Regression-Discontinuity Design - Institute for Policy Research

More documents

Recommendations

Info

Power Considerations Common to Randomized Experiments and RDD 1. Sample size ‐‐ increase N 2. Distribution of outcome variable ‐ normal if possible 3. Misallocation rates ‐ lower the better 4. R‐square of school‐ and student‐level covariates ‐ as high as possible 5. Clustered designs and intra‐class correlations ‐‐ as low as possible or as reduced as possible through the use of covariates explaining clustering
Variance of Impact Estimator in RDD Var rd (α^ 1 ) = σ 2 (1-R rd2 ) np(1-p)(1-ns-co) 2 (1-R ts2 ) • All other terms are the same, except: • R ts2 is the square of the correlation between treatment status and assignment variable – higher the value less power (and more co-linearity)
Page 1 and 2:
Regression‐Discontinuity Design D
Page 3 and 4:
Design Overview Design Examples Vis
Page 5 and 6:
Real Examples of RD Reading First b
Page 7 and 8:
RDD Visual Depiction Comparison Tre
Page 9 and 10:
Two Rationales for RDD 1. Selection
Page 11 and 12:
Two Rationales for RDD 1. Selection
Page 13 and 14:
1. Overrides to the cutoff: Sharp,
Page 15 and 16:
Fuzzy RD Designs • Requires a dis
Page 17 and 18:
Requirements for Estimating Treatme
Page 19 and 20:
Implementation Threats to RD Design
Page 21 and 22:
Example: AYP Data from Texas Densit
Page 23 and 24:
What to do? • This is a big probl
Page 25 and 26:
Analytic Threats to RD Design: Miss
Page 27 and 28:
Nonlinearities in Functional Form
Page 29 and 30:
Here we see a discontinuity between
Page 31 and 32:
Functional Form: Interactions • S
Page 33 and 34:
If we superimpose the regression li
Page 35 and 36:
Here we see an example where the tr
Page 37 and 38:
Adding Nonlinear Terms to the Model
Page 39 and 40:
Adding Nonlinear and Interaction Te
Page 41 and 42:
Adding Terms, Continued • When in
Page 43 and 44:
Example of Parametric Plot used aga
Page 45 and 46:
Non‐parametric Approach lim x↓c
Page 47 and 48:
Non‐parametric plot Local linear
Page 49 and 50:
Semi‐Parametric Plot RD data from
Page 51 and 52:
What to do? • Check to see whethe
Page 53 and 54:
Shadish, Galindo, Wong, V., Steiner
Page 55 and 56: RD Design assumption 2 • Continui
Page 57 and 58: Implementation assumption 2 • No
Page 59 and 60: Analytic assumption 1 continued •
Page 61 and 62: Shadish et al. (under review) Vocab
Page 63 and 64: RDD Example 2
Page 65 and 66: Best case scenario - regression lin
Page 67 and 68: Correct specification of functional
Page 69 and 70: What to do (2) • Parametric appro
Page 71 and 72: What to do (4) • State‐of‐art
Page 73 and 74: What to do (5) • Move to a tie br
Page 75 and 76: Example Cocaine Project • Include
Page 77 and 78: What to do (7) • Estimation throu
Page 79 and 80: 2. Dealing with Overrides to the cu
Page 81 and 82: 2. Overrides to the cutoff • Mult
Page 83 and 84: Specification of Functional Form Pa
Page 85 and 86: Specification of Functional Form No
Page 87 and 88: Overrides to cutoff (2) Alternative
Page 89 and 90: Final estimates Function al form Pa
Page 91 and 92: Addressing Potential Issues with us
Page 93 and 94: IS Wong et al. a Fallible RD or Ins
Page 95 and 96: African‐American Home language Sp
Page 97 and 98: Fallible RD? • Now we look for di
Page 99 and 100: McCrary Test for New Jersey
Page 101 and 102: Summarizing Checks - Conditional Me
Page 103 and 104: Other Kinds of Threats with RD 1. L
Page 105: Variance of Impact Estimator in RA
Page 109 and 110: Power Considerations Unique to RDD
Page 111 and 112: Generalizing Beyond The Cut‐off I
Page 113 and 114: Observed vs Counterfactual Outcomes
Page 115 and 116: Questions About Generalizing From R
Page 117 and 118: What Do We Mean By Comparison Group
Page 119 and 120: Our Purpose • Use experimental da
Page 121 and 122: What Is The Target Parameter? • A
Page 127 and 128: What About The Above The Cut‐Off
Page 129 and 130: Root Mean Square Error In Experimen
Page 131 and 132: Post-Treatment Medicaid Expenditure
Page 133 and 134: Results For Number of Unpaid Worker
Page 135 and 136: Results For Expenditures With Expen
Page 137 and 138: Conclusions re Using a Comparison R
Page 139 and 140: Implications for Practice • Which
Page 141 and 142: RD with Multiple Cutoffs Design (as
Page 143 and 144: Analyzing RDs with Multiple Cutoffs
Page 145 and 146: Examples • Black, Galdo, and Smit
Page 147 and 148: Issue 2: To pool or not pool across
Page 149 and 150: Pooling is Already Common in Social
Page 151 and 152: What is Special about Pooling RD Es
Page 153 and 154: Estimating Treatment Effects across
Page 155 and 156: Estimating Treatment Effects across
Page 157 and 158:
Generalizing Across Multiple Assign
Page 159 and 160:
Multivariate RDD with Two Assignmen
Page 161 and 162:
Recent Education Examples of RDDs w
Page 163 and 164:
Frontier‐specific Effect (τ R )
Page 165 and 166:
Requirements for a valid Multivaria
Page 167 and 168:
Frontier Approach Estimates the dis
Page 169 and 170:
Univariate Approach Addresses dimen
Page 171 and 172:
Monte Carlo Study Wong, V., Steiner
Page 173:
Implications for Practice • Which
show all

Regression-Discontinuity Design - Institute for Policy Research

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?