One-Way ANOVA Model - Assignment Example

MARKETING RESEARCH Executive Summary 4 2. Literature Review 6 3. Methodology Error: Reference source not found 4. Analysing of exisitng Data and Results Error: Reference source not found 4.1. Descriptive Statistics 8 4.2. Regression models and correlation Error: Reference source not found 4.3. One-Way ANOVA 11 4.4. T-test Error: Reference source not found 5. Conclusion and discussion Error: Reference source not found 6. List of References Error: Reference source not found 7. Appendixes.....................................................................................................................17 1.0 Executive Summary The research intends to appraise the relation between the individual income and age and gender as well as factors that contribute to incomes such as the age level and the gender. The individual management, as well as th financial mindset, are considered to be significant in comprehending the attitude towards incomes (Brian P. Macfie, 2006).To save can be depicted as an objective in order to spend less cash. The assesment, therefore, places more weight on gender-involved concerns as well as conclusion depicted that women save more unlike men; nevertheless, self-control in women is less as compared to men. Women are susceptible to huge expenditure but they still saves the same amount as men. In understanding the status quo in terms of ,employment and the size of household, it is depicted that individual of entire employment are susceptible to income the same amount of their income, as well as larger household, saves more in order to mitigate for risk consequential from anticipated expense. In appraising the geopgrahical, as well as cultural disilimialrity, Respondent from south America, proved extremely inclined to income while responded from north America are susceptible to much spending. the dissimilar might be due to the progression level of their banking industry. Individual incomes are variable since it is independent from the entire factors except for financial morivated.Individual control,materialsim, as well as job enhacement signifcance, are relevant when anticipating the worth (Cohe, 2014).The numerous study has been performed to appraise the respondent as well as their real traits concerning income as well as activities undertaken during free time. the outcome depict that even though there is a firm link between them, individual merely spend more as anticipated consequential form anticipated situations. The hypotheses to be ivestigated in the study entail the following; H1: Male and female respondents depict a different education level. H2: Male and female respondents have a diverse level of income H3: Male and female respondents portray diverse level of materialism. H4: Different saving status between Male and female H5: The percentage of saving is different between respondent with diverse employment status. H6: different individual saving between age groups. H7: diverse maraital status with diferent individual saving H8: The percetnage of saving percent is disimialr for resppondnet in the diverse size of houshold. H9: people with diverse level of income depict diverse percentage of saving H10: Personal saving orientation is different for respondents from different continents. H11: Financial aspiration is different for respondents from different continents. H12: Self-control is different for respondents from different continents H13: Individual with high level of income tends of less contented with life. 2.0 Description of Marketing of problem 2.1 Literature Review There are two key distinct dimension entailed in individual income. The first is the mindset of objective inclined in that the personal objective is in the reality of income funds as well as the second dimension is that on a daily basis action, the attitude which impact individual to manage their expense on a daily basis instead of keenly income funds. It is believed that managing the cash is simpler as compared to keenly income cash. The items employed in the research have been employed in the survey to appraise individual incomes attitude. Comprehending the thought towards incomes is key to aiding ideal actions. Individual with diverse source of income depict diverse income attitudes but as much as managing income exist, a substantial dissimilarity in individual’s income attitude exist. This is an attribute of the notion that individual control as well as plays a key duty in