Sociological Methodology最新文献_第2页

Micro-Macro Mediation Analysis in Social Networks 社交网络中的微观-宏观中介分析

IF 3 2区社会学

Sociological Methodology Pub Date : 2024-02-05 DOI: 10.1177/00811750231220950

Scott W. Duxbury

{"title":"Micro-Macro Mediation Analysis in Social Networks","authors":"Scott W. Duxbury","doi":"10.1177/00811750231220950","DOIUrl":"https://doi.org/10.1177/00811750231220950","url":null,"abstract":"Mediation analysis is increasingly used in the social sciences. Extension to social network data, however, has proved difficult because statistical network models are formulated at a lower level of analysis (the dyad) than many outcomes of interest. This study introduces a general approach for micro-macro mediation analysis in social networks. The author defines the average mediated micro effect (AMME) as the indirect effect of a network selection process on an individual, group, or organizational outcome through its effect on an intervening network variable. The author shows that the AMME can be nonparametrically identified using a wide range of common statistical network and regression modeling strategies under the assumption of conditional independence among multiple mediators. Nonparametric and parametric algorithms are introduced to generically estimate the AMME in a multitude of research designs. The author illustrates the utility of the method with an applied example using cross-sectional National Longitudinal Study of Adolescent to Adult Health data to examine the friendship selection mechanisms that indirectly shape adolescent school performance through their effect on network structure.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139804971","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: 0

Incorporating Machine Learning into Sociological Model-Building 将机器学习融入社会学模型构建

IF 3 2区社会学

Sociological Methodology Pub Date : 2024-01-13 DOI: 10.1177/00811750231217734

M. Verhagen

{"title":"Incorporating Machine Learning into Sociological Model-Building","authors":"M. Verhagen","doi":"10.1177/00811750231217734","DOIUrl":"https://doi.org/10.1177/00811750231217734","url":null,"abstract":"Quantitative sociologists frequently use simple linear functional forms to estimate associations among variables. However, there is little guidance on whether such simple functional forms correctly reflect the underlying data-generating process. Incorrect model specification can lead to misspecification bias, and a lack of scrutiny of functional forms fosters interference of researcher degrees of freedom in sociological work. In this article, I propose a framework that uses flexible machine learning (ML) methods to provide an indication of the fit potential in a dataset containing the exact same covariates as a researcher’s hypothesized model. When this ML-based fit potential strongly outperforms the researcher’s self-hypothesized functional form, it implies a lack of complexity in the latter. Advances in the field of explainable AI, like the increasingly popular Shapley values, can be used to generate understanding into the ML model such that the researcher’s original functional form can be improved accordingly. The proposed framework aims to use ML beyond solely predictive questions, helping sociologists exploit the potential of ML to identify intricate patterns in data to specify better-fitting, interpretable models. I illustrate the proposed framework using a simulation and real-world examples.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139531774","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: 0

Marginal-Preserving Imputation of Three-Way Array Data in Nested Structures, with Application to Small Areal Units 嵌套结构中三方阵列数据的保边插值及其在小面积单元上的应用

2区社会学

Sociological Methodology Pub Date : 2023-11-08 DOI: 10.1177/00811750231203218

Loring J. Thomas, Peng Huang, Xiaoshuang Iris Luo, John R. Hipp, Carter T. Butts

引用次数: 0

Micro Effects on Macro Structure in Social Networks 社会网络微观对宏观结构的影响

2区社会学

Sociological Methodology Pub Date : 2023-11-08 DOI: 10.1177/00811750231209040

Scott W. Duxbury

引用次数: 0

Networked Participants, Networked Meanings: Using Networks to Visualize Ethnographic Data 网络参与者，网络意义：利用网络可视化民族志数据

IF 3 2区社会学

Sociological Methodology Pub Date : 2023-09-07 DOI: 10.1177/00811750231195338

Kenneth R. Hanson, Nicholas Theis

引用次数: 0

Trend Analysis with Pooled Data from Different Survey Series: The Latent Attitude Method 不同调查系列汇总数据的趋势分析:潜在态度法

IF 3 2区社会学

Sociological Methodology Pub Date : 2023-09-05 DOI: 10.1177/00811750231193641

Donghui Wang, Yueqi Xie, Junming Huang

引用次数: 0

Evaluation of Respondent-Driven Sampling Prevalence Estimators Using Real-World Reported Network Degree. 使用真实世界报告的网络度评估受访者驱动的抽样患病率估计器。

IF 3 2区社会学

Sociological Methodology Pub Date : 2023-08-01 DOI: 10.1177/00811750231163832

Lisa Avery, Michael Rotondi

引用次数: 0

Choosing an Optimal Method for Causal Decomposition Analysis with Continuous Outcomes: A Review and Simulation Study 具有连续结果的因果分解分析的最优方法选择:综述与仿真研究

