Pub Date : 2018-07-25DOI: 10.1177/0081175018785216
Weihua An
Treatment interference (i.e., one unit’s potential outcomes depend on other units’ treatment) is prevalent in social settings. Ignoring treatment interference can lead to biased estimates of treatment effects and incorrect statistical inferences. Some recent studies have started to incorporate treatment interference into causal inference. But treatment interference is often assumed to follow a simple structure (e.g., treatment interference exists only within groups) or measured in a simplistic way (e.g., only based on the number of treated friends). In this paper, I highlight the importance of collecting data on actual treatment diffusion in order to more accurately measure treatment interference. Furthermore, I show that with accurate measures of treatment interference, we can identify and estimate a series of causal effects that are previously unavailable, including the direct treatment effect, treatment interference effect, and treatment effect on interference. I illustrate the methods through a case study of a social network–based smoking prevention intervention.
{"title":"Causal Inference with Networked Treatment Diffusion","authors":"Weihua An","doi":"10.1177/0081175018785216","DOIUrl":"https://doi.org/10.1177/0081175018785216","url":null,"abstract":"Treatment interference (i.e., one unit’s potential outcomes depend on other units’ treatment) is prevalent in social settings. Ignoring treatment interference can lead to biased estimates of treatment effects and incorrect statistical inferences. Some recent studies have started to incorporate treatment interference into causal inference. But treatment interference is often assumed to follow a simple structure (e.g., treatment interference exists only within groups) or measured in a simplistic way (e.g., only based on the number of treated friends). In this paper, I highlight the importance of collecting data on actual treatment diffusion in order to more accurately measure treatment interference. Furthermore, I show that with accurate measures of treatment interference, we can identify and estimate a series of causal effects that are previously unavailable, including the direct treatment effect, treatment interference effect, and treatment effect on interference. I illustrate the methods through a case study of a social network–based smoking prevention intervention.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"48 1","pages":"152 - 181"},"PeriodicalIF":3.0,"publicationDate":"2018-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175018785216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46496172","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}
Pub Date : 2018-07-13DOI: 10.1177/0081175018786762
Michael Schultz
Conventional model selection evaluates models on their ability to represent data accurately, ignoring their dependence on theoretical and methodological assumptions. Drawing on the concept of underdetermination from the philosophy of science, the author argues that uncritical use of methodological assumptions can pose a problem for effective inference. By ignoring the plausibility of assumptions, existing techniques select models that are poor representations of theory and are thus suboptimal for inference. To address this problem, the author proposes a new paradigm for inference-oriented model selection that evaluates models on the basis of a trade-off between model fit and model plausibility. By comparing the fits of sequentially nested models, it is possible to derive an empirical lower bound for the subjective plausibility of assumptions. To demonstrate the effectiveness of this approach, the method is applied to models of the relationship between cultural tastes and network composition.
{"title":"The Problem of Underdetermination in Model Selection","authors":"Michael Schultz","doi":"10.1177/0081175018786762","DOIUrl":"https://doi.org/10.1177/0081175018786762","url":null,"abstract":"Conventional model selection evaluates models on their ability to represent data accurately, ignoring their dependence on theoretical and methodological assumptions. Drawing on the concept of underdetermination from the philosophy of science, the author argues that uncritical use of methodological assumptions can pose a problem for effective inference. By ignoring the plausibility of assumptions, existing techniques select models that are poor representations of theory and are thus suboptimal for inference. To address this problem, the author proposes a new paradigm for inference-oriented model selection that evaluates models on the basis of a trade-off between model fit and model plausibility. By comparing the fits of sequentially nested models, it is possible to derive an empirical lower bound for the subjective plausibility of assumptions. To demonstrate the effectiveness of this approach, the method is applied to models of the relationship between cultural tastes and network composition.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"48 1","pages":"52 - 87"},"PeriodicalIF":3.0,"publicationDate":"2018-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175018786762","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42436313","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}
Pub Date : 2018-07-13DOI: 10.1177/0081175018777988
John Muñoz, Cristobal Young
False positive findings are a growing problem in many research literatures. We argue that excessive false positives often stem from model uncertainty. There are many plausible ways of specifying a regression model, but researchers typically report only a few preferred estimates. This raises the concern that such research reveals only a small fraction of the possible results and may easily lead to nonrobust, false positive conclusions. It is often unclear how much the results are driven by model specification and how much the results would change if a different plausible model were used. Computational model robustness analysis addresses this challenge by estimating all possible models from a theoretically informed model space. We use large-scale random noise simulations to show (1) the problem of excess false positive errors under model uncertainty and (2) that computational robustness analysis can identify and eliminate false positives caused by model uncertainty. We also draw on a series of empirical applications to further illustrate issues of model uncertainty and estimate instability. Computational robustness analysis offers a method for relaxing modeling assumptions and improving the transparency of applied research.
