Abbreviated Interrupted Time-Series - Institute for Policy Research

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Results: Average SAT scores <strong>for</strong> students reporting high school GPA ≥ 3.00 Avg SAT Scores, Students Reporting B or Better 1,060 1,040 SAT Scores 1,020 1,000 980 960 940 Southeast US GA 90 92 94 96 98 2000 Year
Adding a nonequivalent dependent variable to the time series NEDV: A dependent variable that is predicted not to change because of treatment, but is expected to respond to some or all of the contextually important internal validity threats in the same way as the target outcome
Page 1 and 2: Abbreviated Interrupted Time‐Seri
Page 3 and 4: The effect can be a change in inter
Page 5 and 6: The effect can be a change in slope
Page 7 and 8: The effect can be delayed in time.
Page 9 and 10: Interrupted Time Series Can Provide
Page 11 and 12: Meeting ITS Conditions in Education
Page 13 and 14: • O X O Theoretical Rationales fo
Page 15 and 16: O O O X O • Initial standing asse
Page 17 and 18: Threats to Validity: History • Wi
Page 19 and 20: Threat to SCV • When a treatment
Page 21 and 22: External Validity • The essence o
Page 23 and 24: O X O O O • Now we add a comparis
Page 25 and 26: O O O X O O O O O • Now mean diff
Page 27: Results: Percent of Students Obtain
Page 31 and 32: Note that the outcome variable (ope
Page 33 and 34: Only the targeted dependent variabl
Page 35 and 36: Example: Evaluating No Child Left B
Page 37 and 38: Contrast Type 1 & 2 • Contrast 1:
Page 39 and 40: Hypothetical NCLB effects on public
Page 41 and 42: Analytic Model • NCLB Public vs.
Page 43 and 44: Main NAEP 4 th grade math scores by
Page 45 and 46: Difference in differences in Total
Page 49 and 50: Trend NAEP Time Series Graphs Publi
Page 51 and 52: Trend NAEP 8 th grade math scores b
Page 53 and 54: Public vs Private School Findings
Page 55 and 56: Contrast 2: Comparing States that v
Page 57 and 58: Evidence of Differing Standards Hig
Page 59 and 60: Main NAEP Time Series Graphs States
Page 61 and 62: ], ) ( ) ( ) ( [ ) _ _ ( ) _ _ ( )
Page 65 and 66: Difference in differences in 2009 T
Page 67 and 68: Overall Conclusions • Similar res
Page 69 and 70: Basic Insight of D & J • NCLB pas
Page 71 and 72: D & J Results: 4 th Grade Math
Page 73 and 74: Dee and Jacob Results • Similar c
Page 75 and 76: 4 th Grade Reading
Page 77 and 78: The Use of Multiple Comparison Grou
Page 79 and 80:
Adding Treatment Introduction and R
Page 81 and 82:
Medication decreased hyperactivity,
Page 83 and 84:
Example: Chorpita • A child with
Page 85 and 86:
Multiple Baselines on Different Uni
Page 88 and 89:
The effects of screening for phenyl
Page 90 and 91:
Multilevel Models: Current Work •
Page 92 and 93:
Potential drawbacks of using HLM to
Page 94 and 95:
Stuart Data Example (Stuart, 1967)
Page 96 and 97:
Level 2 data: Persons Level-2 data
Page 98 and 99:
HLM: Linear Model Estimates Final e
Page 100 and 101:
HLM Output from Quadratic Model: Fi
Page 102 and 103:
So Simple: What Could Go Wrong? •
Page 104 and 105:
Problematically • Different combi
Page 106 and 107:
Do you have any examples to share w
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