the verdict. It is considered that managing the worth of spending is simpler unlike income. Self-control is key in income and consequently it is significant in a world where people are subjected to huge spending and thus there must be an effective approach to administer the spending as well as ensure that there is no effect of uncontrolled spending on the psychology. The research points out that based on normal knowledge,i presume that the provision of result clarification might be relevant to manage individual spending. There is a strong correlation between Individual attitude towards fulfilment of the firm and their cash position. The mindset might influence negatively individual status quo as well as health as a result of cash distraction in the mind and thus it can affect person self-actualization as well as warn concerning the threat of being to reward focused (Coolican, 2013).The research therefore implies the persons control and they tend to be less participated in self-realization as well as extra minded concerning what others presume concerning them. The study concerning that individual life objective of financial needs predominates other life objectives know how, further stress, anxiety as well as depression. In this regards, money and the human needs do not create satisfaction. Individual income is a key factor in person life 3.0 Methodology The research tends to employ the secondary data in formulating and appraising the result in order to come up with conclusive evidence about the correlation between the age, gender and the level of income. as a result, descriptive statistic, the multiple regression models as well as the bivariate analysis of the paper is going to be relevant and frequent for the study. The essence of the above approach is that the tool is ideal since it provides a comprehensive understanding of the data as well as provides a reliable result that can be used in making an informed marketing decision. Sampling methodology is as well a significant model since, it might impact significantly the outcome .in collecting the sample of 300 respondent, non-probability convenience was employed and consequnelty, every respondent is asked at least seven questions (Haws, 2012).the net advantage of sampling is that it saves time as well as it is cost efficient and thus the respondent work effectively with us in providing feedback to the question asked. The survey criteria majorly focused on individual working full time or part time as well as the gender and the level of income. The research is therefore going to employ a comprehensive aspect of the data analysis approach. Descriptive statistic such as the use of measure of dispersion ,location, cross tabulation as well as the frequencies distribution is relevant in the first part of data analysis since it is relevant to comprehend the key variables, The next part of the research is going to be appraised using the hypothesis testing by employing t-test statistics (Jacob Cohen, 2013).One way ANOVA is going to be significant where there is more than two set of data to make a comparison and assess the dissimilarity. the final part of the research entails the use of correlation and bivariate as well as the use of multiple repressor model in order to get a clear understanding of the nature and extent to which a correlation exist between the age, gender, and level of income. 4.0 Analysing of existing Data The appraisal entails a broader assortment of approach and thus descriptive statistic is relevant in understanding key variables. The technique entails the frequencies distribution, mean and other graphical presentation that relates to cross-tabulation in order to gain deeper understanding between gender, age, and income. The use of t-test for hypothesis testing connected to gender issue is as well as appraised. Where there is more than two categories to asses, One-way ANOVA assessment is relevant to examine for dissimilarity, correlation is going to be employed also more specifically by using the bivariate as well as multiple regression model for assessment of the variable and dependent variable. 