{"title":"We Ran 9 Billion Regressions: Eliminating False Positives through Computational Model Robustness","authors":"John Muñoz, Cristobal Young","doi":"10.1177/0081175018777988","DOIUrl":"https://doi.org/10.1177/0081175018777988","url":null,"abstract":"False positive findings are a growing problem in many research literatures. We argue that excessive false positives often stem from model uncertainty. There are many plausible ways of specifying a regression model, but researchers typically report only a few preferred estimates. This raises the concern that such research reveals only a small fraction of the possible results and may easily lead to nonrobust, false positive conclusions. It is often unclear how much the results are driven by model specification and how much the results would change if a different plausible model were used. Computational model robustness analysis addresses this challenge by estimating all possible models from a theoretically informed model space. We use large-scale random noise simulations to show (1) the problem of excess false positive errors under model uncertainty and (2) that computational robustness analysis can identify and eliminate false positives caused by model uncertainty. We also draw on a series of empirical applications to further illustrate issues of model uncertainty and estimate instability. Computational robustness analysis offers a method for relaxing modeling assumptions and improving the transparency of applied research.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"48 1","pages":"1 - 33"},"PeriodicalIF":3.0,"publicationDate":"2018-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175018777988","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44889259","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}
Pub Date : 2018-07-09DOI: 10.1177/0081175018782579
P. Jargowsky, Christopher A. Wheeler
Researchers studying income inequality, economic segregation, and other subjects must often rely on grouped data—that is, data in which thousands or millions of observations have been reduced to counts of units by specified income brackets. The distribution of households within the brackets is unknown, and highest incomes are often included in an open-ended top bracket, such as “$200,000 and above.” Common approaches to this estimation problem include calculating midpoint estimators with an assumed Pareto distribution in the top bracket and fitting a flexible multiple-parameter distribution to the data. The authors describe a new method, mean-constrained integration over brackets (MCIB), that is far more accurate than those methods using only the bracket counts and the overall mean of the data. On the basis of an analysis of 297 metropolitan areas, MCIB produces estimates of the standard deviation, Gini coefficient, and Theil index that are correlated at 0.997, 0.998, and 0.991, respectively, with the parameters calculated from the underlying individual record data. Similar levels of accuracy are obtained for percentiles of the distribution and the shares of income by quintiles of the distribution. The technique can easily be extended to other distributional parameters and inequality statistics.
{"title":"Estimating Income Statistics from Grouped Data: Mean-constrained Integration over Brackets","authors":"P. Jargowsky, Christopher A. Wheeler","doi":"10.1177/0081175018782579","DOIUrl":"https://doi.org/10.1177/0081175018782579","url":null,"abstract":"Researchers studying income inequality, economic segregation, and other subjects must often rely on grouped data—that is, data in which thousands or millions of observations have been reduced to counts of units by specified income brackets. The distribution of households within the brackets is unknown, and highest incomes are often included in an open-ended top bracket, such as “$200,000 and above.” Common approaches to this estimation problem include calculating midpoint estimators with an assumed Pareto distribution in the top bracket and fitting a flexible multiple-parameter distribution to the data. The authors describe a new method, mean-constrained integration over brackets (MCIB), that is far more accurate than those methods using only the bracket counts and the overall mean of the data. On the basis of an analysis of 297 metropolitan areas, MCIB produces estimates of the standard deviation, Gini coefficient, and Theil index that are correlated at 0.997, 0.998, and 0.991, respectively, with the parameters calculated from the underlying individual record data. Similar levels of accuracy are obtained for percentiles of the distribution and the shares of income by quintiles of the distribution. The technique can easily be extended to other distributional parameters and inequality statistics.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"48 1","pages":"337 - 374"},"PeriodicalIF":3.0,"publicationDate":"2018-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175018782579","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46483285","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}
Pub Date : 2018-07-01DOI: 10.1177/0081175019886613
Pablo A. Mitnik, D. Grusky
The intergenerational elasticity (IGE) has been assumed to refer to the expectation of children’s income when in fact it pertains to the geometric mean of children’s income. We show that mobility analyses based on the conventional IGE have been widely misinterpreted, are subject to selection bias, and cannot disentangle the different channels for transmitting economic status across generations. The solution to these problems—estimating the IGE of expected income or earnings—returns the field to what it has long meant to estimate. Under this approach, intergenerational persistence is found to be substantially higher, thus raising the possibility that the field’s stock results are misleading.
