Sociological Methods and Research
0049-1241
1552-8294
Mỹ
Cơ quản chủ quản: SAGE Publications Inc.
Lĩnh vực:
Social Sciences (miscellaneous)Sociology and Political Science
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Thông tin về tạp chí
Sociological Methods & Research is a quarterly journal devoted to sociology as a cumulative empirical science. The objectives of SMR are multiple, but emphasis is placed on articles that advance the understanding of the field through systematic presentations that clarify methodological problems and assist in ordering the known facts in an area. Review articles will be published, particularly those that emphasize a critical analysis of the status of the arts, but original presentations that are broadly based and provide new research will also be published. Intrinsically, SMR is viewed as substantive journal but one that is highly focused on the assessment of the scientific status of sociology. The scope is broad and flexible, and authors are invited to correspond with the editors about the appropriateness of their articles.
Các bài báo tiêu biểu
Networks of Interorganizational Relations Network analysis has been used extensively in the study of interorganizational relations. This article reviews the literature over the past fifteen years and organizes it into three theoretical traditions: the resource dependence model, the social class framework, and the institutional model. It is shown that network methods have enabled researchers to describe phenomena, such as interorganizational fields, that were previously inaccessible. It is also shown how social networks help to explain the formation of interorganizational ties and how interorganizational relations, conceptualized as social networks, can explain organizational power as well as the strategies decision makers pursue.
Tập 22 Số 1 - Trang 46-70 - 1993
A SAS Procedure Based on Mixture Models for Estimating Developmental Trajectories This article introduces a new SAS procedure written by the authors that analyzes longitudinal data (developmental trajectories) by fitting a mixture model. The TRAJ procedure fits semiparametric (discrete) mixtures of censored normal, Poisson, zero-inflated Poisson, and Bernoulli distributions to longitudinal data. Applications to psychometric scale data, offense counts, and a dichotomous prevalence measure in violence research are illustrated. In addition, the use of the Bayesian information criterion to address the problem of model selection, including the estimation of the number of components in the mixture, is demonstrated.
Tập 29 Số 3 - Trang 374-393 - 2001
Practical Issues in Structural Modeling Practical problems that are frequently encountered in applications of covariance structure analysis are discussed and solutions are suggested. Conceptual, statistical, and practical requirements for structural modeling are reviewed to indicate how basic assumptions might be violated. Problems associated with estimation, results, and model fit are also mentioned. Various issues in each area are raised, and possible solutions are provided to encourage more appropriate and successful applications of structural modeling.
Tập 16 Số 1 - Trang 78-117 - 1987
Snowball Sampling: Problems and Techniques of Chain Referral Sampling In spite of the fact that chain referral sampling has been widely used in qualitative sociological research, especially in the study of deviant behavior, the problems and techniques involved in its use have not been adequately explained. The procedures of chain referral sampling are not self-evident or obvious. This article attempts to rectify this methodological neglect. The article provides a description and analysis of some of the problems that were encountered and resolved in the course of using the method in a relatively large exploratory study of ex-opiate addicts.
Tập 10 Số 2 - Trang 141-163 - 1981
Multimodel Inference The model selection literature has been generally poor at reflecting the deep foundations of the Akaike information criterion (AIC) and at making appropriate comparisons to the Bayesian information criterion (BIC). There is a clear philosophy, a sound criterion based in information theory, and a rigorous statistical foundation for AIC. AIC can be justified as Bayesian using a “savvy” prior on models that is a function of sample size and the number of model parameters. Furthermore, BIC can be derived as a non-Bayesian result. Therefore, arguments about using AIC versus BIC for model selection cannot be from a Bayes versus frequentist perspective. The philosophical context of what is assumed about reality, approximating models, and the intent of model-based inference should determine whether AIC or BIC is used. Various facets of such multimodel inference are presented here, particularly methods of model averaging.
Tập 33 Số 2 - Trang 261-304 - 2004
Impact of a Confounding Variable on a Regression Coefficient Regression coefficients cannot be interpreted as causal if the relationship can be attributed to an alternate mechanism. One may control for the alternate cause through an experiment (e.g., with random assignment to treatment and control) or by measuring a corresponding confounding variable and including it in the model. Unfortunately, there are some circumstances under which it is not possible to measure or control for the potentially confounding variable. Under these circumstances, it is helpful to assess the robustness of a statistical inference to the inclusion of a potentially confounding variable. In this article, an index is derived for quantifying the impact of a confounding variable on the inference of a regression coefficient. The index is developed for the bivariate case and then generalized to the multivariate case, and the distribution of the index is discussed. The index also is compared with existing indexes and procedures. An example is presented for the relationship between socioeconomic background and educational attainment, and a reference distribution for the index is obtained. The potential for the index to inform causal inferences is discussed, as are extensions.
Tập 29 Số 2 - Trang 147-194 - 2000
A Crash Course in Good and Bad Controls Many students of statistics and econometrics express frustration with the way a problem known as “bad control” is treated in the traditional literature. The issue arises when the addition of a variable to a regression equation produces an unintended discrepancy between the regression coefficient and the effect that the coefficient is intended to represent. Avoiding such discrepancies presents a challenge to all analysts in the data intensive sciences. This note describes graphical tools for understanding, visualizing, and resolving the problem through a series of illustrative examples. By making this “crash course” accessible to instructors and practitioners, we hope to avail these tools to a broader community of scientists concerned with the causal interpretation of regression models.
Tập 53 Số 3 - Trang 1071-1104 - 2024
A Procedure for Surveying Personal Networks The application of network analysis to certain issues in sociology requires measurement of individuals' personal networks. These issues generally involve the impact of structural locations on persons' social lives. One such case is the Northern California Community Study of the personal consequences of residential environments. This article describes and illustrates the methodology we have developed for studying personal networks by mass survey. It reviews the conceptual problems in network definition and measurement, assesses earlier efforts, presents our technique, and illustrates its applications.
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The Performance of RMSEA in Models With Small Degrees of Freedom Given that the root mean square error of approximation (RMSEA) is currently one of the most popular measures of goodness-of-model fit within structural equation modeling (SEM), it is important to know how well the RMSEA performs in models with small degrees of freedom ( df). Unfortunately, most previous work on the RMSEA and its confidence interval has focused on models with a large df. Building on the work of Chen et al. to examine the impact of small df on the RMSEA, we conducted a theoretical analysis and a Monte Carlo simulation using correctly specified models with varying df and sample size. The results of our investigation indicate that when the cutoff values are used to assess the fit of the properly specified models with small df and small sample size, the RMSEA too often falsely indicates a poor fitting model. We recommend not computing the RMSEA for small df models, especially those with small sample sizes, but rather estimating parameters that were not originally specified in the model.
Tập 44 Số 3 - Trang 486-507 - 2015