\ufeffRUNNING HEAD: EDUCATION AND EMPLOYMENT<\/p>\n
A Research Paper to Determine the Relationship between Education and Employment<\/p>\n
Name:<\/p>\n
Course:<\/p>\n
Tutor:<\/p>\n
20th March, 2011.<\/p>\n
The Relationship between Education And Employment<\/p>\n
INRODUCTION<\/p>\n
Education has a crucial role in improving one\u2019s life after schooling. There has existed a relationship between one\u2019s educational level of achievement and his career advancements and therefore well economic well being.<\/p>\n
The purpose of this research paper is to find the correlation that exists between educated individuals in their later life and those have little or no education. This will involve data collection using secondary sources. Then the data will be presented using various methods of data presentation. Using descriptive statistics, the data will be presented using appropriate means, for example using pictograms among any other forms of data presentation. <\/p>\n
The data will be analysed using the various data analysis strategies of inferential statistic, to calculate the correlation that exists between the two variables. <\/p>\n
The study emphasizes the importance of education; to finish high school and attend postsecondary education. It also analyses the benefits of education to students. It also determines the likelihood of employment based on education. Therefore, my main aim is to see if there exists a strong correlation between high level of education and securing employment opportunities employment. <\/p>\n
LITERATURE REVIEW<\/p>\n
In some commonwealth countries the number of female enrolment in schools exceeds the number of male enrolment, besides the deference between female and male is decreasing and this has been in the last thirty years (Elsa Leo-Rhynie, 1999).<\/p>\n
Literacy is a tool that can be used by an individual for upward mobility, especially for economic prosperity. One who is highly educated has a high chance of getting a job and therefore securing a better economic stats than one who is not educated who might find it difficult securing a well paying job. In addition, education is important in improving somebody\u2019s standard of living. An educated person is batter placed in terms of knowledge acquisition as well as participating in the processes of decision making at family, community and even at the whole society at large (Elsa Leo-Rhynie, 1999).<\/p>\n
Literacy for both male and female has improved for the last 20 years for some commonwealth countries; there are some countries whose women\u2019s literacy is considered lower than that one of male. This is partly due to some social responsibilities that are bestrode on female which make them face some challenges in accessing education as opposed to male\u2019s responsibilities which put them in a better position to acquire more knowledge.<\/p>\n
From human\u2019s right perspective there is a strong argument to achieving gender parity in education. This is a venture that is economical and a course worth taking; bearing in mind the gains that are associated with literacy. Educated girls, just like boy, achieve better livelihoods. Besides, they earn their place as responsible citizenry (UNESCO, 2011).Through various efforts either by various governments, UNESCO, NGOS, INGOs, and communities of getting all girls to learn in school by advocacy, legal means, curricula, teacher training and in servicing, among many others ,there are some countries or nation-state that are yet to achieve gender parity n educational provision. This has seen the existence of the economic inequalities that majorly exist between males and female.<\/p>\n
THESIS<\/p>\n
What is the relationship between the level of education achieved by individuals, versus their employment and economic well being?<\/p>\n
GENERAL HYPOTHESIS<\/p>\n
The level of education a person achieves will most likely affect his employment status. The higher the level of education achieved, the higher the chances that the individual is employed and better placed economically.<\/p>\n
HYPOTHESIS ONE VARIABLE PREDICTION<\/p>\n
Mean, represent the average Australian while mode; represents the educational level completed by the average person. Median is a good representation of the educational level completed by the average person <\/p>\n
SEMI-INTERQUARTILE TO INTERQUARTILE RANGE<\/p>\n
Display results to support the fact that the average citizenry has the educational level equivalent to the one found through the mean, median and mode. <\/p>\n
