A total of 213 articles on psychological statistical methods have been published in 11 journals of psychology in Mainland China from 2001 to 2020. There are mainly 10 areas attractive to researchers (sorted by the number of papers): structural equation models (SEM), test reliability, mediation effect, effect size and testing power, longitudinal study, moderation effect, exploratory factor analysis, latent class analysis, common method bias and hierarchical linear models.

Research on structural equation models (with confirmatory factor analysis model as a special case) explore five major aspects: model fit evaluation, model estimation, item parceling, measurement invariance and the extensions of SEM. The last aspect includes exploratory structural equation modeling, factor mixture modeling, high-order factor modeling as well as bifactor modeling. Articles on exploratory factor analysis focus on factor extraction. Modern reliability analysis is inextricably linked with factor models, including three main topics: distinction between coefficient*α* and internal consistency or homogeneity, confidence interval estimation of composite reliability and homogeneity coefficient, and reliability of multilevel data and longitudinal data. Common method bias is also based on factor analysis and studied in three aspects: the relationship between common method bias and common method variance, the influence of common method bias, and the comparison of approaches for testing and controlling common method bias.

Studies on mediation effects can be summarized in four topics: testing approaches and their comparison, mediation effect size, mediation effect testing for categorical variables, and the extensions of mediation models. The simple mediation model was extended to multilevel or multiple mediation models, moderated mediation models and mediated moderation models, as well as mediation models of longitudinal data. Articles on moderation effects mainly explore three issues: the development of latent interaction models from those with mean structure to those without mean structure, and the change from latent interaction models with product indicators to those without product indicators, as well as standardized estimates of latent moderating effect models.

Articles on longitudinal data analysis fall into three main groups. The first is the development of models, which includes hierarchical linear models, latent growth models and its mixture models, piecewise growth models and its mixture models, etc. The second is the development of longitudinal data collecting methods, which include intensive longitudinal and accelerated longitudinal design. The last is missing data handing methods of longitudinal data. Hierarchical linear models were studied in three directions: aggregation adequacy testing used in aggregating the ratings of individual level to team level, hierarchical linear model of categorical variables as outcome variables (including multilevel binomial and multilevel multinomial logit models), hierarchical linear modeling of latent variables (i.e., multilevel structural equation model).

Research on latent class models investigates three main topics: the use of latent class analysis, latent profile analysis and Taxometric techniques in probing latent class structure; precision of classification; regression mixture model (i.e., latent class model including covariates).

Both effect size and testing power are closely associated with hypothesis testing, and studies in this area introduce types and characteristics of effect size, calculation of testing power, alternative approaches and their supplements for testing null hypothesis significance.