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Analyses of Mediating Effects: The Development of Methods and Models WEN Zhonglin;YE Baojuan Advances in Psychological Science    2014, 22 (5): 731-745.   DOI: 10.3724/SP.J.1042.2014.00731 Abstract （3456）            Knowledge map    Mediation models are frequently used in the research of psychology and other social science disciplines. Mediation indicates that the effect of an independent variable on a dependent variable is transmitted through a third variable, which is called mediator. In most applied research, Baron and Kenny’s (1986) causal steps approach has been used to test mediating effect. In recent years, however, many methodological researchers questioned the rationality of the causal steps approach, and some of them even attempted to stop its use. Firstly, we clarify the queries on the causal steps approach one by one. Secondly, we propose a new procedure to analyze mediating effects. The new procedure is better than any single method that constitutes the procedure in terms of Type I error rate and power. The proposed procedure can be conducted by using observed variables and/or latent variables. Mplus programs are supplied for the procedure with observed variables and/or latent variables. Finally, this article introduces the development of mediation models, such as mediation model of ordinal variables, multilevel mediation, multiple mediation, moderated mediation, and mediated moderation. Related Articles | Metrics

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Network analysis and its applications in psychology CAI Yuqing, DONG Shuyang, YUAN Shuai, HU Chuan-Peng Advances in Psychological Science    2020, 28 (1): 178-190.   DOI: 10.3724/SP.J.1042.2020.00178 Abstract （1399）            Knowledge map    Network analysis models (or Network Psychometrics) have been widely used in psychology research in recent years. Unlike latent variable models which conceive observable variables as outcomes of unobservable latent factors, network analysis models apply the graph theory to construct a network to depict the associations among observable variables. The observable variables are treated as nodes and the associations between them are treated as edges. As such, network analysis models reveal the relationships among observable variables and the dynamic system resulted from the interactions between these observable variables. With indices reflecting individual nodes’ characteristics (such as centrality) and network structural characteristics (such as small-worldness), network analysis models provide a new perspective for visualization and for studying various psychological phenomena. In the past decade, network analysis models have been applied in the fields of personality, social, and clinical psychology as well as psychiatry. Future research should continue to develop and improve the methods of network analysis models, making them applicable to more types of data and broader research fields. Related Articles | Metrics

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Statistical Remedies for Common Method Biases Zhou Hao,Long Lirong null    2004, 12 (06): 942-942~950.   Abstract （7053）            Knowledge map    The problem of common method biases has being given more and more attention in the field of psychology, but there is little research about it in China, and the effects of common method bias are not well controlled. Generally, there are two ways of controlling common method biases, procedural remedies and statistical remedies. In this paper, statistical remedies for common method biases are provided, such as factor analysis, partial correlation, latent method factor, structural equation model, and their advantages and disadvantages are analyzed separately. Finally, suggestions of how to choose these remedies are given. Related Articles | Metrics

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A Review of the Theory of Planned Behavior Duan Wenting;Jiang Guangrong null    2008, 16 (02): 315-320.   Abstract （5362）            Knowledge map    Theory of planned behavior (TPB) is the most famous theory about attitude-behavior in social psychology and has been found to be well supported by empirical evidences. According to TPB, intentions to perform behaviors of different kinds can be predicted with high accuracy from attitudes toward the behavior, subjective norms, and perceived behavioral control, account for considerable variance in actual behavior. In this paper, various aspects of TPB were introduced, including its derivation, general picture, measurements, new researches and developments. Other issues that remain unresolved and further studies were discussed in the end Related Articles | Metrics

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Social Identity Theory and It’s Development Zhang Yingrui,  Zuo Bin null    2006, 14 (03): 475-480.   Abstract （4905）            Knowledge map    Social identity theory, developed by Tajfel and Turner et al.，which made new explanations to the group behavior， has become the most influential theory in the field of intergroup relation. The social identity theory developed from the explanations for intergroup behavior, it proposed that group identity is the fundamental cause of intergroup behavior. Recent research provides much evidence for the theory, The social identity theory is important achievement of European psychology localization and has important contributions to the social psychology. At the same time it also waits for further consummates Related Articles | Metrics

