Table 1.

Effects of treatment on performance

 Dependent variable: Parameter Optimum found logistic (1) Best solution OLS (2) No. of unique solutions Poisson (3) Mean solution OLS (4) CT −0.900* 0.285*** −0.240*** −0.098 (0.418) (0.053) (0.059) (0.107) NT −0.444 0.074 0.103* 0.351*** (0.426) (0.051) (0.040) (0.104) CT with storage −0.584 0.238*** −0.532*** −0.400*** (0.350) (0.047) (0.061) (0.121) IT with storage −0.541 0.133*** −0.344*** −0.271* (0.389) (0.048) (0.054) (0.116) NT with storage −0.672* 0.125** −0.121* 0.114 (0.328) (0.043) (0.050) (0.113) Problem 2 2.200*** −0.479*** −0.029 −0.571*** (0.306) (0.032) (0.034) (0.063) Problem 3 1.225*** −0.130** −0.097** −0.038 (0.279) (0.046) (0.034) (0.078)) Problem 4 −0.288 0.345*** −0.019 0.064 (0.326) (0.042) (0.033) (0.064) Problem 5 −0.531 0.270*** 0.006 0.285*** (0.330) (0.037) (0.034) (.062) Log(prob. order) −0.093 −0.042 −0.237*** −0.459*** (0.174) (0.024) (0.018) (0.038) Best pretest 0.102 −0.068*** −0.025** −0.046* (0.063) (0.008) (0.008) (0.020) Own pretest −0.078*** (0.016) Round −0.191*** 0.009 Round2 0.005*** 0.000 Constant −0.865 1.000*** 3.719*** 4.371*** (0.514) (0.074) (0.065) 0.184 Observations 514 514 514 26,214
• Columns 1–3: unit of observation is a whole trial; column 4: unit of observation is a single solution. For columns 2 and 4, the dependent variable is solution distance [measured as log(1+ difference from optimal distance)], so lower numbers correspond to better performance.*P < 0.05; **P < 0.01; ***P < 0.001