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Learn About Multiple Regression With Interactions Between Categorical and Continuous Variables in Survey Data in Stata With Data From the European Social Survey (2016)

By: & Published: 2019 | Product: SAGE Research Methods Datasets Part 2

This SAGE Research Methods Dataset example introduces readers to interaction effects in multiple regression. Multiple regression techniques allow researchers to evaluate whether a continuous dependent variable is a linear function of two or more independent variables. This example demonstrates how to compute and interpret product-term interactions between continuous and categorical variables in Ordinary Least Squares (OLS) regression using a subset of data from the 2016 European Social Survey. We test whether opinions about the benefits of immigration are related to personal values, specifically the prioritization of values of conformity over other basic human values, and whether respondents voted in the last national election or not. In this example, readers are introduced to the basic theory and assumptions underlying this technique, the type of question this technique can be used to answer, and how to produce and report results. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for Stata.

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