ISSN 0439-755X
CN 11-1911/B


    25 October 2022, Volume 54 Issue 10 Previous Issue    Next Issue

    Reports of Empirical Studies
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    Reports of Empirical Studies
    The role of text familiarity in Chinese word segmentation and Chinese vocabulary recognition
    CHEN Mingjing, WANG Yongsheng, ZHAO Bingjie, LI Xin, BAI Xuejun
    2022, 54 (10):  1151-1166.  doi: 10.3724/SP.J.1041.2022.01151
    Abstract ( 183 )   HTML ( 35 )  
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    Hierarchical control in task switching: Electrophysiological evidence
    WU Jianxiao, CAO Bihua, CHEN Yun, LI Zixia, LI Fuhong
    2022, 54 (10):  1167-1180.  doi: 10.3724/SP.J.1041.2022.01167
    Abstract ( 79 )   HTML ( 19 )  
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    Relationship between empathy and emotion recognition in Chinese national music: An event-related potential study evidence
    YANG Jimei, CHAI Jieyu, QIU Tianlong, QUAN Xiaoshan, ZHENG Maoping
    2022, 54 (10):  1181-1192.  doi: 10.3724/SP.J.1041.2022.01181
    Abstract ( 95 )   HTML ( 21 )  
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    Automatic drug use behavior: Characteristics of cue-induced reactivity and behavior extinction
    ZENG Hong, ZHENG Zhiling, LUO Xiaohong, WANG Pengfei, WANG Mengcheng, SU Dequan, YANG Wendeng, HUANG Haijiao, PENG Shuna
    2022, 54 (10):  1193-1205.  doi: 10.3724/SP.J.1041.2022.01193
    Abstract ( 50 )   HTML ( 10 )  
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    The unique role of sleep problems among symptoms of posttraumatic stress disorder: A cross-lagged panel network analysis
    LIANG Yiming, YANG Luxi, XI Juzhe, LIU Zhengkui
    2022, 54 (10):  1206-1215.  doi: 10.3724/SP.J.1041.2022.01206
    Abstract ( 104 )   HTML ( 12 )  
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    Traumatic events have been recognized as important precipitants of sleep problems. Meanwhile, traumatic insomnia is one of the criteria for diagnosing post-traumatic stress disorder (PTSD). However, whether trauma-induced sleep problems are secondary symptoms of PTSD or a core feature of PTSD has not yet reached a consistent conclusion. Recently, the emerging cross-lagged panel network analysis method has played an important role in understanding the role of symptoms in psychopathology. The advantage is that the role of each symptom can be systematically analyzed, and the longitudinal predictive pathway of each symptom can be estimated, thereby inferring the leading symptoms of psychiatric disorders. The present study aims to explore the role of trauma-induced sleep problems in the evolution of PTSD among children and adolescents through the cross-lagged panel network analysis.

    Three months after the Zhouqu debris flow, we started this 2-year longitudinal study. Three assessments were performed at 3 months (T1), 15 months (T2) and 27 months (T3) after the disaster. We enrolled students from 2 primary schools and 3 secondary schools in the hardest-hit areas. Ultimately, 1, 460 children and adolescents completed three rounds of evaluation. At T1, there were 702 students from grades 4 to 6, and 758 students from grades 7 to 9. The average age of the participant was 12.89 (SD = 2.29). Symptoms of PTSD were assessed with the University of California at Los Angeles PTSD Reaction Index based on the DSM-IV. The cross-lagged panel network analysis was conducted using R packages glmnet and qgraph.

    Results showed that at T1→T2, sleep problems had the highest out-expected influence centrality, followed by physiological cue reactivity. They were sources of activation for the nodes receiving its edges, that is, they were easy to activate other symptoms in the PTSD network. Sleep problems at T1 positively predicted a lot of other PTSD symptoms at T2, including intrusive thoughts, nightmares, flashbacks, emotional cue reactivity, restricted positive affect, restricted negative affect, irritability/anger, hypervigilance and exaggerated startle response. The results also revealed several indirect influence paths such as sleep problems predicting nightmares then affecting flashbacks. However, when it comes to T2→T3, it is detachment rather than sleep problems that had the highest out- expected influence. It positively predicted diminished interest, restricted positive affect, sleep problems and irritability/anger. We also found some feedback loop: detachment→restricted positive affect→diminished interest→detachment.

