Pub Date : 2021-10-24DOI: 10.3102/10769986211051379
S. Sinharay
Takers of educational tests often receive proficiency levels instead of or in addition to scaled scores. For example, proficiency levels are reported for the Advanced Placement (AP®) and U.S. Medical Licensing examinations. Technical difficulties and other unforeseen events occasionally lead to missing item scores and hence to incomplete data on these tests. The reporting of proficiency levels to the examinees with incomplete data requires estimation of the performance of the examinees on the missing part and essentially involves imputation of missing data. In this article, six approaches from the literature on missing data analysis are brought to bear on the problem of reporting of proficiency levels to the examinees with incomplete data. Data from several large-scale educational tests are used to compare the performances of the six approaches to the approach that is operationally used for reporting proficiency levels for these tests. A multiple imputation approach based on chained equations is shown to lead to the most accurate reporting of proficiency levels for data that were missing at random or completely at random, while the model-based approach of Holman and Glas performed the best for data that are missing not at random. Several recommendations are made on the reporting of proficiency levels to the examinees with incomplete data.
{"title":"Reporting Proficiency Levels for Examinees With Incomplete Data","authors":"S. Sinharay","doi":"10.3102/10769986211051379","DOIUrl":"https://doi.org/10.3102/10769986211051379","url":null,"abstract":"Takers of educational tests often receive proficiency levels instead of or in addition to scaled scores. For example, proficiency levels are reported for the Advanced Placement (AP®) and U.S. Medical Licensing examinations. Technical difficulties and other unforeseen events occasionally lead to missing item scores and hence to incomplete data on these tests. The reporting of proficiency levels to the examinees with incomplete data requires estimation of the performance of the examinees on the missing part and essentially involves imputation of missing data. In this article, six approaches from the literature on missing data analysis are brought to bear on the problem of reporting of proficiency levels to the examinees with incomplete data. Data from several large-scale educational tests are used to compare the performances of the six approaches to the approach that is operationally used for reporting proficiency levels for these tests. A multiple imputation approach based on chained equations is shown to lead to the most accurate reporting of proficiency levels for data that were missing at random or completely at random, while the model-based approach of Holman and Glas performed the best for data that are missing not at random. Several recommendations are made on the reporting of proficiency levels to the examinees with incomplete data.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"47 1","pages":"263 - 296"},"PeriodicalIF":2.4,"publicationDate":"2021-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43010884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-05DOI: 10.3102/10769986211035507
Seang-Hwane Joo, Yan Wang, J. Ferron, S. N. Beretvas, Mariola Moeyaert, W. Van den Noortgate
Multiple baseline (MB) designs are becoming more prevalent in educational and behavioral research, and as they do, there is growing interest in combining effect size estimates across studies. To further refine the meta-analytic methods of estimating the effect, this study developed and compared eight alternative methods of estimating intervention effects from a set of MB studies. The methods differed in the assumptions made and varied in whether they relied on within- or between-series comparisons, modeled raw data or effect sizes, and did or did not standardize. Small sample functioning was examined through two simulation studies, which showed that when data were consistent with assumptions the bias was consistently less than 5% of the effect size for each method, whereas root mean squared error varied substantially across methods. When assumptions were violated, substantial biases were found. Implications and limitations are discussed.
