ISSN 0439-755X
CN 11-1911/B


    25 April 2022, Volume 54 Issue 4 Previous Issue    Next Issue

    Reports of Empirical Studies
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    Reports of Empirical Studies
    Effects of integration of facial expression and emotional voice on inhibition of return
    ZHANG Ming, WANG Tingting, WU Xiaogang, ZHANG Yue’e, WANG Aijun
    2022, 54 (4):  331-342.  doi: 10.3724/SP.J.1041.2022.00331
    Abstract ( 520 )   HTML ( 69 )  
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    Inhibition of return (IOR) and emotion have the characteristics of guiding attentional bias and improving search efficiency. However, it is not clear whether there is a certain interaction between IOR and emotional stimuli. The study adopted the cue-target paradigm and used audiovisual dual modality to present emotional stimuli to further investigate the interaction between emotion and IOR. In Experiment 1, emotional stimuli were presented in visual single modality or audiovisual dual modality. Experiment 2 further investigated whether the impact of the audiovisual dual modality emotional stimulus on IOR was caused by the emotional stimulus of the auditory modality, that is, whether the emotional stimulus of the auditory modality was processed. The results showed that congruently emotional stimuli in the audiovisual dual modality can weaken IOR, but there was no interaction between incongruent emotional stimuli in the audiovisual dual modality and IOR, and there was no significant difference in the IOR effect between the single modality and audiovisual dual modality. The results showed that the IOR effect was influenced only when the audiovisual dual modality presented the same emotion, which further supported the perceptual inhibition theory of IOR.

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    The neural basis of the continued influence effect of misinformation
    JIN Hua, JIA Lina, YIN Xiaojuan, YAN Shizhen, WEI Shilin, CHEN Juntao
    2022, 54 (4):  343-354.  doi: 10.3724/SP.J.1041.2022.00343
    Abstract ( 303 )   HTML ( 24 )  
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    In this study, the differences between the activation and functional connectivity conditions of related brain regions by task-fMRI were analyzed to reveal the neural basis of the CIEM and provided more evidence for the hypothesis of mental-model-updating and memory-retrieval-failure. The results showed that the inference scores of retraction condition were significantly higher than that of control condition, and the CIEM exists. In the encoding phase, the activation of left middle temporal gyrus in retraction condition was significantly weaker than that in control condition. While in the retrieval phase, the activation of middle frontal gyrus and anterior cingulate gyrus was weaker in retraction, and the functional connectivity between middle frontal gyrus and precentral gyrus was stronger in retraction. The results suggest that the above brain regions may be involved in the formation of the CIEM, and provide evidence from the neural level that the hypothesis of mental-model-updating and memory-retrieval-failure may explain the different phases of the CIEM formation.

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    Transition of latent classes of children’s mathematics anxiety in primary school and the distinctive effects of parental educational involvement: A three-wave longitudinal study
    SI Jiwei, GUO Kaiyue, ZHAO Xiaomeng, ZHANG Mingliang, LI Hongxia, HUANG Bijuan, XU Yanli
    2022, 54 (4):  355-370.  doi: 10.3724/SP.J.1041.2022.00355
    Abstract ( 389 )   HTML ( 13 )  
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    In this study, latent transition analysis was used to investigate the transitions between different subtypes of primary school children's mathematics anxiety and the role of parental educational involvement in the transitions between different subtypes of primary school children's mathematics anxiety. 1720 third and fourth graders in county primary schools were selected as participants, and their mathematics anxiety and perceived parental educational involvement were measured three times, with an interval of one year each time. The results show that: (1) There were three different subgroups of mathematics anxiety in primary school children, including the low mathematics anxiety group, the high mathematics evaluation anxiety group and the high mathematics acquisition anxiety group; (2) As time went by the high mathematics evaluation anxiety group tended to change to the low mathematics anxiety group, the high mathematics acquisition anxiety group tended to change to the high mathematics evaluation anxiety group, and the low mathematics anxiety group were relatively stable; (3) The predictive effect of paternal/maternal educational involvement on the transitions of children's mathematics anxiety subgroups is distinctive for different mathematics anxiety subgroups. The above findings provide an important reference for further understanding the formation mechanism of mathematics anxiety and the formulation of intervention measures.

