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  1. Comment: Actor Orientation and Relational Event Models

    Sociological Methodology, Volume 47, Issue 1, Page 47-56, August 2017.
  2. Rejoinder: DyNAMs and the Grounds for Actor-oriented Network Event Models

    Sociological Methodology, Volume 47, Issue 1, Page 56-67, August 2017.
  3. Exponential-family Random Graph Models for Rank-order Relational Data

    Rank-order relational data, in which each actor ranks other actors according to some criterion, often arise from sociometric measurements of judgment or preference. The authors propose a general framework for representing such data, define a class of exponential-family models for rank-order relational structure, and derive sufficient statistics for interdependent ordinal judgments that do not require the assumption of comparability across raters.
  4. Multiplicative Models For Continuous Dependent Variables: Estimation on Unlogged versus Logged Form

    In regression analysis with a continuous and positive dependent variable, a multiplicative relationship between the unlogged dependent variable and the independent variables is often specified. It can then be estimated on its unlogged or logged form. The two procedures may yield major differences in estimates, even opposite signs.
  5. A New Way to View the Magnitude of the Difference between the Arithmetic Mean and the Geometric Mean and the Difference between the Slopes When a Continuous Dependent Variable Is Expressed in Raw Form Versus Logged Form

    A New Way to View the Magnitude of the Difference between the Arithmetic Mean and the Geometric Mean and the Difference between the Slopes When a Continuous Dependent Variable Is Expressed in Raw Form Versus Logged Form
  6. Estimating Moderated Causal Effects with Time-varying Treatments and Time-varying Moderators: Structural Nested Mean Models and Regression with Residuals

    Individuals differ in how they respond to a particular treatment or exposure, and social scientists are often interested in understanding how treatment effects are moderated by observed characteristics of individuals. Effect moderation occurs when individual covariates dampen or amplify the effect of some exposure. This article focuses on estimating moderated causal effects in longitudinal settings in which both the treatment and effect moderator vary over time.
  7. Decomposition Analysis of Segregation

    Although substantive studies on segregation, such as residential or school segregation by race and occupational segregation by gender, are many in sociology, the analytical methodology is almost exclusively focused on measurement issues. The author introduces a set of two statistical models for the decomposition analysis of segregation.
  8. New Survey Questions and Estimators for Network Clustering with Respondent-driven Sampling Data

    Respondent-driven sampling (RDS) is a popular method for sampling hard-to-survey populations that leverages social network connections through peer recruitment. Although RDS is most frequently applied to estimate the prevalence of infections and risk behaviors of interest to public health, such as HIV/AIDS or condom use, it is rarely used to draw inferences about the structural properties of social networks among such populations because it does not typically collect the necessary data.
  9. Retrospective Reporting of First Employment in the Life-courses of U.S. Women

    The authors investigate the accuracy of young women’s retrospective reporting on their first substantial employment in three major, nationally representative U.S. surveys, examining hypotheses that longer recall duration, employment histories with lower salience and higher complexity, and an absence of “anchoring” biographical details will adversely affect reporting accuracy. The authors compare retrospective reports to benchmark panel survey estimates for the same cohorts.
  10. Visualizing Latent Class Models with Analysis-of-distance BIPLOTS

    The authors propose using categorical analysis-of-distance biplots to visualize the posterior classifications arising from a latent class (LC) model. Using this multivariate plot, it is possible to visualize in two (or three) dimensions the profile of multiple LCs, specifically both the within- and between-class variation, and the overlap or separation of the classes together with the class weights. Furthermore, visualization of the relative density of each of the data patterns associated with a class is possible.