{"title":"Comparison of Within- and Between-Series Effect Estimates in the Meta-Analysis of Multiple Baseline Studies","authors":"Seang-Hwane Joo, Yan Wang, J. Ferron, S. N. Beretvas, Mariola Moeyaert, W. Van den Noortgate","doi":"10.3102/10769986211035507","DOIUrl":"https://doi.org/10.3102/10769986211035507","url":null,"abstract":"Multiple baseline (MB) designs are becoming more prevalent in educational and behavioral research, and as they do, there is growing interest in combining effect size estimates across studies. To further refine the meta-analytic methods of estimating the effect, this study developed and compared eight alternative methods of estimating intervention effects from a set of MB studies. The methods differed in the assumptions made and varied in whether they relied on within- or between-series comparisons, modeled raw data or effect sizes, and did or did not standardize. Small sample functioning was examined through two simulation studies, which showed that when data were consistent with assumptions the bias was consistently less than 5% of the effect size for each method, whereas root mean squared error varied substantially across methods. When assumptions were violated, substantial biases were found. Implications and limitations are discussed.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"47 1","pages":"131 - 166"},"PeriodicalIF":2.4,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48923433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-04DOI: 10.3102/10769986211017480
Francis L. Huang
The presence of clustered data is common in the sociobehavioral sciences. One approach that specifically deals with clustered data but has seen little use in education is the generalized estimating equations (GEEs) approach. We provide a background on GEEs, discuss why it is appropriate for the analysis of clustered data, and provide worked examples using both continuous and binary outcomes. Comparisons are made between GEEs, multilevel models, and ordinary least squares results to highlight similarities and differences between the approaches. Detailed walkthroughs are provided using both R and SPSS Version 26.
聚类数据的存在在社会行为科学中很常见。一种专门处理聚类数据但在教育中很少使用的方法是广义估计方程（GEEs）方法。我们提供了GEE的背景，讨论了为什么它适合分析聚类数据，并提供了使用连续结果和二元结果的实例。对GEE、多级模型和普通最小二乘法结果进行了比较，以突出两种方法之间的异同。使用R和SPSS Version 26提供了详细的演练。
{"title":"Analyzing Cross-Sectionally Clustered Data Using Generalized Estimating Equations","authors":"Francis L. Huang","doi":"10.3102/10769986211017480","DOIUrl":"https://doi.org/10.3102/10769986211017480","url":null,"abstract":"The presence of clustered data is common in the sociobehavioral sciences. One approach that specifically deals with clustered data but has seen little use in education is the generalized estimating equations (GEEs) approach. We provide a background on GEEs, discuss why it is appropriate for the analysis of clustered data, and provide worked examples using both continuous and binary outcomes. Comparisons are made between GEEs, multilevel models, and ordinary least squares results to highlight similarities and differences between the approaches. Detailed walkthroughs are provided using both R and SPSS Version 26.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"47 1","pages":"101 - 125"},"PeriodicalIF":2.4,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43238549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-03DOI: 10.3102/10769986211010467
Esther Ulitzsch, Qiwei He, S. Pohl
Interactive tasks designed to elicit real-life problem-solving behavior are rapidly becoming more widely used in educational assessment. Incorrect responses to such tasks can occur for a variety of different reasons such as low proficiency levels, low metacognitive strategies, or motivational issues. We demonstrate how behavioral patterns associated with incorrect responses can, in part, be understood, supporting insights into the different sources of failure on a task. To this end, we make use of sequence mining techniques that leverage the information contained in time-stamped action sequences commonly logged in assessments with interactive tasks for (a) investigating what distinguishes incorrect behavioral patterns from correct ones and (b) identifying subgroups of examinees with similar incorrect behavioral patterns. Analyzing a task from the Programme for the International Assessment of Adult Competencies 2012 assessment, we find incorrect behavioral patterns to be more heterogeneous than correct ones. We identify multiple subgroups of incorrect behavioral patterns, which point toward different levels of effort and lack of different subskills needed for solving the task. Albeit focusing on a single task, meaningful patterns of major differences in how examinees approach a given task that generalize across multiple tasks are uncovered. Implications for the construction and analysis of interactive tasks as well as the design of interventions for complex problem-solving skills are derived.