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    A developmental model of job burnout dimensions among primary school teachers: Evidence from structural equation model and cross-lagged panel network model
    XIE Min, LI Feng, LUO Yuhan, KE Li, WANG Xia, WANG Yun
    2022, 54 (4):  371-384.  doi: 10.3724/SP.J.1041.2022.00371
    Abstract ( 263 )   HTML ( 18 )  
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    Emotional exhaustion, depersonalization, and reduced personal accomplishment of teacher burnout are relatively independent but also have mutual influences. Research into their developmental relationship is helpful in understanding the developmental process and identifying the early symptoms of job burnout. A total of 3837 primary school teachers took part in this two-wave longitudinal study with intervals of three years. The structural equation model and cross-lagged network model were used for analysis. The results showed that the optimal development model of teacher burnout was “emotional exhaustion and reduced personal accomplishment at T1 predict emotional exhaustion and reduced personal accomplishment at T2, respectively, and depersonalization at T1 predicts depersonalization and reduced personal accomplishment at T2”. There was no gender difference or teaching experience difference in the optional development model. The results emphasize the important role of depersonalization in the development of teacher burnout and have certain theoretical and practical significance for identifying the early symptoms of teacher burnout and for taking corresponding measures to effectively prevent further teacher burnout.

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    Influence of empathic concern on fairness-related decision making: Evidence from ERP
    HE Yijuan, HU Xinmu, MAI Xiaoqin
    2022, 54 (4):  385-397.  doi: 10.3724/SP.J.1041.2022.00385
    Abstract ( 322 )   HTML ( 34 )  
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    Using event-related potential (ERP) and ultimatum game (UG), this study investigated the influence of empathic concern on fairness-related decision making. The experiment adopted a 2 (state empathic concern: empathy vs. non-empathy) × 3 (fairness: fair vs. disadvantageous unfair vs. advantageous unfair) within-subject design. A total of 37 participants participated in the experiment, and they were asked to choose whether to accept offers from different proposers as responders. Behavior results showed that the acceptance rate of empathy condition was higher than that of non-empathy condition for disadvantageous unfair offers, and the opposite result was observed for advantageous unfair offers. ERP results revealed that for disadvantageous unfair offers, the non-empathy condition elicited a more negative-going anterior N1 (AN1) than the empathy condition, and the empathy condition elicited a larger P2 amplitude than the non-empathy condition. In the empathy context, the disadvantageous unfair condition elicited more negative-going medial frontal negativity (MFN) than the advantageous unfair and fair condition. P3 of fair condition was larger than that of disadvantageous unfair condition, which was not modulated by empathy. These results indicated that empathy modulated not only fairness-related decision making behavior, but also early attention and motivation as well as later cognitive and emotional processing in fairness. However, the higher cognitive processes characterized by P3 were only modulated by fairness but not affected by empathy.

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    The effect of customer-initiated support on employee service performance: The self-verification theory perspective
    ZHANG Hui, LIU Yanjun, SHI Yanwei, ZHANG Nan
    2022, 54 (4):  398-410.  doi: 10.3724/SP.J.1041.2022.00398
    Abstract ( 208 )   HTML ( 10 )  
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    Drawing on the self-verification theory, the present study aimed to examine the effect of customer-initiated support on employee service performance (in-role service performance and proactive customer service performance) and explore the mediating role of organization-based self-esteem and the moderating roles of promotion focus and internal locus of control. We collected three-wave time-lagged data from 652 nurses nested within 139 department supervisors. Results from multilevel modeling analysis showed that: (1) customer-initiated support was positively related to employee organization-based self-esteem; (2) organization-based self-esteem was positively related to employee in-role service performance and proactive customer service performance; (3) employee organization-based self-esteem mediated the relationship between customer-initiated support and employee in-role service performance and proactive customer service performance; (4) promotion focus strengthened the positive relationship between customer-initiated support and organization-based self-esteem, such that the positive relationship between customer-initiated support and organization- based self-esteem was stronger for employees with higher promotion focus; (5) internal locus of control weakened the relationship between customer-initiated support and organization-based self-esteem, such that the positive relationship between customer-initiated support and organization-based self-esteem was weaker for employees with higher internal locus of control. These findings shed light on potential underlying mechanisms and boundary conditions of the effect of customer-initiated support on employee service performance and provide new ideas for organizations to improve employee service performance.

