Statistics In Sociology, 1950–2000: A Selective Review

Statistical methods have had a successful half-century in sociology, contributing to a greatly improved standard of scientific rigor in the discipline. I identify three overlapping postwar generations of statistical methods in sociology, based on the kinds of data they address. The first generation, which started in the late 1940s, deals with cross-tabulations and focuses on measures of association and log-linear models, perhaps the area of statistics to which sociology has contributed the most. The second generation, which began in the 1960s, deals with unit-level survey data and focuses on LISREL-type causal models and event-history analysis. The third generation, starting to emerge in the late 1980s, deals with data that do not fall easily into either of these categories, either because they have a different form, such as texts or narratives, or because dependence is a crucial aspect, as with spatial or social network data. There are many new challenges, and the area is ripe for statistical research; several major institutions have recently launched new initiatives in statistics and the social sciences.     

A framework for the comparison of maximum pseudo-likelihood and maximum likelihood estimation of exponential family random graph models

The statistical modeling of social network data is difficult due to the complex dependence structure of the tie variables. Statistical exponential families of distributions provide a flexible way to model such dependence. They enable the statistical characteristics of the network to be encapsulated within an exponential family random graph (ERG) model. For a long time, however, likelihood-based estimation was only feasible for ERG models assuming dyad independence. For more realistic and complex models inference has been based on the pseudo-likelihood. Recent advances in computational methods have made likelihood-based inference practical, and comparison of the different estimators possible.     

Independence, Dependence, Interdependence: some reflections on the subject and personal autonomy

A sociology of disablement needs to redefine the meaning of independence. In Western industrial societies, independence is associated with being able to do things for oneself, to be self-supporting and self-reliant. The paper attempts to show that some of our understanding of independence is influenced by views of the subject and personal autonomy stemming from the Enlightenment philosophy. The main target of the paper is to show that the modernist view of the subject is inadequate. What is missing is the notion of interdependence. The paper suggests an understanding of the subject that recognises the human condition as one of interdependence. It is argued that a reformulation of the subject as both embedded and embodied, bears better to a sociology of disablement.     

Exponential Family Models for sampled and census Network data

Much progress has been made on the development of statistical methods for network analysis in the past ten years, building on the general class of exponential family random graph (ERG) network models first introduced by Holland and Leinhardt (1981) . Recent examples include models for Markov graphs, "p*" models, and actor‐oriented models. For empirical application, these ERG models take a logistic form, and require the equivalent of a network census: data on all dyads within the network. In a largely separate stream of research, conditional log‐linear (CLL) models have been adapted for analyzing locally sampled ("egocentric") network data. While the general relation between log‐linear and logistic models is well known and has been exploited in the case of a priori blockmodels for networks, the relation for the CLL models is different due to the treatment of absent ties. For fully saturated tie independence models, CLL and ERG are equivalent and related via Bayes' rule. For other tie independence models, the two do not yield equivalent predicted values, but we show that in practice the differences are unlikely to be large. The alternate conditioning in the two models sheds light on the relationship between local and complete network data, and the role that models can play in bridging the gap between them.     

Participant Observation in Data Collection

Participant observation is one type of data collection method typically practiced in the qualitative research domain, and is a widely used methodology in varied disciplines, particularly cultural anthropology, sociology, communication studies, and specifically in classroom teaching. It is to gain a close observation with a particular group of individuals and their practices through an involvement with people in their environment, usually over an extended period of time. This paper deals with the definition, the advantages and disadvantages of participant observation.     

Accelerated Failure–Time Mover–Stayer Regression Models for the Analysis of Last–Episode Data

This article introduces new models for the analysis of last–episode data—that is, data about the timing of the last occurrence of a repeatable event. The new models are elaborations and extensions of a particular model for backward recurrence times from a renewal process introduced by Allison (1985) , and they are accelerated failure–time mover–stayer regression models that permit the separation of covariate effects on event timing from those on event occurrence. Using the data of last residential move from the 1990 Census of Population and Housing, an application of the models, which are found to fit the data adequately, shows distinct patterns in the effects of home ownership, gender, race/ethnicity, education, marital experience, and age group on residential mobility. The patterns differ regarding the relative importance and direction of influence between the effects on the duration of residence in the same house or apartment and the effects on the lifetime probability of not experiencing any residential move.     

