Methodology: European Journal of Research Methods for The Behavioral and Social Sciences最新文献_第4页

A general procedure for testing inequality constrained hypotheses in SEM 扫描电镜中检验不等式约束假设的一般程序

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2017-06-02 DOI: 10.1027/1614-2241/A000123

Leonard Vanbrabant, R. Schoot, N. Loey, Y. Rosseel

引用次数: 1

Using the Errors-in-Variables Method in Two-Group Pretest-Posttest Designs 变量误差法在两组测试前后设计中的应用

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2017-03-22 DOI: 10.1027/1614-2241/a000122

A. Counsell, R. Cribbie

引用次数: 3

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs 改良Brown Forsyth的幂和混合模型方法在分割图设计中的应用

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2017-03-22 DOI: 10.1027/1614-2241/a000124

Pablo Livacic-Rojas, G. Vallejo, P. Fernández, Ellián Tuero-Herrero

{"title":"Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs","authors":"Pablo Livacic-Rojas, G. Vallejo, P. Fernández, Ellián Tuero-Herrero","doi":"10.1027/1614-2241/a000124","DOIUrl":"https://doi.org/10.1027/1614-2241/a000124","url":null,"abstract":"Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42853409","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: 2

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints 部分缺失协变量对离散时间生存终点随机对照试验统计效力的影响

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2017-02-16 DOI: 10.1027/1614-2241/A000121

S. Jolani, M. Safarkhani

引用次数: 2

Performance of Combined Models in Discrete Binary Classification 组合模型在离散二值分类中的性能

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2017-02-16 DOI: 10.1027/1614-2241/a000117

Anabela Marques, A. Ferreira, Margarida M. G. S. Cardoso

{"title":"Performance of Combined Models in Discrete Binary Classification","authors":"Anabela Marques, A. Ferreira, Margarida M. G. S. Cardoso","doi":"10.1027/1614-2241/a000117","DOIUrl":"https://doi.org/10.1027/1614-2241/a000117","url":null,"abstract":"Diverse Discrete Discriminant Analysis (DDA) models perform differently in different samples. This fact has encouraged research in combined models which seems particularly promising when the a priori classes are not well separated or when small or moderate sized samples are considered, which often occurs in practice. In this study, we evaluate the performance of a convex combination of two DDA models: the First-Order Independence Model (FOIM) and the Dependence Trees Model (DTM). We use simulated data sets with two classes and consider diverse data complexity factors which may influence performance of the combined model – the separation of classes, balance, and number of missing states, as well as sample size and also the number of parameters to be estimated in DDA. We resort to cross-validation to evaluate the precision of classification. The results obtained illustrate the advantage of the proposed combination when compared with FOIM and DTM: it yields the best results, especially when very small samples are considered. The experimental study also provides a ranking of the data complexity factors, according to their relative impact on classification performance, by means of a regression model. It leads to the conclusion that the separation of classes is the most influential factor in classification performance. The ratio between the number of degrees of freedom and sample size, along with the proportion of missing states in the minority class, also has significant impact on classification performance. An additional gain of this study, also deriving from the estimated regression model, is the ability to successfully predict the precision of classification in a real data set based on the data complexity factors.","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2017-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41421439","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: 0

Using Conditional Association to Identify Locally Independent Item Sets 使用条件关联来识别局部独立的项目集

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2016-12-05 DOI: 10.1027/1614-2241/A000115

J. Straat, L. V. D. Ark, K. Sijtsma

引用次数: 39

Dealing with data streams: An online, row-by-row, estimation tutorial 处理数据流:一个在线的、逐行的评估教程

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2016-12-05 DOI: 10.1027/1614-2241/A000116

Lianne Ippel, M. Kaptein, J. Vermunt

引用次数: 9

A Meta-Analytic Investigation of the Relationship Between Scale-Item Length, Label Format, and Reliability 量表项目长度、标签格式与信度关系的元分析研究

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2016-10-05 DOI: 10.1027/1614-2241/A000112

Tyler Hamby, R. Peterson

引用次数: 7

Comparing the Performance of Improved Classify-Analyze Approaches For Distal Outcomes in Latent Profile Analysis. 比较潜在剖面分析中远端结果的改进分类分析方法的性能。

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2016-10-01 Epub Date: 2016-12-05 DOI: 10.1027/1614-2241/a000114

John J Dziak, Bethany C Bray, Jieting Zhang, Minqiang Zhang, Stephanie T Lanza

{"title":"Comparing the Performance of Improved Classify-Analyze Approaches For Distal Outcomes in Latent Profile Analysis.","authors":"John J Dziak, Bethany C Bray, Jieting Zhang, Minqiang Zhang, Stephanie T Lanza","doi":"10.1027/1614-2241/a000114","DOIUrl":"https://doi.org/10.1027/1614-2241/a000114","url":null,"abstract":"<p><p>Several approaches are available for estimating the relationship of latent class membership to distal outcomes in latent profile analysis (LPA). A three-step approach is commonly used, but has problems with estimation bias and confidence interval coverage. Proposed improvements include the correction method of Bolck, Croon, and Hagenaars (BCH; 2004), Vermunt's (2010) maximum likelihood (ML) approach, and the inclusive three-step approach of Bray, Lanza, & Tan (2015). These methods have been studied in the related case of latent class analysis (LCA) with categorical indicators, but not as well studied for LPA with continuous indicators. We investigated the performance of these approaches in LPA with normally distributed indicators, under different conditions of distal outcome distribution, class measurement quality, relative latent class size, and strength of association between latent class and the distal outcome. The modified BCH implemented in Latent GOLD had excellent performance. The maximum likelihood and inclusive approaches were not robust to violations of distributional assumptions. These findings broadly agree with and extend the results presented by Bakk and Vermunt (2016) in the context of LCA with categorical indicators.</p>","PeriodicalId":18476,"journal":{"name":"Methodology: European Journal of Research Methods for The Behavioral and Social Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5473653/pdf/nihms-834564.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35102499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 54

Normality and Sample Size Do Not Matter for the Selection of an Appropriate Statistical Test for Two-Group Comparisons 正态性和样本量对于两组比较选择适当的统计检验无关紧要

IF 3.1 3区心理学

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2016-06-20 DOI: 10.1027/1614-2241/A000110

A. Poncet, D. Courvoisier, C. Combescure, T. Perneger

引用次数: 35