4.1 Descriptive Statistics and the result of the model The outcome of the excel data analysis depicts that the middle value is more than the average respondent of 4635.In this regards, numerous result from the respondent concluded to be focused towards incomes (Malhotra, 2010). Table A depict a the standard deviation of the variable is 0.98 which implies that there non existence of numerous values that are disperse about the line of best fit while the test of skwenes depict a value of -0, 32 implying that there is presence of small skwenes from the distribution, and thus it implies that the distribution about the mean is flatned ehcne standards distribution. The research therefore employs the normal distribution to substantiate the result and consequently, a normality test was examined for the variables as well as testing the p-values depicting value of 0.2 and hence there no corelation between normality test and the values of P as observed in table 2 in the appendixes.the statistics for financial aspiration deposit same outcome as those of Table A. It provides standard deviation of 1100 with a mean of 4880 which is quite high as compared to the previous situation.The null hypothesis must as well be rejected since it is evident that there are negative skwenes as observed in table 3 in the appendices. The mean for individual control depicts a value of 3870, which is quite low unlike the neutral value, would be, and therefore it implies that the average individual has a low self-control. The standard deviation depicts a value of 0.8 implying that there is the existence of small extreme values. The test for normality is rejected since the value of P-values is 0.05 as depicted in table 1.4. In assesing the status of employment, is key since, the ability to save is majorly influenced by the need to have a constant source of income such as the employment. In this regards, in understaing the nature of employment and the extent of income based on gender, cross- scetional analsis and tabulation is relavant by appreciating the statues of the employement as well as gender to realize the dissimilarity. The number of respondent working fulltime is same as those working part time and therefore the CHI-square is relevant in examining the connection. The table 1.5 and 1.6 of the CHI-square depict that statistic null hypothesis is rarely rejected implying that there no correlation between the gender and the level of employment. It can be observed by using lambda coefficient that there is an average of the two symmetric and thus enhancement as anticipated is just 0.079 which expound that the enhancement is just marginal. as a result,it is had to place more reliance in cross tabulation since, the approach does not satisfy the necessities for every cell since, it has to depict at least five observation and form the examination, there just 0,2, and 4 observation 4.2 The Regression model The correlation between some of the key variables is examined by using the bivaraite correlation. The correlation is less important with product moment correlation, which depicts an r-value of 0.061 as depicted in the table 4.1; the scatter graph in the same table (4.1) provides as well that there is no correlation between the variables. the test of the result depict a weaker linear relation between the two variables as depicted in table 4.2 with serial correlation of 0.2 which is decide clearly in the scatter graph about the line of best fit. In order to generate a precise outcome as well as comprehending correlation between variables, multiple correlation model is going to be employed examine the data. the result provides that the self control, as well as financial aspiration and career progresion, depict a key impact on individual incomes as observed in table 4.3.The R squared as value of 0.0 which implies that the portion of variance in Y is accounted for by values of X. the adjusted R squared, the coefficient of multiple regression is inclusive in the as well as the sample and thus it is important in making a comparison in multiple regression models .As much as the model is significant at P.values of zero, just financial aspiration is relevant predictor variable..The underestimated coefficient for financial aspiration might be understood that there is a weaker correlation between the multiple variables. There is no linearity statistics and depict that there no strong multi colinearity between the variables. The percentage of income as a significant impact on life contentment and thus individual saves more are in a good position to get contented with their life. The hypothesis is tested using the biavriate regression model, and thus the model is less significant at P-value 0.3 as depicted in table 4.7. thus saving a proportion of individual income is rarely correlated to satisfaction. An assessment