$FL2@(#) SPSS DATA FILE MS Windows 16.0.1 ################d#########Y@04 Oct 1118:02:54 ###########################PARTICIP####Participant ########################SAFETY ########################ROADRAGE########################GENDER ########################EXPERIEN########################PREVIOUS################ ########SELFCONT########################PERCEIVE#################Male ######? #Female ########################################################################## ############################################################## ###################################################### ##########PARTICIP=particip SAFETY=safety ROADRAGE=RoadRage GENDER=gender EXPERIEN=experience PREVIOUS=previous SELFCONT=selfcontrol PERCEIVE=perceived########################d################### ###windows1252#######eeddgfiffhedddeiggeefdohhgeefdmhigdeidihjeddgdffkgeeeflhlddeediemdde fdgdnfdeedegogdeldihpiedjdijqgddgdhhrfdegdjgsgeejdhhtgddhdfhufdeidgdvddeldiewged gdfhxdddjdedyfeegdjgzgedfdih{edekdgd| geegdjh}deehdjg~gddgdgffeejeihfdegeidddeidiedeendgfgddfeiffdehddgeededjh gddkdifeedfejiiddiegddedgefhdeekdigiddjdedgddgegeeedidfhideiefegdeedej eeenejfgedheghfdefeggdedgdkjhddhekiddegegddeekdjhgddgffhdeedegihdehdfi hdehdgfeddeelffeefehgfedfedgdddhegdgddjdeegdefdggddemefdeeehegiedegeff gedkeghgddgehhdednekhiedhehghedfehggeejemhhddfeeiiedgeijgddheehgddeddk gddfdegdddiegegddhddfgednelhiddjdheeeeeeigddeleheddeeeheheejekiiddgekj ddehdedidegegjddejdddhedgejihddedjidedeeiigdefeehgednejhgeenehhhddjdii fdegdegfddedjggeefejhgeeleihfeegejijedgefhfddedhg STEP 1 Go to analyze, then click on regression and then click binary logistic Select road rage as dependent and other required variables as covariates Go to save and tick probabilities. Click on continue Click ok to run the nalaysis. The output will be Model Summary Step 1 Cox & Snell R Nagelkerke R Square Square -2 Log likelihood 89.965 a .373 .501 a. Estimation terminated at iteration number 5 because parameter estimates changed by less than .001. Classification Tablea Predicted RoadRage Observed Step 1 RoadRage 0 1 Correct 0 45 12 78.9 1 10 33 76.7 Overall Percentage a. The cut value is .500 Percentage 78.0 Variables in the Equation B Step 1 a S.E. Wald df Sig. Exp(B) safety -.468 .231 4.082 1 .043 .626 gender .037 .565 .004 1 .948 1.038 experience .195 .109 3.187 1 .074 1.215 previous .677 .460 2.170 1 .141 1.968 selfcontrol .329 .124 7.028 1 .008 1.389 perceived .943 .233 16.321 1 .000 2.568 -4.839 1.119 18.704 1 .000 .008 Constant a. Variable(s) entered on step 1: safety, gender, experience, previous, selfcontrol, perceived. PART A Go to analyze, Then click on correlate, then click on bivariate. Select all the variables and transfer to the variables section. Click Ok to run the analysis. THE OUTPUT Correlations safety safety Pearson Correlation capacity capacity Pearson Correlation Sig. (2-tailed) N speed Pearson Correlation Sig. (2-tailed) .218 .393** .000 .001 .052 .000 80 80 80 80 80 .552** 1 .462** .244* .546** .000 .029 .000 80 80 80 1 ** .525** .000 .000 1 .552 .000 80 80 ** ** .351 .462 .351 .400 .000 80 80 80 80 80 Pearson Correlation .218 .244 * ** 1 .261* Sig. (2-tailed) .052 .029 .000 80 80 80 80 80 ** ** ** * 1 N flow flow .001 N alignment alignment ** Sig. (2-tailed) N speed ** Pearson Correlation Sig. (2-tailed) .393 .000 .546 .000 .400 .525 .000 .019 .261 .019 N 80 80 80 80 80 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). PART B.: Controlling for speed Go to analyze, Then click on correlate, then click on partial. Select speed as the controlling variables and put the other variables in the variable section. Click okay to run the analysis. The output is Correlations Control Variables speed safety capacity safety Correlation flow .470 .090 .262 Significance (2-tailed) . .000 .429 .020 df 0 77 77 77 Correlation .470 1.000 .073 .402 Significance (2-tailed) .000 . .522 .000 77 0 77 77 Correlation .090 .073 1.000 .065 Significance (2-tailed) .429 .522 . .569 77 77 0 77 Correlation .262 .402 .065 1.000 Significance (2-tailed) .020 .000 .569 . 77 77 77 0 df flow alignment 1.000 df alignment capacity df STEP 1 Go to analyze, then click on regression and then click binary logistic Select road rage as dependent and other required variables as covariates Go to save and tick probabilities. Click on continue Click ok to run the nalaysis. The output will be Model Summary Step 1 Cox & Snell R Nagelkerke R Square Square -2 Log likelihood 89.965 a .373 .501 a. Estimation terminated at iteration number 5 because parameter estimates changed by less than .001. Classification Tablea Predicted RoadRage Observed Step 1 RoadRage 0 1 Correct 0 45 12 78.9 1 10 33 76.7 Overall Percentage a. The cut value is .500 Percentage 78.0 Variables in the Equation B Step 1 a S.E. Wald df Sig. Exp(B) safety -.468 .231 4.082 1 .043 .626 gender .037 .565 .004 1 .948 1.038 experience .195 .109 3.187 1 .074 1.215 previous .677 .460 2.170 1 .141 1.968 selfcontrol .329 .124 7.028 1 .008 1.389 perceived .943 .233 16.321 1 .000 2.568 -4.839 1.119 18.704 1 .000 .008 Constant a. Variable(s) entered on step 1: safety, gender, experience, previous, selfcontrol, perceived. PART A Go to analyze, Then click on correlate, then click on bivariate. Select all the variables and transfer to the variables section. Click Ok to run the analysis. THE OUTPUT Correlations safety safety Pearson Correlation capacity capacity Pearson Correlation Sig. (2-tailed) N speed Pearson Correlation Sig. (2-tailed) .218 .393** .000 .001 .052 .000 80 80 80 80 80 .552** 1 .462** .244* .546** .000 .029 .000 80 80 80 1 ** .525** .000 .000 1 .552 .000 80 80 ** ** .351 .462 .351 .400 .000 80 80 80 80 80 Pearson Correlation .218 .244 * ** 1 .261* Sig. (2-tailed) .052 .029 .000 80 80 80 80 80 ** ** ** * 1 N flow flow .001 N alignment alignment ** Sig. (2-tailed) N speed ** Pearson Correlation Sig. (2-tailed) .393 .000 .546 .000 .400 .525 .000 .019 .261 .019 N 80 80 80 80 80 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). PART B.: Controlling for speed Go to analyze, Then click on correlate, then click on partial. Select speed as the controlling variables and put the other variables in the variable section. Click okay to run the analysis. The output is Correlations Control Variables speed safety capacity safety Correlation flow .470 .090 .262 Significance (2-tailed) . .000 .429 .020 df 0 77 77 77 Correlation .470 1.000 .073 .402 Significance (2-tailed) .000 . .522 .000 77 0 77 77 Correlation .090 .073 1.000 .065 Significance (2-tailed) .429 .522 . .569 77 77 0 77 Correlation .262 .402 .065 1.000 Significance (2-tailed) .020 .000 .569 . 77 77 77 0 df flow alignment 1.000 df alignment capacity df