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Regression Discontinuity
Regression discontinuity (RD) is a two-group, pre- post, quasi-experimental method for evaluating the effect of an intervention. Assignment to the experimental or comparison group is based on the value of some variable. That variable can be either the baseline value of the outcome itself or some other variable. The effect of the intervention is assessed by looking at the discontinuity of the regression lines between the groups at the cutoff point.
History
The RD design was introduced in 1960 by Donald Thistlewaite and Donald Campbell to evaluate the effects of compensatory educational interventions. Since that time, it has been used in areas such as criminal justice, social welfare, economics, and pharmaceutical health services research, although it is still a relatively rarely used design. Indeed, a recent search ...
- Bayesian Statistics
- Descriptive Statistics
- Central Tendency, Measures of
- Cohen’s d Statistic
- Cohen’s f Statistic
- Correspondence Analysis
- Descriptive Statistics
- Effect Size, Measures of
- Eta-Squared
- Factor Loadings
- Mean
- Median
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- Partial Eta-Squared
- Range
- Relative Measures of Dispersion
- Standard Deviation
- Statistic
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- Variability, Measure of
- Variance
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- z Distribution
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- Data Visualization
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- Visual Analysis
- Visual Display of Quantitative Information
- Hypothesis Testing
- p Value
- Alternative Hypotheses
- Beta
- Critical Value
- Decision Rule
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- Hypothesis
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- Nonsignificance
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- Significance Level, Interpretation and Construction
- Significance, Statistical
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- Type I Error
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- Important Publications
- “Coefficient Alpha and the Internal Structure of Tests”
- “Convergent and Discriminant Validation by the Multitrait–Multimethod Matrix”
- “Meta-Analysis of Psychotherapy Outcome Studies”
- “On the Theory of Scales of Measurement”
- “Probable Error of a Mean, The”
- “Psychometric Experiments”
- “Sequential Tests of Statistical Hypotheses”
- “Structural Holes: The Social Structure of Competition”
- “Technique for the Measurement of Attitudes, A”
- “Validity”
- Aptitudes and Instructional Methods
- Doctrine of Chances, The
- Logic of Scientific Discovery, The
- Nonparametric Statistics for the Behavioral Sciences
- Probabilistic Models for Some Intelligence and Attainment Tests
- Social Network Analysis Methodsand Applications
- Statistical Power Analysis for the Behavioral Sciences
- Strength of Weak Ties
- Structural Equivalence of Individuals in Social Networks
- Teoria Statistica Delle Classi e Calcolo Delle Probabilità
- Inferential Statistics
- Association, Measures of
- Coefficient of Concordance
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- Underrepresented Groups
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- Social Network Analysis
- Alters
- Connectivity
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- Name Generator
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- Degrees of Freedom
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- Heterogeneity
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- Main Effects
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- Regression Artifacts
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- Robust
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- Root Mean Square Error
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- Statistical Procedures
- F Test
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- t Test, Paired Samples
- Accuracy in Parameter Estimation
- Analysis of Covariance
- Analysis of Variance
- Bartlett’s Test
- Barycentric Discriminant Analysis
- Behrens–Fisher t’ Statistic
- Bivariate Regression
- Bonferroni Procedure
- Bootstrapping
- Canonical Correlation Analysis
- Categorical Data Analysis
- Chi-Square Test
- Cluster Analysis
- Confirmatory Factor Analysis
- Contingency Table Analysis
- Contrast Analysis
- Descriptive Discriminant Analysis
- Diagnostic Classification Modeling
- Dummy Coding
- Duncan’s Multiple Range Test
- Dunnett’s Test
- Effect Coding
- Estimation
- Exploratory Factor Analysis
- Fisher’s Least Significant Difference Test
- Friedman Test
- Greenhouse–Geisser Correction
- Hidden Markov Model
- Hierarchical Linear Modeling
- Holm’s Sequential Bonferroni Procedure
- Jackknife
- Kolmogorov–Smirnov Test
- Kruskal–Wallis Test
- Latent Class Analysis
- Latent Growth Modeling
- Latent Profile Analysis
- Least Squares, Methods of
- Logistic Regression
- Mann–Whitney U Test
- Mauchly Test
- Maximum Likelihood Estimation
- McNemar’s Test
- Mean Comparisons
- Missing Data, Imputation of
- Multidimensional Scaling
- Multiple Comparison Tests
- Multiple Comparisons With Modeling Techniques
- Multiple Regression
- Multivariate Analysis of Variance
- Newman–Keuls Test
- Omnibus Tests
- Pairwise Comparisons
- Path Analysis
- Post Hoc Analysis
- Post Hoc Comparisons
- Predictive Discriminant Analysis
- Principal Components Analysis
- Propensity Score Analysis
- Scheffé Test
- Sequential Analysis
- Sign Test
- Stepwise Model Selection
- Stepwise Regression
- Structural Equation Modeling
- Survival Analysis
- Trend Analysis
- Tukey’s Honestly Significant Difference
- Welch’s t Test
- Wilcoxon Rank Sum Test
- Yates’s Correction
- Statistical Tests
- F Test
- t Test, Independent Samples
- t Test, One Sample
- t Test, Paired Samples
- z Test
- Bartlett’s Test
- Behrens–Fisher t’ Statistic
- Chi-Square Test
- Cochran–Armitage Test for Trend
- Duncan’s Multiple Range Test
- Dunnett’s Test
- Fisher’s Least Significant Difference Test
- Friedman Test
- Hosmer-Lemeshow Test
- Kolmogorov–Smirnov Test
- Kruskal–Wallis Test
- Mann–Whitney U Test
- Mauchly Test
- McNemar’s Test
- Multiple Comparison Tests
- Newman–Keuls Test
- Omnibus Tests
- Scheffé Test
- Sign Test
- Tukey’s Honestly Significant Difference
- Welch’s t Test
- Wilcoxon Rank Sum Test
- Structural Equation Modeling
- Theories, Laws, and Principles
- Central Limit Theorem
- Classical Test Theory
- Correspondence Principle
- Critical Theory
- Diffusion of Innovation Theory
- Falsifiability
- Game Theory
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- Grounded Theory
- Item Response Theory
- Likelihood Principle
- Machine Learning
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- Occam’s Razor
- Paradigm
- Positivism
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- Social Capital Theory
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- Structural Paradigm
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- Weber–Fechner Law
- Types of Variables
- Validity of Scores
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