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Research Paradigms and Theoretical Models of Social Exclusion CHENG Su;LIU Lu;ZHENG Yong null    2011, 19 (6): 905-915.   Abstract （1521）            Knowledge map    Social exclusion is regarded as a common phenomenon which has great influence on individual and society. It also has a variety of paradigms that include rejection paradigm, ostracism paradigm, life alone paradigm, and so on, and models that consist of temporal need-threat model, multimotive model, emotional numbness and self-control failure theory. The effects of social exclusion on basic needs, emotion, and self-esteem are still in the arguments. The developments of paradigm application and ecological validity as well as the explorations on social exclusion origins and results in the future are being ignited, as to the localization are also raised. Related Articles | Metrics

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Processing Efficiency Theory to Attentional Control Theory: New Perspective for Anxiety-performance Relationship in Sport Psychology SUN Guoxiao;ZHANG Liwei Advances in Psychological Science    2013, 21 (10): 1851-1864.   DOI: 10.3724/SP.J.1042.2013.01851 Abstract （1949）            Knowledge map    Sport psychology literature extensively reveals that competitive anxiety affects athletic performance (Sarason, 1984). However, the specific mechanisms of such negative relationship occurred between anxiety and performance still need to be further examined. Recently, processing efficiency theory and attentional control theory are further studied for attempting to explain the anxiety-performance relationship specifically in the areas of working memory and executive functions. Based on the research findings (Eysenck & Calvo, 1992), there are two theoretical assumptions for the processing efficiency theory: (1) anxiety impairs processing efficiency more than performance effectiveness, and (2) anxiety impairs the central executive system of working memory. Whereas, attentional control theory is a major development of processing efficiency theory (Eysenck, Derakshan, Santos, & Calvo, 2007). Accordingly, there are also two theoretical assumptions for attentional control theory: (1) anxiety impairs goal-directed attentional system, (2) anxiety impairs efficiency of inhibition and shifting functions. Evidently, processing efficiency theory and attentional control theory both provide the useful frameworks to explain the specific mechanisms of anxiety-performance relationship, which is an imperative topic in sport psychology. Thus, the main purpose of this presentation is twofold: (1) to review the empirical research studies based on these two theories and (2) to recommend the implications for future research. Hopefully, our presentation would promote to further examine other anxiety-performance theories, improve the consistency of research protocol, take the cognitive perspectives into consideration of our research endeavor, and pay more attention to the effect of state anxiety for the purpose of enriching applied research literature. Related Articles | Metrics

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Anthropomorphism: Antecedents and consequences XU Liying, YU Feng, WU Jiahua, HAN Tingting, ZHAO Liang Advances in Psychological Science    2017, 25 (11): 1942-1954.   DOI: 10.3724/SP.J.1042.2017.01942 Abstract （1990）            Knowledge map     Anthropomorphism refers to the psychological process or individual difference of imbuing nonhuman agents with humanlike characteristics, motivations, intentions, or mental states. Elicited agent knowledge, effectance motivation, and sociality motivation have been found as the three key determinants of anthropomorphism. Existing research mainly focused on anthropomorphism of nature, super-nature, animals, machines, brands, and products. Previous research found that, anthropomorphizing nature contributed to pro-environment behavior, while anthropomorphism of animals, machines, brands or products had diversified forms and ambiguous consequences. Future research might be mainly conducted in human-robot interaction area, as well as the relationship between anthropomorphism and cuteness. Related Articles | Metrics

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Item Parceling Strategies in Structural Equation Modeling WU Yan;WEN Zhong-Lin null    2011, 19 (12): 1859-1867.   Abstract （2325）            Knowledge map    Item parceling is a technique using in structural equation modeling (SEM). Parceling can improve the quality of indicators and model fit. Bias that due to parceling was often neglectable and can be corrected. Parceling greatly enhances model parsimony, but it greatly reduces falsifiability of the tested model. It could be summarized that the prerequisites of parceling are unidimension and homogeneity, and the applicability of parceling is the analysis of structural models, rather than measurement models. Parcel-building algorithms and the number of parcels were discussed and recommended. A procedure for item parceling was proposed when the scale was unidimensional. If the scale was multidimensional, internal-consistency approach was recommended such that the items of the same dimension are parceled to one or three indicators for structural equation modeling. Related Articles | Metrics

