Sociological Methodology最新文献_第9页

A General and Robust Estimation Method for the Case-Time-Control Design 情形时间控制设计的一种通用的鲁棒估计方法

IF 3 2区社会学

Sociological Methodology Pub Date : 2019-07-22 DOI: 10.1177/0081175019862259

A. Sjölander, Y. Ning

引用次数: 0

CASM: A Deep-Learning Approach for Identifying Collective Action Events with Text and Image Data from Social Media CASM:一种基于社交媒体文本和图像数据识别集体行动事件的深度学习方法

IF 3 2区社会学

Sociological Methodology Pub Date : 2019-07-19 DOI: 10.1177/0081175019860244

Han Zhang, Jennifer Pan

引用次数: 105

A General Framework for Comparing Predictions and Marginal Effects across Models 跨模型比较预测和边际效应的一般框架

IF 3 2区社会学

Sociological Methodology Pub Date : 2019-06-20 DOI: 10.1177/0081175019852763

Trenton D. Mize, Long Doan, J. S. Long

{"title":"A General Framework for Comparing Predictions and Marginal Effects across Models","authors":"Trenton D. Mize, Long Doan, J. S. Long","doi":"10.1177/0081175019852763","DOIUrl":"https://doi.org/10.1177/0081175019852763","url":null,"abstract":"Many research questions involve comparing predictions or effects across multiple models. For example, it may be of interest whether an independent variable’s effect changes after adding variables to a model. Or, it could be important to compare a variable’s effect on different outcomes or across different types of models. When doing this, marginal effects are a useful method for quantifying effects because they are in the natural metric of the dependent variable and they avoid identification problems when comparing regression coefficients across logit and probit models. Despite advances that make it possible to compute marginal effects for almost any model, there is no general method for comparing these effects across models. In this article, the authors provide a general framework for comparing predictions and marginal effects across models using seemingly unrelated estimation to combine estimates from multiple models, which allows tests of the equality of predictions and effects across models. The authors illustrate their method to compare nested models, to compare effects on different dependent or independent variables, to compare results from different samples or groups within one sample, and to assess results from different types of models.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2019-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175019852763","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43162214","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: 148

Analyzing Meaning in Big Data: Performing a Map Analysis Using Grammatical Parsing and Topic Modeling 分析大数据中的意义:使用语法解析和主题建模进行地图分析

IF 3 2区社会学

Sociological Methodology Pub Date : 2019-06-18 DOI: 10.1177/0081175019852762

Jan Goldenstein, Philipp Poschmann

引用次数: 16

No Longer Discrete: Modeling the Dynamics of Social Networks and Continuous Behavior 不再离散：社交网络和连续行为的动力学建模

IF 3 2区社会学

Sociological Methodology Pub Date : 2019-05-09 DOI: 10.1177/0081175019842263

Nynke M. D. Niezink, T. Snijders, M. V. van Duijn

{"title":"No Longer Discrete: Modeling the Dynamics of Social Networks and Continuous Behavior","authors":"Nynke M. D. Niezink, T. Snijders, M. V. van Duijn","doi":"10.1177/0081175019842263","DOIUrl":"https://doi.org/10.1177/0081175019842263","url":null,"abstract":"The dynamics of individual behavior are related to the dynamics of the social structures in which individuals are embedded. This implies that in order to study social mechanisms such as social selection or peer influence, we need to model the evolution of social networks and the attributes of network actors as interdependent processes. The stochastic actor-oriented model is a statistical approach to study network-attribute coevolution based on longitudinal data. In its standard specification, the coevolving actor attributes are assumed to be measured on an ordinal categorical scale. Continuous variables first need to be discretized to fit into such a modeling framework. This article presents an extension of the stochastic actor-oriented model that does away with this restriction by using a stochastic differential equation to model the evolution of a continuous attribute. We propose a measure for explained variance and give an interpretation of parameter sizes. The proposed method is illustrated by a study of the relationship between friendship, alcohol consumption, and self-esteem among adolescents.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2019-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175019842263","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49387493","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: 22

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":null,"pages":null},"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