This final chapter provides an introduction into multivariate regression modeling. We will cover the logic behind multiple regression modeling and explain the interpretation of a multivariate regression model. We will further cover the assumptions this type of model is based upon. Finally, and using our data, we will provide concrete examples on how to interpret a multiple regression model.
- Multivariate Regression Model
- Extra-curricular Activities
- Standardized Beta Coefficients
- Absolute Influence
- Advanced Statistics Course
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Esarey, J., & Schwindt-Bayer, L. A. (2017). Women’s representation, accountability and corruption in democracies. British Journal of Political Science, 1–32.
Since basically all books listed under bivariate correlation and regression analysis also cover multiple regression analysis, the books I present here go beyond the scope of this textbook here. These books could be interesting further reads, in particular to students, who want to learn more what is covered here.
Heeringa, S. G., West, B. T., & Berglund, P. A. (2017). Applied survey data analysis. Boca Raton: Chapman and Hall/CRC. An overview of different approaches to analyze complex sample survey data. In addition to multiple linear regression analysis the topics covered include different types of maximum likelihood estimations such as logit, probit, and ordinal regression analysis, as well as survival or event history analysis.
Lewis-Beck, C., & Lewis-Beck, M. (2015). Applied regression: An introduction (Vol. 22). Thousand Oaks: Sage A comprehensive introduction into different types of regression techniques.
Pesaran, M. H. (2015). Time series and panel data econometrics. Oxford: Oxford University Press. Comprehensive introduction into different forms of time series models and panel data estimations.
Wooldridge, J. M. (2015). Introductory econometrics: A modern approach. Mason, OH: Nelson Education. Comprehensive book about various regression techniques; it is, however, mathematically relatively advanced.
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Stockemer, D. (2019). Multivariate Regression Analysis. In: Quantitative Methods for the Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-99118-4_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99117-7
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