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Moral foreign language effect and its moderating variables: A systematic review and meta-analysis ZHU Lin, LIU Jinru, LI Jing, LIU Conghui Advances in Psychological Science    2022, 30 (1): 32-50.   DOI: 10.3724/SP.J.1042.2022.00032 Abstract （293）            Knowledge map    Individual decisions in the field of moral judgement are often related to "hurting or sacrificing the innocent" and "tolerating immoral behaviors." Previous studies have shown that when presented with the moral decision-making situation within a foreign language context, the individuals will show a stronger utilitarian and a more tolerant moral evaluation tendency compared with the same situation within the native language context. This phenomenon is defined as the moral foreign language effect. The influence of the language context on the moral judgement has been investigated by numerous studies. However, the results were far from consistent. To this end, we used meta-analysis to explore the effect of the language type (native language vs. foreign language) on the individuals' utilitarian tendency in moral judgments, and we analyzed several moderating variables. A total of 19 papers were retrieved from literature, with 46 independent samples, 97 effect sizes and 9672 participants that met the inclusion criteria of the meta-analysis. First, we analyzed the effect of the language type (native language vs. foreign language) on the utilitarian tendency in moral judgments using the ‘metafor' R package. Next, the potential moderation effects of several factors were examined, including the moral dilemmas story type (personal moral dilemmas vs. impersonal moral dilemmas vs. daily moral evaluation situations), sex, scoring method (two-point scoring vs. multi-point scoring) and language family type (same vs. different). In addition, we used Bayesian factor estimation for secondary exploration of the results that had a nonsignificant moderating effect. Our meta-analysis resulted in the following findings. First, the main effect test indicated that the language type has a significant effect on the utilitarian tendency in moral judgment, with a small but stable moral foreign language effect (g = 0.23). Second, the moderation analysis indicated that the moral foreign language effect was influenced by the story type; there was a small but stable effect of the language type for personal moral dilemmas (g = 0.32), but not for impersonal moral dilemmas (g = 0.11) or daily moral evaluation situations (g = 0.12). The foreign language effect under impersonal moral dilemmas was affected by the scoring method; a significant effect was found under multi-point scoring (g = 0.27), but not under two-point scoring (g = 0.05). On the other hand, there was no significant moderating effect for the sex or language family type. In addition, Bayesian analysis showed only moderate evidence for the absence of moderating effect regarding the factors of sex, scoring methods and language family type. The stability of these conclusions can be further verified in future research. In summary, this study used meta-analysis to systematically explore the robustness and influencing factors of foreign language effects in moral judgment and answered the disputes about the stability of the moral foreign language effect. The results showed a small but relatively stable effect of the language type on the utilitarian orientation in moral judgment. We analyzed the moderating effects of multiple variables, including variables that have not been well-considered in previous studies, such as the scoring methods (two-point scoring vs. multi-point scoring). Our work did not only find the moderating effect of the type of moral dilemmas, but it also revealed the potential impact of the scoring method on the effect size. This provides certain enlightenment and guidance for future empirical studies when selecting the experimental materials and statistical methods. Finally, we used a variety of data processing methods to increase the robustness of the results. For example, robust variance estimation (RVE) was used to control the correlations between dependent effect sizes and compare our results with those of traditional meta-analysis, so as to understand how the results of the meta-analysis are influenced by the correlations between multiple dependent effect sizes. Related Articles | Metrics

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Sweet poison: How does benevolent sexism affect women’s career development? ZHANG Shanshan, XIE Jinyu, WU Min Advances in Psychological Science    2019, 27 (8): 1478-1488.   DOI: 10.3724/SP.J.1042.2019.01478 Abstract （1416）            Knowledge map    Benevolent sexism (BS) is a set of interrelated attitudes toward women that are subjectively positive in tone but viewing women stereotypically in traditional gender roles. These attitudes failed to be detected as prejudice by the perceiver but still reinforces women’s subordinate status. Benevolent sexism revealed in family education and intimate relationships and in workplace contexts restricts the career development of women by disarming them and, rather than compelling them directly, persuading women to internalize these restrictions. To explain the function mechanism, previous studies have investigated how women perceive and react to benevolent sexism with corresponding theories such as stereotype threat, fear of success, and system justification theory. However, the objective and neutral standpoint that the researchers hold in the study of benevolent sexism is worth debating from the perspective of feminist psychology because masculine value has been implicitly admired and heterogeneity among women has been ignored. Considering the recent trend of feminist psychology, some further research ideas are implied and discussed in this review. Related Articles | Metrics

