Member Training: Model Building Approaches

There is a bit of art and experience to model building. You need to build a model to answer your research question but how do you build a statistical model when there are no instructions in the box? 

Should you start with all your predictors or look at each one separately? Do you always take out non-significant variables and do you always leave in significant ones?

There are two basic strategies: Top Down and Bottom Up.

These strategies can be implemented in a number of ways that can be classified as either Automatic or Theory-based.  And these frameworks each have a number of techniques. For example, Lasso and Stepwise techniques are both considered part of the Automatic model building framework.

The best strategy and the appropriate framework to implement that strategy depend on many considerations.


This webinar will also cover:

  • How your hypothesis influences your choices
  • Practical issues that affect your options, like missing data, multicollinearity, and sample size
  • Important model-building concepts like over-fitting and specification error
  • Criteria for deciding whether to keep or drop a predictor
  • When to include interactions, both significant and non-significant
  • Transformations, centering, and high order variables
    The role of model fit in decision making

This webinar will review model building strategies and teach you how to choose an approach that is best suited to your research.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

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About the Instructor

Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.

She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.

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