{"title":"Using Sequence Mining Techniques for Understanding Incorrect Behavioral Patterns on Interactive Tasks","authors":"Esther Ulitzsch, Qiwei He, S. Pohl","doi":"10.3102/10769986211010467","DOIUrl":"https://doi.org/10.3102/10769986211010467","url":null,"abstract":"Interactive tasks designed to elicit real-life problem-solving behavior are rapidly becoming more widely used in educational assessment. Incorrect responses to such tasks can occur for a variety of different reasons such as low proficiency levels, low metacognitive strategies, or motivational issues. We demonstrate how behavioral patterns associated with incorrect responses can, in part, be understood, supporting insights into the different sources of failure on a task. To this end, we make use of sequence mining techniques that leverage the information contained in time-stamped action sequences commonly logged in assessments with interactive tasks for (a) investigating what distinguishes incorrect behavioral patterns from correct ones and (b) identifying subgroups of examinees with similar incorrect behavioral patterns. Analyzing a task from the Programme for the International Assessment of Adult Competencies 2012 assessment, we find incorrect behavioral patterns to be more heterogeneous than correct ones. We identify multiple subgroups of incorrect behavioral patterns, which point toward different levels of effort and lack of different subskills needed for solving the task. Albeit focusing on a single task, meaningful patterns of major differences in how examinees approach a given task that generalize across multiple tasks are uncovered. Implications for the construction and analysis of interactive tasks as well as the design of interventions for complex problem-solving skills are derived.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"47 1","pages":"3 - 35"},"PeriodicalIF":2.4,"publicationDate":"2021-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41989802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-09DOI: 10.3102/10769986221141074
Yajuan Si, R. Little, Ya Mo, N. Sedransk
Nonresponse bias is a widely prevalent problem for data on education. We develop a ten-step exemplar to guide nonresponse bias analysis (NRBA) in cross-sectional studies and apply these steps to the Early Childhood Longitudinal Study, Kindergarten Class of 2010–2011. A key step is the construction of indices of nonresponse bias based on proxy pattern-mixture models for survey variables of interest. A novel feature is to characterize the strength of evidence about nonresponse bias contained in these indices, based on the strength of the relationship between the characteristics in the nonresponse adjustment and the key survey variables. Our NRBA improves the existing methods by incorporating both missing at random and missing not at random mechanisms, and all analyses can be done straightforwardly with standard statistical software.
{"title":"A Case Study of Nonresponse Bias Analysis in Educational Assessment Surveys","authors":"Yajuan Si, R. Little, Ya Mo, N. Sedransk","doi":"10.3102/10769986221141074","DOIUrl":"https://doi.org/10.3102/10769986221141074","url":null,"abstract":"Nonresponse bias is a widely prevalent problem for data on education. We develop a ten-step exemplar to guide nonresponse bias analysis (NRBA) in cross-sectional studies and apply these steps to the Early Childhood Longitudinal Study, Kindergarten Class of 2010–2011. A key step is the construction of indices of nonresponse bias based on proxy pattern-mixture models for survey variables of interest. A novel feature is to characterize the strength of evidence about nonresponse bias contained in these indices, based on the strength of the relationship between the characteristics in the nonresponse adjustment and the key survey variables. Our NRBA improves the existing methods by incorporating both missing at random and missing not at random mechanisms, and all analyses can be done straightforwardly with standard statistical software.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"48 1","pages":"271 - 295"},"PeriodicalIF":2.4,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46105939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-01DOI: 10.3102/1076998621994540
Andrew D. Ho, Sean F. Reardon, Demetra Kalogrides
In this issue, Reardon, Kalogrides, and Ho developed precision-adjusted random effects models to estimate aggregate-level linking error, for populations and subpopulations, for averages and progress over time. We are grateful to past editor Dan McCaffrey for selecting our paper as the focal article for a set of commentaries from our colleagues Daniel Bolt, Mark Davison, Alina von Davier, Tim Moses, and Neil Dorans. These commentaries reinforce important cautions and identify promising directions for future research. In this rejoinder, we clarify aspects of our originally proposed method. (1) Validation methods provide evidence of benefits and risks that different experts may weigh differently for different purposes. (2) Our proposed method differs from “standard mapping” procedures using the National Assessment of Educational Progress not only by using a linear (vs. equipercentile) link but also by targeting direct validity evidence about counterfactual aggregate scores. (3) Multilevel approaches that assume common score scales across states are indeed a promising next step for validation, and we hope that states enable researchers to use more of their common-core-era consortium test data for this purpose. Finally, we apply our linking method to an extended panel of data from 2009 to 2017 to show that linking recovery has remained stable.