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Motivated Information Processing Model: Theory and Applications WU Meng;BAI Xin-Wen Advances in Psychological Science    2012, 20 (11): 1889-1898.   DOI: 10.3724/SP.J.1042.2012.01889 Abstract （1962）            Knowledge map    Based on “groups as information processor perspective”, motivated information processing (MIP) model emphasizes that information processing and sharing depends on two types of motivations, epistemic motivation and social motivation, respectively. Epistemic motivation refers to the willingness to expend effort to achieve a thorough, rich, and accurate understanding of the world. It determines the depth of information processing. Social motivation is defined as the individual preference for outcome distributions between oneself and others. It influences which information will be processed. Epistemic motivation and social motivation, alone and in combination, interpret information processing at both individual and team level, and information sharing at team level. MIP model contributes to the industrial/organizational psychology literature by integrating the dual-process theory and groups as information processor perspective, and by providing a new perspective in the fields of negotiation, creativity, and team effectiveness. Limitations and implications for future study of MIP model are discussed. Related Articles | Metrics

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An Introduction of Researches and Theory of Organizational Innovation Climate Wang Yanfei, Zhu Yu null    2006, 14 (03): 443-449.   Abstract （2106）            Knowledge map    organizational innovation climate is the perception employees hold about innovation in the organization and it consists of workers’ feelings, attitudes, and behavioral tendencies measured by their perceptions. The purpose of this paper is to discuss some important aspects about the theory and research of organizational innovation climate, presenting the origin of the concept，formation procedure, construct and measurement, summarizes some drawbacks in this field, several interesting directions for future research are raised Related Articles | Metrics

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Creating for Others: An Experimental Study of the Effects of Intrinsic Motivation and Prosocial Motivation on Creativity LI Yang; BAI Xinwen Advances in Psychological Science    2015, 23 (2): 175-181.   DOI: 10.3724/SP.J.1042.2015.00175 Abstract （964）            Knowledge map    Scholars of creativity research usually believe that intrinsic motivation is conducive to creativity. However, more and more results have challenged and questioned this conclusion. According to the latest motivated information processing model, prosocial motivation can help people think about not only novel but also useful aspects of ideas to improve the whole creativity. This study used 2×2 between-subjects design. Through the manipulation of intrinsic motivation and prosocial motivation, participants were randomly assigned to one of four conditions and completed a creative task. ANOVA indicated that there was a significant interaction effect of intrinsic motivation and prosocial motivation on creativity, and only when people had high levels of intrinsic motivation coupled with high levels of prosocial motivation, they would be most creative. This study proved the importance of prosocial motivation in creative process, and opened a new perspective of creativity research. Related Articles | Metrics

