Pub Date : 2017-11-24DOI: 10.1027/1614-2241/a000136
J. Rojo-García, J. Sanz-Gómez
The present article features a hierarchical Bayes method applied to solving problems of benchmarking and contemporaneous reconciliation across time series. This method enables the use of high frequency series to be either approximations or one or several related indicators. This method may be applied when facing flow or index disaggregation problems. The authors compare their results to classical procedures (viz., Denton univariate and Rossi multivariate methods) through the use of indicators. This article concludes that the suggested method bestows greater importance on the low frequency series profile, consequently providing smoother solutions than its counterparts.
{"title":"Benchmarking and Reconciliation of Time Series: An Applied Bayesian Method","authors":"J. Rojo-García, J. Sanz-Gómez","doi":"10.1027/1614-2241/a000136","DOIUrl":"https://doi.org/10.1027/1614-2241/a000136","url":null,"abstract":"The present article features a hierarchical Bayes method applied to solving problems of benchmarking and contemporaneous reconciliation across time series. This method enables the use of high frequency series to be either approximations or one or several related indicators. This method may be applied when facing flow or index disaggregation problems. The authors compare their results to classical procedures (viz., Denton univariate and Rossi multivariate methods) through the use of indicators. This article concludes that the suggested method bestows greater importance on the low frequency series profile, consequently providing smoother solutions than its counterparts.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":"13 1","pages":"123–134"},"PeriodicalIF":3.1,"publicationDate":"2017-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43614013","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 : 2017-11-24DOI: 10.1027/1614-2241/a000139
Jiin‐Huarng Guo, W. Luh
The present study considered two independent exponential distributions with the hypothesis for testing equivalence of lifetime means or failure rates, and aimed to determine the required uncensored sample size based on power, sampling cost, and censoring proportion simultaneously in the case of right censoring. Approximate sample size formulas with an iterative procedure were proposed and an uncensored sample size allocation ratio was derived to minimize the total cost for a designated power or maximize statistical power for a limit cost. R codes are provided for easy application. The proposed methods are validated in terms of Type I errors and statistical power in a simulation study, and are recommended for the future use.
{"title":"Sample Size Calculations for Testing Equivalence of Two Exponential Distributions With Right Censoring: Allocation With Costs","authors":"Jiin‐Huarng Guo, W. Luh","doi":"10.1027/1614-2241/a000139","DOIUrl":"https://doi.org/10.1027/1614-2241/a000139","url":null,"abstract":"The present study considered two independent exponential distributions with the hypothesis for testing equivalence of lifetime means or failure rates, and aimed to determine the required uncensored sample size based on power, sampling cost, and censoring proportion simultaneously in the case of right censoring. Approximate sample size formulas with an iterative procedure were proposed and an uncensored sample size allocation ratio was derived to minimize the total cost for a designated power or maximize statistical power for a limit cost. R codes are provided for easy application. The proposed methods are validated in terms of Type I errors and statistical power in a simulation study, and are recommended for the future use.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":"13 1","pages":"144–156"},"PeriodicalIF":3.1,"publicationDate":"2017-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42059452","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 : 2017-09-08DOI: 10.1027/1614-2241/a000135
G. Moors, I. Vriens, J. Gelissen
The form-resistant hypothesis states that alternative ways of measuring the same values should be small if method-specific features are taken into account. However, previous research that compared rating and ranking questionnaires for measuring values has shown mixed results. We suggest that adopting a latent class segmentation approach helps to explain these mixed results by identifying segments with similar item preference structures and segments linked to one format only. Our approach is applied to a Dutch survey on work values. In both ranking and rating mode, we find two similar segments reflecting the intrinsic and extrinsic preference structure, while other segments differed between modes. In line with the modified form-resistant hypothesis, the results suggest the same latent preference structure has guided particular segments in a population to respond similarly to rating and ranking questions.
