Subjects: Psychology >> Statistics in Psychology Subjects: Psychology >> Psychological Measurement submitted time 2022-03-08
Abstract：
In the first two decades of the twenty-first century, the hotspots of the methodological research on structural equation models (SEM) in China's mainland generally involve the following five aspects: model development, parameter estimation, model evaluation, measurement invariance and special data processing. Remarkably, there is more progress in model development (i.e., different variations of SEM) amongst the above aspects. After an overview of the background knowledge of these hotspots, we presented the main research topics and methodological achievements under each hotspot. We also discussed the recent progress of the foreign methodological studies on SEM and the future research directions.
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Subjects: Psychology >> Statistics in Psychology submitted time 2022-03-01
Abstract：
In social science research fields, single-level research often adopts cluster sampling or multi-stage sampling to obtain samples, resulting in the fact that the data structure is multi-level. Thus, researchers have to control for errors from the higher level in their single-level studies.
Hierarchical linear model (HLM) suffers from limitations in dealing with such issue. First, HLM's unique advantage to focus on random effects and cluster-specific inferences cannot be reflected in single-level research. Second, the disadvantages of HLM are amplified in single-level research. (1) HLM's assumptions about random effects are harder to satisfy and test. Violation of these assumptions may result in parameter estimation bias. (2) HLM is more likely to produce convergence problems. (3) For single-level studies, HLM is complex in theory, modeling, software operation and interpretation of results. Thus, HLM is difficult to generalize in a single level study with multi-level error.
Design-based methods (DBM), including cluster-robust standard errors (CRSE), generalized estimation equation (GEE), and fixed effects model (FEM), represent a category of logical and valid procedures to analyze multi-level data. By correcting for the standard errors of fixed effects, DBM circumvents the issues of partitioning residuals and variables into different levels while accurately estimate parameters. Thus, DBM can address multi-level data within the single-level framework, which is very friendly to single-level researchers.
Contrast to HLM, DBM is more parsimonious in modeling, simpler in operating, more efficient in running and more robust in estimating for single-level research. Therefore, at least under the condition of single-level research with multi-level error, DBM is an ideal alternative to HLM.
After a detailed introduction of DBM and its advantages, a simulation data set were used to demonstrate the effectiveness of DBM in controlling for multi-level error in single-level mediation studies (i.e., 1-1-1 mediation model). The results showed that although both HLM and DBM were accurate in estimating the within-cluster component of the mediating effect, the former underestimated the standard errors of mediating effect and each mediating path coefficient. In addition, all of the DBMs are simpler than HLM in terms of operations, especially the FEM. FEM is not only possible to operate through SPSS, but also unnecessary to center the variables in level 1 and control between-cluster variables. What’s more, through the popular SPSS mediating analysis macro PROCESS, FEM can realize both casual steps approach and coefficients product approach with bootstrap confidence interval for various complex mediation models.
Finally, following suggestions were given for practitioners to select appropriate methods to accommodate clustering in single-level research. (1) DBM is suggested to control the multi-level error in single-level study, especially FEM. (2) If researchers are interested in between-cluster fixed effects, CRSE and GEE is recommended. (3) When researchers have sufficient background knowledge of HLM, and need to focus on random effects, they should collect multi-level data deliberately, especially to ensure that the sample size of level 2 is sufficient. (4) It is recommended to retain the cluster identification information when collecting data, so as to prevent the actual level of data from exceeding the expectant level, leading to the failure to control the multi-level error.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Statistics in Psychology submitted time 2022-02-02
Abstract：
"Mediated moderation is frequently used in psychological research to reveal the phenomenon of a moderating effect being indirectly realized through mediating variables. This paper introduces the concept and advantages of a second type of mediated moderation (meMO-II). Then, we compare meMO-II with other models that combine mediation and moderation. Additionally, we propose the meMO-II modeling approach and analysis process, which we then demonstrated with a real example. We also introduce meMO-II analysis methods based on latent variables, advances in meMO-II modeling approaches, and variations in meMO-II. This offers a valuable contribution to moderating mechanism research.
Subjects: Psychology >> Statistics in Psychology Subjects: Psychology >> Psychological Measurement submitted time 2020-07-28
Abstract： "
Subjects: Psychology >> Educational Psychology submitted time 2020-05-07
Abstract： Statistical learning (SL), which was first addressed in the seminal study on speech segmentation of infants by Saffran et al. (1996), is a process of detecting the statistical regularities such as transitional probability in continuous flow of stimuli. Previous studies have proven the general existence of SL, and in recent years close attention has been placed on its specificity and its impact on other cognitive activities, especially revealing the cognitive neural mechanisms of SL and its interaction with language by exploring the process and the specificity of SL. According to the multimodal data from brain and behavior measures, future studies should seek more behavioral and neural indexes to evaluate the performance of SL, to explore the dynamic changes in neural activities of different types of SL and to construct the connection between neural correlates and behavioral performance, which will help to have an in-depth understanding of SL. Based on previous discoveries on the interaction between SL and language, future studies could determine whether SL is an effective intervention to improve language acquisition and how it works in the improvement, through exploring the effect of music SL training on second language learning of adult learners.
Peer Review Status:Awaiting Review
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