Gender Case Processing Summary GenderCasesValidMissingTotalNPercentNPercentNPercentIncome_afterFemale135100.0%00.0%135100.0%Male155100.0%00.0%155100.0% Tests of Normality GenderKolmogorov-SmirnovaShapiro-WilkStatisticdfSig.StatisticdfSig.Income_afterFemale.540135.000.230135.000Male.536155.000.295155.000 a. Lilliefors Significance Correction Income_after Histograms Normal Q-Q Plots Interpretation: T-Test Paired Samples Statistics MeanNStd. DeviationStd. Error MeanPair 1Income_after38965.5829030033.9811763.656Income_before33448.2429016018.070940.613 Paired Samples Correlations NCorrelationSig.Pair 1Income_after & Income_before2901.000.000 Paired Samples Test Paired DifferencestdfSig. (2-tailed)MeanStd. DeviationStd. Error Mean95% Confidence Interval of the DifferenceLowerUpperPair 1Income_after – Income_before5517.34214015.912823.0433897.4247137.2606.704289.000 Interpretation: Correlations QoLIncome_afterQoLPearson Correlation1-.828**Sig. (2-tailed) .000N290290Income_afterPearson Correlation-.828**1Sig. (2-tailed).000 N290290 **. Correlation is significant at the 0.01 level (2-tailed). Interpretation: There is an association between QoL and income_after. This means the null hypothesis will be rejected because the significance level is < 0.05 and this shows there is a correlation (Wasserstein et al. 2019). The significance level is used to make a decision concerning rejecting or failing to reject the null hypothesis (Park 2015). Therefore alternative hypothesis will be accepted. Correlation have been essential part of statistical analysis as it provide association information between variables (Aggarwal and Ranganathan 2016). The correlation coefficient is -.828, which means that the association is strong and the negative value means that the association is reversed. A correlation coefficient more than 0.6 indicate a strong association between the variables. This finding is interesting as it indicate that QoL is negatively affected with increasing income. Future research in this area should considering this in their research design. (142 words) Reference Aggarwal, R. and Ranganathan, P., 2016. Common pitfalls in statistical analysis: The use of correlation techniques. Perspectives in clinical research, 7(4), p.187. Park, H.M., 2015. Hypothesis testing and statistical power of a test. Wasserstein, R.L., Schirm, A.L. and Lazar, N.A., 2019. Moving to a world beyond “p< 0.05”.
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