WHOSE CIVIC COMMUNITY? TESTING ALTERNATIVE HYPOTHESES OF THE RELATIONSHIP BETWEEN CIVIC COMMUNITY AND RACIAL INEQUALITY IN ARREST RATES

This analysis extends prior research on racial inequality in arrest rates by testing opposing hypotheses derived from the civic community tradition in sociology. One interpretation of this perspective suggests that communities with a locally-oriented economic infrastructure, an abundance of civic institutions, and civically engaged citizens should exhibit lower racial disparity in arrest rates. On the other hand, recent writings exposing a potential “dark side” of social capital suggest that civic community may exacerbate such racial disparties. Using spatial regression models to analyze data from nearly 1,800 U.S. counties, the current analysis examines these competing hypotheses regarding the effects of civic community. Results suggest mixed support for both the traditional and “dark side” predictions. Implications for theory and empirical research are discussed.     

MULTIPLE IMPUTATION OF INCOMPLETE CATEGORICAL DATA USING LATENT CLASS ANALYSIS

We propose using latent class analysis as an alternative to log-linear analysis for the multiple imputation of incomplete categorical data. Similar to log-linear models, latent class models can be used to describe complex association structures between the variables used in the imputation model. However, unlike log-linear models, latent class models can be used to build large imputation models containing more than a few categorical variables. To obtain imputations reflecting uncertainty about the unknown model parameters, we use a nonparametric bootstrap procedure as an alternative to the more common full Bayesian approach. The proposed multiple imputation method, which is implemented in Latent GOLD software for latent class analysis, is illustrated with two examples. In a simulated data example, we compare the new method to well-established methods such as maximum likelihood estimation with incomplete data and multiple imputation using a saturated log-linear model. This example shows that the proposed method yields unbiased parameter estimates and standard errors. The second example concerns an application using a typical social sciences data set. It contains 79 variables that are all included in the imputation model. The proposed method is especially useful for such large data sets because standard methods for dealing with missing data in categorical variables break down when the number of variables is so large.     

Paneldaten in der Soziologie: Fixed Effects Paradigma und empirische Praxis in Panelregression und Ereignisanalyse

In sociology there is a growing body of literature discussing the ability of quantitative research methods to test causal inferences. This discourse introduces panel data as an important instrument to defend the interpretation of coefficients as effects: by focusing on what is going on within individuals, panel data allow for longitudinal empirical modelling, which reflects the inherent semantic of scientific hypotheses. As a consequence, unobserved heterogenity is absorbed and alternative explanations to the presumed causal mechanisms are largely ruled out. However, a within-, or rather Fixed Effects-estimator is needed to realize this potential of panel data. Consequently, such within-estimators are well established as standard in the methodological discourse on panel data. We explain this standard with references to the mechanics of within-estimators in panel-regression and event-history analysis. Finally, inspection of contributions to the two major German language journals for sociology shows that within-estimators are rarely used in empirical practice. We conclude that the potential of panel data to control for heterogeneity is largely underused in empirical practice. At the same time, our inspection of studies illustrates that the benefits of panel data are multidimensional and go beyond statistical virtues.     

Modeling Repeatable Events Using Discrete‐Time Data: Predicting Marital Dissolution

I join two methodologies by illustrating the application of multilevel modeling principles to hazard‐rate models with an emphasis on procedures for discrete‐time data that contain repeatable events. I demonstrate this application using data taken from the 1995 National Survey of Family Growth (NSFG) to ascertain the relationship between multiple covariates and risk of subsequent marital dissolution. I consider both fixed‐ and random‐effects versions of the multilevel model, as well as a Generalized Estimating Equation alternative to estimating random effects. I compare results obtained from the various estimators, noting why differences occur, and recommend when to choose the various alternatives. I also provide a set of SAS and STATA programs that can be used to analyze the NSFG data.     

