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  1. Causal Inference with Networked Treatment Diffusion

    Treatment interference (i.e., one unit’s potential outcomes depend on other units’ treatment) is prevalent in social settings. Ignoring treatment interference can lead to biased estimates of treatment effects and incorrect statistical inferences. Some recent studies have started to incorporate treatment interference into causal inference. But treatment interference is often assumed to follow a simple structure (e.g., treatment interference exists only within groups) or measured in a simplistic way (e.g., only based on the number of treated friends).
  2. Estimating the Relationship between Time-varying Covariates and Trajectories: The Sequence Analysis Multistate Model Procedure

    The relationship between processes and time-varying covariates is of central theoretical interest in addressing many social science research questions. On the one hand, event history analysis (EHA) has been the chosen method to study these kinds of relationships when the outcomes can be meaningfully specified as simple instantaneous events or transitions.
  3. Limitations of Design-based Causal Inference and A/B Testing under Arbitrary and Network Interference

    Randomized experiments on a network often involve interference between connected units, namely, a situation in which an individual’s treatment can affect the response of another individual. Current approaches to deal with interference, in theory and in practice, often make restrictive assumptions on its structure—for instance, assuming that interference is local—even when using otherwise nonparametric inference strategies.
  4. Rejoinder: On the Assumptions of Inferential Model Selection—A Response to Vassend and Weakliem

    I am grateful to Professors Vassend and Weakliem for their comments on my paper (this volume, pp. 52–87) and its admittedly unusual approach to model selection and to the Sociological Methodology editors for the opportunity to respond. My goal here is not to defend the inferential information criterion (IIC) against all the points brought out by Vassend (this volume, pp. 91–97) and Weakliem (this volume, pp. 88–91). My paper aimed to (1) show how methodological assumptions interfere with inferences about theory and (2) develop a practical approach to minimize this interference.
  5. The Problem of Underdetermination in Model Selection

    Conventional model selection evaluates models on their ability to represent data accurately, ignoring their dependence on theoretical and methodological assumptions. Drawing on the concept of underdetermination from the philosophy of science, the author argues that uncritical use of methodological assumptions can pose a problem for effective inference. By ignoring the plausibility of assumptions, existing techniques select models that are poor representations of theory and are thus suboptimal for inference.
  6. Rejoinder: Can We Weight Models by Their Probability of Being True?

    We thank the commenters for thoughtful, constructive engagement with our paper (this volume, pp. 1–33). Throughout this discussion, there is strong consensus that model robustness analysis is essential to sociological research methods in the twenty-first century. Indeed, both O’Brien (this volume, pp. 34–39) and Western (this volume, pp. 39–43) identify examples of sociological research that is plagued by uncertainty over modeling decisions and how those decisions can change the results and conclusions of the analyses.
  7. We Ran 9 Billion Regressions: Eliminating False Positives through Computational Model Robustness

    False positive findings are a growing problem in many research literatures. We argue that excessive false positives often stem from model uncertainty. There are many plausible ways of specifying a regression model, but researchers typically report only a few preferred estimates. This raises the concern that such research reveals only a small fraction of the possible results and may easily lead to nonrobust, false positive conclusions. It is often unclear how much the results are driven by model specification and how much the results would change if a different plausible model were used.
  8. Qualitative Comparative Analysis in Critical Perspective

    Qualitative comparative analysis (QCA) appears to offer a systematic means for case-oriented analysis. The method not only offers to provide a standardized procedure for qualitative research but also serves, to some, as an instantiation of deterministic methods. Others, however, contest QCA because of its deterministic lineage. Multiple other issues surrounding QCA, such as its response to measurement error and its ability to ascertain asymmetric causality, are also matters of interest.

  9. Comparing Regression Coefficients Between Same-sample Nested Models Using Logit and Probit: A New Method

    Logit and probit models are widely used in empirical sociological research. However, the common practice of comparing the coefficients of a given variable across differently specified models fitted to the same sample does not warrant the same interpretation in logits and probits as in linear regression. Unlike linear models, the change in the coefficient of the variable of interest cannot be straightforwardly attributed to the inclusion of confounding variables. The reason for this is that the variance of the underlying latent variable is not identified and will differ between models.

  10. A Theory of Resonance

    The metaphor of resonance often describes the fit between a message and an audience’s worldviews. Yet scholars have largely ignored the cognitive processes audiences use to interpret messages and interactions that determine why certain messages and other cultural objects appeal to some but not others. Drawing on pragmatism, we argue that resonance occurs as cultural objects help people puzzle through practical challenges they face or construct. We discuss how cognitive distance and the process of emotional reasoning shape the likelihood of cultural resonance.