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    A comparison of standard residual methods and a mixture hierarchical model for detecting non-effortful responses
    LIU Yue, LIU Hongyun, YOU Xiaofeng, YANG Jianqin
    2022, 54 (4):  411-425.  doi: 10.3724/SP.J.1041.2022.00411
    Abstract ( 176 )  
    Assessment datasets contaminated by non-effortful responses may lead to serious consequences if not handled appropriately. Previous research has proposed two different strategies: down-weighting and accommodating. Down-weighting tries to limit the influence of aberrant responses on parameter estimation by reducing their weight. The extreme form of down-weighting is the detection and removal of irregular responses and response times (RTs). The standard residual-based methods, including the recently developed residual method using an iterative purification process, can be used to detect non-effortful responses in the framework of down-weighting. In accommodating, on the other hand, one tries to extend a model in order to account for the contaminations directly. This boils down to a mixture hierarchical model (MHM) for responses and RTs. However, to the authors’ knowledge, few studies have compared standard residual methods and MHM under different simulation conditions. It is unknown which method should be applied in different situations. Meanwhile, MHM has strong assumptions for different types of responses. It would be valuable to examine the performance of the method when the assumptions are violated. The purpose of this study is to compare standard residual methods and MHM under a fully crossed simulation design. In addition, specific recommendations for their applications are provided.
    The simulation study included two scenarios. In simulation scenario I, data were generated under the assumptions of MHM. In simulation scenario II, the assumptions of MHM concerning non-effortful responses and RTs were both violated. Simulation scenario I had three manipulated factors. (1) Non-effort prevalence (π), which was the proportion of individuals with non-effortful responses. It had three levels: 0%, 20% and 40%. (2) Non-effort severity ($\pi_{i}^{non}$), which was the proportion of non-effortful responses for each non-effortful individual. It varied between two levels: low and high. When $\pi_{i}^{non}$ was low, $\pi_{i}^{non}$was generated from U (0, 0.25); while when $\pi_{i}^{non}$ was high, $\pi_{i}^{non}$was generated from U (0.5, 0.75), where “U” denoted a uniform distribution. (3) Difference between RTs of non-effortful and effortful responses (dRT). The difference between RTs from two groups, dRT, had two levels, small and large. The logarithm of RTs of non-effortful responses were generated from normal distribution N (μ,0.5 2), where μ=-1 when dRT was small, μ=-2when dRT was large. For generating the non-effortful responses, we followed Wang, Xu and Shang (2018), with the probability of a correct response gj setting at 0.25 for all non-effortful responses. In simulation scenario II, only the first two factors were considered. Non-effortful RTs were generated from a uniform distribution with a lower bound of exp(-5) and upper bound being the 5th percentile of RT on item j with τ=0. The probability of a correct response for non-effortful responses was dependent on the ability level of each examinee. In all the conditions, sample size was fixed at I = 2,000 and test length was fixed at J = 30. For each condition, 30 replications were generated. For effortful responses, Responses and RTs were simulated from van der Linden’s (2007) hierarchical model. Item parameters were generated with aj~U(1,2.5), bj~N(0,1), αj~U(1.5,2.5),βj~U(-0.2,0.2). For simulees, the person parameters (θi, τi) were generated from a bivariate normal distribution with the mean vector of μ=(0,0)’ and the covariance matrix of $\Sigma=\left[\begin{array}{cc}1 & 0.25 \\ 0.25 & 0.25\end{array}\right]$. Four methods were compared under each condition: the original standard residual method (OSR), conditional estimate standard residual (CSR), conditional estimate with fixed item parameters standard residual method using iterative purifying procedure (CSRI), and MHM. These methods were implemented in R and JAGS using a Bayesian MCMC sampling method for parameter calibration. Finally, these methods were evaluated in terms of convergence rate, detection accuracy and parameter recovery.
    The results are presented as following. First of all, MHM suffered from convergence issues, especially for the latent variable indicating non-effortful responses. On the contrary, all the standard residual methods achieved convergence successfully. The convergence issues were more serious in simulation scenario II. Secondly, when all the items were assumed to have effortful responses, the false positive rate (FPR) of MHM was 0. Although the standard residual methods had FPR around 5% (the nominal level), the accuracy of parameter estimates was similar for all these methods. Third, when data were contaminated by non-effortful responses, CSRI had higher true positive rate (TPR) almost in all the conditions. MHM showed lower TPR but lower false discovery rate (FDR), exhibiting even lower TPR in simulation scenario II. When $\pi_{i}^{non}$ was high, CSRI and MHM showed more advantages over the other methods in terms of parameter recovery. However, when $\pi_{i}^{non}$ was high and dRT was small, MHM generally had higher RMSE than CSRI. Compared to simulation scenario I, MHM performed worse in simulation scenario II. The only problem CSRI needed to deal with was its overestimation of time discrimination parameter across all the conditions except for when π=40% and dRT was large. In a real data example, all the methods were applied to a dataset collected for program assessment and accountability purposes from undergraduates at a mid-sized southeastern university in USA. Evidences from convergence validity showed that CSRI and MHM might detect non-effortful responses more accurately and obtain more precise parameter estimates for this data.