{"title":"The Intergenerational Elasticity of What? The Case for Redefining the Workhorse Measure of Economic Mobility","authors":"Pablo A. Mitnik, D. Grusky","doi":"10.1177/0081175019886613","DOIUrl":"https://doi.org/10.1177/0081175019886613","url":null,"abstract":"The intergenerational elasticity (IGE) has been assumed to refer to the expectation of children’s income when in fact it pertains to the geometric mean of children’s income. We show that mobility analyses based on the conventional IGE have been widely misinterpreted, are subject to selection bias, and cannot disentangle the different channels for transmitting economic status across generations. The solution to these problems—estimating the IGE of expected income or earnings—returns the field to what it has long meant to estimate. Under this approach, intergenerational persistence is found to be substantially higher, thus raising the possibility that the field’s stock results are misleading.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"50 1","pages":"47 - 95"},"PeriodicalIF":3.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175019886613","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47416330","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}
Pub Date : 2018-06-18DOI: 10.1177/0081175018778299
Kristen Olson, Jolene D Smyth, Beth Cochran
Survey researchers often ask a series of attitudinal questions with a common question stem and response options, known as battery questions. Interviewers have substantial latitude in deciding how to administer these items, including whether to reread the common question stem on items after the first one or to probe respondents’ answers. Despite the ubiquity of use of these items, there is virtually no research on whether respondent and interviewer behaviors on battery questions differ over items in a battery or whether interview behaviors are associated with answers to these questions. This article uses a nationally representative telephone survey with audio-recorded interviews and randomized placement of items within four different batteries to examine interviewer and respondent behaviors and respondent answers in battery questions. Using cross-classified random-effects models, the authors find strong evidence that there is more interviewer–respondent interaction on items asked earlier in the battery. In addition, interviewer and respondent behaviors are associated with both substantive and nonsubstantive answers provided to battery items, especially if the interviewer decided to reread or probe with the response options. These results suggest that survey designers should follow recommendations to randomize battery items and consider the importance of standardization of question administration when designing battery questions.
{"title":"Item Location, the Interviewer–Respondent Interaction, and Responses to Battery Questions in Telephone Surveys","authors":"Kristen Olson, Jolene D Smyth, Beth Cochran","doi":"10.1177/0081175018778299","DOIUrl":"https://doi.org/10.1177/0081175018778299","url":null,"abstract":"Survey researchers often ask a series of attitudinal questions with a common question stem and response options, known as battery questions. Interviewers have substantial latitude in deciding how to administer these items, including whether to reread the common question stem on items after the first one or to probe respondents’ answers. Despite the ubiquity of use of these items, there is virtually no research on whether respondent and interviewer behaviors on battery questions differ over items in a battery or whether interview behaviors are associated with answers to these questions. This article uses a nationally representative telephone survey with audio-recorded interviews and randomized placement of items within four different batteries to examine interviewer and respondent behaviors and respondent answers in battery questions. Using cross-classified random-effects models, the authors find strong evidence that there is more interviewer–respondent interaction on items asked earlier in the battery. In addition, interviewer and respondent behaviors are associated with both substantive and nonsubstantive answers provided to battery items, especially if the interviewer decided to reread or probe with the response options. These results suggest that survey designers should follow recommendations to randomize battery items and consider the importance of standardization of question administration when designing battery questions.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"48 1","pages":"225 - 268"},"PeriodicalIF":3.0,"publicationDate":"2018-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175018778299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49645804","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}
Pub Date : 2018-01-08DOI: 10.1177/0081175017747122
M. Studer, E. Struffolino, A. Fasang
The relationship between processes and time-varying covariates is of central theoretical interest in addressing many social science research questions. On the one hand, event history analysis (EHA) has been the chosen method to study these kinds of relationships when the outcomes can be meaningfully specified as simple instantaneous events or transitions. On the other hand, sequence analysis (SA) has made increasing inroads into the social sciences to analyze trajectories as holistic “process outcomes.” We propose an original combination of these two approaches called the sequence analysis multistate model (SAMM) procedure. The SAMM procedure allows the study of the relationship between time-varying covariates and trajectories of categorical states specified as process outcomes that unfold over time. The SAMM is a stepwise procedure: (1) SA-related methods are used to identify ideal-typical patterns of changes within trajectories obtained by considering the sequence of states over a predefined time span; (2) multistate event history models are estimated to study the probability of transitioning from a specific state to such ideal-typical patterns. The added value of the SAMM procedure is illustrated through an example from life-course sociology on how (1) time-varying family status is associated with women’s employment trajectories in East and West Germany and (2) how German reunification affected these trajectories in the two subsocieties.