{"title":"Similarities Between Ranking and Rating Measures of Values Preferences: Evidence From a Latent Class Segmentation Approach","authors":"G. Moors, I. Vriens, J. Gelissen","doi":"10.1027/1614-2241/a000135","DOIUrl":"https://doi.org/10.1027/1614-2241/a000135","url":null,"abstract":"The form-resistant hypothesis states that alternative ways of measuring the same values should be small if method-specific features are taken into account. However, previous research that compared rating and ranking questionnaires for measuring values has shown mixed results. We suggest that adopting a latent class segmentation approach helps to explain these mixed results by identifying segments with similar item preference structures and segments linked to one format only. Our approach is applied to a Dutch survey on work values. In both ranking and rating mode, we find two similar segments reflecting the intrinsic and extrinsic preference structure, while other segments differed between modes. In line with the modified form-resistant hypothesis, the results suggest the same latent preference structure has guided particular segments in a population to respond similarly to rating and ranking questions.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":"13 1","pages":"113–122"},"PeriodicalIF":3.1,"publicationDate":"2017-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44460221","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 : 2017-09-08DOI: 10.1027/1614-2241/a000134
W. H. Finch
Growth curve modeling (GCM) is an important and commonly used methodology in the social sciences for examining change over time in a variable value. While much of the empirical research examining the performance of various estimators under a variety of conditions has focused on continuous (and typically normally distributed) observed indicators, in practice researchers frequently make use of categorical indicators with anywhere from two to as many as seven categories. Given the popularity of GCMs, along with the frequent use of categorical indicators, and the relative dearth of simulation research focusing on estimation of these models with such variables, the current study focused on the issue of parameter estimation accuracy as related to the number of categorical indicators, and the number of categories per indicator. Results of this research found that for models with only a linear component, parameter estimation was very accurate for as few as four indicators with two categories each and a sample size of 200. On the other hand, when the underlying model included both linear and quadratic terms, parameter estimation accuracy suffered for a small number of dichotomous indicators unless the sample size was 1,000 or more. However, with six or more indicator variables, and/or at least three categories, parameter estimation accuracy remained high.
{"title":"Investigation of Parameter Estimation Accuracy for Growth Curve Modeling With Categorical Indicators: Impact of Number of Measurement Occasions and Number of Categories","authors":"W. H. Finch","doi":"10.1027/1614-2241/a000134","DOIUrl":"https://doi.org/10.1027/1614-2241/a000134","url":null,"abstract":"Growth curve modeling (GCM) is an important and commonly used methodology in the social sciences for examining change over time in a variable value. While much of the empirical research examining the performance of various estimators under a variety of conditions has focused on continuous (and typically normally distributed) observed indicators, in practice researchers frequently make use of categorical indicators with anywhere from two to as many as seven categories. Given the popularity of GCMs, along with the frequent use of categorical indicators, and the relative dearth of simulation research focusing on estimation of these models with such variables, the current study focused on the issue of parameter estimation accuracy as related to the number of categorical indicators, and the number of categories per indicator. Results of this research found that for models with only a linear component, parameter estimation was very accurate for as few as four indicators with two categories each and a sample size of 200. On the other hand, when the underlying model included both linear and quadratic terms, parameter estimation accuracy suffered for a small number of dichotomous indicators unless the sample size was 1,000 or more. However, with six or more indicator variables, and/or at least three categories, parameter estimation accuracy remained high.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":"13 1","pages":"98–112"},"PeriodicalIF":3.1,"publicationDate":"2017-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43232047","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 : 2017-09-08DOI: 10.1027/1614-2241/a000133
Jan Hochweber, J. Hartig
In repeated cross-sections of organizations, different individuals are sampled from the same set of organizations at each time point of measurement. As a result, common longitudinal data analysis methods (e.g., latent growth curve models) cannot be applied in the usual way. In this contribution, a multilevel structural equation modeling approach to analyze data from repeated cross-sections is presented. Results from a simulation study are reported which aimed at obtaining guidelines on appropriate sample sizes. We focused on a situation where linear growth occurs at the organizational level, and organizational growth is predicted by a single organizational level variable. The power to identify an effect of this organizational level variable was moderately to strongly positively related to number of measurement occasions, number of groups, group size, intraclass correlation, effect size, and growth curve reliability. The Type I error rate was close to the nominal alpha level under all conditions.
{"title":"Analyzing Organizational Growth in Repeated Cross-Sectional Designs Using Multilevel Structural Equation Modeling","authors":"Jan Hochweber, J. Hartig","doi":"10.1027/1614-2241/a000133","DOIUrl":"https://doi.org/10.1027/1614-2241/a000133","url":null,"abstract":"In repeated cross-sections of organizations, different individuals are sampled from the same set of organizations at each time point of measurement. As a result, common longitudinal data analysis methods (e.g., latent growth curve models) cannot be applied in the usual way. In this contribution, a multilevel structural equation modeling approach to analyze data from repeated cross-sections is presented. Results from a simulation study are reported which aimed at obtaining guidelines on appropriate sample sizes. We focused on a situation where linear growth occurs at the organizational level, and organizational growth is predicted by a single organizational level variable. The power to identify an effect of this organizational level variable was moderately to strongly positively related to number of measurement occasions, number of groups, group size, intraclass correlation, effect size, and growth curve reliability. The Type I error rate was close to the nominal alpha level under all conditions.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":"13 1","pages":"83–97"},"PeriodicalIF":3.1,"publicationDate":"2017-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41610254","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 : 2017-06-02DOI: 10.1027/1614-2241/A000130
Ines Devlieger, Y. Rosseel
Abstract. Theoretical researchers consider Structural Equation Modeling (SEM) to be the preferred method to study the relationships among latent variables. However, SEM has the disadvantage of requiring a large sample size, especially if the model is complex. Furthermore, since SEM estimates all parameters simultaneously, one misspecification in the model may influence the whole model. For these reasons, applied researchers often use a two-step Factor Score Regression (FSR) approach. In the first step, factor scores are calculated for the latent variables, which are used to perform a linear regression in the second step. However, this method results in incorrect regression coefficients. Croon (2002) developed a method that corrects for this bias. We combine this method of Croon (2002) with path analysis, resulting in Factor Score Path Analysis. This method results in correct path coefficients and has some advantages over SEM: it requires smaller sample sizes, can handle more complex models and the method ...