THE SECOND HYPOTHESIS IS VARIABLE PREDICTION<\/p>\n
LINEAR REGRESSION<\/p>\n
The relationship between unemployment rate and level of education should be a strong one a negative whereas the relationship between education level and level of employment should be a strong positive correlation. <\/p>\n
DATA COLLECTION STAGE<\/p>\n
Data that is related to the rates of employment and unemployment and those certifications in Certification of Education considered highest, need to be collected. Australian Bureau of statistic conducted a nationwide census. The census figures include both females and males, in the labor force. The Census figures gave accurate picture of the employment situation in Australian as shown below.<\/p>\n
DATA COLLECTED<\/p>\n
EDUCATIONAL LEVEL PERCENTAGE EMPLOYMENT RATE PERCENTAGE UNEMPLOYMENT RATE WEIGHTING<\/p>\n
Earned Doctorate 75.3 % 3.6 % 10<\/p>\n
Master\u2019s degree 76.2 % 4.5 % 9<\/p>\n
Degree in medicine, dentistry, veterinary medicine or optometry 78.9 % 2.6 % 8<\/p>\n
University certificate or diploma above bachelor level 73.6 % 4.2 % 7<\/p>\n
Bachelor\u2019s degree 77.2 % 4.7 % 6<\/p>\n
University certificate or diploma below bachelor<\/p>\n
Level 67.1 % 5.2 % 5<\/p>\n
College, CEGEP or other non-university certificate or diploma 74.2 % 5.0 % 4<\/p>\n
Apprenticeship or trades certificate or diploma 68.1 % 6.2 % 3<\/p>\n
High school certificate or equivalent 63.8 % 7.3 % 2<\/p>\n
No certificate, diploma or degree 38.1 % 11.1 % 1<\/p>\n
Employment rate unemployment rate and various educational levels<\/p>\n
(adapted from Australian Bureau of statistics.)<\/p>\n
DATA ANALYSIS <\/p>\n
Levels of degrees were weighted. A weighting of ten; the highest and hardest degree that one could achieve, and less common. A weighting of one; would indicate that the level of education completed was low, for example, situations where the individuals had no certificate, diploma or degree. The rate of unemployment and employment is calculated as a percentage. <\/p>\n
ONE VARIABLE ANALYSIS<\/p>\n
INDEPENDENT VARIABLE LEVEL OF EDUCATION ACHIEVED DATA ANALYSIS<\/p>\n
USING MEASURES OF CENTRAL TENDENCIES<\/p>\n
Ranking from 1-10 based on level of education First Quartile, Q 1 is 1.5, the Inter Quartile range (IQR) = -4.5 Outliers < -4.5 Q 3 + 1.5 (IQR) = 15.5 Outliers > 15.5 There are no Outliers <\/p>\n
outlier calculations 2.5 % , semi inter quartile range; SIQR 5 %, IQR 8 %, third quartile q 3 3 % , q 1 0.54, z\u2013 scores 8.25, variance 2.872, standard deviation 10 % , maximum 1 %, minimum 9 %, range is not applicable, mode 5.5 %, and median 5.5 % which is also equals to the mean.<\/p>\n
ANALYSIS<\/p>\n
Mean and median = 5.5; indicates that the average level of education achieved and the normal person in a population would achieve an education level of 5.5, which is between a University certificate or diploma below bachelor\u2019s degree and a bachelor\u2019s degree. <\/p>\n
DEPENDENT VARIABLE ANALYSIS<\/p>\n
That is the analysis of unemployment. Unemployment mean = 5.44%. This means that the average unemployment in Australian is 5.44%. However, the average unemployment rate in Australian is 6.0% in 2008. But in 2009, the unemployment rate rose to 9.50%, possibly due to the oncoming recession at that time. Therefore, the mean rate that is calculated from the set of data is actually lower than the statistical unemployment rate and is not actually that high. <\/p>\n
q 1 \u2013 1.5(IQR) = 2.7 % outliers < 2.7 q 3 + 1.5 (IQR) = 7.7 % outliers > 7.7 % 2.6 % and 11.1 % are outliers outlier calculations 1% SIQR 2 % IQR 6.2 % q 3 4.2 % q 1 0.898 Z \u2013 scores 5074 variance 2.25 standard deviation 11.1 % maximum 2.6 % minimum 8.5 % range not applicable mode 5.1 % median 5.44 % which is equal to the mean.<\/p>\n
ANALYSIS<\/p>\n
As the Level of Education achieved increases, the levels of unemployment decrease. Logically, as people with certain skill come up and are identified to poses appropriate skill, most employers tend to employ them. In other words, specialists in certain areas are more likely to be hired than non-specialists who have not met the educational qualification. <\/p>\n
DEPENDENT VARIABLE ANALYSIS<\/p>\n
Employment Australian\u2019s statistical employment rate is 63.6% as of 2008. The calculated mean in this case study is 69.25%, which indicates that the theoretical employment rate is higher than it actually is. Q 1 \u2013 1.5(IQR) = 53.45 % Outliers < 53.45 % Q 3 + 1.5 (IQR) = 89.95 % Outliers > 89.95 % 38.1 % is an outlier. <\/p>\n
