# Assignment 8: Signature Assignment

An analysis and synthesis of a 500 sample dataset was done in an effort to understand education in our different areas: gender, age, marital status and parental education. The descriptive statistics for various variables and the relationship between the variables are reported in this paper. Pearson’s correlation, regression analysis, ANOVA, and multivariate analysis of variance were some of the statistical tests conducted using this dataset. A discussion of the findings is given with reference to peer reviewed journals and finally a report – appropriate for a non-statistician audience -of the findings is provided.
Education and gender
Among a total of 1419 participants who were involved in this study, there were 622 (43.8%) males and the rest 797 (56.2%) were females (Output 2). This information is also displayed graphically in Figure 3 where it is evident that there were more female participants than males. In addition, there were 8 missing data on highest degree in a total sample of 1419 participants. The highest number of respondents (725) had a high school degree (51%) while those who had less than a high school degree followed with 17.1% (243). Individuals who had a bachelor degree were also substantially many, 236 (16.6%) while participants with a graduate degree were 106 (7.5%). Respondents who had a junior college degree were the least, 101 (7.1%). A graphical display of the respondents’ highest degree is also shown in form of a bar graph (Figure 1).
According to output 3 (Table 3), the mean highest year of school completed for male respondents was 13.54 with a standard deviation of 2.945. The 5% trimmed mean was 13.55 years and the median year of school completed was 13. The minimum highest year of school completed for males was 4 years whereas the maximum highest year of school completed was 20 years. This data is negatively skewed (-.013) and a kurtosis of .079 indicating that the data assumes normal distribution.
The mean highest year of school completed for female participants was 12.99 with a standard deviation of 2.834. The 5% trimmed mean was 13.03 and the median highest year of school completed was 12.00. The minimum highest year of school completed for females was 0 years whereas the maximum year is 20 years. This data has a negative skewness of -.249 and a kurtosis of 1.259 (output 3). It is conclusive that males have a higher level of education compared to females as far as highest year of school completed is concerned. In addition, it is possible to have females who have not completed one year in school but the least number of years completed by males is 4 years.
From output 4, the Pearson correlation coefficient between respondent’s gender and respondent’s highest degree (for a sample N = 1411) was significant, r = -.076, 2-tailed p = .004. It is important to note that the correlation is negative indicating that there is a significant negative relationship between gender and highest degree attained by respondents. Table 5 also shows that there was a significant relationship between gender of the respondent and the highest year of school completed for N = 1415, r = -.093, 2-tailed p < .05. Again this relationship is negative as indicated by the negative Pearson correlation coefficient. It is pertinent to note that there is a perfect correlation, r = 1 between each variable and itself. The R2 value for gender against highest degree is (-.076)2 = 0.005776 which can be converted to 1%. This indicates that 1 percent variability in level of education (highest degree) was as a result of the individual’s gender. The R2 value for gender against highest year of school completed is -.0932 = 0.008649. When converted to a percentage, this becomes 1% indicating that 1 percent variability in level of education (highest year of school completed) was as a result of the participant’s gender. It is evident that although there was a significant relationship between gender and education, gender accounts for only 1% in level of education whereas the rest, 99%, is as a result of other variables. Parental education and respondent’s education The mean highest year school completed by mother is 11.56 with a standard deviation of 3.45 (N = 902) whereas for 902 participants, the mean highest year school completed by father is 11.37 with a standard deviation of 4.10 (output 6). From the model summary in output 7, the R value is .362 indicating a somewhat strong correlation between parental education and the respondent’s education. The R2 value is .131 which is equivalent to 13.1 percent. This implies that parental education accounted 13.1 percent variation in respondent’s level of education. The remaining 86.9 percent was variation in respondent’s education due to other factors other than parental education. The analysis of variance for this model is shown in Table 8. The average sum of squares for this model was 164.37 with 2 degrees of freedom. The F-ratio, 67.94 is significant at p = .001 (F (2 899) = 67.37, p = .001). The regression model therefore indicates that parental education significantly predicts the respondent’s education level. Output 9 is helpful in identifying the contributions of each parent’s education level on respondent’s education. The Y intercept, b0 was .245 indicating that when x (parental education) is zero, the respondent’s level of education is .245. The two equations derived from Table 10 are Y =.086x + .245 for mother’s education and Y = .042x + .245 for father’s education. From the first equation, it is evident that when maternal education increases by a factor of 1, the respondent’s education increased by .086. Likewise, an increase in paternal education by a factor of 1 leads to an increase in respondent’s education by .042. It is therefore clear that maternal education has a higher contribution to respondent’s education compared to paternal education. Maternal education has a significant contribution to respondent’s education as indicated by a significant t value, t = 6.24, p = .001. Paternal education also contributes to the respondents education significantly, t = 3.61, p =.001. The beta value for mother’s education is .25 whereas that of father’s education is .14 indicating that maternal education has a higher contribution to respondent’s education compared to paternal education. Age and Education Output 10 shows the mean age for 1409 respondents as 46.60 years with a standard deviation of 17.30. A Pearson correlation between respondent’s highest degree and the age of the respondent showed that there was a significant relationship, r = -.090, p = .001 for 1409 participants. The relationship was however negative indicating that as the age of the respondent increases, the highest degree achieved reduces. There exists a perfect relationship, r = 1, between every variable and itself. It is conclusive that