Model building, like the entire process of data analysis itself, is an iterative process. Models are used to provide data reduction and to give you some insight into the population about which you are trying to make inference. It’s important to first set your expectations for a how a model should characterize a dataset before you actually apply a model to data. Then you can check to see how your model conforms to your expectation. Often, there will be features of the dataset that do not conform to your model and you will have to either refine your model or examine the data collection process.