The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Instrumental Variables in Sociology and the Social Sciences
Abstract
Instrumental variable (IV) methods provide a powerful but underutilized tool to address many common problems with observational sociological data. Key to their successful use is having IVs that are uncorrelated with an equation's disturbance and that are sufficiently strongly related to the problematic endogenous covariates. This review briefly defines IVs, summarizes their origins, and describes their use in multiple regression, simultaneous equation models, factor analysis, latent variable structural equation models, and limited dependent variable models. It defines and contrasts three methods of selecting IVs: auxiliary instrumental variable, model implied instrumental variable, and randomized instrumental variable. It provides overidentification tests and weak IV diagnostics as methods to evaluate the quality of IVs. I review the use of IVs in models that assume heterogeneous causal effects. Another section summarizes the use of IVs in contemporary sociological publications. The conclusion suggests ways to improve the use of IVs and suggests that there are many areas in which IVs could be profitably used in sociological research.
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Cites
Mostly Harmless Econometrics: An Empiricist's Companion
Angrist, J. D., & Pischke, J.-S. (2009) · doi:10.1515/9781400829828 · Cited by 9,467
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · Journal of Educational Psychology · doi:10.1037/h0037350 · Cited by 9,436
Microeconometrics: Methods and Applications
Cameron, A. C., & Trivedi, P. K. (2005) · doi:10.1017/cbo9780511811241 · Cited by 8,092
Mostly Harmless Econometrics: An Empiricist's Companion
Angrist, J. D., & Pischke, J.-S. (2008) · doi:10.2307/j.ctvcm4j72 · Cited by 5,051
Identification of Causal Effects Using Instrumental Variables
Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476902 · Cited by 4,230
Identification and estimation of local average treatment effects
Imbens, G. W., & Angrist, J. D. (1994) · Econometrica · doi:10.2307/2951620 · Cited by 4,101
Angrist, J. D., & Krueger, A. B. (2001) · Journal of Economic Perspectives · doi:10.1257/jep.15.4.69 · Cited by 2,355
Counterfactuals and Causal Inference: Methods and Principles for Social Research
Morgan, S. L., & Winship, C. (2007) · doi:10.1017/cbo9780511804564 · Cited by 1,146
Maximum Likelihood Estimation of the Polychoric Correlation Coefficient
Olsson, U. (1979) · Psychometrika · doi:10.1007/bf02296207 · Cited by 1,127
Bound, J., Jaeger, D. A., & Baker, R. M. (1995) · Journal of the American Statistical Association · doi:10.2307/2291055 · Cited by 1,018
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Metadata
- Authors
- Bollen, K. A.
- Venue
- Annual Review of Sociology
- Publisher
- Annual Reviews
- Published
- 2012-08-11
- Volume
- 38
- Issue
- 1
- Pages
- 37-72
- ISSN
- 0360-0572 (ISSN-L)
- 1545-2115
- OpenAlex
- W2104615113
- Cited by
- 235
- References
- 14
- FWCI
- 1.79
- Type
- journal article
- Language
- en
- Open access
- closed
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