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The Seven Deadly Sins of Program Evaluation

The Seven Deadly Sins of Program Evaluation

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The Seven Deadly Sins of Program Evaluation

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  1. The Seven Deadly Sins of Program Evaluation William Ashton, Ph.D.

  2. This Talk is for … • Everyone -- Especially ATOD Professionals • Some Experience with Program Evaluation • Cookbook • Look Out For • Solutions • Hot Topic -- Outcomes • One Step Beyond

  3. A brief quote ... “Clearly, evaluation can evoke strong emotions, negative associations, and genuine fear.” -- Michael Q. Patton

  4. Alternate Title A Psych Geek Talks about Boring & Technical Research Methodology & Program Evaluation Stuff

  5. Seven Deadly Sins of Program Evaluation Please See Insert 1

  6. A History of Evaluation • Process Data • documenting services delivered (e.g. clients seen, talks given, participants at talks) • Outcome Data • documenting changes for populations receiving services (e.g. increase in family cohesion, increase in knowledge of drug refusal skills) • Effects • documenting changes for populations receiving services that are due to the program -- and only the program

  7. Counterfactuals • Did My Program Make a Difference • Compared to What? • Counterfactual -- should have beens • Jim Fixx -- outcome: died while jogging at 51 • Counterfactual: ‘should have’ died when he was 40 • What difference did jogging make? • jogging had a life-lengthening effect

  8. Another Counterfactual Example • Program -- 1999-2000 school year you implement an anti-smoking program for eight-graders • Outcome -- Number of eight-grade tobacco violations drops from 1998-1999 to 1999-2000. • Did your smoking program work … or ... • Counterfactual -- principal shifts school’s enforcement focus away from tobacco to weapons & threats in 1999-2000-- violations would have dropped anyway!

  9. The 7 Deadly Sins are ... 1. Using Bad Measures 2. Underestimating Regression 3. Underestimating Maturation 4. Underestimating Testing Effects 5. Underestimating Local History 6. Selected Groups 7. Using Bad Statistics

  10. Deadly Sin #1 • Bad Measures • Tests • Surveys • questionnaires • Archival Data • data you get from someone else • published survey data, school records

  11. BAD Tests • Look Out For • Homemade tests • Solutions: Use published (standardized) tests • Look For -- Internal Consistency (Reliability), a test’s ability to measure the trait and not error. (Cronbach) a > .72 • Look For -- test-retest reliability, a test’s ability to measure the same trait twice. r > .70

  12. BAD Archival Data • Archival Data -- data you get from someone else • Examples: • number of eight-grade ATOD violations • number of high school tobacco violations • juvenile court referrals • police report on gang activity • office referrals at Ensley Aveune High School

  13. BAD Archival Data • Problem: Are the same procedures being used -- year to year -- to record data • Examples: • principal shifts school’s enforcement focus away from tobacco to weapons & threats • new school secretary records many tobacco violations as “other drug” violations • harddrive crashes -- all ATOD&V data from Ensley Avenue High School is lost for 1999.

  14. BAD Archival Data • Look Out For • All archive data • Solutions • Sherlock Holmes approach • Look For • changes in policy • changes in personnel • read the find print

  15. Pair off and ... • Describe your program • Describe how you could use bad measures in your program. • Have your partner do the same • Five minutes total

  16. Seven Deadly Sins of Program Evaluation Please See Insert 2

  17. Deadly Sin #2 Underestimating Regression • When measuring the same thing twice • extreme scores will become less extreme for no real reason • Look Out For • Giving a person the same test twice • Forming groups based upon a pre-test score.