IF 3 2区社会学

Sociological Methodology Pub Date : 2023-07-17 DOI: 10.1177/00811750231183711

S. Park, Suyeon Kang, Chioun Lee

{"title":"Choosing an Optimal Method for Causal Decomposition Analysis with Continuous Outcomes: A Review and Simulation Study","authors":"S. Park, Suyeon Kang, Chioun Lee","doi":"10.1177/00811750231183711","DOIUrl":"https://doi.org/10.1177/00811750231183711","url":null,"abstract":"Causal decomposition analysis is among the rapidly growing number of tools for identifying factors (“mediators”) that contribute to disparities in outcomes between social groups. An example of such mediators is college completion, which explains later health disparities between Black women and White men. The goal is to quantify how much a disparity would be reduced (or remain) if we hypothetically intervened to set the mediator distribution equal across social groups. Despite increasing interest in estimating disparity reduction and the disparity that remains, various estimation procedures are not straightforward, and researchers have scant guidance for choosing an optimal method. In this article, the authors evaluate the performance in terms of bias, variance, and coverage of three approaches that use different modeling strategies: (1) regression-based methods that impose restrictive modeling assumptions (e.g., linearity) and (2) weighting-based and (3) imputation-based methods that rely on the observed distribution of variables. The authors find a trade-off between the modeling assumptions required in the method and its performance. In terms of performance, regression-based methods operate best as long as the restrictive assumption of linearity is met. Methods relying on mediator models without imposing any modeling assumptions are sensitive to the ratio of the group-mediator association to the mediator-outcome association. These results highlight the importance of selecting an appropriate estimation procedure considering the data at hand.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42569427","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: 0

A Model of Dynamic Flows: Explaining Turkey’s Interprovincial Migration 动态流动模型：土耳其省际移民的解释

IF 3 2区社会学

Sociological Methodology Pub Date : 2023-07-11 DOI: 10.1177/00811750231184460

O. Aksoy, S. Yıldırım

{"title":"A Model of Dynamic Flows: Explaining Turkey’s Interprovincial Migration","authors":"O. Aksoy, S. Yıldırım","doi":"10.1177/00811750231184460","DOIUrl":"https://doi.org/10.1177/00811750231184460","url":null,"abstract":"The flow of resources across nodes over time (e.g., migration, financial transfers, peer-to-peer interactions) is a common phenomenon in sociology. Standard statistical methods are inadequate to model such interdependent flows. We propose a hierarchical Dirichlet-multinomial regression model and a Bayesian estimation method. We apply the model to analyze 25,632,876 migration instances that took place between Turkey’s 81 provinces from 2009 to 2018. We then discuss the methodological and substantive implications of our results. Methodologically, we demonstrate the predictive advantage of our model compared to its most common alternative in migration research, the gravity model. We also discuss our model in the context of other approaches, mostly developed in the social networks literature. Substantively, we find that population, economic prosperity, the spatial and political distance between the origin and destination, the strength of the AKP (Justice and Development Party) in a province, and the network characteristics of the provinces are important predictors of migration, whereas the proportion of ethnic minority Kurds in a province has no positive association with in- and out-migration.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48567827","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: 1

From Sequences to Variables: Rethinking the Relationship between Sequences and Outcomes 从序列到变量:重新思考序列与结果的关系

2区社会学

Sociological Methodology Pub Date : 2023-06-15 DOI: 10.1177/00811750231177026

Satu Helske, Jouni Helske, Guilherme K. Chihaya

{"title":"From Sequences to Variables: Rethinking the Relationship between Sequences and Outcomes","authors":"Satu Helske, Jouni Helske, Guilherme K. Chihaya","doi":"10.1177/00811750231177026","DOIUrl":"https://doi.org/10.1177/00811750231177026","url":null,"abstract":"Sequence analysis is increasingly used in the social sciences for the holistic analysis of life-course and other longitudinal data. The usual approach is to construct sequences, calculate dissimilarities, group similar sequences with cluster analysis, and use cluster membership as a dependent or independent variable in a regression model. This approach may be problematic, as cluster memberships are assumed to be fixed known characteristics of the subjects in subsequent analyses. Furthermore, it is often more reasonable to assume that individual sequences are mixtures of multiple ideal types rather than equal members of some group. Failing to account for uncertain and mixed memberships may lead to wrong conclusions about the nature of the studied relationships. In this article, the authors bring forward and discuss the problems of the “traditional” use of sequence analysis clusters as variables and compare four approaches for creating explanatory variables from sequence dissimilarities using different types of data. The authors conduct simulation and empirical studies, demonstrating the importance of considering how sequences and outcomes are related and the need to adjust analyses accordingly. In many typical social science applications, the traditional approach is prone to result in wrong conclusions, and similarity-based approaches such as representativeness should be preferred.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134890272","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: 0