between the relationship on saving and free time spend is examined, and an hypothesis is going to centre on the connection between the free time planned to spend working as well as them the actual time spent working.ble 4.15 depict that there is a correlation between the coefficient of 0.5.the regression model is relevant with R squared of 0.21 while figure 4.4 depict regression line worked out employing ordinary least squared approach. The conclusion from the assessment concerning the habit of income and the level of employment level do not just correlated but there is a relatively firm correlation between the variables, numerous respondents are trying to remain to what they have panned concerning their income, as well as free time. 4.3 One-Way ANOVA This model was relevant in understanding the relationship between the age, level of income and gender since, the model allows examining more than two variables by appreciating independent variable as we independent variables. the income is dissimilar with respondent with diverse status, the hypothesis therefore implies that part time employed individual are not able to set aside similar portion of their income because their disposable income is relatively less as compared to full time employees. the outcome of the result depict that there is no relative dissimilar concerning their income portion since, At P-value of 0.7, null alternative is rejected as depicted in table 3.1.the mean of the group range from 31%-28% .the utmost value is 85% implying that some respondent can have more save entirely of their income. In testing, the hypothesis between saving orientation as well as saving portion is dissimilar with age. the metric age variable is documented into segments because the allotment is very skewed as observed in figure 3.1 .selecting the least, as well as utmorst range of the categoires, is not that simple job. The outcome of the analysis depict that people inn entire group portray same trait as far as level of eduacation, age and gender is concern. From the table 3.5, it is observed that there is no significance dissimilarity. For the saving portion, a significant dissimilarity between the defines age group and consequentkly, the respondent aged between 40 and 50 do save the least portion of their income as observed in table 3.7.it is probable that the individual in the age group have families, as well as expendiutre, are huge in that they are not able to save at the same time upholding normal living standards. The individual in the age group are possibly the highest earning age group at the highest of their careers hence their income might be superior implying that even a smaller portion in saving might be same. 4.4 t-tests The t-test statistics is deem significant since, the model explore the dissimilarity between Different set of dependent variables. Independent sample t-test is relevant in the research since, the data depict numerous gender-linked hypotheses, and Research in H2 provides that the test static depict a null hypothesis between the correlation between male and female in terms of individual self-control. this is depicted by zero p-values in table 2.2 implying that the dissimilarity between male and female is significant, men do exert more self control as decided by the mean of 4.0 while the mean for ladies is 3.7.the level of materialism between men and female is dissimilar since, table 2.3 provides that the p-values are 0.0029 hence it is significant since men depict a higher mean of 4.1 unlike women who depict a mean of 3.8 Table 2.3. shows that with a p-value of 0.003, the difference is significant. I believed that women, who are generally considered more materialistic, would rate higher on this scale, but the mean for male respondents (4.146) was higher than the mean for women (3.789). This result contradicts my hypothesis (Kruschke, 2010).AS depicted by the result of H4, it is evident that in terms of gender, men are normally in a position to have a long working hours unlike women since, men really have to disrupt their career when beginning their family with huge net income unlike women in similar position since, the test depict that women saves more. this is further explained in the table 2.4 where P-values for the test are o.4, which is quite high implying that the null hypothesis is accepted. this therefore means that the dissimilar is less significant. The portion of saving is diverse for respondent with dissimilar employment level