing to the international scope, publications in China’s mainland are limited in narrow range. Many topics need to keep up with the international pace, which is a direction that Chinese scholars need to make efforts. Another future direction is to learn from other disciplines to promote the development of interdisciplinary.

Key words: longitudinal research, reciprocal effect, growth trajectory, dynamic changes