    This is the first study to explore activation paths of PTSD symptoms among children and adolescents through the cross-lagged panel network analysis. These findings have improved the understanding of the role of trauma-induced sleep problems in the long-term development of PTSD. The results showed that sleep problems at 3 months after the disaster activated a large number of symptoms in PTSD at 15 months after the disaster. Therefore, it is inferred that early sleep problems were the core symptom in the development of PTSD among children and adolescents in the early post-disaster period. However, its predictability decreased in the later period (15 months to 27 months). In conclusion, these findings emphasize the time specificity of the impact of traumatic sleep problems on PTSD symptoms. We recommend that trauma-induced sleep problems should be given greater priority in the diagnostic criteria for PTSD among children and adolescents in the early post-disaster period. Meanwhile, early psychological assistance should vigorously develop treatments based on sleep problems to prevent the occurrence and development of PTSD. One year or longer after the traumatic event, the intervention target should be set to physiological cue reactivity and detachment.

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    The effects of marital quality on coparenting: A cross-level mediation analysis based on the common fate model
    LIU Yiting, FAN Jieqiong, CHEN Bin-Bin
    2022, 54 (10):  1216-1233.  doi: 10.3724/SP.J.1041.2022.01216
    Abstract ( 117 )   HTML ( 25 )  
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    Gain or Loss? Examining the double-edged sword effect of challenge demand on work-family enrichment
    XU Shan, ZHANG Yucheng, ZHANG Bingran, SHI Junqi, YUAN Mengsha, REN Yingwei
    2022, 54 (10):  1234-1247.  doi: 10.3724/SP.J.1041.2022.01234
    Abstract ( 60 )   HTML ( 11 )  
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    A new method for estimating the optimal sample size in generalizability theory based on evolutionary algorithm: Comparisons with three traditional methods
    LI Guangming, QIN Yue
    2022, 54 (10):  1262-1276.  doi: 10.3724/SP.J.1041.2022.01262
    Abstract ( 35 )  
    Generalizability Theory (GT) is widely applied in psychological measurement and evaluation. A larger generalizability coefficient often indicates a higher reliability the test may have. Generalizability coefficients can be improved by increasing sample sizes. However, the size of a sample would be subject to budget constraints. Therefore, it is important to examine how to effectively determine the size of a sample considering the budget constraints. The existing literature has been largely limited to traditional methods, such as the differential optimization method, the Lagrange method and the Cauchy Schwartz inequality method.
    These traditional methods have limited scope of application and their typical conditions are hard to satisfy. In addition, there is no unified comparison available. Fortunately, with the increased use of high performance computing, the Constrained Optimization Evolutionary Algorithms (COEAs) becomes highly feasible.
    This paper expands and compares the four methods—the differential optimization method, Lagrange method, Cauchy Schwartz inequality method, and COEAs—determine the best solution to the optimal sample size problem under the budget constraints in GT. Specifically, this paper compares the applicability of the four methods using three generalizability designs, including p × i × r, (r: p) × i and p × i × r × o designs. The results are presented as follows:
    (1) In the optimization performance of two-facet generalizability design of p × i × r and (r: p) × i, the performance of COEAs is slightly better than that of the traditional methods, whereas the performance of three traditional methods is equivalent. Although COEAs and the traditional methods have showed similar accuracy, the former has better compliance concerning budget constraints.
    (2) In the optimization performance of three-facet generalizability design of p × i × r × o, the performance of COEAs is obviously better than that of the traditional methods. The least ideal generalizability coefficient is obtained using the differential optimization method, whereas its budget compliance is the best; the generalizability coefficient obtained by Lagrange method is the best, but higher than the budget. The Cauchy inequality method obtains a better generalizability coefficient under special budget constraints. But, the performance of COEAs is slightly better than that of Cauchy Schwartz inequality method, especially closer to the budget constraints.
    (3) In terms of the algorithm complexity, COEAs obtains an obviously smaller algorithm complexity than do the traditional methods. The complexity of the three traditional methods is relatively high. However, COEAs does not rely on the derivation of mathematical formulas, and the algorithm is relatively less complex.
    (4) In terms of the algorithm applicability, COEAs is significantly better than the traditional methods. The applicability of the three traditional methods is relatively narrow. However, COEAs does not rely on a specific generalizability design or a budget expression, and, therefore, the applicability of COEAs is stronger.
    (5) In terms of the algorithm generalizability, COEAs is obviously better than the traditional methods. The limited mathematical principles make it difficult to extend the three traditional methods to more complex generalizability designs, and thus, the feasibility of calculation is poor. Howerve, COEAs has revealed stronger generalizability.
    (6) In terms of the possibility of getting the best solution, COEAs is also better than the traditional methods. Because evolutionary algorithm is a probabilistic algorithm, multiple tests can be conducted to obtain better results for optimal sample sizes. Under some conditions, COEAs can determine better solutions, which, however, is impossible for three traditional methods.
    (7) These results suggest that COEAs is superior to three traditional methods in estimating the optimal sample size problem under the budget constraints in GT. It is recommended that researchers use COEAs in future research to determine an optimal sample size in their psychological measurement and evaluation.
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    Exploration of change point analysis in detecting speededness based on response time data with known/unknown item parameters
    ZHONG Xiaoyuan, YU Xiaofeng, MIAO Ying, QIN Chunying, PENG Yafeng, TONG Hao
    2022, 54 (10):  1277-1292.  doi: 10.3724/SP.J.1041.2022.01277
    Abstract ( 35 )   HTML ( 2 )  
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    In recent years, response time has been attracting rapidly growing attention in psychometric research, likely due to the increasing availability of (item-level) response time data through computer-based testing and online survey data collection. Compared to the conventional item response data that are often dichotomous or polytomous, response time is continuous and can provide much more information. Aberrant response behaviors are frequently encountered during testing, and it could cause various negative effects. Change point analysis (CPA) is a well-established statistical process control method to detect changes in a sequence, and it has provided testing professionals a new lens through to understand test-taking behavior at both the examinee and item levels.

