If you are going to implement a quantitative design for your thesis or dissertation, you will probably be using some form of null hypothesis significance testing. It may have been a while since you took your graduate-level statistics course, so the following is a brief refresher about what a null hypothesis is. Null Hypothesis Significance Testing In most quantitative research questions, there are both null hypotheses (noted as H0)…

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Before you begin to collect data for your thesis or dissertation, it may be helpful for you to review the different types of data and scales of measurement available to you. You can use the following cheat sheet as a reference guide as you prepare to collect your dissertation data. Scales of Measurement Nominal: (a.k.a., categorical) refers to characteristic data that have no numeric value (i.e., ethnicity)      Dichotomous:…

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Most statistics courses tend to focus on parametric statistics; however, you might find that as you prepare to analyze your dissertation data, parametric statistics might not be an appropriate choice for your research. The following are some of the differences between parametric and nonparametric statistics. Parametric Statistics Parametric statistics are any statistical tests based on underlying assumptions about data’s distribution. In other words, parametric statistics are based on the…

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You have successfully defended your dissertation proposal, and now you have your dissertation data. It might seem like the next logical step would be to run your analysis according to the analysis plan from your proposal. However, it is important to make sure that the statistics test you plan to run is appropriate for the data that you obtained. Sticking to a rigid plan for which statistics test to…

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