关键词: 追踪研究, 相互影响, 增长趋势, 动态变化

Abstract:

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