Multiple Regression and Beyond: An Introduction to Multiple Regression and Structural Equation Modeling

By T. Keith. 2014

698 citations recorded by the source corpus.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/544baed9c3d115bcc599a594f91236abbec0bbdc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

1. Ikuya Aizawa
2. Zhiyong Zhang
3. Yujiao Mai
4. Gene Thompson
5. Zhonglin Wen
6. H. Rose
7. Samantha Curle
8. Noora Shrestha

Citations in the corpus

• Detecting Multicollinearity in Regression Analysis (2020, methodology): “Multicollinearity increases variance of the regression coefficients making them unstable, which brings problem to interpret the coefficients [5].”
• Exploring the role of self-efficacy beliefs and learner success in English medium instruction (2019, methodology): “assumptions for multiple regression were confirmed for each analysis: linearity, independence, homoscedascity, and normal distribution of errors (Keith 2015).”
• Comparing Exploratory Structural Equation Modeling and Existing Approaches for Multiple Regression with Latent Variables (2018, positive): “Multiple regression is an essential methodological tool in modern social science, especially in psychological and educational research (Keith, 2014).”