We used SPSS to conduct the analysis. Click Next and select Tenure squared (created with Transform, Compute). Then click Next and add Tenure cubed. Model R R Change Statistics Square R Square F df1 df2 Sig. F Change Change Change 1 .045a .002 .002 .352 1 174 .554 2 .261b .068 .066 12.241 1 173 .001 3 .288c .083 .015 2.775 1 172 .098 Notice that adding tenure cubed increased the R2 by a small and insignificant amount. Accordingly, we drop back to the quadratic model. Also notice that the strength of association is a helluva lot lower than it was with ladybugs. Well, explaining the behavior of humans is a bit more difficult than explaining the behavior of ladybugs. ANOVAa Model Sum of df Mean F Sig. Squares Square Regression .243 1 .243 .352 .554b 1 Residual 120.283 174 .691 Total 120.526 175 Regression 8.192 2 4.096 6.308 .002c 2 Residual 112.334 173 .649 Total 120.526 175 Regression 9.975 3 3.325 5.173 .002d 3 Residual 110.551 172 .643 Total 120.526 175 a. Dependent Variable: Cont_Comm b. Predictors: (Constant), Tenure_Years c. Predictors: (Constant), Tenure_Years, Tenure2 d. Predictors: (Constant), Tenure_Years, Tenure2, Tenure3 Presenting the Results Sequential polynomial regression analysis was employed to investigate the nature of the relationship between tenure and continuance commitment. After evaluating a linear model, each additional step involved entering the next highest power of the predictor (tenure, in years). This continued until the addition of the next highest power increased the fit of the model to the data by an
We used SPSS to conduct the analysis. Click Next and select Tenure squared (created with Transform, Compute). Then click Next and add Tenure cubed. Model R R Square Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .045a .002 .002 .352 1 174 .554 2 .261b .068 .066 12.241 1 173 .001 3 .288c .083 .015 2.775 1 172 .098 Notice that adding tenure cubed increased the R2 by a small and insignificant amount. Accordingly, we drop back to the quadratic model. Also notice that the strength of association is a helluva lot lower than it was with ladybugs. Well, explaining the behavior of humans is a bit more difficult than explaining the behavior of ladybugs. ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression .243 1 .243 .352 .554b Residual 120.283 174 .691 Total 120.526 175 2 Regression 8.192 2 4.096 6.308 .002c Residual 112.334 173 .649 Total 120.526 175 3 Regression 9.975 3 3.325 5.173 .002d Residual 110.551 172 .643 Total 120.526 175 a. Dependent Variable: Cont_Comm b. Predictors: (Constant), Tenure_Years c. Predictors: (Constant), Tenure_Years, Tenure2 d. Predictors: (Constant), Tenure_Years, Tenure2, Tenure3 Presenting the Results Sequential polynomial regression analysis was employed to investigate the nature of the relationship between tenure and continuance commitment. After evaluating a linear model, each additional step involved entering the next highest power of the predictor (tenure, in years). This continued until the addition of the next highest power increased the fit of the model to the data by an
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