A p* primer: logit models for social networks

A major criticism of the statistical models for analyzing social networks developed by Holland, Leinhardt, and others [Holland, P.W., Leinhardt, S., 1977. Notes on the statistical analysis of social network data; Holland, P.W., Leinhardt, S., 1981. An exponential family of probability distributions for directed graphs. Journal of the American Statistical Association. 76, pp. 33–65 (with discussion); Fienberg, S.E., Wasserman, S., 1981. Categorical data analysis of single sociometric relations. In: Leinhardt, S. (Ed.), Sociological Methodology 1981, San Francisco: Jossey-Bass, pp. 156–192; Fienberg, S.E., Meyer, M.M., Wasserman, S., 1985. Statistical analysis of multiple sociometric relations. Journal of the American Statistical Association, 80, pp. 51–67; Wasserman, S., Weaver, S., 1985. Statistical analysis of binary relational data: Parameter estimation. Journal of Mathematical Psychology. 29, pp. 406–427; Wasserman, S., 1987. Conformity of two sociometric relations. Psychometrika. 52, pp. 3–18] is the very strong independence assumption made on interacting individuals or units within a network or group. This limiting assumption is no longer necessary given recent developments on models for random graphs made by Frank and Strauss [Frank, O., Strauss, D., 1986. Markov graphs. Journal of the American Statistical Association. 81, pp. 832–842] and Strauss and Ikeda [Strauss, D., Ikeda, M., 1990. Pseudolikelihood estimation for social networks. Journal of the American Statistical Association. 85, pp. 204–212]. The resulting models are extremely flexible and easy to fit to data. Although Wasserman and Pattison [Wasserman, S., Pattison, P., 1996. Logit models and logistic regressions for social networks: I. An introduction to Markov random graphs and p*. Psychometrika. 60, pp. 401–426] present a derivation and extension of these models, this paper is a primer on how to use these important breakthroughs to model the relationships between actors (individuals, units) within a single network and provides an extension of the models to multiple networks. The models for multiple networks permit researchers to study how groups are similar and/or how they are different. The models for single and multiple networks and the modeling process are illustrated using friendship data from elementary school children from a study by Parker and Asher [Parker, J.G., Asher, S.R., 1993. Friendship and friendship quality in middle childhood: Links with peer group acceptance and feelings of loneliness and social dissatisfaction. Developmental Psychology. 29, pp. 611–621].     

Covariance Models For Latent Structure In Longitudinal Data

We present several approaches to modeling latent structure in longitudinal studies when the covariance itself is the primary focus of the analysis. This is a departure from much of the work on longitudinal data analysis, in which attention is focused solely on the cross-sectional mean and the influence of covariates on the mean. Such analyses are particularly important in policy-related studies, in which the heterogeneity of the population is of interest. We describe several traditional approaches to this modeling and introduce a flexible, parsimonious class of covariance models appropriate to such analyses. This class, while rooted in the tradition of mixed effects and random coefficient models, merges several disparate modeling philosophies into what we view as a hybrid approach to longitudinal data modeling. We discuss the implications of this approach and its alternatives especially on model interpretation. We compare several implementations of this class to more commonly employed mixed effects models to describe the strengths and limitations of each. These alternatives are compared in an application to long-term trends in wage inequality for young workers. The findings provide additional guidance for the model formulation process in both statistical and substantive senses.     

Dynamic Discrete-time Duration Models: Estimation via Markov Chain Monte Carlo

Discrete-time or grouped duration data, with one or multiple types of terminating events, are often observed in social sciences or economics. In this paper we suggest and discuss dynamic models for flexible Bayesian nonparametric analysis of such data. These models allow simultaneous incorporation and estimation of baseline hazards and time-varying covariate effects, without imposing particular parametric forms. Methods for exploring the possibility of time-varying effects, as for example the impact of nationality or unemployment insurance benefits on the probability of reemployment, have recently gained increasing interest. Our modeling and estimation approach is fully Bayesian and makes use of Markov Chain Monte Carlo (MCMC) simulation techniques. A detailed analysis of unemployment duration data, with full-time job, part-time job and other causes as terminating events, illustrates our methods and shows how they can be used to obtain refined results and interpretations.     

Sociology as giant killer: What should we do with Goliath’s armor?

This paper argues that economic theories and econometric methods can constitute useful tools for the sociological study of employment and gender. Work by the author and by Paula England is used to illustrate this point. His research interests include labor markets, economic sociology, statistical methods, and policy analysis. This article is based on a paper presented at the Southern Sociological Association meetings held in Nashville, Tennessee, March 1988.     

