Sample size is a critical part of any quantitative research study.
My Statistical Data Analysis tutorial page provides a good explanation of the basic principles of probability and statistical inference. If you understand these concepts, you will understand why sample size is so important to the effectiveness of your study.
In very basic terms, the larger your sample size, the more likely you will be able to find statistically significant results.
Since sample size is so important in making statistical inferences, your committee naturally wants to be sure that your dissertation research uses an adequate sample size to effectively address your research questions. That is why you should include a "sample size justification" section in your Methods Chapter.
Power Analysis is a family of statistical procedures which are used to justify the appropriate sample size for testing a given statistical hypothesis.
Sample size calculation refers to using power analysis to determine an appropriate sample size for testing your research hypotheses.
In basic terms, "Statistical Power" is the likelihood of achieving a statistically significant result if your research hypothesis is actually true. In other words, if the null hypothesis is false, what is the probability that your research results will be significant at the p < 0.05 level? If the likelihood is good (e.g., at least 80% chance), then your sample size would be considered adequate (i.e. statistically powerful).
See my Power Analysis Tutorial for more information.
When I help you with the statistical considerations for your dissertation proposal, I will perform a power analysis to justify your sample size and I will determine which statistical methods are appropriate for your data analysis section. I also provide readily available statistics help to make sure that you fully understand all of the statistics that I used for your research.
Simply contact me by phone or email to get started.
1-800-357-0321 or 1-630-936-4771 | Steve@StatisticallySignificantConsulting.com