age is a significant predictor of respondent’s education (highest degree). Output 12 shows the model summary for this relationship and the r value is .090 showing that correlation between age of the respondent and respondent’s highest degree. The R Square value is .008, which is equivalent to 1% upon conversion to a percentage. This implies that although age is a predictor of respondent’s education, it causes a variability of 1% only with the rest 99% being variability due to other factors. The ANOVA for this model is shown in output 13 where the sum of squares was 15.84 whereas the mean square was 11.56 and 1 degree of freedom. The F-ratio was significant at p < .05 (F (1 1407) = 11.56, p = .001). This indicates that age is a significant predictor of the respondent’s education (highest degree). From the regression coefficient in output 14, it is possible to indicate the exact effect of age on education. The regression equation in this case is Y = -.006x + 1.75 indicating that an increase in the respondent’s age by a factor of 1 leads to .006 decrease in respondent’s education (highest degree). When the respondent’s age is zero, the respondents education level is about -.006. The t value for this model is significant, t = -3.40, p = .001 indicating that age has a significant reduction in the respondent’s education. The beta value also shows that an increase in respondent’s age accounts for -.09 decrease in the respondent’s level of education. When examining the contribution of age on respondent’s highest year of school completed, it is evident that the mean highest year of school completed was 13.23 with a standard deviation of 2.90 for 1413 respondents. For the same number of respondents, the mean age for respondents was 46.55 years (output 15). Table 16 shows the Pearson correlation coefficient for respondent’s age and highest year of school completed is significant, r = -.163, p = .001 (single- tailed). The negative correlation indicates that as the age of the respondent increases, the highest year of school completed decreases. The R Square value is 0.027 which is equivalent to 2.7%. This shows that the respondent’s age has 2.7 % variability in the respondent’s highest year of school completed. The model summary in Table 17 shows that R = .163 which shows correlation coefficient of age and highest year of school completed, a weak relationship. The R Square value is .027 which is 2.7 percent variability of respondent’s highest year of school completed as a result of age. Furthermore, the regression coefficient in output 18 shows that a change in the respondent’s age by a factor of 1 leads to a decrease in the respondent’s highest year of school completed by .027. When the respondent’s age is zero, the highest year of school completed is 14.50 hence the regression equation is Y = -.027x + 14.50. The beta value is -.163 indicating that a one unit variation in age causes a .163 decrease in highest year of school completed. Moreover, the t value is significant, t = -6.22, p = .001 indicating that respondent’s age causes a significant reduction in the highest year of school completed. While it is evident that the respondent’s education can be predicted by age, it is not possible to show the direction of causality as well as accounting for other variables other than age. Marital status and education On conducting a multivariate analysis on marital status and education, it was evident that there were 627 married participants, 139 widowed respondents, 228 divorced participants, 57 separated and 357 never married individuals (output 19). The multivariate tests showed a significant Wilks’ lambda .953, F = 8.62, df = 8 and p = .001. The Roy’s Largest Root as well as the Pillai’s Trace were also significant, .046, F = 16.28, df = 4.00 and p = .001 and .048, F = 8.55, df = 8.00 and p = .001 respectively. This is an indication that respondent’s education is significantly predicted by the respondent’s marital status. The tests for between-subjects effects indicated that marital status significantly affects both the respondent’s highest degree as well as the highest year of school completed. The sum of squares for marital status influence on respondent’s highest degree was 57.51 while that of highest year of school completed was 515.89. The mean squares for respondent’s highest degree and highest year of school completed were 14.38 and 128.97 respectively and both had 4 degrees of freedom. The F values for highest degree and highest year of school completed were 10.78 and 16.03 respectively and both were significant, p = .001 for both (Output 21). The R square for respondent’s highest degree is .030 indicating that marital status contributed 3% of variability in respondent’s highest degree. On the other hand, the R square value for highest year of school completed was .044 indicating that marital status caused 4.1% variability in respondent’s highest year of school completed. It can therefore be concluded that marital status has a higher contribution on respondent’s highest year of school completed than on respondent’s highest degree. To identify which marital status actually caused a variation in respondent’s education, Tukey’s HSD post-hoc test was conducted. Tukey HSD test showed that there was a significant difference, mean difference of .68, p < .05 and 95% CI (.38 – .97), between married and widowed respondents in determining respondent’s highest degree. Tukey HSD test however showed that there was no significant difference between married and divorced, p = .77, mean difference of .10, 95% CI (-.14 – .35), and married and separated, p = .35, mean difference of .29, 95% CI (-.14 – .73) in determining respondent’s highest degree. Table 22 also shows that there was no significant difference between married and never married individuals in their contribution to respondent’s highest degree (mean difference = .02, p = 1.0, 95% CI (-.19 – .23)). Tukey HSD indicate that there was a significant difference (p < .05) between widowed and divorced respondent’s in determining the respondent’s highest degree. This was also the case between widowed and never married individuals but there was no significant difference (p = .21) in respondent’s highest degree when comparing widowed and separated individuals. There was no significant difference in respondent’s highest degree when comparing divorced and separated as well as divorced and never married participants (p = .81 and p = .89 respectively). However, a significant difference (p <.05) in respondent’s highest degree occurred between widowed and divorced individuals. There was no significant difference in respondent’s highest degree when comparing separated and all other forms of marital status (p > .05 for all cases) whereas there was a significant difference (p = .001) in highest degree when comparing never married and widowed participants.

