Artificial Data Example 2

MOST practice decision-making

Example 2: Smoking cessation

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If you are interested in practicing fitting the ANOVA models, constructing interaction plots, and deciding which  components to include in the hypothetical intervention, follow steps 1 – 6 of the instructions below.

If you are interested only in practicing the decision-making process, skip steps 1 – 4, and begin on step 5.

Instructions

  1. Read the description of the experiment provided below.
  2. Download the artificial data set and import the data into your preferred statistical software package (e.g. SPSS, STATA).
  3. Estimate the ANOVA model, including main effects and interactions.
  4. Construct any interaction plots that may be useful in the decision-making process.
  5. Use the ANOVA output and relevant interaction plots to practice the decision-making process (you can use the file provided if you wish to skip steps 1—4).
  6. Check the answer key file to determine whether you chose one of the best combinations of components and component levels to build your hypothetical intervention.

Description of pediatric obesity example

A smoking cessation researcher is interested in developing an intervention.  The researcher wishes to estimate the effects of five intervention components:

  1. PATCH (use of nicotine patch);
  2. GUM (adlib use of nicotine gum);
  3. PRECOUN (in-person precessation counseling before the quit date);
  4. CESSCOUN (in-person cessation counseling for two weeks after the quit date);
  5. PHONE (phone counseling during weeks 3—6).

The researcher decides to use a 25 factorial design for the experiment, N=512.  The first four factors correspond to the first four components.  Each has 2 levels: yes (included in the intervention package, coded 1 in the data set) and no (not included, coded -1 in the data set).  The 5th factor corresponds to PHONE, and has the following 2 levels: intensive (weekly counseling, coded 1 in the data set), and minimal (a single session, coded -1 in the data set).  The outcome variable is a scale measuring the participant’s beliefs about their ability to quit smoking; a higher score indicates more self-efficacy.