{"title":"Factor score path analysis: An alternative for SEM?","authors":"Ines Devlieger, Y. Rosseel","doi":"10.1027/1614-2241/A000130","DOIUrl":"https://doi.org/10.1027/1614-2241/A000130","url":null,"abstract":"Abstract. Theoretical researchers consider Structural Equation Modeling (SEM) to be the preferred method to study the relationships among latent variables. However, SEM has the disadvantage of requiring a large sample size, especially if the model is complex. Furthermore, since SEM estimates all parameters simultaneously, one misspecification in the model may influence the whole model. For these reasons, applied researchers often use a two-step Factor Score Regression (FSR) approach. In the first step, factor scores are calculated for the latent variables, which are used to perform a linear regression in the second step. However, this method results in incorrect regression coefficients. Croon (2002) developed a method that corrects for this bias. We combine this method of Croon (2002) with path analysis, resulting in Factor Score Path Analysis. This method results in correct path coefficients and has some advantages over SEM: it requires smaller sample sizes, can handle more complex models and the method ...","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":"13 1","pages":"31-38"},"PeriodicalIF":3.1,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46596221","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 : 2017-06-02DOI: 10.1027/1614-2241/A000128
M. V. D. Bergh, V. Schmittmann, J. Vermunt
Abstract. Researchers use latent class (LC) analysis to derive meaningful clusters from sets of categorical variables. However, especially when the number of classes required to obtain a good fit is large, interpretation of the latent classes may not be straightforward. To overcome this problem, we propose an alternative way of performing LC analysis, Latent Class Tree (LCT) modeling. For this purpose, a recursive partitioning procedure similar to divisive hierarchical cluster analysis is used: classes are split until a certain criterion indicates that the fit does not improve. The advantage of the LCT approach compared to the standard LC approach is that it gives a clear insight into how the latent classes are formed and how solutions with different numbers of classes relate. We also propose measures to evaluate the relative importance of the splits. The practical use of the approach is illustrated by the analysis of a data set on social capital.
{"title":"Building latent class trees, with an application to a study of social capital","authors":"M. V. D. Bergh, V. Schmittmann, J. Vermunt","doi":"10.1027/1614-2241/A000128","DOIUrl":"https://doi.org/10.1027/1614-2241/A000128","url":null,"abstract":"Abstract. Researchers use latent class (LC) analysis to derive meaningful clusters from sets of categorical variables. However, especially when the number of classes required to obtain a good fit is large, interpretation of the latent classes may not be straightforward. To overcome this problem, we propose an alternative way of performing LC analysis, Latent Class Tree (LCT) modeling. For this purpose, a recursive partitioning procedure similar to divisive hierarchical cluster analysis is used: classes are split until a certain criterion indicates that the fit does not improve. The advantage of the LCT approach compared to the standard LC approach is that it gives a clear insight into how the latent classes are formed and how solutions with different numbers of classes relate. We also propose measures to evaluate the relative importance of the splits. The practical use of the approach is illustrated by the analysis of a data set on social capital.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":"13 1","pages":"13-22"},"PeriodicalIF":3.1,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43823789","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 : 2017-06-02DOI: 10.1027/1614-2241/a000129
Albert Maydeu-Olivares, Dexin Shi
Residual correlations and covariances provide effect sizes of the misfit of covariance structure models. In a simulation study, we found that accurate confidence intervals (CIs) for standardized residual covariances are obtained even in small samples (N = 100), regardless of model size, degree of model misspecification, and data distribution. Standardized residual covariances also provide information about the source of misfit in poorly fitting models. From this viewpoint, they may be considered an alternative to modification indices. We compared the empirical Type I errors and power rates of standardized residual covariances and modification indices and found that both procedures provide nearly identical rates across the simulation conditions investigated. Residual correlations and standardized residual covariances provide very similar results.
