hat
San yo pa done yo anvan tout koreksyon, li difisil pou konnen egzakteman ki jan pi plis efikas yon diferans-an-diferans estimator ta ka nan ka sa a. Men, nou ka gade nan lòt eksperyans ki gen rapò pou yon lide ki graj. Deng et al. (2013) rapòte ke lè l sèvi avèk yon fòm estimatè diferans-an-diferans yo, yo te kapab diminye divèjans estimasyon yo pa apeprè 50% nan twa diferan eksperyans sou entènèt; Rezilta ki sanble yo te rapòte pa Xie and Aurisset (2016) . Sa a rediksyon divèjans 50% vle di ke chèchè yo Kontagyon Emosyonèl ta ka yo te kapab koupe echantiyon yo nan mwatye si yo te itilize yon metòd analiz yon ti kras diferan. Nan lòt mo, ak yon chanjman ti nan analiz la, 350,000 moun ta ka yo te touye patisipasyon nan eksperyans lan.
eng
Without having the raw data, it is difficult to know exactly how much more efficient a difference-in-differences estimator would have been in this case. But we can look at other related experiments for a rough idea. Deng et al. (2013) reported that by using a form of the difference-in-differences estimator, they were able to reduce the variance of their estimates by about 50% in three different online experiments; similar results have been reported by Xie and Aurisset (2016). This 50% variance reduction means that the Emotional Contagion researchers might have been able to cut their sample in half if they had used a slightly different analysis method. In other words, with a tiny change in the analysis, 350,000 people might have been spared participation in the experiment.