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Lynn Hrabik, MPH, RD, CD Matthew Walsh, MPH, PhD

Powering Up to Prevent the “Oops!”: Strengthening Evaluation with Preparation. Lynn Hrabik, MPH, RD, CD Matthew Walsh, MPH, PhD. LOGIC MODEL. Good Question Design. Have you heard of joint use agreements or know where a local gym is?. Response Options _ Dichotomous.

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Lynn Hrabik, MPH, RD, CD Matthew Walsh, MPH, PhD

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  1. Powering Up to Prevent the “Oops!”: Strengthening Evaluation with Preparation Lynn Hrabik, MPH, RD, CD Matthew Walsh, MPH, PhD

  2. LOGIC MODEL

  3. Good Question Design Have you heard of joint use agreements or know where a local gym is?

  4. Response Options _ Dichotomous Did you eat 5 cups of vegetables yesterday? • Yes • No

  5. Response Options - Ordinal How often do you eat vegetables? • Never • Sometimes • Most of the time • Always

  6. Response Options - Continuous How many servings of vegetables did you eat yesterday? • Enter Number (0-50) ____

  7. Response Options - Nominal Name the vegetables you ate yesterday? • Carrots • Beets • Tomatoes • Peppers

  8. STATISTICS Image source: http://science.leidenuniv.nl/index.php/ibl/pep/people/Tom_de_Jong/Teaching Let the questions drive the statistics – not the other way around

  9. BRAINSTORMING STUDY QUESTIONS • AODA (Alcohol and other drug Abuse) • Smoke-Free Multi-Unit Housing • Obesity Prevention • General Prevention (Injury, Domestic Abuse, Mental Health)

  10. HYPOTHESES • Ho: Null Hypothesis • Exposing our population to this intervention will have NOeffect • on what we are evaluating • Ha: Alternative Hypothesis • Exposing our population to this intervention will have AN effect • on what we are evaluating

  11. POWER • Why Calculate Power? • Often Required • Saves Resources • Manages Expectations • Forces Focus On Study Design Before Data Collection • Sets Priorities of Key Question(s)

  12. POWER PRIMER Type 1 Error Type 2 Error Power Effect Size Sample Size Hypotheses Ho: Null Ha: Alternative

  13. TYPE I/TYPE II ERROR Image source: http://effectsizefaq.com/2010/05/31/i-always-get-confused-about-type-i-and-ii-errors-can-you-show-me-something-to-help-me-remember-the-difference/

  14. GUILT OR INNOCENCE • The Confidence Level: Times out of 100 that a jury would correctly conclude that the defendant is not guilty • Type II Error: Times out of 100 that a jury would incorrectly conclude that the defendant is not guilty • Type I Error: Times out of 100 that a jury would incorrectly conclude that the defendant is guilty • Power: Times out of 100 that a jury would correctly conclude that the defendant is guilty Guilty Not Guilty Jury Verdict Not Guilty 1 2 3 4 Guilty

  15. GREEK • (1- α): The Confidence Level The number of times out of 100 that we will correctly conclude there is no effect • β : Type II Error The number of times out of 100 that we will incorrectly conclude there is no effect • α : Type I Error The number of times out of 100 that we will incorrectly conclude there is no effect • (1- β): Power The number of times out of 100 that we will correctly conclude there is an effect 1 2 3 4 Image source: http://allpsych.com/researchmethods/errors.html

  16. POWER • If you have three of these ingredients you can calculate the fourth. • Level of α (Type I error) • Level of β (Type II error) • Sample Size • Effect Size (expected difference and variance) • Effect Size: The strength of a ‘treatment’ relative to the noise in the measurement

  17. NO MORE GREEK • 95%: The Confidence Level Percent of time we will correctly conclude there is no effect • 20%: Type II Error Percent of time we will incorrectly conclude there is no effect • 5%: Type I Error Percent of time we will incorrectly conclude there is no effect • 80%: Power Percent of time we will correctly conclude there is an effect 1 2 3 4 Image source: http://allpsych.com/researchmethods/errors.html

  18. STUDY DESIGN Important considerations data analyst probably shouldn't help you with: A. Data Collection Method(s) B. Target Population/Study Subjects C. Treatments/Interventions … Most aspects of study design have an impact on how to determine detectable Effect Sizes and thus Power

  19. LITERATURE REVIEW What are the questions currently of interest in your area of expertise or for your department? Plausible Effect Sizes and resonable response rates need to be researched using past literature.

  20. POWER PRIMER Type 1 Error Type 2 Error Power Standard Error Sample Means Sample Size Hypotheses Ho: Null Ha: Alternative http://intuitor.com/statistics/T1T2Errors.html

  21. RESOURCES Statistical Programs (EpiInfo/SAS) Online Power Calculation Apps http://homepage.stat.uiowa.edu/~rlenth/Power/ http://intuitor.com/statistics/T1T2Errors.html

  22. Summary Power calculations force reflection on aspects of study design and resource allocation that are often required to get meaningful results. SHOW – TRANSFORM WISCONSIN POWER CALCULATIONS

  23. Have questions? Want more information? hrabik@wisc.edu 920-833-0051 walsh2@wisc.edu 608-821-1268

  24. Data Analysis Resources • http://learningstore.uwex.edu/Collecting-Analyzing-Data-C237.aspx

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