5. Multi-predictor Models

Design researchers are often collecting data under a variety of conditions, each of which qualifies as a predictor in its own right. The advantage of the linear terms is that they can be combined, which allows estimating the simultaneous influence of multiple predictors in one model. We will start with models, where all predictors act mutually independent on the outcome. In reality, it often happens that predictors are not independent, but act conditional upon each other. Conditional effects models can adjust a model for common violations of linearity, saturation and amplification, but can also uncover relevant structures, or test theories. The chapter ends with polynomial models, which allow to fit non-linear relationships between metric predictors and outcome.