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Understanding mechanisms of prediction error cost in Chinese reading for older adults LI Lin, ZHAO Sainan, ZHANG Lijuan, WANG Jingxin Advances in Psychological Science    2022, 30 (1): 1-14.   DOI: 10.3724/SP.J.1042.2022.00001 Abstract （404）            Knowledge map    An important question for research on reading across the lifespan concerns whether mechanisms of cognitive processing undergo only quantitative changes or also qualitative changes with aging. To process written language effectively, readers use their existing knowledge to make predictive inferences about linguistic information. Quite often this will facilitate the processing of newly acquired information but will sometimes incur a processing cost due to predictive error. As Older adults appear to rely more heavily on lexical prediction during reading (Zhao et al., 2019, 2021). However, it is currently unknown whether, like young adults, they experience a processing cost due to predictive error, and whether the magnitude of this cost differs across age adult groups. Accordingly, the present research aims to understand the processing consequence of predictive error in both young and older adults, using methods that can shed light on both the behavioral and neural bases of these effects. This will be achieved using novel co-registration methods that synchronize the recording of electroencephalographic (EEG) signals with eye movements, so that behavioral and neural indices of language processing can be acquired simultaneously, in real-time, during natural reading. In particular, this approach will enable the analysis of fixated-related potentials (FRPs), which are averaged EEG waveforms time-locked to a fixation on a target word in a sentence during normal reading. Study 1 will manipulate whether a target word is predictable from the prior sentence context, using contexts in which the target word is predictable, ones in which it is unpredictable, and neutral contexts containing an unpredictable word. Crucially, comparisons of an unpredictable word in neutral compared to constraining contexts will provide a measure of prediction error, which is the cost incurred when the target word is unpredicted in a constraining context. The study will investigate the behavioral and neural correlates of this prediction error using a combination of eye movement measures and FRPs for target words. Moreover, by investigating age differences in these effects (i.e., for young compared to older adults) the study will reveal whether this prediction error differs across adult age groups. Study 2 will test these effects further by examining both the contribution to the prediction error cost of parafoveal information availability and individual differences in visual, cognitive and linguistic abilities. To examine the contribution of these individual differences, we will comprehensively assess the visual, cognitive and linguistic abilities of young and older adult participants prior their taking part in experiments. We will obtain information about participants' educational background, vocabulary knowledge and recent reading experience to match participants in terms of formal educational experience and to obtain indices of linguistic experience. In addition, we will assess processing speed, working memory, and inhibition as measures of cognitive capabilities. The data obtained will be used for the linear mixed-effects modelling of Study 3. Experiment 1 will use the boundary paradigm to investigate age differences in the prediction error cost when parafoveal information is available or not. The aim of this experiment is to establish whether limiting the availability of parafoveal information about an upcoming word differentially impacts lexical prediction by young and older adults. Experiment 2 will use masking text paradigm to investigate the aging effects on prediction error cost under high or low working memory load conditions. The aim of this experiment is to explore the effect of working memory load on prediction processing mechanism of young and older readers. Finally, in Experiment 3, the older adult participants will be divided into good and poor reading skill groups to examine whether there is a difference in the prediction error cost for older participants with good and poor reading skills as compared to skilled young adult readers. This will reveal how reading skills mediates predictive processing by older adults. Study 3 will use linear mixed-effects modelling and data-mining methods. All relevant factors will be included in the model analysis as covariates to investigate their effects on the prediction processing of older readers. Moreover, survival analysis and distribution analysis will be adopted to investigate the time course and individual differences of the above-mentioned effects (using data from Study 1 and 2). The findings from these studies will provide important insights into the nature of effects of cognitive aging and individual differences in visual, cognitive and linguistic abilities on neural and cognitive indices of word prediction in reading, and will form the basis for future models of these effects in Chinese reading. Moreover, the findings will shed light on the contribution of parafoveal processing, memory load, and reading skill on the predictive abilities of older adult readers. 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The duality of attachment pattern: Trait attachment and state attachment JIA Chenglong, LIU Tingting, SUN Li, QIN Jinliang Advances in Psychological Science    2020, 28 (4): 626-637.   DOI: 10.3724/SP.J.1042.2020.00626 Abstract （1044）            Knowledge map    Traditional attachment theory suggests that an individual’s attachment-related psychological and behavioral patterns are relatively stable. However, from information processes or life-span development perspective, attachment patterns have trait-state duality, i.e. attachment patterns are both relatively stable and context-sensitive. An individual’s attachment pattern in a particular context is the consequence of his or her trait attachment and contextual feature interactions. The interaction patterns between trait attachment and attachment priming effects provide a window to understand the duality of attachment and their relationships. And the two-stages model of attachment activation offers a framework for integrating and understanding these patterns. Future studies should optimize the manipulation check of attachment priming, meanwhile, take the interaction between attachment avoidance and attachment anxiety in consideration. More attention should be paid to the individual differences in the high attachment anxiety group which may clarify the patterns of interaction between trait and state attachment. Moreover, the time course of how attachment coping strategies impact the effects of attachment priming should be concerned, which could reveal the mechanism of the interactions between trait and state attachment. Related Articles | Metrics