{"title":"Effect Sizes of Model Misfit in Structural Equation Models: Standardized Residual Covariances and Residual Correlations","authors":"Albert Maydeu-Olivares, Dexin Shi","doi":"10.1027/1614-2241/a000129","DOIUrl":"https://doi.org/10.1027/1614-2241/a000129","url":null,"abstract":"Residual correlations and covariances provide effect sizes of the misfit of covariance structure models. In a simulation study, we found that accurate confidence intervals (CIs) for standardized residual covariances are obtained even in small samples (N = 100), regardless of model size, degree of model misspecification, and data distribution. Standardized residual covariances also provide information about the source of misfit in poorly fitting models. From this viewpoint, they may be considered an alternative to modification indices. We compared the empirical Type I errors and power rates of standardized residual covariances and modification indices and found that both procedures provide nearly identical rates across the simulation conditions investigated. Residual correlations and standardized residual covariances provide very similar results.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":"13 1","pages":"23–30"},"PeriodicalIF":3.1,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44916148","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 : 2017-06-02DOI: 10.1027/1614-2241/A000123
Leonard Vanbrabant, R. Schoot, N. Loey, Y. Rosseel
Abstract. Researchers in the social and behavioral sciences often have clear expectations about the order and/or the sign of the parameters in their statistical model. For example, a researcher might expect that regression coefficient β1 is larger than regression coefficients β2 and β3. To test such a constrained hypothesis special methods have been developed. However, the existing methods for structural equation models (SEM) are complex, computationally demanding, and a software routine is lacking. Therefore, in this paper we describe a general procedure for testing order/inequality constrained hypotheses in SEM using the R package lavaan. We use the likelihood ratio (LR) statistic to test constrained hypotheses and the resulting plug-in p value is computed by either parametric or Bollen-Stine bootstrapping. Since the obtained plug-in p value can be biased, a double bootstrap approach is available. The procedure is illustrated by a real-life example about the psychosocial functioning in patients with fac...
{"title":"A general procedure for testing inequality constrained hypotheses in SEM","authors":"Leonard Vanbrabant, R. Schoot, N. Loey, Y. Rosseel","doi":"10.1027/1614-2241/A000123","DOIUrl":"https://doi.org/10.1027/1614-2241/A000123","url":null,"abstract":"Abstract. Researchers in the social and behavioral sciences often have clear expectations about the order and/or the sign of the parameters in their statistical model. For example, a researcher might expect that regression coefficient β1 is larger than regression coefficients β2 and β3. To test such a constrained hypothesis special methods have been developed. However, the existing methods for structural equation models (SEM) are complex, computationally demanding, and a software routine is lacking. Therefore, in this paper we describe a general procedure for testing order/inequality constrained hypotheses in SEM using the R package lavaan. We use the likelihood ratio (LR) statistic to test constrained hypotheses and the resulting plug-in p value is computed by either parametric or Bollen-Stine bootstrapping. Since the obtained plug-in p value can be biased, a double bootstrap approach is available. The procedure is illustrated by a real-life example about the psychosocial functioning in patients with fac...","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":"13 1","pages":"61-70"},"PeriodicalIF":3.1,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44064733","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 : 2017-03-22DOI: 10.1027/1614-2241/a000122
A. Counsell, R. Cribbie
Culpepper and Aguinis (2011) highlighted the benefit of using the errors-in-variables (EIV) method to control for measurement error and obtain unbiased regression estimates. The current study investigated the EIV method and compared it to change scores and analysis of covariance (ANCOVA) in a two-group pretest-posttest design. Results indicated that the EIV method’s estimates were unbiased under many conditions, but the EIV method consistently demonstrated lower power than the change score method. An additional risk with using the EIV method is that one must enter the covariate reliability into the EIV model, and results highlighted that estimates are biased if a researcher chooses a value that differs from the true covariate reliability. Obtaining unbiased results also depended on sample size. Our conclusion is that there is no additional benefit to using the EIV method over change score or ANCOVA methods for comparing the amount of change in pretest-posttest designs.
{"title":"Using the Errors-in-Variables Method in Two-Group Pretest-Posttest Designs","authors":"A. Counsell, R. Cribbie","doi":"10.1027/1614-2241/a000122","DOIUrl":"https://doi.org/10.1027/1614-2241/a000122","url":null,"abstract":"Culpepper and Aguinis (2011) highlighted the benefit of using the errors-in-variables (EIV) method to control for measurement error and obtain unbiased regression estimates. The current study investigated the EIV method and compared it to change scores and analysis of covariance (ANCOVA) in a two-group pretest-posttest design. Results indicated that the EIV method’s estimates were unbiased under many conditions, but the EIV method consistently demonstrated lower power than the change score method. An additional risk with using the EIV method is that one must enter the covariate reliability into the EIV model, and results highlighted that estimates are biased if a researcher chooses a value that differs from the true covariate reliability. Obtaining unbiased results also depended on sample size. Our conclusion is that there is no additional benefit to using the EIV method over change score or ANCOVA methods for comparing the amount of change in pretest-posttest designs.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":"13 1","pages":"1–8"},"PeriodicalIF":3.1,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41884541","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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