    In conclusion, CSRI generally performed better than the other methods across all the conditions. It is highly recommended to use this method in practice because: (1) It showed acceptable FPR and fairly accurate parameter estimates even when all responses were effortful; (2) It was free of strong assumptions, which meant that it would be robust under various situations; (3) It showed most advantages when $\pi_{i}^{non}$ was high in terms of the detection of non-effortful responses and the improvement of the parameter estimation. In order to improve the estimation of time discrimination parameter in CSRI, the robust estimation methods that down-weight flagged response patterns can be used as an alternative to directly removing non-effortful responses (i.e., the method in the current study). MHM can perform well when all its assumptions are met and $\pi_{i}^{non}$ is high, dRT is large. However, some parameters have difficulty in convergence under MHM, which will limit its application in practice.
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    Comparison of missing data handling methods in cognitive diagnosis: Zero replacement, multiple imputation and maximum likelihood estimation
    SONG Zhilin, GUO Lei, ZHENG Tianpeng
    2022, 54 (4):  426-440.  doi: 10.3724/SP.J.1041.2022.00426
    Abstract ( 166 )  
    The problem of missing data is common in research, and there is no exception for cognitive diagnostic assessment (CDA). Some studies have revealed that both the presence of missing values and the selection of different missing data processing methods would affect the results of CDA. Therefore, it is necessary to attach more attention to the problem in CDA and choose appropriate methods to deal with it. Although the problem in CDA has been explored before, previous studies did not consider multiple imputation (MI) and full information maximum likelihood (FIML), which are widely used in the field of missing data analysis. Moreover, previous studies neglected the comparison using empirical data and saturation models such as GDINA model. In summary, the main purpose of this study are to introduce MI and FIML into CDA, thus making a comprehensive comparison of different missing data handling methods, and further putting forward suggestions for handling missing data in practice.
    Simulation study considered six factors: (1) Sample size: 200 participants, 400 participants, and 1000 participants; (2) Test length: 15 test items and 30 test items; (3) Quality of items: high quality, medium quality, and low quality; (4) Missing data mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR); (5) Missing rate: 10%, 20%, and 30%; (6) Missing data handling methods: zero replacement (ZR), MI-CART, MI-PMM, MI-LOGREG.BOOT, Expectation-Maximization algorithm (EM), and FIML. The GDINA model was used, and the analysis process was realized by the GDINA package in R software. Secondly, the PISA 2015 computer-based mathematics data were applied to compare the practical value of the proposed methods.
    The results of simulation study revealed that: (1) Missing data results in a decrease in estimation accuracy. The absolute value of Bias and RMSE both increased and PCCR values of all methods decreased as the sample size, test length and the quality of the items decreased and the missing rate increased; (2) When estimating item parameters, EM performed best, followed by MI. Meanwhile, FIML and ZR methods were unstable; (3) When estimating the KS of participants, EM and FIML performed best as the missing data mechanism was MAR or MCAR. When the missing data mechanism was MNAR, EM, FIML and ZR performed best. The empirical study results further supported the simulation research results. It showed that: (1) For all empirical indicators, EM, FIML, and MI-PMM perform best on one or more indicators; (2) The results obtained under the empirical study and simulation study under the MNAR mechanism are very similar; (3) EM performs well on all indicators, and ZR and FIML methods are slightly worse than EM, followed by MI-PMM, LOGREG.BOOT and MI-CART.
    In addition, based on the research results, the following suggestions were provided: (1) EM and FIML should be the first choice. However, if researchers do not want to get the complete data set, FIML could be used as a priority for missing data handling; (2) When the missing data mechanism was MAR or MCAR and the test length was not enough, researchers should avoid using the ZR method to deal with missing data. Finally, this paper ends with the prospects of future researches: (1) The multilevel scoring situation should also be studied; (2) The effectiveness of these methods should be tested in longitudinal research; (3) The performance of more methods of information matrix can be further compared in calculating the standard error to handle missing data; (4) Future research could focus on the missing mechanisms of data onto the real data.
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