{"title":"Validation Methods for Aggregate-Level Test Scale Linking: A Rejoinder","authors":"Andrew D. Ho, Sean F. Reardon, Demetra Kalogrides","doi":"10.3102/1076998621994540","DOIUrl":"https://doi.org/10.3102/1076998621994540","url":null,"abstract":"In this issue, Reardon, Kalogrides, and Ho developed precision-adjusted random effects models to estimate aggregate-level linking error, for populations and subpopulations, for averages and progress over time. We are grateful to past editor Dan McCaffrey for selecting our paper as the focal article for a set of commentaries from our colleagues Daniel Bolt, Mark Davison, Alina von Davier, Tim Moses, and Neil Dorans. These commentaries reinforce important cautions and identify promising directions for future research. In this rejoinder, we clarify aspects of our originally proposed method. (1) Validation methods provide evidence of benefits and risks that different experts may weigh differently for different purposes. (2) Our proposed method differs from “standard mapping” procedures using the National Assessment of Educational Progress not only by using a linear (vs. equipercentile) link but also by targeting direct validity evidence about counterfactual aggregate scores. (3) Multilevel approaches that assume common score scales across states are indeed a promising next step for validation, and we hope that states enable researchers to use more of their common-core-era consortium test data for this purpose. Finally, we apply our linking method to an extended panel of data from 2009 to 2017 to show that linking recovery has remained stable.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"46 1","pages":"209 - 218"},"PeriodicalIF":2.4,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49049001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-31DOI: 10.3102/1076998621992554
R. Colombi, S. Giordano, G. Tutz
A mixture of logit models is proposed that discriminates between responses to rating questions that are affected by a tendency to prefer middle or extremes of the scale regardless of the content of the item (response styles) and purely content-driven preferences. Explanatory variables are used to characterize the content-driven way of answering as well as the tendency to middle or extreme categories. The proposed model is extended to account for the presence of response styles in the case of several items, and the association among responses is described, both when they are content driven or dictated by response styles. In addition, stochastic orderings, related to the tendency to select middle or extreme categories, are introduced and investigated. A simulation study describes the effectiveness of the proposed model, and an application to a questionnaire on attitudes toward ethnic minorities illustrates the applicability of the modeling approach.
{"title":"A Rating Scale Mixture Model to Account for the Tendency to Middle and Extreme Categories","authors":"R. Colombi, S. Giordano, G. Tutz","doi":"10.3102/1076998621992554","DOIUrl":"https://doi.org/10.3102/1076998621992554","url":null,"abstract":"A mixture of logit models is proposed that discriminates between responses to rating questions that are affected by a tendency to prefer middle or extremes of the scale regardless of the content of the item (response styles) and purely content-driven preferences. Explanatory variables are used to characterize the content-driven way of answering as well as the tendency to middle or extreme categories. The proposed model is extended to account for the presence of response styles in the case of several items, and the association among responses is described, both when they are content driven or dictated by response styles. In addition, stochastic orderings, related to the tendency to select middle or extreme categories, are introduced and investigated. A simulation study describes the effectiveness of the proposed model, and an application to a questionnaire on attitudes toward ethnic minorities illustrates the applicability of the modeling approach.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"46 1","pages":"682 - 716"},"PeriodicalIF":2.4,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45838221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-29DOI: 10.3102/1076998621994366
Yue Liu, Hongyun Liu
The prevalence and serious consequences of noneffortful responses from unmotivated examinees are well-known in educational measurement. In this study, we propose to apply an iterative purification process based on a response time residual method with fixed item parameter estimates to detect noneffortful responses. The proposed method is compared with the traditional residual method and noniterative method with fixed item parameters in two simulation studies in terms of noneffort detection accuracy and parameter recovery. The results show that when severity of noneffort is high, the proposed method leads to a much higher true positive rate with a small increase of false discovery rate. In addition, parameter estimation is significantly improved by the strategies of fixing item parameters and iteratively cleansing. These results suggest that the proposed method is a potential solution to reduce the impact of data contamination due to severe low test-taking effort and to obtain more accurate parameter estimates. An empirical study is also conducted to show the differences in the detection rate and parameter estimates among different approaches.
