*** Check and Correct for Skew** mvtest norm Valuation, stats(all) lnskew0 Logged_Valuation = Valuation mvtest norm Logged_Valuation, stats(all) *** Check for order effects** reg Logged_Valuation Perspective_Order , cluster(Subject) reg Logged_Valuation Gamble_Order , cluster(Subject) *** Center variables*** program center args v c confirm variable `v' confirm new variable `c' quietly sum `v' gen `c' = `v' - r(mean) end center Perspective Pers_C center Deliberation_Time DT_C center Probability_Win Prob_Win_C center Outcome_Win Out_Win_C center EV EV_C center Perspective_Order Perspective_Order_Center center Gamble_Order Gamble_Order_C ***Analyses*** *Perspective and Deliberation Time Main Effects and Interaction (H1)** xi3: reg Logged_Valuation Pers_C*DT_C, cluster(Subject) *Variance Tests* robvar Logged_Valuation, by(Deliberation_Time) bysort Subject: robvar Logged_Valuation, by(Deliberation_Time) *Effect of Probabilities and Outcomes* xi3: reg Logged_Valuation Prob_Win_C*DT_C , cluster(Subject) xi3: reg Logged_Valuation Out_Win_C*DT_C , cluster(Subject) **Graph** **Figure 1**** mean Valuation , over( Perspective Deliberation_Time) cluster(Subject) matrix m = e(b) matrix m = m' matrix s = e(V) matrix s2 = J(6,1,0) matrix s2[1,1] = s[1,1]^0.5 matrix s2[2,1] = s[2,2]^0.5 matrix s2[3,1] = s[3,3]^0.5 matrix s2[4,1] = s[4,4]^0.5 matrix s2[5,1] = s[5,5]^0.5 matrix s2[6,1] = s[6,6]^0.5 matrix list s2 preserve clear svmat m svmat s2 gen n = _n gen buyer = 0 replace buyer = 1 if n >3 gen time = 1 if n == 1 | n ==4 replace time = 2 if n == 2 | n ==5 replace time = 3 if n == 3 | n ==6 gen ll = m1 - s21 gen ul = m1 + s21 twoway (line m1 time if buyer == 0) (line m1 time if buyer == 1) /// (rcap ll ul time if buyer == 0) (rcap ll ul time if buyer == 1), /// xlabel( 1 "5 sec" 2 "10 sec" 3 "15 sec") xtitle("") /// ylabel(,format(%9.2fc)) ytitle(Valuation in US-Dollar) /// legend ( off) /// xsize(4) xscale(r(0.8 3.2))