﻿BLACKWOOD, LOUIS (2016). EJSP CODING FOR STUDIES 1 - 4 STUDY 1 CODING Coding for ChooseY variable. compute R_Choose_R = 3-Choose_R. compute choose = mean (Choose_1, R_Choose_R). if (choose=2) chooseY = 1 . if (choose=1) chooseY = -1 . execute . Coding for 'negotiating strategy condition'. if (cont_a=1) negstrat = 0 . if (cont_b=1) negstrat = 0 . if (con_1a=1) negstrat = 1 . if (con_1b=1) negstrat = 1 . if (con_2a=1) negstrat = -1 . if (con_2b=1) negstrat = -1 . execute . Coding for 'effective' variable. compute Effect_6_r = 8-Effect_6. compute Effect_3_r = 8-Effect_3. compute effective_4items = mean.3 (Effect_1, Effect_6_r, Effect_3_r, Effect_4). Coding for 'prototypical' variable. compute Proto_1_1_r = 8-Proto_1_1. compute Proto_1_5_r = 8-Proto_1_5. compute prototypical_4items = mean.3 (Proto_1_1_r, Proto_1_2, Proto_1_3, Proto_1_5_r). Contrasts. if (negstrat=0) ognoinfo = 2 . if (negstrat~=0) ognoinfo = -1 . execute . STUDY 2 CODING Coding for 'chooseA' variable. if (choose=2) chooseA = -1 . if (choose=1) chooseA = 1 . execute . Coding for 'ogpref' variable. if (control=1) ogpref = 0 . if (condition_1=1) ogpref = 1 . if (condition_2=1) ogpref = -1 . execute . value labels ogpref 1 'OG prefers A' 0 'no info control' -1 'OG prefers not A' . execute. Coding for 'effective' variable. compute Norm_2_1R = 8 - Norm_2_1. compute effective = mean (Effec_1_1, Effec_3_1, Norm_2_1R). Coding for 'protoypical' variable. compute Proto_2_1R = 8 - Proto_2_1. compute prototypical = mean (Proto_1_1, Proto_2_1R). Contrasts. if (ogpref=0) ognoinfo = 2 . if (ogpref~=0) ognoinfo = -1 . execute . STUDY 3 CODING Coding for 'ChooseN' variable. compute R_Choose_R = 3-Choose_R. compute ChooseO = mean (Choose, R_Choose_R). compute ChooseN = 3 - ChooseO. Coding for 'ognegotiate' variable if (con_1a=1) ognegotiate = 1 . if (con_2a=1) ognegotiate = 1 . if (con_1b=1) ognegotiate = -1 . if (con_2b=1) ognegotiate = -1 . execute . Coding for 'N_effective' variable. compute Effect_6_r = 8-Effect_6. compute Effect_3_r = 8-Effect_3. compute Q52_1_r = 8-Q52_1. compute Q52_4_r = 8-Q52_4. compute effect_better = mean.1 (Effect_1, Q52_1_r). compute effect_effective = mean (Effect_3_r, Q52_3). compute effect_influence_og = mean (Effect_4, Q52_4_r). compute effect_influential_og = mean (Effect_6_r, Q52_6). compute effectiveness = mean(effect_better, effect_effective, effect_influence_og, effect_influential_og ) . execute . compute N_effective = 8 - effectiveness. Coding for 'N_prototypical' variable. compute Proto_1_1_r = 8-Proto_1_1. compute Proto_1_5_r = 8-Proto_1_5. compute Q48_2_r = 8-Q48_2. compute Q48_3_r = 8-Q48_3. compute proto_typical = mean.1 (Proto_1_1_r, Q48_1). compute proto_characteristic = mean.1 (Proto_1_2, Q48_2_r). compute proto_values = mean.1 (Proto_1_3, Q48_3_r). compute proto_embody = mean.1 (Proto_1_5_r, Q48_5). compute prototypicality = mean(proto_typical, proto_characteristic, proto_values, proto_embody ) . execute . compute N_prototypical = 8 - prototypicality. STUDY 4 CODING Recoding to correct data entry. RECODE Q11.2_1 Q11.2_2 Q11.2_3 Q11.2_4 Q11.2_6 Q11.2_7 (2=1) (3=2) (4=3) (5=4) (6=5) (9=6) (10=7) (MISSING=SYSMIS). EXECUTE. Coding for 'ChooseN' variable. compute rQ10.1 = 3-Q10.1. compute ChooseN = sum(Q10.2, rQ10.1). Coding for 'ognegotiate' variable. if (Q8.1=1) ognegotiate = -1. if (Q8.2=1) ognegotiate = 1. execute. Coding for 'N_effective' variable. compute rQ11.1_3 = 8-Q11.1_3. compute rQ11.1_6 = 8-Q11.1_6. compute rQ11.2_3 = 8-Q11.2_3. compute rQ11.2_6 = 8-Q11.2_6. compute Q11.1 = mean.4 (Q11.1_1, rQ11.1_3, Q11.1_4, rQ11.1_6). compute Q11.2 = mean.4 (Q11.2_1, rQ11.2_3, Q11.2_4, rQ11.2_6). compute rQ11.1 = 8 - Q11.1. compute N_effective = mean.1 (rQ11.1, Q11.2). Coding for 'N_prototypical' variable. compute RQ12.1_1 = 8-Q12.1_1. compute RQ12.1_5 = 8-Q12.1_5. compute RQ12.2_1 = 8-Q12.2_1. compute RQ12.2_5 = 8-Q12.2_5. compute Q12.1 = mean.3 (RQ12.1_1, Q12.1_2, Q12.1_3, RQ12.1_5). compute Q12.2 = mean.3 (RQ12.2_1, Q12.2_2, Q12.2_3, RQ12.2_5). compute rQ12.1 = 8 - Q12.1. compute N_prototypical = mean.1 (rQ12.1, Q12.2).