Quantitative Methods Have Their Strengths and Weaknesses. Discuss.

1569 Words Jan 18th, 2010 7 Pages
Quantitative methods have their strengths and weaknesses. Discuss.

Quantitative methods, like all social research methods, have their own set of strengths and weaknesses. This essay will attempt to critically assess those characteristics and draw a comparison between quantitative methods and qualitative methods. The quantitative versus qualitative debate is an interesting topic in Sociological studies. In Miles and Huberman's 1994 book Qualitative Data Analysis, quantitative researcher Fred Kerlinger is quoted as saying, "There's no such thing as qualitative data. Everything is either 1 or 0". To this another researcher, Donald Campbell, asserts, "All research ultimately has a qualitative grounding". This essay will look at both sides of
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A good example would be a survey of a father and son’s occupations. The independent variable would be the father’s occupation and the son’s occupation would be the dependant. This is because the father is the possible cause of the son's occupation. The results of such a study would be shown in a table of findings. The survey could look at manual and non-manual workers, and a random sample of 100 people could be used, depending on the researcher. This would be so the researcher could be confident within specifiable limits that any correlation is probably not a chance finding. An important point to make is that quantitative researchers do not like to change statements of correlation into casual statements. For example, quantitative researchers would not confidently state that a father’s occupation is significant cause of a son’s occupation.

For a descriptive study with a wide focus, the main interest should be estimating the effect of everything that is likely to affect the dependent variable, so you include as many independent variables as resources allow. For the large sample sizes that you should use in a descriptive study, including these variables does not lead to substantial loss of precision in the effect statistics, but the danger is that the more effects you look for, the more likely the true value of at least one of them lies outside
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