Abstract
This is the sixth and final volume of Sociological Methodology under my editorship at the University of Illinois. It has been my privilege to serve as editor of this highly regarded and influential journal. In July, I will pass on the editorship to Duane Alwin of Penn State University. The American Sociological Association has recently informed me that the impact factor in 2014 of our journal was ranked fifth of 142 sociology journals. Therefore, I am doubly pleased to pass on the journal in fine shape to Duane’s able hands.
In this year’s volume, we feature again a symposium, “Life-course Sequence Analysis.” Sequence analysis was introduced into sociology by Andrew Abbott over two decades ago, and the second-wave sequence analysis has seen a resurgence of much interest in sociology, a topic that is also close to one of my own recent research interests. The main article in the symposium—“A ‘Global Interdependence’ Approach to Multidimensional Sequence Analysis” by Nicolas Robette, Xavier Bry, and Éva Lelièvre—examines a method for analyzing mothers’ and daughters’ employment history sequences. The paper is followed by a balanced set of commentaries by Cees H. Elzinga, Wen Fan and Phyllis Moen, Anette Eva Fasang, Jacques-Antoine Gauthier, Eliza K. Pavalko, Raffaella Piccarreta, and Matthias Studer as well as a rejoinder by the authors of the main article. The commentators from institutions in five countries represent expertise in life course research and sequence analysis. Proposing a new method is never plain sailing; the symposium shows how many aspects of a method we must consider to evaluate a proposal fully, and it should give the reader an up-to-date assessment of sequence analysis in life course research.
Following the symposium are three sections, containing a total of eight chapters. The two articles in the section following the symposium both deal with some aspects of big data. Daniel Tumminelli O’Brien, Robert J. Sampson, and Christopher Winship’s chapter, “Ecometrics in the Age of Big Data: Measuring and Assessing ‘Broken Windows’ Using Large-scale Administrative Records,” represents a second moment of ecometric analysis, with the first moment measuring the disorder of neighborhoods in things such as graffiti and litter (as in Raudenbush and Sampson’s earlier work published in volume 29 of this journal). These large-scale administrative records certainly qualify as big data. Another form of big data can be found in news reports, which provide a typical unstructured text data. In “A Progressive Supervised-learning Approach to Generating Rich Civil Strife Data,” Peter F. Nardulli, Scott L. Althaus, and Matthew Hayes argue for a collaborative, hybrid approach that combines machine-based and human-centric approaches to content analysis for extracting information from unstructured text. In the age of big data, these two chapters provide a timely report on what data analysts can do to utilize two different data forms.
The next section of the volume contains three chapters related to data collection, management, and analysis. Stephen L. Morgan and Emily S. Taylor Poppe, in “A Design and a Model for Investigating the Heterogeneity of Context Effects in Public Opinion Surveys,” suggest that for data collection, randomized survey experiments on representative samples, when coupled with facilitative primes, can assist modeling selection into variable context effects, thus revealing heterogeneity at the population level. In research practice, the mismatch between data and methods and the difficulties caused by messy data can lead to inaccurate conclusions. In “An Introduction to the General Monotone Model with Application to Two Problematic Data Sets,” Michael R. Dougherty, Rick P. Thomas, Ryan P. Brown, Jeffrey S. Chrabaszcz, and Joe W. Tidwell show how theoretical conclusions can be affected by these issues and demonstrate the general monotone model for analyzing such messy data. Ethnographers typically face the challenges of managing, presenting, and analyzing context-dependent data generated during fieldwork. In the final chapter of the section, “Beyond Text: Using Arrays to Represent and Analyze Ethnographic Data,” Corey M. Abramson and Daniel Dohan introduce an interactive visual approach called ethnoarray for addressing these challenges.
The final section of the volume contains three chapters focused on some aspects of inequality analysis. Building on an empirical Bayes framework, Xiang Zhou, in “Shrinkage Estimation of Log-odds Ratios for Comparing Mobility Tables,” proposes a shrinkage estimator for comparing mobility tables in stratification research that improves estimation efficiency by “borrowing strength” across multiple mobility tables. In “Can Non-full-probability Internet Surveys Yield Useful Data? A Comparison with Full-probability Face-to-face Surveys in the Domain of Race and Social Inequality Attitudes,” Alicia D. Simmons and Lawrence D. Bobo investigate the potential usefulness of web-based surveys relying on non-full-probability sampling for the analysis of race and social inequality attitudes. The decomposition of inequality effects by race and gender is a common practice. The standard DiNardo-Fortin-Lemieux (DFL) decomposition analysis may produce biased estimates. In the final chapter of the section, “Decomposition of Gender or Racial Inequality with Endogenous Intervening Covariates: An Extension of the DiNardo-Fortin-Lemieux Method,” Kazuo Yamaguchi introduces a combination of the DFL method with Heckman’s two-step method for testing and eliminating bias in DFL estimation when some intervening covariates are endogenous while bringing race and gender into the center of causal analysis.
The Sociological Methodology team is grateful to all authors who submitted papers to the journal, whether or not their papers are published in this volume, and to the reviewers and our board members whose devoted work guaranteed the very high standard of the publications. In the past six years, my managing editor, Lisa Savage, has stuck with me through thick and thin even though she changed her regular job with a professional publisher, and I thank her for her reliability and consistency. I also thank our editorial assistant, Andrea Wilbon Hartman, for her promptness and enthusiasm in performing various kinds of editorial assistance; copy editor Stephanie Magean and the copy editors of Sage Publications for keeping up the quality of writing; and Athena Liao, who assisted in the art design that graces the cover and for her contributions to four previous cover designs. This is the fourth year in a row that we have published a symposium with an art design either directly using “data” from the symposium or indirectly reflecting its theme. This year’s art design reflects the theme of life course analysis. My appreciation also goes to Jim Ballinger and Sara Sarver, who coordinated the publication process at Sage, and Janine Chiappa McKenna, Karen Edwards, and the Publications Committee of the American Sociological Association for their continuing support. We would like to acknowledge the material support from the Department of Sociology and the College of Liberal Arts and Sciences at the University of Illinois at Urbana-Champaign that houses our editorial office in the historic building of Lincoln Hall. Last but not least, I would like to express my appreciation to the American Sociological Association for the privilege of editing this highly important journal in our profession and for the opportunity to serve the discipline through this editorship.