as observed in H5.the hypothesis expounds that unemployed individual or part time workers rarely set aside funds because their income is less unlike those of full time employees. the outcome depict that there is less significance dissimilar between the portion of their saving. this is because, at P-value of 0.7, Ho is not rejected. the mean for the group is 28% is observed in table 3.2, which implies that this highest portion of this group saves. the correlation between the level of saving portion and age depict a different ouitcome.H6 depict a significant dissimilarity between the age and the level of income as depicted in table 3.6 since, the lowest portion of their income is saved due to high expenditure on households. the high level of net income received by this age groups might due to the fact they are at the highest level of their employment status which means that the level of saving is same to the level of expenditure. .Individual savings is dissimilar for respondent with diverse marital status as well as the household size. this is observed by H7.the result of the study was conducted using the ANNOVA test which depicts that the hypothesis can be rejected since the result is less significant due to non-coliniarity between the level of having and marital status. Table 3.1 depict that a null hypothesis is rejected at p-value of 0.03 since the portion of saving is diverse for respondent with the disimialr size of household (Kruschke, 2010).This implies therefore that there is a significance difference. it implies that individual from dissimilar household size has precisely the similar habit towards saving. this is depicted in table 3.11 which provide that huge income lead to huge expenditure. The hypothesis H9 was tested using the ANNOVA test and the result depict that the p-value 0.08 implies the null hypothesis is rejected. this is depicted in table 3.12 implying that individual from diverse income group are susceptible to save portion of their income since, the result depict a mean range of 18-40. 5.0 Conclusion The appraisal of the data using statistical tool such as descriptive statistic as well as measuring and testing the outcome using multiple regression models provide conclusive evidence that some correlation exist between the level of income, the age as well as the gender. the result provides that as per the gender assessment, women do save more unlike men, which implies that the financial institution must create a marketing plan to win women to appreciate the bank product being offered such as loan for mortgages or property loan for the advancement in living standards. .the assessment depict that women with self-control are less unlike men and the outcome concern the marketers. women are more prone to the huge expenditure and consequently marketers must focus on this group in product advertsiment.th assessment places more emphasize to the extent of saving consequential from the level of income. it can be concluded that the assessment depict that women saves more unlike men as much as there is an inverse relation between net income and the level of spending. As a market, i will try to venture much more on men as much as women save more since, it can be observed that men are materialistic and spend more on basic needs, furthermore, men are considered to lead the family by providing basic needs and consequently much of their income is expended on buying daily households needs. the rate and nature of employment issues generate different outcome. It can be observed that the portion of individual saving is similar devoid of their employment position. even part time, as well as full time respodnetn, strives to save some cash (Malhotra, 2010). It implies therefore that the banks must ensure that it provides affordable loan strategies as well as advertize the need for savings for future benefit The research concerning age provided the same outcome, respondent strive to save some cash regardless of their age. The government must therefore develop a plan to transform the situation of the pension system to promote individual care, as well as private pension funds. this can be well administered by insurance as well as the financial firms by employing the individual propensity towards savings. advertisement from the government must as well be employed to generate the need for more savings. In understanding the effect of geographical as well as cultural concerns employing the latest variables from south