Statistical Analysis of List Experiments
By Graeme Blair, Kosuke Imai. Political Analysis, 2012
554 citations recorded by the source corpus.
Subfields: Pharmacology, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/6be23d2ce0b9d67c0bc0e9493d8227ea1808e5f6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Pharmacology
In Statistics and Probability
- Kenneth F. Greene
- Bethany Park
- A. Coppock
- Margaret Moor
- Graeme Blair
- Kosuke Imai
- Sandip Sukhtankar
- K. Muralidharan
Citations in the corpus
- When to Worry about Sensitivity Bias: A Social Reference Theory and Evidence from 30 Years of List Experiments (2020, methodology): “In the 166 list experiments for which we have sufficient information, 160 pass the design-effects test described in Blair and Imai (2012).”
- Corruption as a Self-Fulfilling Prophecy: Evidence from a Survey Experiment in Costa Rica (2015, unknown): “The first is called honesty given protection: given the protection afforded by the crosswise model, all respondents are assumed to respond as prompted by the technique (cf. Gingerich, 2010; Blair and Imai, 2012).”
- Building State Capacity: Evidence from Biometric Smartcards in India (2016, background): “Duflo et al. (2012) find, for example, that digital cameras and monetary incentives increased teacher attendance and test scores in Indian schools (when implemented in schools run by an NGO).”
- National Culture and Social Desirability Bias in Measuring Public Service Motivation (2016, background): “The direct questions of all sensitive statements should be located after the three control items in the control group (Blair & Imai, 2012).”
- Using the Predicted Responses from List Experiments as Explanatory Variables in Regression Models (2015, methodology): “Second, statistical methods have been developed for testing whether the key assumptions of list experiments are violated and for reducing the potential biases that arise from violation of such assumptions (Blair and Imai 2012).”
- Paying Attention to Inattentive Survey Respondents (2019, background): “Under two assumptions—nodesign effect and no liars—the difference between the average response under treatment and control consistently estimates the level of support for the sensitive item (Imai 2011; Blair and Imai 2012).”