Financial Status Influences the Well-Being of an Individual - Statistics Project Example

Part II Part II b Part II C Descriptive Statistics N Minimum Maximum Mean Std. Deviation Variance Skewness Kurtosis Statistic Statistic Statistic Statistic Std. Error Statistic Statistic Statistic Std. Error Statistic Std. Error AGE OF RESPONDENT 1969 18 89 48.19 .399 17.687 312.834 .295 .055 -.794 .110 HIGHEST YEAR OF SCHOOL COMPLETED 1972 0 20 13.53 .070 3.127 9.775 -.398 .055 1.169 .110 SUBJECTIVE CLASS IDENTIFICATION 1957 1 4 2.39 .016 .714 .510 -.190 .055 -.397 .111 R EVER INJECT DRUGS 1703 1 2 1.98 .004 .155 .024 -6.135 .059 35.681 .119 Valid N (listwise) 1687 Part III a) The measures of central tendency and variability of the financial status and depression Descriptive Statistics N Range Minimum Maximum Mean Std. Deviation Variance Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Statistic Depression 182 50 0 50 11.65 .610 8.228 67.697 Overall financial status 182 2 1 3 2.26 .049 .662 .438 Valid N (listwise) 182 PART III PART III a Financial status influences well-being of an individual. Financial status affects the human functioning, including their physical and mental health. That is, inadequate finances can result to depression in an individual. In this regard, depression and financial status are positively correlated hence they affect the well-being across the life span. These two variables are part of socioeconomic factors that affects the psychological health of an individual. Inadequate finances can be an act of poverty which in turn considered a risk factor for declines in mental health among people. Therefore, financial status and depression are critical factors in determining well-being of individual hence appropriate measures to the report. b) histograms with the normal curve for the variable post-traumatic stress disorder by gender The frequency histogram shows an almost normally distributed curve. However, the explicit results show a negatively skewed distribution. The frequency for males is more than that of the female gender. This shows that more males suffer from post-traumatic stress disorder as compared to male. The number of people that experience from post-traumatic disorder-from the sample-are 230. Out of the 230, 130 are males while 100 are females as per the histogram. Part IV a.) distribution for subjective class identification Descriptives Statistic Std. Error SUBJECTIVE CLASS IDENTIFICATION Mean 2.39 .016 95% Confidence Interval for Mean Lower Bound 2.36 Upper Bound 2.42 5% Trimmed Mean 2.40 Median 2.00 Variance .510 Std. Deviation .714 Minimum 1 Maximum 4 Range 3 Interquartile Range 1 Skewness -.190 .055 Kurtosis -.397 .111 The distribution of subjective class identification closely matches the normal curve. This shows that 68% of the observations fall within one standard deviation of the mean 2.39. On the other hand, 99.7% of the observations fall within 3 standard deviation of the mean. The normal Q-Q plots also confirms the normal distribution of the subjective class identification. b.) the 95% confidence interval for post-traumatic stress disorder Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Posttraumatic Stress Disorder 205 89.1% 25 10.9% 230 100.0% Descriptives Statistic Std. Error Posttraumatic Stress Disorder Mean 13.54 .603 95% Confidence Interval for Mean Lower Bound 12.35 Upper Bound 14.73 5% Trimmed Mean 12.90 Median 11.00 Variance 74.534 Std. Deviation 8.633 Minimum 1 Maximum 46 Range 45 Interquartile Range 11 Skewness 1.175 .170 Kurtosis 1.353 .338 The confidence interval for Posttraumatic Stress Disorder is 12.35 and 14.73 for lower bound and upper bound respectively. This means that we are 95% confident that the true mean of Posttraumatic Stress Disorder is between 12.35 and 14.74. The corresponding α=0.05. c) a variable that is not normally distributed and correct its distribution Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent AGE OF RESPONDENT 1969 99.7% 5 0.3% 1974 100.0% Descriptives Statistic Std. Error AGE OF RESPONDENT Mean 48.19 .399 95% Confidence Interval for Mean Lower Bound 47.41 Upper Bound 48.98 5% Trimmed Mean 47.73 Median 47.00 Variance 312.834 