{"title":"Estimating the Relationship between Time-varying Covariates and Trajectories: The Sequence Analysis Multistate Model Procedure","authors":"M. Studer, E. Struffolino, A. Fasang","doi":"10.1177/0081175017747122","DOIUrl":"https://doi.org/10.1177/0081175017747122","url":null,"abstract":"The relationship between processes and time-varying covariates is of central theoretical interest in addressing many social science research questions. On the one hand, event history analysis (EHA) has been the chosen method to study these kinds of relationships when the outcomes can be meaningfully specified as simple instantaneous events or transitions. On the other hand, sequence analysis (SA) has made increasing inroads into the social sciences to analyze trajectories as holistic “process outcomes.” We propose an original combination of these two approaches called the sequence analysis multistate model (SAMM) procedure. The SAMM procedure allows the study of the relationship between time-varying covariates and trajectories of categorical states specified as process outcomes that unfold over time. The SAMM is a stepwise procedure: (1) SA-related methods are used to identify ideal-typical patterns of changes within trajectories obtained by considering the sequence of states over a predefined time span; (2) multistate event history models are estimated to study the probability of transitioning from a specific state to such ideal-typical patterns. The added value of the SAMM procedure is illustrated through an example from life-course sociology on how (1) time-varying family status is associated with women’s employment trajectories in East and West Germany and (2) how German reunification affected these trajectories in the two subsocieties.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"48 1","pages":"103 - 135"},"PeriodicalIF":3.0,"publicationDate":"2018-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175017747122","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42422241","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}
Pub Date : 2017-08-01Epub Date: 2017-04-27DOI: 10.1177/0081175017701180
Geoffrey T Wodtke, Daniel Almirall
Individuals differ in how they respond to a particular treatment or exposure, and social scientists are often interested in understanding how treatment effects are moderated by observed characteristics of individuals. Effect moderation occurs when individual covariates dampen or amplify the effect of some exposure. This article focuses on estimating moderated causal effects in longitudinal settings where both the treatment and effect moderator vary over time. Effect moderation is typically examined using covariate by treatment interactions in regression analyses, but in the longitudinal setting, this approach may be problematic because time-varying moderators of future treatment may be affected by prior treatment-for example, moderators may also be mediators-and naively conditioning on an outcome of treatment in a conventional regression model can lead to bias. This article introduces to sociology moderated intermediate causal effects and the structural nested mean model for analyzing effect moderation in the longitudinal setting. It discusses problems with conventional regression and presents a new approach to estimation that avoids these problems (regression-with-residuals). The method is illustrated using longitudinal data from the PSID to examine whether the effects of time-varying exposures to poor neighborhoods on the risk of adolescent childbearing are moderated by time-varying family income.