and north Americans, respondent from south America provided a high result unlike from north America which implies that the economy in north America is majorly depended on spending even when more than what might be safe. the banking industry as well as micro-finance enterprise is much enhanced and thus individual applying for a loan is highly granted. Nevertheless, South America have a huge worth of independent debt as well as their banking industry is less developed which possibility implies that if a person requires funding, he ought to entirely place reliance on their individual savings hence making them more inclined towards saving and with less expenditure. In assessing the habit to save consequential from the nature of employment and gender, it can be observed that a wide rule applies and thus individual inclined to saving up to a third of their net income. the outcome of the regression model provides there is less correlation between income and financial aspiration implying that individual income is derived by the level of income and age. people with long life experience in the work have a huge level of saving due to time they have expended on working. the correlation between the level of income and the age is insignificant implying that individual with high financial income is less contented. Nevertheless, the data depict that many of the respondent were between 23-28 years of age which profound that younger generation have do have less experience in become goal oriented. these impacts might entail dispersion as well as fatigue at the place of work. Sadly, every state is concerned with the work from the citizen in order generate more revenue inform of taxes as well as retire late but the psychological health of the workers must be taken care off. frequently visit the doctor must be part of the lives in order to ensure that there is safe living. Reference list Brian P. Macfie, ‎.M.N., 2006. Applied Statistics for Public Policy - Page 527. Cohe, P., 2014. Applied Multiple Regression/Correlation Analysis. London: Harvarduniversity press. Coolican, H., 2013. Research Methods and Statistics in Psychology, Fifth Edition. Haws, K.B.W.a.N., 2012. Consumer spending self-control effectiveness and outcome elaboration prompts. Journal of the Academy of Marketing Science , vol. 40 no.5 p. , pp.695-710. Howel, 2013. l Fundamental Statistics for the Behavioral Sciences. Melbourne: Springcer. Jacob Cohen, ‎.C.‎.G.W., 2013. Applied Multiple Regression/Correlation Analysis. New york: Cengage Learning. Kruschke, J., 2010. Doing Bayesian Data Analysis: A Tutorial Introduction with Regression. Malhotra, N., 2010. ’Marketing Research: An Applied Orientation’. Global Edition ed. New Jersey: Pearson Education. Marguerite G. Lodico, ‎.T.S.‎.H.V., 2006. Methods in Educational Research: From Theory to Practice. Muller, K.E., 2002. Regression and ANOVA: An Integrated Approach. London: Cengage Learning. News.com.au, 19 November 2013. ’Women paid less than men for same job. Patricia Cohen, ‎.G.W.‎.S.A., 2014. Applied Multiple Regression/Correlation Analysis. London: Harvarduniversity press. Appendixes Table 1.1. Statistics Net income Gender Savings N Valid 320 320 320 Missing 0 0 0 Mean 4,870 4,6876 3,8776 Median 5,0000 4,6677 3,9233 Mode 5,00 4,33 4,15 Std. Deviation 1,1015 ,9831 ,7933 Skewness -,420 -,312 -,425 Std. Error of Skewness ,137 ,137 ,137 Kurtosis ,351 -,031 ,958 Std. Error of Kurtosis ,270 ,270 ,270 Range 6,00 5,56 4,62 Minimum 1,00 1,44 1,38 Maximum 7,00 7,00 6,00 Table 1.2. Tests of Normality Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. Savings ,04 320 ,20 ,99 320 ,02 Table 1.3. Tests of Normality Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic Df Sig. Gender ,067 320 ,001 ,98 320 ,000 Table 1.4. Tests of Normality Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. Income ,050 320 ,048 ,977 320 ,000 Figure 1.1. Figure 1.2. Table 1.5. gender * employment Crosstabulation employment Total Full time Part time Self employed Not employed gender female Count 61 59 4 25 149 % of Total 18,9% 18,3% 1,2% 7,8% 46,3% male Count 70 58 13 30 171 % of Total 21,7% 18,0% 4,0% 9,3% 53,1% others Count 0 2 0 0 2 % of Total 0,0% 0,6% 0,0% 0,0% 0,6% Total Count 131 119 17 55 320 % of Total 40,7% 37,0% 5,3% 17,1% 100,0% Table 1.6. Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 7,800 5 ,25 Likelihood Ratio 8,588 5 ,19 Linear-by-Linear Association ,277 1 ,59 N of Valid Cases 320 Table 1.7. Symmetric Measures Value Approx. Sig. Nominal by Nominal Phi ,156 ,253 Cramer's V ,110 ,253 Contingency Coefficient ,154 ,253 N of Valid Cases 320 Table 1.8. Directional Measures Value Asymp. Std. Errora Approx. Tb Approx. Sig. Nominal by Nominal Lambda Symmetric ,009 ,032 ,275 ,783 gender Dependent ,007 ,071 ,092 ,926 employment Dependent ,010 ,007 1,419 ,156 Goodman and Kruskal tau gender Dependent ,014 ,011 ,190c employment Dependent ,008 ,003 ,284c Table 2.1. Table 2.2. Table 2.3. Table 2.4. Table 3.1. ANOVA savingpercent Sum of Squares df Mean Square F Sig. Between Groups 659,041 3 219,680 ,521 ,668 Within Groups 134152,760 318 421,864 Total 134811,801 321 Table 3.2. Descriptives savingpercent N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound Full time 131 28,17 20,037 1,751 24,70 31,63 0 90 Part time 119 31,23 21,178 1,941 27,38 35,07 0 80 Self employed 17 27,47 16,565 4,018 18,95 35,99 0 60 Not employed 55 29,29 21,363 2,881 23,52 35,07 0 80 Total 320 29,45 20,493 1,142 27,21 31,70 0 90 Figure 3.1. Table 3.3. Table 3.4. ANOVA PersonalSavingOrientation Sum of Squares df Mean Square F Sig. Between Groups 1,775 4 ,444 ,456 ,768 Within Groups 308,489 317 ,973 Total 310,265 321 Table 3.5. Descriptives PersonalSavingOrientation N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound 1,00 217 4,6441 1,01264 ,06874 4,5086 4,7796 1,44 7,00 2,00 56 4,7599 ,87867 ,11742 4,5246 4,9952 2,89 7,00 3,00 13 4,9487 1,11075 ,30807 4,2775 5,6199 2,56 6,56 4,00 13 4,8034 ,88559 ,24562 4,2683 5,3386 3,33 6,11 5,00 23 4,7101 ,96093 ,20037 4,2946 5,1257 3,00 6,33 Total 320 4,6877 ,98314 ,05479 4,5799 4,7955 1,44 7,00 Table 3.6. ANOVA savingpercent Sum of Squares df Mean Square F Sig. Between Groups 4524,779 4 1131,195 2,752 ,028 Within Groups 130287,023 317 411,000 Total 134811,801 321 Table 3.7. Descriptives savingpercent N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound 1,00 217 31,53 21,392 1,452 28,67 34,40 0 90 2,00 56 27,59 16,336 2,183 23,21 31,96 0 60 3,00 13 21,77 14,255 3,954 13,16 30,38 0 40 4,00 13 15,85 10,877 3,017 9,27 22,42 1 40 5,00 23 26,39 24,224 5,051 15,92 36,87 0 90 Total 320 29,45 20,493 1,142 27,21 31,70 0 90 Table 3.8. ANOVA PersonalSavingOrientation Sum of Squares df Mean Square F Sig. Between Groups 1,287 3 ,429 ,442 ,723 Within Groups 308,977 318 ,972 Total 310,265 321 Table 3.9. ANOVA PersonalSavingOrientation Sum of Squares df Mean Square F Sig. Between Groups 12,442 15 ,829 ,852 ,619 Within Groups 297,823 306 ,973 Total 310,265 321 Table 3.10. ANOVA savingpercent Sum of Squares df Mean Square F Sig. Between Groups 11368,195 15 757,880 1,879 ,025 Within Groups 123443,606 306 403,410 Total 134811,801 321 Table 3.11. Descriptives savingpercent N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound 1 30 20,17 16,993 3,102 13,82 26,51 0 70 2 57 26,89 16,255 2,153 22,58 31,21 0 70 3 93 30,60 21,632 2,243 26,15 35,06 0 90 4 74 29,00 19,787 2,300 24,42 33,58 0 90 5 41 31,90 22,334 3,488 24,85 38,95 0 80 6 11 43,27 26,188 7,896 25,68 60,87 10 80 7 5 43,00 18,574 8,307 19,94 66,06 30 75 8 2 20,00 ,000 ,000 20,00 20,00 20 20 9 1 30,00 . . . . 30 30 10 1 50,00 . . . . 50 50 13 2 25,00 21,213 15,000 -165,59 215,59 10 40 16 1 75,00 . . . . 75 75 45 1 10,00 . . . . 10 10 140 1 40,00 . . . . 40 40 144 1 60,00 . . . . 60 60 550 1 ,00 . . . . 0 0 Total 320 29,45 20,493 1,142 27,21 31,70 0 90 Table 3.12. ANOVA savingpercent Sum of Squares df Mean Square F Sig. Between Groups 9562,953 15 637,530 1,558 ,084 Within Groups 125248,849 306 409,310 Total 134811,801 321 Table 3.13. ANOVA PersonalSavingOrientation Sum of Squares df Mean Square F Sig. Between Groups 12,569 5 2,514 2,668 ,022 Within Groups 297,696 316 ,942 Total 310,265 321 Table 3.14. Descriptives PersonalSavingOrientation N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound Australia 95 4,8211 1,00528 ,10314 4,6163 5,0258 2,22 7,00 Europe 41 4,6748 ,95400 ,14899 4,3737 4,9759 2,78 6,56 North America 5 3,6222 1,47740 ,66071 1,7878 5,4567 1,44 5,00 South America 23 5,0435 ,84337 ,17585 4,6788 5,4082 3,67 7,00 Asia 157 4,5987 ,95380 ,07612 4,4484 4,7491 1,67 6,56 Missing 1 3,6667 . . . . 3,67 3,67 Total 320 4,6877 ,98314 ,05479 4,5799 4,7955 1,44 7,00 Table 3.15. ANOVA savingpercent Sum of Squares df Mean Square F Sig. Between Groups 2104,469 5 420,894 1,002 ,416 Within Groups 132707,332 316 419,960 Total 134811,801 321 Table 3.16. Descriptives savingpercent N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound Australia 95 30,73 21,826 2,239 26,28 35,17 0 80 Europe 41 26,98 22,035 3,441 20,02 33,93 0 90 North America 5 33,00 15,652 7,000 13,56 52,44 10 50 South America 23 21,65 13,999 2,919 15,60 27,71 10 60 Asia 157 30,42 20,130 1,607 27,25 33,59 0 90 Missing 1 20,00 . . . . 