Decision Making Using Data From a Factorial Experiment: Practice Data Sets

In the optimization phase of MOST the investigator conducts an optimization trial, which is an experiment to gather information needed in deciding which components and component levels will be selected for inclusion in the optimized intervention.

In general, intervention scientists do not have much experience with this kind of decision making. In our work, we have found it extremely helpful to practice the decision-making process using artificial data WELL BEFORE analyzing the empirical data. This provides a way of gaining a little experience in advance.

We have provided for your use two artificial data sets generated to resemble data from actual implementations of MOST. Both simulate factorial optimization trials. Here is how we recommend using each data set:

  1. Conduct the factorial ANOVA on the data.
  2. Prepare plots of any interactions that are likely to be important in decision making.
  3. Hold a meeting of all decision makers, and go through the exercise of selecting components and component levels based on the factorial ANOVA results and plots.

Citation

, (2019). Decision Making Using Data From a Factorial Experiment: Practice Data Sets [Data set]. Scholarsphere. https://doi.org/10.26207/ex6w-h218

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Work Title Decision Making Using Data From a Factorial Experiment: Practice Data Sets
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Open Access
Creators
  1. The Methodology Center, Penn State
Keyword
  1. data analysis
  2. practice data sets
License In Copyright (Rights Reserved)
Work Type Dataset
Publication Date 2019
DOI doi:10.26207/ex6w-h218
Deposited May 17, 2021

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  • Added ex2_anova_plots.docx
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  • Added ex2_instructions.txt
  • Added README.txt
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