Bandingkan metode

	Regression Discontinuity in Elections	Most Similar Systems Design
Bidang	Political Science	Political Science
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	2008	1970
Pencetus	David S. Lee (electoral application); broader RD tradition	John Stuart Mill (method of difference); Przeworski & Teune (systems framing)
Tipe	Quasi-experimental causal design using a vote-share threshold	Small-N comparative case-selection design
Sumber perintis	Lee, D. S. (2008). Randomized Experiments from Non-random Selection in U.S. House Elections. Journal of Econometrics, 142(2), 675–697.	Przeworski, A., & Teune, H. (1970). The Logic of Comparative Social Inquiry. New York: Wiley-Interscience. ISBN: 9780471701422
Alias	Close-election RD, Electoral regression discontinuity, Vote-share RD design, Incumbency-advantage RD	MSSD, Most similar cases design, Mill's method of difference, Comparable cases strategy
Terkait	3	3
Ringkasan	Regression discontinuity in elections is a quasi-experimental design that exploits the sharp winning threshold in electoral contests to estimate causal effects of holding office. Just above the threshold a candidate or party wins; just below, it loses. In very close races, which side ends up just over the line is plausibly as good as random, so comparing the later outcomes of bare winners and bare losers identifies the causal effect of winning — most famously the incumbency advantage — without confounding by candidate or district quality.	The most similar systems design (MSSD) is a small-N comparative strategy that selects cases as alike as possible on many background characteristics but differing on the outcome of interest. By matching cases so that most potential confounders are held roughly constant, the design isolates the few factors that vary alongside the outcome as the candidate causes. Rooted in John Stuart Mill's method of difference and named by Przeworski and Teune, it is a cornerstone of comparative politics for drawing causal inferences from a handful of countries or cases.