Statistics Money1 INVmoney1 LNmoney1 SQmoney1 SQRTmoney1 N Valid 132 132 132

Statistics money1 invmoney1 lnmoney1 sqmoney1

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Statistics Money1 INVmoney1 LNmoney1 SQmoney1 SQRTmoney1 N Valid 132 132 132 132 132 Missing 1 1 1 1 1 Mean 572727,29 ,1629 11,0844 4,7000E11 669,3449 Median 600000,00 ,0000 13,3047 3,6000E11 774,5967 Std. Deviation 378242,387 ,36808 4,99377 4,16667E11 354,48071 Skewness -,220 1,847 -1,761 ,299 -,896 Std. Error of Skewness ,211 ,211 ,211 ,211 ,211 Kurtosis -1,381 1,459 1,200 -1,638 -,502 Std. Error of Kurtosis ,419 ,419 ,419 ,419 ,419 We conclude that the results of our transformations were not satisfactory. The kurtosis remains for all but the Square root transformation (here it is -1.19 which is acceptable). However for the Square root transformation we get a highly skewed distribution in return (-4.25). There is some improvement in the kurtosis for the Log transformation, from -3.08 to 2.86. (Note that it has become a positive instead of a negative kurtosis). But, this is only a marginal improvement (and the distribution again has become highly skewed (-8.35). We decide that none of the transformations substantially improves the distribution. Which is why we maintain the original variable. D. Check whether all variables in your regression model are of interval or ratio level. What data substantiate your answer/decision? Why is your answer/decision based on these data correct? It is difficult to establish measurement levels, without having access to the original data (to see how the variables were actually measured). However the frequency distributions (requested for 3b) seem to indicate that the scores are of at least an interval level of measurement. -6-
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SPSS Assignments Quantitative Research Methods (BPRA247) Assignment: Multiple regression analysis part 1: Assumptions and dummies The key criterion is whether the distances between the scores are (or can be considered) as meaningful/equal. This would allow us to perform the necessary calculations. With ordinal variables, we can rank the scores, but we cannot say anything meaningful about the distance. Likert items are in themselves are ordinal (we cannot quantify the distance between ‘strongly agree’ and ‘agree’). However, it is customary to consider combined scales of Likert items as interval. E. Based on your answer to question 3d, do you still consider regression analysis to be the appropriate technique to use here? Or would the levels of measurements lead you to suggest a different analysis technique? What data substantiate your answer/decision? Why is your answer/decision based on these data correct? Yes, regression analysis is an appropriate technique, seeing that we are dealing with a dependent variable of interval level and independent variables of interval level. The research question asks us to investigate a causal claim. Question 4 In this question you are going to check the other three assumptions. Generally proceed as follows: Explain whether and to what extent you consider an assumption to be met. If the assumption is not met, suggest a way to solve this problem. Run a new regression model in which you incorporate the proposed solution. Determine the effect of this solution. To what extent does it constitute a substantial improvement over the baseline model (see 4a).
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