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Random intercept latent transition analysis (RI-LTA): Separating the between-subject variation from the within-subject variation WEN Congcong, ZHU Hong Advances in Psychological Science    2021, 29 (10): 1773-1782.   DOI: 10.3724/SP.J.1042.2021.01773 Abstract （531）            Knowledge map    Traditional latent transition analysis (LTA) is usually done using single-level modeling, but can also be viewed as a two-level modeling from a multi-level perspective. In 2020, Muthén and Asparouhov proposed a so-called random intercept latent transition analysis (RI-LTA) model which separates between-subject variation from within-subject variation. By integrating a random intercept factor, latent class transitions are represented on the within level, whereas the between level captures the variability across subjects. The random intercept factor f is the most important. If the factor loadings on the random intercept factor are large, this indicates that the item probabilities are large and thus the cases have large differences on these items. From this perspective, RI-LTA can be viewed as absorbing the measurement non-invariance of the model. Due to large item differences, the different latent classes are easy to distinguish. These differences are absorbed by the random intercept factor but are not set to influence the latent class variables. Therefore, the off-diagonal values of the transition probability matrix are larger. In traditional LTA, large differences across classes are not absorbed by the random intercept factor, which leads to smaller off-diagonal but larger diagonal values of the transition probability matrix. Performing RI-LTA in Mplus software can be done in three to four steps. First, implementing LCA across different time points; second, implementing traditional LTA and RI-LTA; third, saving the parameter estimates obtained in the second step and using them as population values to do a Monte Carlo simulation study; fourth, in the event of previous knowledge or existing applications, one may include covariates or distal outcomes in the model. Researchers can also perform multiple-group analysis, Markov chain mover-stayer analysis, multi-level RI-LTA, or longitudinal factor analysis to have deeper insight into the data. In the current study, a two-wave longitudinal data collection from undergraduates attending in the year 2016 at a research-oriented university was used to demonstrate how to implement RI-LTA in Mplus. The first three steps used were as described in the previous paragraph. For the fourth step, we performed a multiple-group analysis and investigated the interaction effects by including a “type of university enrolment” covariate. Results showed that students of the class labeled “strong intrinsic and extrinsic motivation” class tended to switch to “strong intrinsic motivation but low extrinsic motivation” class and “low intrinsic and extrinsic motivation” class at a 33.0% transition probability of staying in the original class with RI-LTA analysis, while these students tended to stay in the original class at a 68.9% staying transition probability with traditional LTA analysis. This indicated that RI-LTA avoided overestimation on the transition probabilities of students staying in the original class and allowed for clearer interpretation of the data. The RI-LTA model was shown to be better than the traditional LTA model in this situation. By including a “type of university enrolment” covariate, the multiple-group analysis indicated that measurement invariance should be established. Most of the regression coefficients of latent classes on covariate were not significant except c1#1 on dummy2, which was significant at a value of -2.364. This indicated that students who were enrolled via the independent admission examinations and endorsed the “low intrinsic and extrinsic motivation” class were fewer than the recommended students We also found that the interaction effects of the covariate and c1 on c2 were not significant. Thus, a more parsimonious measurement invariant multiple-group analysis including a covariate but without interaction effect model should be chosen. Future research could use Monte Carlo simulation studies to investigate the applicability of RI-LTA, for example by manipulating sample sizes, numbers of indicators, latent classes, and time points. Inspired by multi-level modeling, the implementation of multi-level RI-LTA in statistical software should also be explored further. Related Articles | Metrics

