The complete code used to derive these models is provided in that tutorial. In the segment on multiple linear regression, we created three successive models to estimate the fall undergraduate enrollment at the University of New Mexico. Note that all code samples in this tutorial assume that this data has already been read into an R variable and has been attached.īefore comparing regression models, we must have models to compare. This dataset contains information used to estimate undergraduate enrollment at the University of New Mexico (Office of Institutional Research, 1990). Be sure to right-click and save the file to your R working directory. Try this link, it seems like the data, but will require more work to get it into csv format). This tutorial will explore how the basic HLR process can be conducted in R.īefore we begin, you may want to download the sample data (.csv) used in this tutorial (UPDATE: the data is no longer online. Hierarchical linear regression (HLR) can be used to compare successive regression models and to determine the significance that each one has above and beyond the others. Thankfully, once the potential independent variables have been narrowed down through theoretical and practical considerations, a procedure exists to help us identify which predictors make a significant statistical contribution to our model. Furthermore, they can become exceedingly convoluted when things such as polynomials and interactions are explored. Regression models can become increasingly complex as more variables are included in an analysis.
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