THE METHODOLOGICAL INCLINATIONS OF GENDER SCHOLARSHIP IN MAINSTREAM SOCIOLOGY JOURNALS

This article examines the types of research methods sociologists studying women and gender use in articles published in mainstream journals. The research is based on an analysis of 1,826 gender-content articles published between 1984 and 1993 in 15 major sociology journals. Specifically, we explore whether feminist-oriented articles use different types of data, data collection methods, and statistical techniques than other articles addressing women and gender. The relationship between the rank of an academic journal, author's sex, and type of research method is also examined. We find that a large majority of gender-content articles are based on secondary, quantitative data. When the first author is a man, articles are somewhat more likely to be based on secondary data and to use quantitative statistical techniques. In general, we find that feminist research in the discipline is based on a melange of methodological approaches, reflecting the contested nature of feminist epistemologies.     

PERMUTATION MODELS FOR RELATIONAL DATA

A common problem in sociology, psychology, biology, geography, and management science is the comparison of dyadic relational structures (i.e., graphs). Where these structures are formed on a common set of elements, a natural question that arises is whether there is a tendency for elements that are strongly connected in one set of structures to be more—or less—strongly connected within another set. We may ask, for instance, whether there is a correspondence between golf games and business deals, trade and warfare, or spatial proximity and genetic similarity. In each case, the data for such comparisons may be continuous or discrete, and multiple relations may be involved simultaneously (e.g., when comparing multiple measures of international trade volume with multiple types of political interactions). We propose here an exponential family of permutation models that is suitable for inferring the direction and strength of association among dyadic relational structures. A linear-time algorithm is shown for MCMC simulation of model draws, as is the use of simulated draws for maximum likelihood estimation (MCMC-MLE) and/or estimation of Monte Carlo standard errors. We also provide an easily performed maximum pseudo-likelihood estimation procedure for the permutation model family, which provides a reasonable means of generating seed models for the MCMC-MLE procedure. Use of the modeling framework is demonstrated via an application involving relationships among managers in a high-tech firm.     

Social Survey Research1 in Japanese Sociology

Abstract: In this paper, we look at the history of social survey development in Japanese sociology. First, the history of social research in Japan before World War II is explored. Second, the introduction of survey research to Japan during the American occupation after World War II is examined, and third, the present state and roles of social survey research in Japanese sociology is discussed. Social research was introduced as an administrative tool for the government. Sociology and social research were developed under British empiricism and American pragmatism, but Japanese academia has been based on a metaphysical approach. Social research introduced as a practical tool long had difficulty in being accepted by Japanese academia. For this reason, most sociologists in universities did not use social survey research for practical purposes, but pursued qualitative methodologies for analyzing data to gain academic prestige even after Social Stratification and Mobility (SSM) and Sabro Yasuda's research projects spread social survey methods in the field of Japanese sociology. Such academics did not think that findings acquired through qualitative case studies had to be confirmed through quantitative data to serve a practical purpose, nor did they believe that quantitative data could be better understood when examined along side qualitative data. Social survey methods have been opposed by those who have favored case‐study analysis methods in Japanese sociology. Needless to say, this opposition is fruitless. I propose that professional sociologists in Japanese universities should use social survey research for practical problems more frequently. This is the best way to establish sociology and social research as a science in Japanese society.     

Missing Data in Sociological Research: An Overview of Recent Trends and an Illustration for Controversial Questions, Active Nonrespondents and Targeted Samples

In an age of telemarketers, spam emails, and pop-up advertisements, sociologists are finding it increasingly difficult to achieve high response rates for their surveys. Compounding these issues, the current political and social climate has decreased many survey respondents’ likelihood of responding to controversial questions, which are often at the heart of much research in the discipline. Here we discuss such implications for survey research in sociology using: a content analysis of the prevalence of missing data and survey research methods in the most cited articles in top sociology journals, a case study highlighting the extraction of meaningful information through an example of potential mechanisms driving the non-random missing data patterns in the Religion Among Academic Scientists dataset, and qualitative responses from non-responders in this same case. Implications are likely to increase in importance given the ubiquitous nature of survey research, missing data, and privacy concerns in sociological research.     

Public relations and historical sociology: Historiography as reflexive critique

The essay focuses on thinking about thinking about PR history. The space between history and sociology encompasses theoretical and conceptual frames and can be drawn upon to consider PR in time, across times and between times. It reflects upon the purposes and practices of historical sociology and foregrounds themes relevant to public relations, its histories and methodological approaches. The paper, which is methodological at the strategic rather than the technical level, argues that public relations historians can usefully engage with theoretical issues and problems delineated in historical sociology and historical theory. Evolutionary, functionalist and typological approaches and the cultural logics of historical periodization are discussed and contextualized.