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Intensive longitudinal data analysis: Models and application ZHENG Shufang, ZHANG Lijin, QIAO Xinyu, PAN Junhao Advances in Psychological Science    2021, 29 (11): 1948-1969.   DOI: 10.3724/SP.J.1042.2021.01948 Abstract （382）            Knowledge map    In the fields of psychology, education, and clinical science, researchers have devoted increasing attention to the intraindividual dynamics of behaviors, minds, and treatment effects over time, making personalized modeling a growing concern. Traditional cross-sectional and longitudinal studies only have a few measurement time points for each individual, which is not suitable for studying intraindividual dynamics. Intensive longitudinal design collects a set of measures from individuals at multiple time points with higher frequency over longer periods. With its strengths in more immediate, accurate, and authentic assessments, this design is more suitable to investigate the dynamics and mechanisms of intraindividual processes. With the development of mobile phones and other mobile devices, researchers can conveniently collect intensive longitudinal data for various aspects of psychology, including individual emotion, personality, cognition, and behavior patterns. The intensive longitudinal design has recently become one of the most prominent and promising approaches in psychological research, but most of these studies still relied on traditional analyzing methods. We first reviewed a conventional method of intensive longitudinal data analysis, the multilevel linear model (MLM), and discussed its limitations in analyzing intensive longitudinal data. We then introduced the principles, empirical applications, strengths, and weaknesses of two advanced modeling methods, dynamic structural equation model (DSEM) and group iterative multiple model estimation (GIMME). DSEM is a top-down approach of modeling intensive longitudinal data while GIMME is a bottom-up one, both being implemented in commonly used software. DSEM is one of the most promising methods for intensive longitudinal modeling and can be regarded as a multilevel extension of the dynamic factor model (DFM). It combines the strengths of various modeling approaches, including multilevel modeling, time-series modeling, structural equational model (SEM), and time-varying effects modeling (TVEM). GIMME is a dynamic network method initially proposed for functional magnetic resonance imaging (fMRI) data analysis and has recently been applied to intensive longitudinal data analysis. It combines individual- and group-level information to estimate network models at both levels, bridging nomothetic (population) and idiographic (individual) approaches to intensive longitudinal data analysis. By introducing these two advanced modeling methods, the current review can help deepen the understanding of the top-down approach and bottom-up approach and clarify their strengths and weaknesses in the intensive longitudinal data analysis. To help empirical researchers better understand the modeling of DSEM and GIMME and show the advantages of the two models compared with MLM, we provided a tutorial on how to analyze the intensive longitudinal data with the three models (i.e., MLM, DSEM, and GIMME), respectively. We presented the analyzing processes step by step and explained how to interpret the results of these models accordingly. By comparing the output results of the three models, the current review summarized the characteristics of each model. The corresponding Mplus and R codes were provided in the appendixes. Along with the three modeling methods mainly introduced in the current review, we also provided a general introduction of other common modeling methods in the intensive longitudinal data analysis. The current review summarized the popular models in the intensive longitudinal data analysis on their strengths and weaknesses and guided researchers to select suitable modeling methods in different situations. The current review contributes to the development and application of the advanced methods of intensive longitudinal data analysis and helps researchers better understand the dynamic process behind the intensive longitudinal data. Related Articles | Metrics

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Testosterone and human decision-making LIAO Jiajun, LI Hong, WU Yin Advances in Psychological Science    2019, 27 (9): 1607-1621.   DOI: 10.3724/SP.J.1042.2019.01607 Abstract （2398）            Knowledge map    Testosterone is one of the steroid hormones (i.e. androgen). Early research has shown that testosterone played a large role in the human aggressive and impulsive behavior. There is increasing interest in the effects of testosterone on human decision-making, including social (i.e. trust, cooperation, altruism, and competition) and economic decision-making (i.e. risk taking). In general, there is a positive association between testosterone level and risk-seeking behavior in economic decision-making. In the social domain, high testosterone levels are associated with more aggressive, dominant, and fairness behavior. Testosterone administration also reduced interpersonal trust. Note that some findings are hard to replicate, and more research is needed to investigate potential moderators. Future research could fruitfully explore the role of testosterone in consumer decision-making, adolescent’s social behavior and clinical application. Related Articles | Metrics

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Resilience: The Psychological Mechanism for Recovery and Growth during Stress Yu Xiaonan,Zhang Jianxin null    2005, 13 (05): 658-665.   Abstract （2918）            Knowledge map    Resilience refers to the effective coping and adaptation although faced with loss, hardship, or adversity. This biological imperative for self-protection will be exhibited when people are faced with stress, threats, or life changes. Coping resources or protective factors on personal, family, and social aspects interact with each other into a dynamic system in order to resist the effect of adversity. The process model argues that resilience refers to the higher homeostasis than original level, and it is different from recovery. The hierarchy of resilience demonstrates its adaptive nature on different developmental stages, and the meaning of resilience varies according to the specific situations. Although there is no consensus on the measurement of resilience, some scales have been employed widely for their convenience and efficiency. The aim of resilience research is to examine strength and promote adaptation of people, and resilience interventions conducted by schools, clinical institutions, communities, and enterprises have been proven good effects. Related Articles | Metrics