Learn About Multiple Regression With Interactions Between Continuous Variables In Survey Data In R With Data From The General Social Survey 2016
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Learn about Multiple Regression with Interactions Between Continuous Variables in Survey Data in R with Data from the General Social Survey (2016)
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 variables in Ordinary Least Squares (OLS) regression using a subset of data from the 2016 General Social Survey. We test whether agreement with a statement that men should go out to work and women should look after the home and family is related to literacy and to belief in the role of government in providing support. 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 R.
Learn about Multiple Regression with Interactions Between Continuous Variables in Survey Data in SPSS with Data from the General Social Survey (2016)
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 variables in Ordinary Least Squares (OLS) regression using a subset of data from the 2016 General Social Survey. We test whether agreement with a statement that men should go out to work and women should look after the home and family is related to literacy and to belief in the role of government in providing support. 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 SPSS.
Theory-Based Data Analysis for the Social Sciences
This book presents the elaboration model for the multivariate analysis of observational quantitative data. This model entails the systematic introduction of "third variables" to the analysis of a focal relationship between one independent and one dependent variable to ascertain whether an inference of causality is justified. Two complementary strategies are used: an exclusionary strategy that rules out alternative explanations such as spuriousness and redundancy with competing theories, and an inclusive strategy that connects the focal relationship to a network of other relationships, including the hypothesized causal mechanisms linking the focal independent variable to the focal dependent variable. The primary emphasis is on the translation of theory into a logical analytic strategy and the interpretation of results. The elaboration model is applied with case studies drawn from newly published research that serve as prototypes for aligning theory and the data analytic plan used to test it; these studies are drawn from a wide range of substantive topics in the social sciences, such as emotion management in the workplace, subjective age identification during the transition to adulthood, and the relationship between religious and paranormal beliefs. The second application of the elaboration model is in the form of original data analysis presented in two Analysis Journals that are integrated throughout the text and implement the full elaboration model. Using real data, not contrived examples, the text provides a step-by-step guide through the process of integrating theory with data analysis in order to arrive at meaningful answers to research questions.