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The article explores the role that subjective evidence of causality and associated counterfactuals and counterpotentials might play in the social sciences where comparative cases are scarce. This scarcity rules out statistical inference based upon frequencies and usually invites in-depth ethnographic studies. Thus, if causality is to be preserved in such situations, a conception of ethnographic causal inference is required. Ethnographic causality inverts the standard statistical concept of causal explanation in observational studies, whereby comparison and generalization, across a sample of cases, are both necessary prerequisites for any causal inference. Ethnographic causality allows, in contrast, for causal explanation prior to any subsequent comparison or generalization. [ABSTRACT FROM AUTHOR]

Given the questionnaire design and the nature of the problem, partially ordered data that are neither completely ordered nor completely unordered are frequently encountered in social, behavioral, and medical studies. However, early developments in partially ordered data analysis are very limited and restricted only to cross-sectional data. In this study, we propose a Bayesian two-level regression model for analyzing repeated partially ordered responses in longitudinal data. The first-level model is defined for partially ordered observations of interest that are taken at each time point nested within individuals, while the second-level model is defined for individuals to assess the effects of their characteristics on the first-level model. A full Bayesian approach with the Markov chain Monte Carlo algorithm is developed for statistical inference. Simulation studies demonstrate the satisfactory performance of the developed methodology. The methodology is then applied to a longitudinal study on adolescent smoking behavior. [ABSTRACT FROM AUTHOR]

Periodic changes in occupational classifications make it difficult to obtain consistent measures of social class over time, potentially jeopardizing research on class-based trends. The severity of this problem depends, in part, on the measurement strategies used to address those changes. The authors propose that when a sample has been coded partly with one occupational classification and partly with another, Krippendorff's index α be used to identify the best strategy for measuring class consistently across the two classifications and to assess the reliability of the class measure employed in the final analyses. This index can be computed regardless of the metric of the class variable; it can be used to compare measures based on different class schemes or that use different metrics; and statistical inference is straightforward, even with a complex sampling design. The authors put the index to work in conducting a case study of the effects of the switch from the 1970 to the 1980 U.S. Census Bureau Classification of Occupations on the reliability of Erikson–Goldthorpe–Portocarero class measures. Their findings indicate that measurement strategies that seem a priori equally reasonable vary substantially in terms of their reliability, and that the bulk of this variation is accounted for by the extent to which the strategies rely on subjective judgments about the relationships between occupational and class classifications. Most importantly, as long as the best-performing measurement strategies are used, the switch in occupational classifications appears to be substantially less consequential than has been previously argued. A computer program made available as a companion to the paper makes estimation of Krippendorff's α, and statistical inference, very simple endeavors for nominal class variables. [ABSTRACT FROM AUTHOR]

We delineate the underlying homogeneity assumption, procedural variants, and implications of the comparative method and distinguish this from Mill’s method of difference. We demonstrate that additional units can provide “placebo” tests for the comparative method even if the scope of inference is limited to the two units under comparison. Moreover, such tests may be available even when these units are the most similar pair of units on the control variables with differing values of the independent variable. Small-n analyses using this method should therefore, at a minimum, clearly define the dependent, independent, and control variables so they may be measured for additional units, and specify how the control variables are weighted in defining similarity between units. When these tasks are too difficult, process tracing of a single unit may be a more appropriate method. We illustrate these points with applications to two studies. [ABSTRACT FROM AUTHOR]

Stochastic actor-based models for network dynamics have the primary aim of statistical inference about processes of network change, but may be regarded as a kind of agent-based models. Similar to many other agent-based models, they are based on local rules for actor behavior. Different from many other agent-based models, by including elements of generalized linear statistical models they aim to be realistic detailed representations of network dynamics in empirical data sets. Statistical parallels to micro–macro considerations can be found in the estimation of parameters determining local actor behavior from empirical data, and the assessment of goodness of fit from the correspondence with network-level descriptives. This article studies several network-level consequences of dynamic actor-based models applied to represent cross-sectional network data. Two examples illustrate how network-level characteristics can be obtained as emergent features implied by microspecifications of actor-based models. [ABSTRACT FROM PUBLISHER]