calculations 1% SIQR 2 % IQR 6.2 % q 3 4.2 % q 1 -0.459 Z \u2013 scores 129.05 % variance 11.36 % standard deviation 78.9 % maximum 38.1 % minimum 40.8 % range n\/a mode 74.75 % median 69.25 % mean<\/p>\n
SECOND VARIABLE ANALYSIS UNEMPLOYMENT<\/p>\n
The correlation for the Rate of Unemployment and levels of education is 0.8346 which is strong and negatively sloped as the r value was near 1 and the linear regression sloped downwards. <\/p>\n
y = 9.3596e -0.1121x 0.7364 Exponential y = – 0.0231x 3 + 0.517x 2 – 3.9053x + 14.007 0.9204 Cubic y = 0.1356x 2 – 2.1462x + 12.023 0.8879 Quadratic y = – 0.6545x + 9.04 0.6965 Linear Equation r 2 REGRESSION TYPE<\/p>\n
TWO VARIABLE ANALYSIS EMPLOYMENT AND LEVEL OF EDUCATION<\/p>\n
The correlation for the Rate of Employment and level of education attained is 0.7415 which was strong and positive because the value was close to 1 and the linear regression sloped upwards. <\/p>\n
y = 51.706e 0.0499x 0.4938 Exponential y = 0.1764x 3 – 3.6957x 2 + 24.991x + 20.717 0.8763 Cubic y = – 0.7848x 2 + 11.566x + 35.853 0.8018 Quadratic y = 2.9327x + 53.12 0.5498 Linear Equation r 2 regression type<\/p>\n
There is a correlation that exists, that is as the level of education increases, there is a significant increase in the rate of employment while on the other hand, there is a decrease in the unemployment rate. <\/p>\n
There exists a discrepancy at \u2018level 8\u2019 Degree in Medicine, Dentistry, Veterinary Medicine or Optometry, Unemployment Rate is 2.6% and Employment peaks at 78.9%. This is caused by the fact that these are the caliber of professionals who are rare to come by in the society thus most of them have got opportunity in terms of employment.<\/p>\n
LIMITATIONS<\/p>\n
Employment and Unemployment rates do not add up to 100%. This is due to hidden unemployment, untruth about employment status, businesses which are not registered, as well as those people who are out of work due to sicknesses, injuries among many other reasons.<\/p>\n
Interpolating and extrapolating does not make sense. No level of education that is \u2018in between\u2019 another. For example, a Bachelor and half a degree does not exist. Census includes figures that have got various discrepancies for example, it give statistical data that do not take into consideration the various inequalities that exists in the society. Thus, we can end up with politically manipulated figures so as to create some political mileage of the leaders. An example may be inflated rates of employment. This is because some leaders rise on the platform of leadership on the promises of creating more employment, which they hardly realize, so to cover up for their under achievement, they may instigate these figures. <\/p>\n
Immigrants issue may also pose some challenges in the data collected and therefore the inferences that are made may not reflect the actual picture on the ground. Besides, respondents may not be completely honest, or not available for example the may be away during census <\/p>\n
CONCLUSION<\/p>\n
There exists a strong correlation between the level of education one achieves and his chances of securing employment. As the level of education increases, so does the rate of employment increases as well. This is backed by evidence of linear regression of a cubic relation. <\/p>\n
Results are similar to hypothesis. However, in the hypothesis, it was predicted that the relationship would be linear, but instead, is a cubic function. Relationship between Unemployment rate and Employment rate; regression are opposite of each other. That is as one tends to increase, the other tends to decrease.<\/p>\n
References<\/p>\n
Elsa Leo-Rhynie, C. S. (1999). Gender mainstreaming in education: a reference manual for<\/p>\n
governments and other stakeholders. London: Commonwealth Secretariat.<\/p>\n
Statistics, A. B. (2011). Census data. Retrieved March 20th, 2011, from Australian Bureau of<\/p>\n
Statistics HYPERLINK “http:\/\/www.abs.gov.au\/websitedbs\/D3310114.nsf\/home\/census+data?opendocument#from-” http:\/\/www.abs.gov.au\/websitedbs\/D3310114.nsf\/home\/census+data?opendocument#from-banner=LN<\/p>\n
UNESCO. (2011). Education: Gender equality in education. Retrieved March 20, 2011, from:<\/p>\n
HYPERLINK “http:\/\/www.unesco.org\/new\/en\/education\/themes\/leading-the-international-agenda\/gender-and-education\/” http:\/\/www.unesco.org\/new\/en\/education\/themes\/leading-the-international-agenda\/gender-and-education\/<\/p>\n","protected":false},"excerpt":{"rendered":"
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