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Designing A Useful And Understandable Research Paper

Randomness within the survey selection process and within the survey itself can help protect against bias that may seep in unknowingly. Determining the variables within this project will also help guide the data and provide a way to describe it relatively. With race being the independent variable, the dependent variable for this project would be the varying levels of depression each black man would ascribe in the survey. Further breakdowns of the data could also be included to infer other relationships such as race, address, marital status and any other social label that could be applied to black males.

A target population of 1000 seems appropriate for the scale of this project. A larger group of subjects is preferable but the resources available dictate what size is appropriate for this investigation. Using a survey, the results from the survey, SPSS or Excel are all the tools necessary to complete this investigation. Knowledge about correct data collection procedures will help make the study valid and eliminate possible errors caused by the construct of the project itself. When analyzing the data there are many choices to choose from, but it is important to continually examine the data and test for

Keeping this data private is of the utmost ethical importance. Research is about learning and attaining new knowledge that can be used for the goodness of mankind. It is important to keep this principle in mind when conducting any academic investigation into social and personal issues such as race and depression.
References

Cresewell, J. (nd). Chapter Eight: Quantitative Methods Power Point Presentation. Provided by customer.

Shadish, W.R., Cook, T.D., & Campbell, D.T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton- Mifflin.

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References

Cresewell, J. (nd). Chapter Eight: Quantitative Methods Power Point Presentation. Provided by customer.

Shadish, W.R., Cook, T.D., & Campbell, D.T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton- Mifflin.
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