Whether or not you run experiments, there are elements of experimental design that affect how you need to analyze many types of studies.
The most fundamental of these are replication, randomization, and blocking. These key design elements come up in studies under all sorts of names: trials, replicates, multi-level nesting, repeated measures. Any data set that requires mixed or multilevel models has some of these design elements.
In this training webinar you’ll learn:
- What these fundamental elements really mean and how to recognize them
- How they differ from and work with crossing and nesting
- How they come together to inform the analysis and the inferences you can make
- How simple changes in the design can have big impacts on the complexity of the analysis
- The use of these elements in common designs such as randomized blocks, Latin squares, cross-overs, and multilevel.
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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