{"title":"Estimating Moderated Causal Effects with Time-varying Treatments and Time-varying Moderators: Structural Nested Mean Models and Regression with Residuals.","authors":"Geoffrey T Wodtke, Daniel Almirall","doi":"10.1177/0081175017701180","DOIUrl":"https://doi.org/10.1177/0081175017701180","url":null,"abstract":"<p><p>Individuals differ in how they respond to a particular treatment or exposure, and social scientists are often interested in understanding how treatment effects are moderated by observed characteristics of individuals. Effect moderation occurs when individual covariates dampen or amplify the effect of some exposure. This article focuses on estimating moderated causal effects in longitudinal settings where both the treatment and effect moderator vary over time. Effect moderation is typically examined using covariate by treatment interactions in regression analyses, but in the longitudinal setting, this approach may be problematic because time-varying moderators of future treatment may be affected by prior treatment-for example, moderators may also be mediators-and naively conditioning on an outcome of treatment in a conventional regression model can lead to bias. This article introduces to sociology moderated intermediate causal effects and the structural nested mean model for analyzing effect moderation in the longitudinal setting. It discusses problems with conventional regression and presents a new approach to estimation that avoids these problems (regression-with-residuals). The method is illustrated using longitudinal data from the PSID to examine whether the effects of time-varying exposures to poor neighborhoods on the risk of adolescent childbearing are moderated by time-varying family income.</p>","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"47 1","pages":"212-245"},"PeriodicalIF":3.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175017701180","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35786320","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}
Pub Date : 2017-08-01DOI: 10.1177/0081175017733457
Christoph Stadtfeld, James Hollway, Per Block
Dynamic Event Processes in Social Networks. Karlsruhe, Germany: KIT Scientific. Tranmer, M., C. S. Marcum, F. B. Morton, D. P. Croft, and S. R. de Kort. 2015. “Using the Relational Event Model (REM) to Investigate the Temporal Dynamics of Animal Social Networks.” Animal Behaviour 101:99–105. Vu, D., A. Lomi, D. Mascia, and F. Pallotti. 2017. “Relational Event Models for Longitudinal Network Data with an Application to Interhospital Patient Transfers.” Statistics in Medicine 36(14):2265–87. Vu, D., P. Pattison, and G. Robins. 2015. “Relational Event Models for Social Learning in MOOCs.” Social Networks 43:121–35.
{"title":"Rejoinder: DyNAMs and the Grounds for Actor-oriented Network Event Models","authors":"Christoph Stadtfeld, James Hollway, Per Block","doi":"10.1177/0081175017733457","DOIUrl":"https://doi.org/10.1177/0081175017733457","url":null,"abstract":"Dynamic Event Processes in Social Networks. Karlsruhe, Germany: KIT Scientific. Tranmer, M., C. S. Marcum, F. B. Morton, D. P. Croft, and S. R. de Kort. 2015. “Using the Relational Event Model (REM) to Investigate the Temporal Dynamics of Animal Social Networks.” Animal Behaviour 101:99–105. Vu, D., A. Lomi, D. Mascia, and F. Pallotti. 2017. “Relational Event Models for Longitudinal Network Data with an Application to Interhospital Patient Transfers.” Statistics in Medicine 36(14):2265–87. Vu, D., P. Pattison, and G. Robins. 2015. “Relational Event Models for Social Learning in MOOCs.” Social Networks 43:121–35.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"47 1","pages":"56 - 67"},"PeriodicalIF":3.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175017733457","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47492980","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}
Pub Date : 2017-08-01DOI: 10.1177/0081175017730075
Christoph Stadtfeld, James Hollway
This is the second volume of Sociological Methodology (SM) under my editorship. The work presented here spanned several years in that some of the papers were initially reviewed and/or accepted under previous editors. Most of the contributions included here exemplify the principle articulated by Jim Davis in the epigraph to the dedication, that the focus of substantive interests of sociologists may be capricious, but their methods are cumulative. This is apparent from the papers published here, which cover a broad range of methodological problems and are grounded in prior innovations and extend that work. We have organized the material in the present volume into seven major sections: (1) a symposium on dynamic network models, (2) more on social network models, (3) multiplicative models for continuous dependent variables, (4) causal inference, (5) decomposing segregation, (6) survey measurement, and (7) data visualization.
{"title":"Prologue","authors":"Christoph Stadtfeld, James Hollway","doi":"10.1177/0081175017730075","DOIUrl":"https://doi.org/10.1177/0081175017730075","url":null,"abstract":"This is the second volume of Sociological Methodology (SM) under my editorship. The work presented here spanned several years in that some of the papers were initially reviewed and/or accepted under previous editors. Most of the contributions included here exemplify the principle articulated by Jim Davis in the epigraph to the dedication, that the focus of substantive interests of sociologists may be capricious, but their methods are cumulative. This is apparent from the papers published here, which cover a broad range of methodological problems and are grounded in prior innovations and extend that work. We have organized the material in the present volume into seven major sections: (1) a symposium on dynamic network models, (2) more on social network models, (3) multiplicative models for continuous dependent variables, (4) causal inference, (5) decomposing segregation, (6) survey measurement, and (7) data visualization.","PeriodicalId":48140,"journal":{"name":"Sociological Methodology","volume":"47 1","pages":"xvi - xxv"},"PeriodicalIF":3.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0081175017730075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46960512","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：