first-edits

README.md

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-**projoint** is a package for more general, more straightforward, and more creative conjoint analysis. It estimates---either *profile-level* or *choice-level*---Marginal Means (MMs) and Average Marginal Component Effects (AMCEs) based on a conjoint survey experiment. It produces more reliable estimates after correcting measurement error bias and other problems known in the literature (e.g., having the same levels for the attribute of interest). Furthermore, it presents a more general framework so that researchers can answer a range of substantively important questions more straightforwardly.
+**projoint** is a package for general, straightforward, and creative conjoint analysis. It estimates---either *profile-level* or *choice-level*---Marginal Means (MMs) and Average Marginal Component Effects (AMCEs) based on a conjoint survey experiment. It produces more reliable estimates after correcting measurement error bias and other problems known in the literature (e.g., having the same levels for the attribute of interest). Furthermore, it presents a more general framework so that researchers can answer a much wider range of substantively important questions.
 
 ### Installation
 
@@ -39,9 +39,9 @@ plot(output)
 
 ### Relevant Article
 
-For our framework and methods, please read and cite the following article:
+Our framework and methods can be found in this paper:
 
-* Katherine Clayton, Yusaku Horiuchi, Aaron R. Kaufman, Gary King, and Mayya Komisarchik. “Correcting Measurement Error Bias in Conjoint Survey Experiments”. Working Paper. [[Paper](https://gking.harvard.edu/sites/scholar.harvard.edu/files/gking/files/conerr.pdf)] [[Supplementary Appendix](https://gking.harvard.edu/sites/scholar.harvard.edu/files/gking/files/conerr-supp.pdf)]
+* Katherine Clayton, Yusaku Horiuchi, Aaron R. Kaufman, Gary King, and Mayya Komisarchik. “Correcting Measurement Error Bias in Conjoint Survey Experiments”. Working Paper. [[Paper](https://gking.harvard.edu/conjointE)]
   + **Abstract:** Conjoint survey designs are spreading across the social sciences due to their unusual capacity to estimate many causal effects from a single randomized experiment. Unfortunately, by their ability to mirror complicated real-world choices, these designs often generate substantial measurement errors and thus bias. We replicate both the data collection and analysis from eight prominent conjoint studies, all of which closely reproduce published results, and show that a large proportion of observed variation in answers to conjoint questions is effectively random noise. We then discover a common empirical pattern in how measurement error appears in conjoint studies and, with it, introduce an easy-to-use statistical method to correct the bias.
 
 ### Notes