Every dissertation methodology requires a data analysis plan. The plan is critical because it tells the reader what analysis will be conducted to examine each of the research hypotheses. In the data plan, data cleaning, transformations, and assumptions of the analyses should be addressed, in addition to the actual analytic strategy selected.
Statistics Solutions can assist with the development of your quantitative data analysis plan. We offer the following services:
Edit your research questions and null/alternative hypotheses;
Write your data analysis plan; specify specific statistics to address the research questions, the assumptions of the statistics, and justify why they are the appropriate statistics; provide references;
Justify your sample size/power analysis, and provide references;
Explain your data analysis plan so you are comfortable and confident with what the plan entails;
Two (2) hours of additional support with your statistician should you or your advisors have any questions.
Data analysis plan complete in 1-2 weeks.
Discover How We Assist to Edit Your Dissertation Chapters
Aligning theoretical framework, gathering articles, synthesizing gaps, articulating a clear methodology and data plan, and writing about the theoretical and practical implications of your research are part of our comprehensive dissertation editing services.
Bring dissertation editing expertise to chapters 1-5 in timely manner.
Track all changes, then work with you to bring about scholarly writing.
Ongoing support to address committee feedback, reducing revisions.
Data Analysis Plan-Quantitative
Data analysis plans are presented in the methodology and used in the IRB process. They describe the procedures to analyze quantitative data. It is important in the scientific process, as it facilitates the ability for other researchers to replicate the study. The procedures, or selection of statistical tests, are driven by two factors: the way the research questions is phrased and the level of measurement of the data.
Research questions with variables that have the same level of measurement, but different phraseology, lead to different test selections. For example, here are some possible research questions using a dependent variable of Engagement Life Scores (ranging from 0-100) and an independent variable of Meditation condition (yes vs. no).
Is there a difference in Engagement scores by Meditation condition? Given the language of differences on a scale variable by a dichotomous independent variable, leads us to an independent t-test. Questions of difference in general lead to t-tests, ANOVA’s, and MANOVA’s.
Does Meditation condition predict Engagement scores? Given the language of prediction using a scale variable leads us to a linear regression. Questions regarding prediction in general lead to various regression tests (linear, binary, ordinal, and multinominal).
Is there a relationship between Engagement scores and Meditation condition? Given the language of relationships between a scale variable and a dichotomous nominal variable leads us to a point-biserial correlation. Questions regarding the relationship between variables in general lead to various correlations (Pearson, Spearman, point-biserial, partial).
Fortunately, there are decision trees that can help in the selection of statistical tests. But we are not done yet…the selection of the test is the first step. The second step is to describe the assumptions of the test.