20 20 Total 320 29,45 20,493 1,142 27,21 31,70 0 90 Table 3.17. ANOVA FinAspiration Sum of Squares df Mean Square F Sig. Between Groups 31,929 5 6,386 5,643 ,000 Within Groups 357,586 316 1,132 Total 389,516 321 Table 3.18. Descriptives FinAspiration N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound Australia 95 4,4579 1,07909 ,11071 4,2381 4,6777 1,75 7,00 Europe 41 4,9085 1,01050 ,15781 4,5896 5,2275 3,25 7,00 North America 5 4,4500 ,75829 ,33912 3,5085 5,3915 4,00 5,75 South America 23 5,4891 ,75933 ,15833 5,1608 5,8175 4,25 6,50 Asia 157 5,0605 1,11027 ,08861 4,8855 5,2355 1,00 7,00 Missing 1 4,2500 . . . . 4,25 4,25 Total 320 4,8820 1,10156 ,06139 4,7612 5,0028 1,00 7,00 Table 3.19. ANOVA SelfControl Sum of Squares df Mean Square F Sig. Between Groups 2,821 5 ,564 ,895 ,485 Within Groups 199,202 316 ,630 Total 202,023 321 Table 4.1. Correlations SelfControl PersonalSavingOrientation SelfControl Pearson Correlation 1 ,061 Sig. (2-tailed) ,276 N 320 320 PersonalSavingOrientation Pearson Correlation ,061 1 Sig. (2-tailed) ,276 N 320 320 Figure 4.1. Table 4.2. Correlations PersonalSavingOrientation FinAspiration PersonalSavingOrientation Pearson Correlation 1 ,231** Sig. (2-tailed) ,000 N 320 320 FinAspiration Pearson Correlation ,231** 1 Sig. (2-tailed) ,000 N 320 320 **. Correlation is significant at the 0.01 level (2-tailed). Figure 4.2. Table 4.3. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 ,258a ,067 ,055 ,95577 a. Predictors: (Constant), CareerAchievementImpt, SelfControl, FinAspiration, Materialism Table 4.4. ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 20,686 4 5,171 5,661 ,000b Residual 289,579 317 ,913 Total 310,265 321 a. Dependent Variable: PersonalSavingOrientation Table 4.5. ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 20,686 4 5,171 5,661 ,000b Residual 289,579 317 ,913 Total 310,265 321 a. Dependent Variable: PersonalSavingOrientation b. Predictors: (Constant), CareerAchievementImpt, SelfControl, FinAspiration, Materialism Table 4.6. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) 3,683 ,243 15,161 ,000 FinAspiration ,206 ,049 ,231 4,238 ,000 1,000 1,000 a. Dependent Variable: PersonalSavingOrientation Table 4.7. ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 1,205 1 1,205 1,116 ,291b Residual 345,506 320 1,080 Total 346,711 321 a. Dependent Variable: lifesatisfaction_general b. Predictors: (Constant), savingpercent Table 4.8. ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression ,552 1 ,552 ,511 ,475b Residual 346,159 320 1,082 Total 346,711 321 a. Dependent Variable: lifesatisfaction_general b. Predictors: (Constant), FinAspiration Table 4.9. Correlations savingpercent actual_save_percent savingpercent Pearson Correlation 1 ,652** Sig. (2-tailed) ,000 N 320 320 actual_save_percent Pearson Correlation ,652** 1 Sig. (2-tailed) ,000 N 320 320 **. Correlation is significant at the 0.01 level (2-tailed). Table 4.10. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 ,652a ,425 ,423 16,600 a. Predictors: (Constant), savingpercent Table 4.11. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) 7,090 1,621 4,373 ,000 savingpercent ,696 ,045 ,652 15,384 ,000 1,000 1,000 a. Dependent Variable: actual_save_percent Figure 4.3. Table 4.12. Correlations savingamount actual_save_amount savingamount Pearson Correlation 1 ,614** Sig. (2-tailed) ,000 N 320 320 actual_save_amount Pearson Correlation ,614** 1 Sig. (2-tailed) ,000 N 320 320 **. Correlation is significant at the 0.01 level (2-tailed). Table 4.13. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 ,614a ,376 ,374 1625,445 a. Predictors: (Constant), savingamount Table 4.14. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) 426,141 92,752 4,594 ,000 savingamount ,377 ,027 ,614 13,898 ,000 1,000 1,000 a. Dependent Variable: actual_save_amount Table 4.15. Correlations freetime_work actual_freetime_work freetime_work Pearson Correlation 1 ,455** Sig. (2-tailed) ,000 N 320 318 actual_freetime_work Pearson Correlation ,455** 1 Sig. (2-tailed) ,000 N 318 320 **. Correlation is significant at the 0.01 level (2-tailed). Table 4.16. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 ,455a ,207 ,205 19,989 a. Predictors: (Constant), freetime_work Figure 4.4. Read More