{"title":"Detecting Noneffortful Responses Based on a Residual Method Using an Iterative Purification Process","authors":"Yue Liu, Hongyun Liu","doi":"10.3102/1076998621994366","DOIUrl":"https://doi.org/10.3102/1076998621994366","url":null,"abstract":"The prevalence and serious consequences of noneffortful responses from unmotivated examinees are well-known in educational measurement. In this study, we propose to apply an iterative purification process based on a response time residual method with fixed item parameter estimates to detect noneffortful responses. The proposed method is compared with the traditional residual method and noniterative method with fixed item parameters in two simulation studies in terms of noneffort detection accuracy and parameter recovery. The results show that when severity of noneffort is high, the proposed method leads to a much higher true positive rate with a small increase of false discovery rate. In addition, parameter estimation is significantly improved by the strategies of fixing item parameters and iteratively cleansing. These results suggest that the proposed method is a potential solution to reduce the impact of data contamination due to severe low test-taking effort and to obtain more accurate parameter estimates. An empirical study is also conducted to show the differences in the detection rate and parameter estimates among different approaches.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"46 1","pages":"717 - 752"},"PeriodicalIF":2.4,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44643360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-29DOI: 10.3102/10769986211003283
Xiangyi Liao, D. Bolt
Four-parameter models have received increasing psychometric attention in recent years, as a reduced upper asymptote for item characteristic curves can be appealing for measurement applications such as adaptive testing and person-fit assessment. However, applications can be challenging due to the large number of parameters in the model. In this article, we demonstrate in the context of mathematics assessments how the slip and guess parameters of a four-parameter model may often be empirically related. This observation also has a psychological explanation to the extent that both asymptote parameters may be manifestations of a single item complexity characteristic. The relationship between lower and upper asymptotes motivates the consideration of an asymmetric item response theory model as a three-parameter alternative to the four-parameter model. Using actual response data from mathematics multiple-choice tests, we demonstrate the empirical superiority of a three-parameter asymmetric model in several standardized tests of mathematics. To the extent that a model of asymmetry ultimately portrays slips and guesses not as purely random but rather as proficiency-related phenomena, we argue that the asymmetric approach may also have greater psychological plausibility.
{"title":"Item Characteristic Curve Asymmetry: A Better Way to Accommodate Slips and Guesses Than a Four-Parameter Model?","authors":"Xiangyi Liao, D. Bolt","doi":"10.3102/10769986211003283","DOIUrl":"https://doi.org/10.3102/10769986211003283","url":null,"abstract":"Four-parameter models have received increasing psychometric attention in recent years, as a reduced upper asymptote for item characteristic curves can be appealing for measurement applications such as adaptive testing and person-fit assessment. However, applications can be challenging due to the large number of parameters in the model. In this article, we demonstrate in the context of mathematics assessments how the slip and guess parameters of a four-parameter model may often be empirically related. This observation also has a psychological explanation to the extent that both asymptote parameters may be manifestations of a single item complexity characteristic. The relationship between lower and upper asymptotes motivates the consideration of an asymmetric item response theory model as a three-parameter alternative to the four-parameter model. Using actual response data from mathematics multiple-choice tests, we demonstrate the empirical superiority of a three-parameter asymmetric model in several standardized tests of mathematics. To the extent that a model of asymmetry ultimately portrays slips and guesses not as purely random but rather as proficiency-related phenomena, we argue that the asymmetric approach may also have greater psychological plausibility.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"46 1","pages":"753 - 775"},"PeriodicalIF":2.4,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49036230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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