Subjects: Psychology >> Social Psychology Subjects: Psychology >> Developmental Psychology submitted time 2023-05-17
Abstract： Perception of groups develops from an early age. Previous studies focused on groups with perceptual-salient cues like gender and race. As highlighted in the intuitive theories of social categorization, children perceive social groups as natural kinds or serving functional roles of social obligation. However, the priority ofthese two aspects affecting children’s group perception is yet to be explored. Our current research summarized these two aspects into physical-type and societal-type cues. Physical-type cues are identified by perceptual-salient attributes related to people like color, gender, and socioeconomic status (SES). Societal-type cues reflect shared attitudes, beliefs, and values among group members, such as common interests, group belongings, and norms. It has previously been found that children start to endorse prescriptive norms around age five. Therefore, we assume that children’s preferences for societal-type cues will increase across ages 3 to 8, with a critical period of 5 to 6 years of age. Study 1 was tested online. A total of 215 children (108 males) ages 3 to 8 were recruited. Three physical-type and three societal-type cues were paired under nine experimental conditions. Two tasks were conducted in random order between the participants: The Triad ClassificationTask and the Exclusion Task. Both tasksrequired participantsto categorize targets based on one of the two given cues (each represented by one cue-type). In the Triad Classification Task, children needed to select one target from two peers, and in the Exclusion Task, they needed to exclude one target. Study 2 tested 3- to 8-year-old children offline (3- to 4-year-olds: 32 children; 5- to 6-year-olds: 21 children; 7- to 8-year-olds: 20 children). Six cues were combined into two experimental conditions(gender × color × norm vs. SES × common interest × belonging). Children were tested using the Opening Social Categorization Task, in which they categorized eight targets into two groups, and reported the reasons for categorization. Results of the two studies demonstrated that 3-to 8-year-olds could apply physical-type and societal-type cuesto group perception. Specifically, childrenrely more on societal-type cues than physical-type cues as they grow up. The 3- to 4-year-olds preferred societal-type cues in social categorization tasks with two choices (Study 1), and physical-type cues in tasks offering three choices(Study 2). Children aged 5 to 8 displayed preferencesforsocietal-type cuesin the tasks of Study 1, whereasshowed no cue preferences in Study 2. Therefore, for young children (3- to 6- year-olds), their preferencesforsocietal-type cues were sensitive to the number of cues provided in the social categorization tasks, and offline versus online measurements. Moreover, children’s cue-type preferences differed significantly between 3- to 4-year-olds (preferred physical-type cues) and 7- to 8-year-olds(preferredsocietal-type cues). Thus, the critical period for developing a preference for societal cues was 5 to 6 years of age. Thisstudy constructs a new framework of physical-type and societal-type cues to understand children’ssocial categorization and group perception. These two types of cues reflect children’s perceptual and conceptual foundation in theirsocial categorization. Across ages, children’s ability to apply physical-type and societal-type cuessupportsthe intuitive theory of social categorization that children are naturally perceived as groups from two aspects. Physical and societal aspects may be the basic dimensions of group perception. Future research could extend the present findingsto othersocial categories, and more importantly, provide more neurobiological evidence for children’s biases toward societal-type cues.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Structural equation modeling (SEM) is an important statistical method in social science research. In the first two decades of the 21st century, great progress has been made in methodological research on SEM in China’s mainland. The publications cover five aspects: model development, parameter estimation, model evaluation, measurement invariance and the special data processing in SEM. SEM development includes the research on measurement models, structural models, and complete models, as well as the SEM in population heterogeneity studies and longitudinal studies. The research on the measurement models involves bi-factor model, exploratory structural equation model, measurement models for special design (e.g., random intercept factor analysis model, fixed-links model, and the Thurston model), and formative measurement models. The research on the structural models involves the actor-partner interdependence model. The research on the complete models focuses on item parceling. The SEM in the study of population heterogeneity involves latent class/profile model, factor mixture model, and multi-level latent class model. The SEM in longitudinal studies includes models describing development trajectories and differences, such as the latent growth model, the piecewise growth model, the latent class growth model, the growth mixture model, the piecewise growth mixture model, the latent transition model and the cross-lagged model. The publications on parameter estimation methods mainly involve the introduction of methodology (including the partial least square method and the Bayesian method) and the comparison of different parameter estimation methods. Advances in the model evaluation include fit indices and their corresponding critical values, selection of fit indices, model evaluation criteria beyond fit indices, and comparison and selection among alternative models. The development of measurement invariance involves three topics: (1) the introduction of different models with testing process and model evaluation criteria for measurement invariance analysis; (2) measurement invariance analysis in a particular model or data (e.g., second order factor model and ordered categorical data); (3) new methods of measurement invariance analysis (e.g., alignment and projection method). In addition, research into special data processing methods in SEM addresses issues of missing data, non-continuous data, non-normal data, and latent variable scores. Finally, recent advances in SEM methodological research abroad are introduced to help researchers understand some cutting-edge topics in this field, which offers implications for future directions of SEM methodological research.
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.
Peer Review Status:
Awaiting Review
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： "
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