Methodology: European Journal of Research Methods for The Behavioral and Social Sciences最新文献_第2页

Looking for a Consensus in the Discussion About the Concept of Validity: A Delphi Study 在效度概念讨论中寻求共识:德尔菲研究

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2018-10-01 DOI: 10.1027/1614-2241/a000157

Sandra Liliana Camargo, A. Herrera, A. Traynor

引用次数: 6

Comparing the Performance of Agree/Disagree and Item-Specific Questions Across PCs and Smartphones 比较PC和智能手机中同意/不同意和项目特定问题的表现

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2018-07-01 DOI: 10.1027/1614-2241/a000151

Jan Karem Höhne, M. Revilla, Timo Lenzner

{"title":"Comparing the Performance of Agree/Disagree and Item-Specific Questions Across PCs and Smartphones","authors":"Jan Karem Höhne, M. Revilla, Timo Lenzner","doi":"10.1027/1614-2241/a000151","DOIUrl":"https://doi.org/10.1027/1614-2241/a000151","url":null,"abstract":"The use of agree/disagree (A/D) questions is a common technique to measure attitudes. For instance, this question format is employed frequently in the Eurobarometer and International Social Survey Programme (ISSP). Theoretical considerations, however, suggest that A/D questions require a complex processing. Therefore, many survey researchers have recommended the use of item-specific (IS) questions, since they seem to be less burdensome. Parallel to this methodological discussion is the discussion around the use of mobile devices for responding to surveys. However, until now, evidence has been lacking as to whether the use of mobile devices for survey response affects the performance of established question formats. In this study, implemented in the Netquest panel in Spain (N = 1,476), we investigated the cognitive effort and response quality associated with A/D and IS questions across PCs and smartphones. For this purpose, we applied a split-ballot design defined by device type and question format. Our analyses revealed longer response times for IS questions than A/D questions, irrespective of the device type and scale length. Also, the IS questions produced better response quality than their A/D counterparts. All in all, the findings indicate a more conscientious response to IS questions compared to A/D questions.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":"14 1","pages":"109–118"},"PeriodicalIF":3.1,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48970377","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}

引用次数: 15

Modeling Intraindividual Variability in Three-Level Multilevel Models 三层次多层次模型中个体内变异性的建模

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2018-07-01 DOI: 10.1027/1614-2241/a000150

S. Nestler, K. Geukes, M. Back

引用次数: 8

The Consequences of Varying Measurement Occasions in Discrete-Time Survival Analysis 离散时间生存分析中测量场合变化的后果

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2018-06-21 DOI: 10.1027/1614-2241/a000145

M. Moerbeek, L. Hesen

引用次数: 1

Agreement on the Classification of Latent Class Membership Between Different Identification Constraint Approaches in the Mixture Rasch Model 混合Rasch模型中不同识别约束方法之间潜在类隶属度分类的一致性

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2018-06-21 DOI: 10.1027/1614-2241/a000148

Yi-Jhen Wu, Insu Paek

{"title":"Agreement on the Classification of Latent Class Membership Between Different Identification Constraint Approaches in the Mixture Rasch Model","authors":"Yi-Jhen Wu, Insu Paek","doi":"10.1027/1614-2241/a000148","DOIUrl":"https://doi.org/10.1027/1614-2241/a000148","url":null,"abstract":"When using the mixture Rasch model, the model identification constraints are either to set the equal means for all classes in the assumed normal ability distributions (equal ability mean constraint in short), or to set the sum of item difficulties to be zero for each class. In real data analysis, however, both constraints are not always sufficient to establish a common scale across latent classes unless some items are specified as anchor items in the estimation. If these two conventional constraint approaches recover the class membership as good as the anchor item constraint approach, the conventional constraint approaches may be considered useful for the purpose of class membership classification. This study investigated agreement on class membership between one conventional constraint (the equal ability mean) and the anchor item constraint approaches. Results showed high agreement between these two constraint approaches, indicating that the conventional constraint of the equal mean ability approach may be used to recover the latent class membership although item profiles are not correctly estimated across latent classes.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":"14 1","pages":"82–93"},"PeriodicalIF":3.1,"publicationDate":"2018-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43688635","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}

引用次数: 1

Assessing a Bayesian Embedding Approach to Circular Regression Models 评估循环回归模型的贝叶斯嵌入方法

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2018-06-21 DOI: 10.1027/1614-2241/a000147

J. Cremers, T. Mainhard, I. Klugkist

引用次数: 6

Bayesian Latent Class Models for the Multiple Imputation of Categorical Data 范畴数据多重输入的贝叶斯潜在类模型

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2018-06-21 DOI: 10.1027/1614-2241/a000146

