Rate of Drop-Outs in Oklahoma

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Though there has been a decrease in the number of dropouts in many countries in the recent past, but there still exist countries where the rate has remained too high. The students either drop out from primary or high school but hardly do they reach colleges. There are numerous reasons why this so. In most areas, the urban area do have high rate compared to rural but the converse is true in some countries. The best example is seen in Oklahoma where the rural areas have relatively higher dropout rates as compared to the urban areas (Bolo, 2001).

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Many side effects are associated with this vice. An assessment of the trends of employment for Canada’s, labor force confirms that unemployment rates for younger employees are higher compared to adults. Furthermore, rates of unemployment are higher for the employees that are least educated (Ontario, 2007). In the following analysis, a correlation between age and the rate of drop-out is determined. Another variable determined is the effects of the level of income on the rates of drop-outs. It is also due to high rate of dropouts that has led to the high rate of criminology in Oklahoma (Otieno, 2001).

Effects of age on the rate of dropouts

It has been assumed that all the high school students are in the age limit of 14 and 25 years. All the students above 28 are considered as between 25 and 27. Very few go to high school below 14 and hence the number can be ignored (Kaufman, 1996).

All values are in percentages and few round offs are made. The percentage gave the best since it considered the whole population and hence each is a fraction of the whole (Bolo, 2001).

Effects of age on drop-out rates

Age 14-16 17-19 20-22 23-24 25-27
Drop-outs (%) 03 05 07 03 02

Correlation between age and drop-out rates

The drop-out rate is highest between 20 and 22. Between 14 and 16, the children are still under close supervision of the parents. The reach adolescent age 17 and 18 and then starts to think that they can be dependent. At this point the close supervision is absent and the peer pressure increases. It is normal that few students can reach 23 before clearing high school hence low drop-outs in this age bracket. Negligible number in the age bracket 25 and 27 (Ontario 2007). When two lines of best fit are drawn (from graph one) using scatter diagram in illustrating the effects of age, a correlation factor of 1.5 is obtained.

The numbers of points left in and out are almost equal. There has been a gradual decrease over the years.

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Age 14-16 17-19
Rate (%) 03 05

Correlation = change in % rate divide by change is time (age)

  • = 5- 3/ (18-15) %/time
  • =2/ (3)
  • = +0.67%/yr.

The relation thus; +0.67 = change (%)/time (yrs)

  • Change = +0.67x time
  • C = +0.67t.

The correlation indicates that time (age) and rate of dropout are positively related i.e. the rate of dropout and time are directly proportional in the first section. A strong negative correlation indicated in the second (Rumberger, 1986). As time progress, the rate of dropouts decreases. The correlation has a lot of importance in the first section because it is at this stage that majority of youths are in school and hence the second can be ignored. We therefore can say that age and rate of drop-outs are positively related (Ontario, 2007).

Effects of income on drop-out rates

All the people who do not earn are taken to earn between 100 and 399. The percentage give the best comparison as the whole population is looked at. All those who earn US $1599.5 are considered to earn 1600 (round off).

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The following is the data obtained.

Family income Us $ per month 100-399 400-699 700-999 1000-1299 1300 -1599 1600-1899 1900-2199 2200-2499 2500 and above
Rates of drop-out % 04 09 11 14 15 13 07 04 02

Stem and leaf diagram on fourth page.

Correlation factor

The correlation factor is complex to determine. However, two sections of the graph can be used to determine the correlation. The data is divided into two and the correlation factors can be determined separately in each section. It shows an increase then a decrease. The increase is between one hundred and three hundred and ninety nine, then a decrease from four hundred to above five hundred. It can be assumed that between 400 and 699, many children go to high school due to moderate income. Although quite a good number make it to high school but the drop-out must be either from the lack of fee with time or other related fact ors like peer pressure (Dewar, 2004) The rates decrease for the families earning 1600 and further lower for the families earning as the income increases. In this range, the families are stable and the fee is not a problem. Though this stability, but the low drop-out level might arise from other secondary factors like peer pressure, inability to learn, loss of parents and the likes (Lyle, 2009).

The correlation factor is positive in the first section (100-1599) and then negative in the second 1600 and above. It has been concluded that the rural has higher drop-out rates as compared to urban because the majority of people from rural have low earning income as compared to urban areas. This has caused the number of drop-outs to be high in rural compared to urban areas (Otto, 2003).

Correlation factor between 100 and 1599 is 0.014 as calculated below. This is the first section of the graph. The first value and the third give the line of best fit in the first section. All the mid earnings are considered in obtaining the difference hence the rate (Dewar, 2004).

Correlation = change in rate divided by change is earning. (Earnings are independent variable but rate is dependent variable. Rater depends on earning but the reverse is not true) (Kaufman, 1996)

  • Correlation = (11-4) / (749.5-249.5)
  • = 7 /500
  • =0.014.

Therefore; 0.014 x change in earning = change in drop-out rate.

The correlation factor between 1600 and above is negative.

Income and rate of drop-out

From the scatter diagram (figure two), is evident that income and rate of dropouts have complex correlation. A line of best fit drawn indicates a curve (Lyle, 2009). The rate increases then drops as the income increases. The correlation factor cannot be determined using a line. When the whole data is analyzed it shows that the two are weakly negatively related. The upper values can be ignored since few students reach those ages before they clear high school (Zola, 2000). The families with income between one hundred and one ninety nine must have had low drop-out rate due to the low number that make it to high school. They only obtain the basic primary education and quit due to less or no funds for high school (Osonald, 2000).


Bolo, H. (2001). Economic Returns to Schooling Decisions. Research in Higher Education. Rotor Publishers.

Dewar, D. (2004). Scatter Diagrams: Leader Manual and Instructional Guide. Publisher, QCI International.

Kaufman, P., Marilyn, M., and McMillan, R. (1996). Comparison of High School Dropout Rates in US. DIANE Publishers.

Lyle, B. (2009). High school dropout and its consequences. Congressional Research Service Publishers.

Ontario, P. (2007). Dropouts’ rates in Canada. Web.

Otto, P. (2003). Educational attainment and health: Evidence from a sample of older adults. Popo publishers.

Osonald, R. (2000). High school dropouts: Causes, Consequences, and Cure. Phi Delta Kappa Educational Foundation. Gnosticta Publisher.

Rumberger, R. (1986). High school dropouts: A problem for Research, Policy, and Practice. Publisher Stanford Education Policy Institute, Stanford University.

Scatter diagram. Otieno, J. (2001). Web.

Zola, H. (2000). Effects of age on the rate of dropouts. Delaldela publishers.

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