Regression Analysis– Does Dropping out of School Impact the Rate of Violent Crimes The rate of school dropouts and the rate of violent crimes in U.S. were being suspected to have correlation since long time ago. Yet, up until recently, only the psychological methodology was being used to establish a link between these two social problems. Applying mathematical research to deal with the issue, was being thought of as too complicated, since computer technology wasnt commonly available. In the light of recent developments in the field of sociology, it became much easier even for non-professionals, to use algebraic formulas, as the ultimate research tool that would help solving social dilemmas. The method of linear regression does not only show whether there is a link between two seemingly separate social trends, but it also allows us to theoretically project these trends into the future, so that our current social policies can be adjusted accordingly.
We will base our analysis on the statistical data of violent crime and school dropouts, available at U.S. Department of Justice and Child Trends Data Bank. The dropout rate among youth between 16 to 24 shows that there is a steady decline in number of students who leave school, without getting a diploma. In 1972 the dropout ratio accounted for 15%, while in 2002 it was being reduced to 11%. The crime rate statistics also shows us that there is decline in number of registered cases of violence, although the crime reduction rate is not quite as steady as school dropouts reduction. In 1973 it accounted for 47.7 (trend estimate) and 22.8 in 2002.
Crime is a social construct Discuss. This composition will look at crime and its different criminological interpretations. Crime is an umbrella word which covers a diverse range of issues and is dependant upon the theoretical stand point of the writer. Although the wordings of the explanations differ, the implications are consistent (Newburn, 2007. Doherty, 2005). Mclaughlin et al (2006) seems the ...
In order for us to come to conclusion about whether there is any relation between these two trends, we will have to draw a so-called best-fit line through analyzed data, which would allow us to figure out whether a linear interconnection exists between two statistics ratios. The formula y=mx+b would have to be used, where y and x are two variables and m and b are the slope and Y-intercept. This is a classical way of figuring out a correlation ratio, yet if we suspect that there is a linear correlation is present, it is not necessary to use this formula every time, as the mean of determining constants m and b. The linear regression method in Microsoft Excel allows us to find correlation coefficient r by simply plotting the available data in appropriate columns. Here is the available data, which will be used in our analysis, where the first column represents year, the second high school dropouts ratio and the third violent crimes trends: 1975 5.8 48 1980 6 49.4 1985 45.2 5.2 1990 44.1 4 1995 13.9 46.1 1996 12.8 41.6 1997 13 38.8 1998 13.9 36 1999 13 32.1 2000 12.4 27.4 2001 13 24.7 2002 12.3 22.8 The violent crime trend we will use as a dependent variable and the school dropouts trend as an independent one. The linear regression analysis results are going to be as follows: 0.750 r, -0.866 r, 8.017 std.
error of estimate, 12 observations, 1 predictor variable, Violent Crime Trends dependent variable: variables coefficients std. error t (df=10) p-value 95%, lower 95% upper, intercept a = 48.5539, High School Drop Outs b = -1.00558 0.18373 -5.47 .0003 -1.41496 -0.59620.The summary of analysis is what allows us to fid a correlation. Regression ratio is calculated to be 1,925.3594 1 1,925.3594 29.96 .0003, Residual 642.7498 10 64.2750. The Total appears to be 2,568.1092 11. From this we can conclude that there is linear correlation between crime rate and school dropouts rate, as if we were to draw a best-fit line, its course wouldnt deviate too much from general decline coefficient, because correlation coefficient in this case is 0.750. The results of linear regression analysis show very low deviation margins, which is the proof that dependable variable corresponds to the statistical progress of independent variable, which is a crime rate, in our case.
Comparative and ratio analysis are two of the most common types of analyses used in examining a company’s fiscal records, and both used the same information contained in a firm’s financial statements. This paper is written better understand the role of each type of analysis in evaluating a company this paper expounds on such involvement. Definition Ratio analysis assesses the association among the ...
At the same time, we cant say that the best-fit line points out to direct geometrical relation between two variables. From this we can conclude that there are other factors play role in making impossible for us to suggest that reduction of school dropouts ratio would automatically cause the crime ratio to correspond accordingly. This is because it was quite impossible to include other decisive motivations of violent crime in this analysis, such as living quality progress (or regress), demographical dynamics ratio and the racial crime profiling coefficient. The same applies to school dropout statistics. In our analysis we used an average trend figures, which we were only able to obtain by compressing available data. The reason why sociologists always refer to linear correlation between violent crime and school dropouts ratio is because it directly relates crime to social inequality and its being accepted as dogma that there are no other factors, which cause children to leave school, but the social ones.
Yet, as many sociological surveys show, about 50% of all dropouts occur due to childrens unwillingness to continue with their studies. Also, the dropouts data in this analysis is based on self-reported principle. But is it a fact that this data is the subject of certain manipulations, as certain categories of children are being over sampled, in order to lower margins of general dropouts ratio. The results of linear regression analysis in this case, proves that it is the potential criminals fail at school and not the schools fail to prevent children with inclination towards the crime, to continue with their studies, which would enable them to become a productive members of society. Even though there is no doubt that there is a correlation between these two social problems, itd be much more appropriate if the character of dropouts data was included into linear regression analysis. This would enable us to obtain more accurate figures, and in its turn, this would allow us to come up with better social forecast, as there is a direct relation between antisocial behaviors and dropping out of school.
High School dropout is referred to student quitting high school prematurely or before graduation. Many reasons and factors are responsible for dropping out of school. Dropping out of school may be a singular reason of the child or a logical agreement between the child and the parent. Some reasons are due to intention to go find work, avoidance of bullying, depression from poor grades, and ...
It has to be remembered that leaving school has only incidental relation with students failure to meet educational requirements. I think, since school is nothing else but the miniature model of society, itd even make more sense to use a geometrical progression method, in order to find relation between criminal behavior out on the street and irresponsible behavior at school. In my opinion, the biggest challenge for the member of learning team, while applying linear regression method for identifying connection between two seemingly unrelated issues, is to consider all influential factors. The multiple regression method might prove to be more effective, in this respect. Still, there is a way to analyze complicated matter by using linear regression approach. For this, the researcher will have to analyze components of the social issue, one at the time, and then combine the results.
To conclude this paper, let us to summarize conclusions, based upon linear regression analysis: 1) Although, there is an undeniable link between school dropouts and violent crime statistics, it is impossible to predict whether calculated correlation is going to remain a definite category in this respect, for further than 2-3 years ahead, since correlation coefficient appears not to be as high as it seems, when we simply compare two statistical scales. 2) It is possible to obtain more accurate figures, but more factors of influence would have to be considered during the analysis.
Burnette, C. Factors Affecting Placement Failure: An Analysis of Sacramento County Juvenile Offenders. California State University. 24 May, 2000.
9 Jan. 2005. http://www.csus.edu/indiv/w/wassmerr/burnette.pdf. High School Dropout Rates Child Trends Data Bank. 2003. 9 Jan. 2005. http://www.childtrendsdatabank.org/indicators/1Hig hSchoolDropout.cfm United States. U.S. Department of Justice.
Bureau of Justice Statistics. National Crime Victimization Survey Violent Crime Trends, 1973-2003. 12 Sept. 2004. 9 Jan. 2005. http://www.ojp.usdoj.gov/bjs/glance/tables/viortrd tab.htm.