    In this paper, we first gave a comprehensive summary and analysis of the application of change-point analysis in the field of psychometrics. Then we took test speededness as an example to illustrate how the CPA method can be used to detect aberrant behavior using item response time data. Two CPA-based statistics were introduced, as well as their rationale. In the simulation study, there were two cases of response time data: one was that the item parameters were known, the other was that the item parameter were unknown. Response time under speededness was simulated using the gradual-change log-normal model for response time. Two CPA-based test statistics, the Likelihood Ratio Test and the Wald Test, were used to detect aberrant response behaviors. The critical values were obtained through Monte Carlo simulations and compared with the approximate critical values in a previous study. Based on the chosen critical values, we examined the performance of the likelihood ratio test and Wald test in detecting speeded responses, specifically in terms of power and empirical Type-I error.

    On the one hand, the critical values are almost identical for the Wald and the likelihood ratio test. They vary substantially at different nominal α levels, but do not differ much across different test lengths. On the other hand, results indicate that the proposed method is much more powerful based on the critical values than conventional methods that use item response data. When the item parameters are known, the power was close to 1 for most of the conditions while keeping the type-I error rate well-controlled. When the item parameters are unknown, the power of the statistic decreases slightly, but its lowest value reaches 0.89. In the case of factors other than item parameters, the results under the same conditions are only slightly decreased. See Tables 1 and 2 for more detailed results, which show that unknown item parameters may have a negative impact on performance. Real data analysis also demonstrates the performance of the method.

    This study applied CPA based on response time data and offered a very promising approach to detecting aberrant response behavior. Through the simulation study, we demonstrated that it was possible to use fixed critical values in different test lengths, which makes the application of the method straightforward. CPA is very flexible. This study assumed that the log-normal model fitted the response time data, but the method is not bounded by that assumption.

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