JCS Focus |《社会学方法与研究》最新目录与摘要

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社会学国际顶刊

Sociological Methods & Research

（《社会学方法与研究》）

最新目录及摘要

This article explores how researchers adapt to disruptions that cost them access to their field sites, advancing a uniquely sociological perspective on the dynamics of flexibility and adaptation in qualitative methods. Through interviews with 31 ethnographers whose access was preempted or eliminated, I find that adaptation varied systematically based on when during the fieldwork process researchers' access was disrupted. The timing of the disruption shaped the relevance and implications of common conditions that affect fieldwork, such as funding availability, institutionalized time constraints, and sunk costs. Consequently, despite a lack of common conventions or training in how to adapt to losing access, adaptations took one of three general forms, which I refer to as turning home, pivoting, and following. I highlight specific challenges associated with each of these forms and offer insights for navigating them. Building from my findings, I make the case that the logistics of being flexible and adapting are part of a hidden curriculum in qualitative methods, and I discuss how interrogating the conditions that structure these aspects of fieldwork advances research and pedagogy in qualitative methodology.

The rise of social media has opened countless opportunities to explore social science questions with new data and methods. However, research on socioeconomic inequality remains constrained by limited individual-level socioeconomic status (SES) measures in digital trace data. Following Bourdieu, we argue that the commercial and entertainment accounts Twitter users follow reflect their economic and cultural capital. Adapting a political science method for inferring political ideology, we use correspondence analysis to estimate the SES of 3,482,652 Twitter users who follow the accounts of 339 brands in the United States. We validate our estimates with data from the Facebook Marketing application programming interface, self-reported job titles on users’ Twitter profiles, and a small survey sample. The results show reasonable correlations with the standard proxies for SES, alongside much weaker or nonsignificant correlations with other demographic variables. The proposed method opens new opportunities for innovative social research on inequality on Twitter and similar online platforms.

Directed acyclic graphs (DAGs) are now a popular tool to inform causal inferences. We discuss how DAGs can also be used to encode theoretical assumptions about nonprobability samples and survey nonresponse and to determine whether population quantities including conditional distributions and regressions can be identified. We describe sources of bias and assumptions for eliminating it in various selection scenarios. We then introduce and analyze graphical representations of multiple selection stages in the data collection process, and highlight the strong assumptions implicit in using only design weights. Furthermore, we show that the common practice of selecting adjustment variables based on correlations with sample selection and outcome variables of interest is ill-justified and that nonresponse weighting when the interest is in causal inference may come at severe costs. Finally, we identify further areas for survey methodology research that can benefit from advances in causal graph theory.  

Probes are follow-ups to survey questions used to gain insights on respondents’ understanding of and responses to these questions. They are usually administered as open-ended questions, primarily in the context of questionnaire pretesting. Due to the decreased cost of data collection for open-ended questions in web surveys, researchers have argued for embedding more open-ended probes in large-scale web surveys. However, there are concerns that this may cause reactivity and impact survey data. The study presents a randomized experiment in which identical survey questions were run with and without open-ended probes. Embedding open-ended probes resulted in higher levels of survey break off, as well as increased backtracking and answer changes to previous questions. In most cases, there was no impact of open-ended probes on the cognitive processing of and response to survey questions. Implications for embedding open-ended probes into web surveys are discussed.

Lagged dependent variables (LDVs) are often used as predictors in ordinary least squares (OLS) models in the social sciences. Although several estimators are commonly employed, little is known about their relative merits in the presence of classical measurement error and different longitudinal processes. We assess the performance of four commonly used estimators: (1) the standard OLS estimator, (2) an average of past measures (AVG), (3) an instrumental variable (IV) measured at one period previously (IV), and (4) an IV derived from information from more than one time before (IV2). We also propose a new estimator for fixed effects models—the first difference instrumental variable (FDIV) estimator. After exploring the consistency of these estimators, we demonstrate their performance using an empirical application predicting primary school test scores. Our results demonstrate that for a Markov process with classic measurement error (CME), IV and IV2 estimators are generally consistent; LDV and AVG estimators are not. For a semi-Markov process, only the IV2 estimator is consistent. On the other hand, if fixed effects are included in the model, only the FDIV estimator is consistent. We end with advice on how to select the appropriate estimator.

