Marginal models for the association structure of hierarchical binary responses

Clustered binary responses are often found in ecological studies. Data analysis may include modeling the marginal probability response. However, when the association is the main scientific focus, modeling the correlation structure between pairs of responses is the key part of the analysis. Second-order generalized estimating equations (GEE) are established in the literature. Some of them are more efficient in computational terms, especially facing large clusters. Alternating logistic regression (ALR) and orthogonalized residual (ORTH) GEE methods are presented and compared in this paper. Simulation results show a slightly superiority of ALR over ORTH. Marginal probabilities and odds ratios are also estimated and compared in a real ecological study involving a three-level hierarchical clustering. ALR and ORTH models are useful for modeling complex association structure with large cluster sizes.     

Model selection criteria in beta regression with varying dispersion

We address the issue of model selection in beta regressions with varying dispersion. The model consists of two submodels, namely: for the mean and for the dispersion. Our focus is on the selection of the covariates for each submodel. Our Monte Carlo evidence reveals that the joint selection of covariates for the two submodels is not accurate in finite samples. We introduce two new model selection criteria that explicitly account for varying dispersion and propose a fast two step model selection scheme which is considerably more accurate and is computationally less costly than usual joint model selection. Monte Carlo evidence is presented and discussed. We also present the results of an empirical application.     

Saddlepoint p-values for group of linear rank tests

Many nonparametric tests in one sample problem, matched pairs, and competingrisks under censoring have the same underlying permutation distribution. This article proposes a saddlepoint approximation to the exact p-values of these tests instead of the asymptotic approximations. The performance of the saddlepoint approximation is assessed by using simulation studies that show the superiority of the saddlepoint methods over the asymptotic approximations in several settings. The use of the saddlepoint to approximate the p-values of class of two sample tests under complete randomized design is also discussed.     

The inverse power Lindley distribution

Several probability distributions have been proposed in the literature, especially with the aim of obtaining models that are more flexible relative to the behaviors of the density and hazard rate functions. Recently, two generalizations of the Lindley distribution were proposed in the literature: the power Lindley distribution and the inverse Lindley distribution. In this article, a distribution is obtained from these two generalizations and named as inverse power Lindley distribution. Some properties of this distribution and study of the behavior of maximum likelihood estimators are presented and discussed. It is also applied considering two real datasets and compared with the fits obtained for already-known distributions. When applied, the inverse power Lindley distribution was found to be a good alternative for modeling survival data.     

Ignorability conditions for frequentist non parametric analysis of conditional distributions with incomplete data

Rubin (1976 Rubin, D.B. (1976). Inference and missing data. Biometrika 63(3):581–592.[Crossref], [Web of Science ®] , [Google Scholar]) derived general conditions under which inferences that ignore missing data are valid. These conditions are sufficient but not generally necessary, and therefore may be relaxed in some special cases. We consider here the case of frequentist estimation of a conditional cdf subject to missing outcomes. We partition a set of data into outcome, conditioning, and latent variables, all of which potentially affect the probability of a missing response. We describe sufficient conditions under which a complete-case estimate of the conditional cdf of the outcome given the conditioning variable is unbiased. We use simulations on a renal transplant data set (Dienemann et al.) to illustrate the implications of these results.     

Public Sociology on Twitter: a Space for Public Pedagogy?

Research has examined various elements of Twitter; however, no scholarship has explored how sociologists currently use the platform. This empirically driven paper explores how individuals that self-identify as sociologists on Twitter use the popular micro-blogging social media site. A total of 152,977 tweets from Twitter profiles of 130 sociologists were collected and examined using qualitative media analysis. The potential use of Twitter allows the sociologist to become both the generator and interlocutor of dialogue with publics. We frame our data analysis and discussion around the core theme of expertise - namely, the role that expertise plays in the use of Twitter by sociologists. Our findings indicate that when sociologists used Twitter as sociologists (i.e., drawing upon their stated research expertise) little direct engagement with publics occurred. Thus, while sociologists appear to be using Twitter as a space for public sociology, the use of this interactive platform is mostly limited to the generation of content, a finding consistent with Burawoy’s traditional form of public sociology. Suggestions for future research are noted.     

Standard Bearers: Qualitative Sociologists’ Experiences with IRB Regulation

In response to the system for regulating research with human subjects, researchers have raised two apparently contradictory concerns: that IRBs are excessively inconsistent (often raised by biomedical researchers), and that they are excessively standardizing (often raised by qualitative interview researchers). Why does standardization appear as the dominant theme in qualitative researchers’ experiences with their IRBs? And how do qualitative researchers experience standardization in their IRB encounters? We focus on IRBs role as regulatory bureaucracies, which typically rely heavily on standardized communication and decisions to process information and make large numbers of decisions in a timely manner. We explore the role of standardization in IRB regulation of qualitative research in an analysis of semi-structured interviews with 26 qualitative sociologists from six research universities and three liberal arts colleges in the Northeastern United States. In a regulatory regime oriented toward the norms of experimental research, these frictions resulted partly from a lack of appropriate standardized language and decision-templates, but also from the inherent difficulties of applying standardized decisions to work that is unpredictable, unique, and difficult to routinize.     

Cognitive Sociology in France

While cognitive sociology is a relatively new area in U.S. sociology, the subfield has a lengthy history in French sociology. Developing a typology based upon the existing literature, we identify three branches of cognitive sociology in France. The first was initiated by Raymond Boudon, one of the scholars most responsible for popularizing the area, who envisioned cognitive sociology as helping delineate the role of beliefs in rational action. Second, another group of researchers seeks to found sociology upon a naturalistic basis, thus drawing upon the disciplines constitutive of cognitive science (e.g., psychology, cognitive neuroscience, and cognitive anthropology). Finally, the third approach to cognitive sociology in France takes inspiration from linguistics as a foundation for sociological investigations. We conclude the paper by discussing the relationship between these three sub-fields and by examining the relationship between French and American cognitive sociologies in order to identify fruitful directions for future research.     

Networking in France. Is there a French School of Social Network Analysis?

Social Network Analysis (SNA) is a sociology based on interaction that visualizes and models relations between actors. Whereas interaction is approached by classical scholars, we had to wait until the 1970s and the birth of computer science to see social networks analysis develop. This article investigates the influence of SNA in France from the 1980s and wonder if there is a French school of SNA? To do so, we first resume social networks history and highlight its contribution to sociology. Second, we analyze the trajectory and profile of five “disciplinary entrepreneurs,” whose role in the field is important as they master three necessary languages for SNA: English, Mathematics and Computer Science. Third, in order to put back those individuals in their social structures, we cross SNA with the different French sociological tradition(s) (according to topics and methods). Last, we wonder if the institutionalization process succeeded in the creation of institutions from which a French SNA would be able to expand?