Multiple Linear Regression

Multiple linear regression (multiple regression) is a type of correlational test in which the research is interested in finding the strength of a correlation between multiple variables. In multiple linear regression, multiple variables are used as predictors. 'Here, the researcher is interested in the relationship between the predicted variables (dependent) and predictor variables (also known as the independent variables).

Independent variables in multiple regression are usually quantitatively measured variables using summative response, interval, or ratio scales (Lawrence, Meyer, & Guarino, 2017)

Multiple Linear Regression uses the same general equation as linear regression, but accommodates for multiple IV's.

Contribution by: Thomas Fox, WCSU Cohort 8

Reference

Lawrence, S., Meyer, G, & Guarino, A.J. (2017). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: Sage Publications

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Collinearity

When conducting a multiple linear regressions, you to see if the data meets the assumption of collinearity. Therefore, you need to locate the Coefficients table in your results under the heading Collinearity Statistics, under which are two subheadings, Tolerance and VIF.

If the VIF value is greater than 10, or the Tolerance is less than 0.1, then you have concerns over multicollinearity. Otherwise, your data has met the assumption of collinearity and can be written up something like this:

Contribution by: Sheri Prendergast, WCSU Cohort 8

Dart, A., (2013). Reporting Multiple Regressions in APA format-Part One. Retrieved from: http://www.adart.myzen.co.uk/reporting-multiple-regressions-in-apa-format-part-one/