A linear model is often used to find the effect of a binary treatment 𝐷 on a noncontinuous outcome 𝑌 with covariates 𝑋. Particularly, a binary 𝑌 gives the popular “linear probability model (LPM),” but the linear model is untenable if 𝑋 contains a continuous regressor. This raises the question: what kind of treatment effect does the ordinary least squares estimator (OLS) to LPM estimate? This article shows that the OLS estimates a weighted average of the 𝑋-conditional heterogeneous effect plus a bias. Under the condition that 𝐸(𝐷|𝑋) is equal to the linear projection of 𝐷 on 𝑋, the bias becomes zero, and the OLS estimates the “overlap-weighted average” of the 𝑋-conditional effect. Although the condition does not hold in general, specifying the 𝑋-part of the LPM such that the 𝑋 -part predicts 𝐷 well, not 𝑌, minimizes the bias counter-intuitively. This article also shows how to estimate the overlap-weighted average without the condition by using the “propensity-score residual” 𝐷−𝐸(𝐷|𝑋). An empirical analysis demonstrates our points.

We combine empirical experimental research on biased argument processing with a computational theory of group deliberation to overcome the micro–macro problem of sociology and to clarify the role of biased processing in debates around energy. We integrate biased processing into the framework of argument communication theory in which agents exchange arguments about a certain topic and adapt opinions accordingly. Our derived mathematical model fits significantly better to the experimentally observed attitude changes than the neutral argument processing assumption made in previous models. Our approach provides new insight into the relationship between biased processing and opinion polarization. Our analysis reveals a sharp qualitative transition from attitude moderation to polarization at the individual level. At the collective level, we find that weak biased processing significantly accelerates group decision processes, whereas strong biased processing leads to a meta-stable conflictual state of bi-polarization that becomes persistent as the bias increases.

The Gini index is probably the most commonly used indicator to measure inequality. For continuous distributions, the Gini index can be computed using several equivalent formulations. However, this is not the case with discrete distributions, where controversy remains regarding the expression to be used to estimate the Gini index. We attempt to bring a better understanding of the underlying problem by regrouping and classifying the most common estimators of the Gini index proposed in both infinite and finite populations, and focusing on the biases. We use Monte Carlo simulation studies to analyse the bias of the various estimators under a wide range of scenarios. Extremely large biases are observed in heavy-tailed distributions with high Gini indices, and bias corrections are recommended in this situation. We propose the use of some (new and traditional) bootstrap-based and jackknife-based strategies to mitigate this bias problem. Results are based on continuous distributions often used in the modelling of income distributions. We describe a simulation-based criterion for deciding when to use bias corrections. Various real data sets are used to illustrate the practical application of the suggested bias corrected procedures.

Empirical studies on individual behaviour often, implicitly or explicitly, assume a single type of decision rule. Other studies do not specify behavioural assumptions at all. We advance sociological research by introducing (random) regret minimization, which is related to loss aversion, into the sociological literature and by testing it against (random) utility maximization, which is the most prominent decision rule in sociological research on individual behaviour. With an application to neighbourhood choice, in a sample of four European cities, we combine stated choice experiment data and discrete choice modelling techniques and find a considerable degree of decision rule-heterogeneity, with a strong prevalence of regret minimization and hence loss aversion. We also provide indicative evidence that decision rules can affect expected neighbourhood demand at the macro level. Our approach allows identifying heterogeneity in decision rules, that is, the degree of regret/loss aversion, at the level of choice attributes such as the share of foreigners when comparing neighbourhoods, and can improve sociological practice related to linking theories and social research on decision-making.

Rating scales are ubiquitous in the social sciences, yet may present practical difficulties when response formats change over time or vary across surveys. To allow researchers to pool rating data across alternative question formats, the article provides a generalization of the ordered logit model that accommodates multiple scale formats in the measurement of a single rating construct. The resulting multiscale ordered logit model shares the interpretation as well as the proportional odds (or parallel lines) assumption with the standard ordered logit model. A further extension to relax the proportional odds assumption in the multiscale context is proposed, and the substitution of the logit with other convenient link functions is equally straightforward. The utility of the model is illustrated from an empirical analysis of the determinants of respondents’ confidence in democratic institutions that combines data from the European Social Survey, the General Social Survey, and the European and World Values Survey series.

《中国社会学学刊》（The Journal of Chinese Sociology）于2014年10月由中国社会科学院社会学研究所创办。作为中国大陆第一本英文社会学学术期刊，JCS致力于为中国社会学者与国外同行的学术交流和合作打造国际一流的学术平台。JCS由全球最大科技期刊出版集团施普林格·自然(Springer Nature)出版发行，由国内外顶尖社会学家组成强大编委会队伍，采用双向匿名评审方式和“开放获取”(open access)出版模式。JCS已于2021年5月被ESCI收录。2022年，JCS的CiteScore分值为2.0（Q2），在社科类别的262种期刊中排名第94位，位列同类期刊前36%。2023年，JCS在科睿唯安发布的2023年度《期刊引证报告》（JCR）中首次获得影响因子并达到1.5（Q3）。