  18. Don’t Form Groups Based upon Pre-Test Scores

  19. Don’t Form Extreme Groups Please See Insert 3

  20. Deadly Sin #2 Solution • Don’t Form Extreme Groups • Form groups based upon random assignment • flip a coin!

  21. Deadly Sin #3 Underestimating Maturation • Participants “Grow Up” between pre-test and post-test • Example: Behavioral and Emotional Rating Scale’s Interpersonal Strength subscale shows an increase between the pre-test (beginning of ninth grade) and post-test (end of ninth grade) • Effect of program which targeted individual risk factors … or ... • normal growth in Interpersonal Strength during the first year of high school?

  22. Maturation • Look Out For • Long test-retest intervals (some tests list acceptable intervals) • test-retest intervals during “growth spurts” • Solution • Avoid above warning signs • Use a control group

  23. Deadly Sin #4 Underestimating Testing Effects • Pre-test influences both behavior and/or responses on the post-test. • Example: IQ Tests Pre-test ® Refusal Skill Training ® Post-test • Is the positive outcome on the post-test caused by the training or the pre-test?

  24. Testing • Look Out For • Obvious (Transparent) tests • Highly Inflammatory (Reactive) tests • Solutions • Avoid warning signs • Use a control group

  25. Deadly Sin #5 Underestimating “Local History” • other non-treatment event influences treatment group Example • 80% of FAST families evicted during FAST program • School-wide anti-drug curriculum • Drug-related death at school

  26. Local History • Look Out For • Single group-- pre/post-test designs • Solution • Sherlock Holmes Approach • Use a control group

  27. In groups of four … • Find new people • Form a group of four • Describe your program • Each person describe how either a regression, maturation, testing or local history sin could effect your program • Help out your partners! • Ten to Fifteen Minutes

  28. Seven Deadly Sins of Program Evaluation • Please See Insert 4

  29. Control Groups Participants are randomly assigned to either the control or treatment group • Control group is given tests, but not the treatment • This creates a counterfactual

  30. Control Group Random Assignment Pre-test 8 Weeks Nothing Post-test TreatmentGroup Random Assignment Pre-test 8 Week FAST Curriculum Post-test Control Group Design

  31. Control Group Design • Random Control/Treatment Design Eliminates • Regression • Maturation • Testing • Local History

  32. Deadly Sin #6 Selected Groups • Instead of Randomly Forming groups … • Participants get to choose which group to join • Groups formed by a criterion • FAST - teachers identify children most likely to benefit • Regression

  33. Selected Groups • Look Out For • Participants Choosing • Participants Being Selected • Solution • Random Assignment to control and treatment groups

  34. Deadly Sin #7 BadStatistics • Conducting Multiple Statistical Tests • Conducting Statistical Tests on Small Samples

  35. Conducting Multiple Statistical Tests … or begging for a Type I Statistical Error • Look Out For • Conducting several t-tests or chi-squared tests • Solution • Find a statistician

  36. Finding A Statistician • Local College • Psychology, Sociology or Math Department • Professor • Class Project • Senior Thesis • Remember College Time-Line

  37. Conducting Statistical Tests on Small Samples … or begging for a Type II Statistical Error • Look Out For • groups with less than 15 persons • Solution • Don’t do statistics • Find a statistician • Get more people

  38. Find a new partner and ... • Describe your program • Discuss how you would use a random control/treatment group design • What problems would you encounter trying to randomly assign participates to control versus treatment groups?

  39. Inspirational Quote “Bad data is free. Good data costs money.” -- Bill Ashton

  40. The Cost of Evaluation • Does your funder require Effects Evaluation? • Yes, then get evaluation money from funder • No, then ask yourself, “do I need to do this?” • Will evaluation increase your chances of getting new funding? • Yes, then find funding for evaluation and accept risk

  41. Rights of Use of This Material • Some trainers are very protective of their materials – they’re afraid that they’re giving away their business. I feel that freely distributing information like this is just good advertising for a trainer or consultant. So please use my material as you see fit; with the provision that you, in print, reference me. Please use the following information – in full: • William Ashton, Ph.D. • The City University of New York, York College • Department of Political Science and Psychology • www.york.cuny.edu/~washton