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Alex Lyman; Bryce Hepner; Lisa P. Argyle; Ethan C. Busby; Joshua R. Gubler; David Wingate – Sociological Methods & Research, 2025
Generative artificial intelligence (AI) has the potential to revolutionize social science research. However, researchers face the difficult challenge of choosing a specific AI model, often without social science-specific guidance. To demonstrate the importance of this choice, we present an evaluation of the effect of alignment, or human-driven…
Descriptors: Artificial Intelligence, Computer Simulation, Open Source Technology, Social Science Research
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An, Weihua – Sociological Methods & Research, 2023
In this article, I present a new multivariate regression model for analyzing outcomes with network dependence. The model is capable to account for two types of outcome dependence including the mean dependence that allows the outcome to depend on selected features of a known dependence network and the error dependence that allows the outcome to be…
Descriptors: Multivariate Analysis, Regression (Statistics), Models, Correlation
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Narjis, Ghulam; Shabbir, Javid – Sociological Methods & Research, 2023
The randomized response technique (RRT) is an effective method designed to obtain the stigmatized information from respondents while assuring the privacy. In this study, we propose a new two-stage RRT model to estimate the prevalence of sensitive attribute ([pi]). A simulation study shows that the empirical mean and variance of proposed estimator…
Descriptors: Comparative Analysis, Incidence, Efficiency, Models
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Smith, Jeffrey A.; Burow, Jessica – Sociological Methods & Research, 2020
Agent-based modeling holds great potential as an analytical tool. Agent-based models (ABMs) are, however, also vulnerable to critique, as they often employ stylized social worlds, with little connection to the actual environment in question. Given these concerns, there has been a recent call to more fully incorporate empirical data into ABMs. This…
Descriptors: Simulation, Models, Networks, Cultural Influences
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Duxbury, Scott W. – Sociological Methods & Research, 2023
This study shows that residual variation can cause problems related to scaling in exponential random graph models (ERGM). Residual variation is likely to exist when there are unmeasured variables in a model--even those uncorrelated with other predictors--or when the logistic form of the model is inappropriate. As a consequence, coefficients cannot…
Descriptors: Graphs, Scaling, Research Problems, Models
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Dittrich, Dino; Leenders, Roger Th. A. J.; Mulder, Joris – Sociological Methods & Research, 2019
Currently available (classical) testing procedures for the network autocorrelation can only be used for falsifying a precise null hypothesis of no network effect. Classical methods can be neither used for quantifying evidence for the null nor for testing multiple hypotheses simultaneously. This article presents flexible Bayes factor testing…
Descriptors: Correlation, Bayesian Statistics, Networks, Evaluation Methods
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Wang, Cheng; Butts, Carter T.; Hipp, John; Lakon, Cynthia M. – Sociological Methods & Research, 2022
The recent popularity of models that capture the dynamic coevolution of both network structure and behavior has driven the need for summary indices to assess the adequacy of these models to reproduce dynamic properties of scientific or practical importance. Whereas there are several existing indices for assessing the ability of the model to…
Descriptors: Models, Goodness of Fit, Comparative Analysis, Computer Software
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Liu, Kayuet; Bearman, Peter S. – Sociological Methods & Research, 2015
Autism prevalence has increased rapidly in the United States during the past two decades. We have previously shown that the diffusion of information about autism through spatially proximate social relations has contributed significantly to the epidemic. This study expands on this finding by identifying the focal points for interaction that drive…
Descriptors: Autism, Pervasive Developmental Disorders, Incidence, Interaction
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Blackwell, Matthew; Honaker, James; King, Gary – Sociological Methods & Research, 2017
We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model…
Descriptors: Error of Measurement, Correlation, Simulation, Bayesian Statistics
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Stamey, James D.; Beavers, Daniel P.; Sherr, Michael E. – Sociological Methods & Research, 2017
Survey data are often subject to various types of errors such as misclassification. In this article, we consider a model where interest is simultaneously in two correlated response variables and one is potentially subject to misclassification. A motivating example of a recent study of the impact of a sexual education course for adolescents is…
Descriptors: Bayesian Statistics, Classification, Models, Correlation
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Song, Xi; Mare, Robert D. – Sociological Methods & Research, 2015
Most intergenerational social mobility studies are based upon retrospective data, in which samples of individuals report socioeconomic information about their parents, an approach that provides representative data for offspring but not the parental generation. When available, prospective data on intergenerational mobility, which are based on a…
Descriptors: Social Mobility, Simulation, Income, Models
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Vuolo, Mike – Sociological Methods & Research, 2017
Often in sociology, researchers are confronted with nonnormal variables whose joint distribution they wish to explore. Yet, assumptions of common measures of dependence can fail or estimating such dependence is computationally intensive. This article presents the copula method for modeling the joint distribution of two random variables, including…
Descriptors: Sociology, Research Methodology, Social Science Research, Models