Std. Deviation 17.687 Minimum 18 Maximum 89 Range 71 Interquartile Range 28 Skewness .295 .055 Kurtosis -.794 .110 Tests of Normality Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. AGE OF RESPONDENT .064 1969 .000 .972 1969 .000 a. Lilliefors Significance Correction When testing for normality, the normality table and the normal Q-Q plots become important as the numerical and graphical methods respectively. The normality table represents the result for Shapiro-Wilk Test and the Kolmogorov-Smirnov Test. In this case, we will use Shapiro-Wilk Test because the data is less than 2000. The sig. value (0.000) of the Shapiro-Wilk Test is less than 0.05 hence the data is not normally distributed. The data significantly deviate from a normal distribution. We have also used the Q-Q plot to determine normality graphically. The data points stray from the line in a non-linear fashion hence the data is not normally distributed. To correct the distribution of age of the respondents, I have increased sample size from 1974 to 2500. As you increase the sample size, the sample mean moves towards the population mean hence the distribution moves towards normal distribution. The distribution has significantly improved but I prefer the original version of the variable because it is more accurate. Part V a) The variables to be determined are salary and education. Where salary is the dependent variable and education is the independent variable. H0: the salary of an individual is determined by the level of education H1: the salary of an individual is not determined by the level of education Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Correlations B Std. Error Beta Zero-order Partial Part 1 (Constant) 18.119 .413 43.829 .000 Level of Education -.725 1.063 -.046 -.682 .496 -.046 -.046 -.046 a. Dependent Variable: Salary ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 14.719 1 14.719 .466 .496b Residual 6829.263 216 31.617 Total 6843.982 217 a. Dependent Variable: Salary b. Predictors: (Constant), Level of Education The significance level is α=0.05 and the p-value=0.496 The P-value (0.496)> α=0.05 hence we accept the null hypothesis that salary of an individual is determined by the level of education. b) An analysis of the pre and post-test analysis H0: there is no significance difference in mean between pretest anxiety and posttest analysis H1: there is significance difference in mean between pretest anxiety and posttest analysis Paired Samples Statistics Mean N Std. Deviation Std. Error Mean Pair 1 Anxiety_Pre 78.86 50 11.763 1.664 Anxiety Post 72.36 50 9.542 1.349 Paired Samples Correlations N Correlation Sig. Pair 1 Anxiety_Pre & Anxiety_Post 50 .790 .000 Paired Samples Test Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 Anxiety_Pre - Anxiety_Post 6.500 7.223 1.022 4.447 8.553 6.363 49 .000 The significance level is α=0.05 and the p-value=0.00 The P-value (0.00) < α=0.05 hence we reject the null hypothesis that there is no significance difference in mean between pretest anxiety and posttest analysis. Hence, we conclude that there is significance difference in mean between pretest anxiety and posttest analysis. c) the correlation between income and depression H0: there is a negative correlation between income and depression H0: there is a positive correlation between income and depression Descriptive Statistics Mean Std. Deviation N Monthly Income 1455.9716 657.14640 82 Depression .4112 .49437 107 Correlations Monthly Income Depression Monthly Income Pearson Correlation 1 -.020 Sig. (1-tailed) .431 N 82 82 Depression Pearson Correlation -.020 1 Sig. (1-tailed) .431 N 82 107 At 95% confidence level, the p-value=0.431 while the significance level=0.05. The p-value> α=0.05. We therefore accept the null hypothesis that there is a negative correlation between income and depression. The hypothesis is supported by the correlation. The Pearson correlation coefficient between monthly income and depression is -0.020. This shows that an increase in income will lead to a reduction to depression. References Carver, R. H., & Nash, J. G. (2011). Doing data analysis with SPSS version 18.0. Boston, MA: Brooks/Cole Cengage Learning. Read More