# Our Service Charter

1. ### Excellent Quality / 100% Plagiarism-Free

We employ a number of measures to ensure top quality essays. The papers go through a system of quality control prior to delivery. We run plagiarism checks on each paper to ensure that they will be 100% plagiarism-free. So, only clean copies hit customers’ emails. We also never resell the papers completed by our writers. So, once it is checked using a plagiarism checker, the paper will be unique. Speaking of the academic writing standards, we will stick to the assignment brief given by the customer and assign the perfect writer. By saying “the perfect writer” we mean the one having an academic degree in the customer’s study field and positive feedback from other customers.
2. ### Free Revisions

We keep the quality bar of all papers high. But in case you need some extra brilliance to the paper, here’s what to do. First of all, you can choose a top writer. It means that we will assign an expert with a degree in your subject. And secondly, you can rely on our editing services. Our editors will revise your papers, checking whether or not they comply with high standards of academic writing. In addition, editing entails adjusting content if it’s off the topic, adding more sources, refining the language style, and making sure the referencing style is followed.
3. ### Confidentiality / 100% No Disclosure

We make sure that clients’ personal data remains confidential and is not exploited for any purposes beyond those related to our services. We only ask you to provide us with the information that is required to produce the paper according to your writing needs. Please note that the payment info is protected as well. Feel free to refer to the support team for more information about our payment methods. The fact that you used our service is kept secret due to the advanced security standards. So, you can be sure that no one will find out that you got a paper from our writing service.
4. ### Money Back Guarantee

If the writer doesn’t address all the questions on your assignment brief or the delivered paper appears to be off the topic, you can ask for a refund. Or, if it is applicable, you can opt in for free revision within 14-30 days, depending on your paper’s length. The revision or refund request should be sent within 14 days after delivery. The customer gets 100% money-back in case they haven't downloaded the paper. All approved refunds will be returned to the customer’s credit card or Bonus Balance in a form of store credit. Take a note that we will send an extra compensation if the customers goes with a store credit.

We have a support team working 24/7 ready to give your issue concerning the order their immediate attention. If you have any questions about the ordering process, communication with the writer, payment options, feel free to join live chat. Be sure to get a fast response. They can also give you the exact price quote, taking into account the timing, desired academic level of the paper, and the number of pages.

Excellent Quality
Zero Plagiarism
Expert Writers

or