Sociological Methodology最新文献_第9页

Getting the Within Estimator of Cross-Level Interactions in Multilevel Models with Pooled Cross-Sections: Why Country Dummies (Sometimes) Do Not Do the Job 在汇集截面的多层模型中获得跨层相互作用的内估计量:为什么国家假人(有时)不能做这项工作

IF 3 2区社会学

Sociological Methodology Pub Date : 2018-11-15 DOI: 10.1177/0081175018809150

Marco Giesselmann, Alexander W. Schmidt-Catran

{"title":"Getting the Within Estimator of Cross-Level Interactions in Multilevel Models with Pooled Cross-Sections: Why Country Dummies (Sometimes) Do Not Do the Job","authors":"Marco Giesselmann, Alexander W. Schmidt-Catran","doi":"10.1177/0081175018809150","DOIUrl":"https://doi.org/10.1177/0081175018809150","url":null,"abstract":"Multilevel models with persons nested in countries are increasingly popular in cross-country research. Recently, social scientists have started to analyze data with a three-level structure: persons at level 1, nested in year-specific country samples at level 2, nested in countries at level 3. By using a country fixed-effects estimator, or an alternative equivalent specification in a random-effects framework, this structure is increasingly used to estimate within-country effects in order to control for unobserved heterogeneity. For the main effects of country-level characteristics, such estimators have been shown to have desirable statistical properties. However, estimators of cross-level interactions in these models are not exhibiting these attractive properties: as algebraic transformations show, they are not independent of between-country variation and thus carry country-specific heterogeneity. Monte Carlo experiments consistently reveal the standard approaches to within estimation to provide biased estimates of cross-level interactions in the presence of an unobserved correlated moderator at the country level. To obtain an unbiased within-country estimator of a cross-level interaction, effect heterogeneity must be systematically controlled. By replicating a published analysis, we demonstrate the relevance of this extended country fixed-effects estimator in research practice. The intent of this article is to provide advice for multilevel practitioners, who will be increasingly confronted with the availability of pooled cross-sectional survey data.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"49 1","pages":"190 - 219"},"PeriodicalIF":3.0,"publicationDate":"2018-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175018809150","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44725827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 36

Comment: The Inferential Information Criterion from a Bayesian Point of View 评论:贝叶斯观点下的推理信息准则

IF 3 2区社会学

Sociological Methodology Pub Date : 2018-08-01 DOI: 10.1177/0081175018794489

O. Vassend

引用次数: 0

Comment: Evidence, Plausibility, and Model Selection 评论：证据、合理性和模型选择

IF 3 2区社会学

Sociological Methodology Pub Date : 2018-08-01 DOI: 10.1177/0081175018793654

D. Weakliem

引用次数: 0

Prologue 开场

IF 3 2区社会学

Sociological Methodology Pub Date : 2018-08-01 DOI: 10.1177/0081175018799359

Anonymous

引用次数: 0

Dedication: Allan McCutcheon: Latent Class Analyst 奉献:Allan McCutcheon:潜在阶级分析师

IF 3 2区社会学

Sociological Methodology Pub Date : 2018-08-01 DOI: 10.1177/0081175018791565

A. McCutcheon, Allan Lee

引用次数: 0

Deciding on the Starting Number of Classes of a Latent Class Tree. 决定潜在类树的起始类数。

IF 3 2区社会学

Sociological Methodology Pub Date : 2018-08-01 Epub Date: 2018-06-21 DOI: 10.1177/0081175018780170

Mattis van den Bergh, Geert H van Kollenburg, Jeroen K Vermunt

{"title":"Deciding on the Starting Number of Classes of a Latent Class Tree.","authors":"Mattis van den Bergh, Geert H van Kollenburg, Jeroen K Vermunt","doi":"10.1177/0081175018780170","DOIUrl":"https://doi.org/10.1177/0081175018780170","url":null,"abstract":"<p><p>In recent studies, latent class tree (LCT) modeling has been proposed as a convenient alternative to standard latent class (LC) analysis. Instead of using an estimation method in which all classes are formed simultaneously given the specified number of classes, in LCT analysis a hierarchical structure of mutually linked classes is obtained by sequentially splitting classes into two subclasses. The resulting tree structure gives a clear insight into how the classes are formed and how solutions with different numbers of classes are substantively linked to one another. A limitation of the current LCT modeling approach is that it allows only for binary splits, which in certain situations may be too restrictive. Especially at the root node of the tree, where an initial set of classes is created based on the most dominant associations present in the data, it may make sense to use a model with more than two classes. In this article, we propose a modification of the LCT approach that allows for a nonbinary split at the root node, and we provide methods to determine the appropriate number of classes in this first split, based either on theoretical grounds or on a relative improvement of fit measure. This novel approach also can be seen as a hybrid of a standard LC model and a binary LCT model, in which an initial, oversimplified but interpretable model is refined using an LCT approach. Furthermore, we show how to apply an LCT model when a nonstandard LC model is required. These new approaches are illustrated using two empirical applications: one on social capital and the other on (post)materialism.</p>","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"48 1","pages":"303-336"},"PeriodicalIF":3.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175018780170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36859532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Comment: Some Challenges When Estimating the Impact of Model Uncertainty on Coefficient Instability 评论：在估计模型不确定性对系数不稳定性的影响时面临的一些挑战

IF 3 2区社会学

Sociological Methodology Pub Date : 2018-08-01 DOI: 10.1177/0081175018790569

Robert M. O’Brien

引用次数: 1

Comment: Bayes, Model Uncertainty, and Learning from Data 评论：贝叶斯、模型不确定性和从数据中学习

IF 3 2区社会学

Sociological Methodology Pub Date : 2018-08-01 DOI: 10.1177/0081175018799095

B. Western

引用次数: 1

Nonlinear Autoregressive Latent Trajectory Models 非线性自回归潜在轨迹模型

IF 3 2区社会学

Sociological Methodology Pub Date : 2018-08-01 DOI: 10.1177/0081175018789441

Shawn Bauldry, K. Bollen

{"title":"Nonlinear Autoregressive Latent Trajectory Models","authors":"Shawn Bauldry, K. Bollen","doi":"10.1177/0081175018789441","DOIUrl":"https://doi.org/10.1177/0081175018789441","url":null,"abstract":"Autoregressive latent trajectory (ALT) models combine features of latent growth curve models and autoregressive models into a single modeling framework. The development of ALT models has focused primarily on models with linear growth components, but some social processes follow nonlinear trajectories. Although it is straightforward to extend ALT models to allow for some forms of nonlinear trajectories, the identification status of such models, approaches to comparing them with alternative models, and the interpretation of parameters have not been systematically assessed. In this paper we focus on two forms of nonlinear autoregressive latent trajectory (NLALT) models. The first form allows for a quadratic growth trajectory, a popular form of nonlinear latent growth curve models. The second form derives from latent basis models, or freed loading models, that allow for arbitrary growth processes. We discuss details concerning parameterization, model identification, estimation, and testing for the two forms of NLALT models. We include a simulation study that illustrates potential biases that may arise from fitting alternative models to data derived from an autoregressive process and individual-specific nonlinear trajectories. In addition, we include an extended empirical example modeling growth trajectories of weight from birth through age 2.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"48 1","pages":"269 - 302"},"PeriodicalIF":3.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175018789441","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42277749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Rejoinder: Can We Weight Models by Their Probability of Being True? 复辩状：我们能根据模型为真的概率对其进行加权吗？

IF 3 2区社会学

Sociological Methodology Pub Date : 2018-08-01 DOI: 10.1177/0081175018796841

John Muñoz, Cristobal Young

引用次数: 1