Law and science share many perspectives, but they also differ in important ways. While much of science is concerned with the effects of causes (EoC), relying upon evidence accumulated from randomized controlled experiments and observational studies, the problem of inferring the causes of effects (CoE) requires its own framing and possibly different data. Philosophers have written about the need to distinguish between the “EoC” and “the CoE” for hundreds of years, but their advice remains murky even today. The statistical literature is only of limited help here as well, focusing largely on the traditional problem of the “EoC.” Through a series of examples, we review the two concepts, how they are related, and how they differ. We provide an alternative framing of the “CoE” that differs substantially from that found in the bulk of the scientific literature, and in legal cases and commentary on them. Although in these few pages we cannot fully resolve this issue, we hope to begin to sketch a blueprint for a solution. In so doing, we consider how causation is framed by courts and thought about by philosophers and scientists. We also endeavor to examine how law and science might better align their approaches to causation so that, in particular, courts can take better advantage of scientific expertise. [ABSTRACT FROM PUBLISHER]

For survey methodologists, latent class analysis (LCA) is a powerful tool for assessing the measurement error in survey questions, evaluating survey methods, and estimating the bias in estimates of population prevalence. LCA can be used when gold standard measurements are not available and applied to essentially any set of indicators that meet certain criteria for identifiability. LCA offers quality inference, provided the key threat to model validity—namely, local dependence—can be appropriately addressed either in the study design or in the model-building process. Three potential causes threaten local independence: bivocality, behaviorally correlated error, and latent heterogeneity. In this article, these threats are examined separately to obtain insights regarding (a) questionnaire designs that reduce local dependence, (b) the effects of local dependence on parameter estimation, and (c) modeling strategies to mitigate these effects in statistical inference. The article focuses primarily on the analysis of rare and sensitivity outcomes and proposes a practical approach for diagnosing and mitigating model failures. The proposed approach is empirically tested using real data from a national survey of inmate sexual abuse where measurement errors are a serious concern. Our findings suggest that the proposed modeling strategy was successful in reducing local dependence bias in the estimates, but its success varied by the quality of the indicators available for analysis. With only three indicators, the biasing effects of local dependence can usually be reduced but not always to acceptable levels. [ABSTRACT FROM AUTHOR]

Set-theoretic methods and Qualitative Comparative Analysis (QCA) in particular are case-based methods. There are, however, only few guidelines on how to combine them with qualitative case studies. Contributing to the literature on multi-method research (MMR), we offer the first comprehensive elaboration of principles for the integration of QCA and case studies with a special focus on case selection. We show that QCA's reliance on set-relational causation in terms of necessity and sufficiency has important consequences for the choice of cases. Using real world data for both crisp-set and fuzzy-set QCA, we show what typical and deviant cases are in QCA-based MMR. In addition, we demonstrate how to select cases for comparative case studies aiming to discern causal mechanisms and address the puzzles behind deviant cases. Finally, we detail the implications of modifying the set-theoretic cross-case model in the light of case-study evidence. Following the principles developed in this article should increase the inferential leverage of set-theoretic MMR. [ABSTRACT FROM PUBLISHER]

The increasing availability of data from multisite randomized trials provides a potential opportunity to use instrumental variables (IV) methods to study the effects of multiple hypothesized mediators of the effect of a treatment. We derive nine assumptions needed to identify the effects of multiple mediators when using site-by-treatment interactions to generate multiple instruments. Three of these assumptions are unique to the multiple-site, multiple-mediator case: (1) the assumption that the mediators act in parallel (no mediator affects another mediator); (2) the assumption that the site-average effect of the treatment on each mediator is independent of the site-average effect of each mediator on the outcome; and (3) the assumption that the site-by-compliance matrix has sufficient rank. The first two of these assumptions are nontrivial and cannot be empirically verified, suggesting that multiple-site, multiple-mediator IV models must be justified by strong theory. [ABSTRACT FROM PUBLISHER]