D. Vidotto, J. Vermunt, K. Van Deun

{"title":"Bayesian Latent Class Models for the Multiple Imputation of Categorical Data","authors":"D. Vidotto, J. Vermunt, K. Van Deun","doi":"10.1027/1614-2241/a000146","DOIUrl":"https://doi.org/10.1027/1614-2241/a000146","url":null,"abstract":"Latent class analysis has been recently proposed for the multiple imputation (MI) of missing categorical data, using either a standard frequentist approach or a nonparametric Bayesian model called Dirichlet process mixture of multinomial distributions (DPMM). The main advantage of using a latent class model for multiple imputation is that it is very flexible in the sense that it can capture complex relationships in the data given that the number of latent classes is large enough. However, the two existing approaches also have certain disadvantages. The frequentist approach is computationally demanding because it requires estimating many LC models: first models with different number of classes should be estimated to determine the required number of classes and subsequently the selected model is reestimated for multiple bootstrap samples to take into account parameter uncertainty during the imputation stage. Whereas the Bayesian Dirichlet process models perform the model selection and the handling of the parameter uncertainty automatically, the disadvantage of this method is that it tends to use a too small number of clusters during the Gibbs sampling, leading to an underfitting model yielding invalid imputations. In this paper, we propose an alternative approach which combined the strengths of the two existing approaches; that is, we use the Bayesian standard latent class model as an imputation model. We show how model selection can be performed prior to the imputation step using a single run of the Gibbs sampler and, moreover, show how underfitting is prevented by using large values for the hyperparameters of the mixture weights. The results of two simulation studies and one real-data study indicate that with a proper setting of the prior distributions, the Bayesian latent class model yields valid imputations and outperforms competing methods.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":"14 1","pages":"56–68"},"PeriodicalIF":3.1,"publicationDate":"2018-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41370291","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}

引用次数: 7

Multiple Imputation by Predictive Mean Matching When Sample Size Is Small 样本量小时预测均值匹配的多重脉冲

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2018-04-23 DOI: 10.1027/1614-2241/a000141

Kristian Kleinke

引用次数: 34

Estimating a Three-Level Latent Variable Regression Model With Cross-Classified Multiple Membership Data 交叉分类多隶属度数据的三水平潜变量回归模型估计

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2018-04-23 DOI: 10.1027/1614-2241/a000143

Audrey J. Leroux, S. Beretvas

{"title":"Estimating a Three-Level Latent Variable Regression Model With Cross-Classified Multiple Membership Data","authors":"Audrey J. Leroux, S. Beretvas","doi":"10.1027/1614-2241/a000143","DOIUrl":"https://doi.org/10.1027/1614-2241/a000143","url":null,"abstract":"The current study proposed a new model, termed the cross-classified multiple membership latent variable regression (CCMM-LVR) model that provides an extension to the three-level latent variable regression (HM3-LVR) model that can be used with cross-classified multiple membership data, for example, in the presence of student mobility across schools. The HM3-LVR model is beneficial for testing more flexible hypotheses about growth trajectory parameters and handles pure clustering of participants within higher-level (level-3) units. However, the HM3-LVR model involves the assumption that students remain in the same cluster (school) throughout the duration of the time period of interest. The CCMM-LVR model appropriately models the participants’ changing clusters over time. The impact of ignoring mobility in the real data was investigated by comparing parameter estimates, standard error estimates, and model fit indices for the model (CCMM-LVR) that appropriately modeled the cross-classified multiple membership structure with results when this structure was ignored (HM3-LVR).","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":"14 1","pages":"30–44"},"PeriodicalIF":3.1,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43905996","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}

引用次数: 6

Comparison of Uni- and Multidimensional Models Applied in Testlet-Based Tests 基于测试集的测试中单模型与多维模型的比较

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2017-11-24 DOI: 10.1027/1614-2241/a000137

Alejandro Hernández‐Camacho, J. Olea, F. J. Abad

{"title":"Comparison of Uni- and Multidimensional Models Applied in Testlet-Based Tests","authors":"Alejandro Hernández‐Camacho, J. Olea, F. J. Abad","doi":"10.1027/1614-2241/a000137","DOIUrl":"https://doi.org/10.1027/1614-2241/a000137","url":null,"abstract":"The bifactor model (BM) and the testlet response model (TRM) are the most common multidimensional models applied to testlet-based tests. The common procedure is to estimate these models using different estimation methods (see, e.g., DeMars, 2006). A possible consequence of this is that previous findings about the implications of fitting a wrong model to the data may be confounded with the estimation procedures they employed. With this in mind, the present study uses the same method (maximum marginal likelihood [MML] using dimensional reduction) to compare uni- and multidimensional strategies to testlet-based tests, and assess the performance of various relative fit indices. Data were simulated under three different models, namely BM, TRM, and the unidimensional model. Recovery of item parameters, reliability estimates, and selection rates of the relative fit indices were documented. The results were essentially consistent with those obtained through different methods (DeMars, 2006), indicating that the effect of the estimation method is negligible. Regarding the fit indices, Akaike Information Criterion (AIC) showed the best selection rates, whereas Bayes Information Criterion (BIC) tended to select a model which is simpler than the true one. The work concludes with recommendations for practitioners and proposals for future research.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":"13 1","pages":"135–143"},"PeriodicalIF":3.1,"publicationDate":"2017-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42189419","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}

引用次数: 4