Instrumental variables methods in experimental criminological research: what, why and how

By Joshua D. Angrist. 2006

219 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Soil Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/38722dbbd32a105d7e907fdf2f5a5776afff71d7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Sociology and Political Science

1. G. Bascle
2. E. Hedberg
3. S. Thompson
4. Stephen Burgess
5. Decio Coviello
6. C. Katz
7. E. Gondolf
8. Mario Mariniello

In Soil Science

1. G. Bascle
2. E. Hedberg
3. S. Thompson
4. Stephen Burgess
5. Dylan S. Small
6. Decio Coviello
7. C. Katz
8. E. Gondolf

In Statistics and Probability

1. Stephen Burgess
2. S. Thompson
3. Dylan S. Small
4. G. Bascle
5. E. Hedberg
6. Decio Coviello
7. C. Katz
8. E. Gondolf

Citations in the corpus

• Do Study Abroad Programs Enhance the Employability of Graduates? (2015, unknown): “This is known in the literature as the “local average treatment effect” (LATE) (see Angrist and Imbens, 1994; Angrist et al., 1996). Angrist and Imbens (1994) show that 2SLS estimates capture the effect of ”treatment” on the population whose treatment status is influenced by the instrument; they ref”
• Controlling for endogeneity with instrumental variables in strategic management research (2008, methodology): “Researchers should refrain from using such methods and use, instead, a linear regression for the first stage (see Angrist, 2001, 2006; Angrist and Krueger, 2001; and, for a more general treatment, Kelejian, 1971).”
• Body-Worn Cameras and Citizen Interactions with Police Officers: Estimating Plausible Effects Given Varying Compliance Levels (2017, methodology): “Also, given that our outcome is dichotomous, a generalized liner model is more appropriate in cases of low event rates, especially when employing controls, but for simple models the least squares approach provides unbiased estimates (Angrist, 2006).”
• THEORETICAL AND RESEARCH SUPPORT FOR THE DULUTH MODEL: A REPLY TO DUTTON AND CORVO (2007, background): “A recent article in the Journal of Experimental Criminology endorses instrumental variable analysis as an alternative approach to experimental studies that face implementation problems, as well as an approach that has been widely used in public health research where experimental designs are frequent”
• A review of instrumental variable estimators for Mendelian randomization (2015, background): “(The latter is usually one, although analyses with multiple exposure variables have also been considered.(115,116)) When there is more than one IV for a single exposure, separate causal estimates can be calculated using each IV in turn.”
• Publicity requirements in public procurement: Evidence from a regression discontinuity design ☆ (2012, methodology): “48 See Section 2 for details on publicity requirements.”
• Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects (2015, unknown): “The possibility of including multiple risk factors in an instrumental-variable analysis is discussed in many econometric textbooks (8), and applied instrumental-variable analyses involvingmultiple risk factors have been performed (9, 10), but we are unaware of any application of the approach in gene”