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Mohammed, M. A.; Ibrahim, A. I. N.; Siri, Z.; Noor, N. F. M. – Sociological Methods & Research, 2019
In this article, a numerical method integrated with statistical data simulation technique is introduced to solve a nonlinear system of ordinary differential equations with multiple random variable coefficients. The utilization of Monte Carlo simulation with central divided difference formula of finite difference (FD) method is repeated n times to…
Descriptors: Monte Carlo Methods, Calculus, Sampling, Simulation
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
Muñoz, J. F.; Álvarez-Verdejo, E.; García-Fernández, R. M. – Sociological Methods & Research, 2018
Many poverty measures are estimated by using sample data collected from social surveys. Two examples are the poverty gap and the poverty severity indices. A novel method for the estimation of these poverty indicators is described. Social surveys usually contain different variables, some of which can be used to improve the estimation of poverty…
Descriptors: Poverty, Simulation, Income, Socioeconomic Status
Blackwell, Matthew; Honaker, James; King, Gary – Sociological Methods & Research, 2017
Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model…
Descriptors: Error of Measurement, Monte Carlo Methods, Data Collection, Simulation
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
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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