# Data Analysis Plan

Every quantitative dissertation methodology and IRB (Institutional Review Board) application necessitates a comprehensive data analysis plan. This plan is pivotal as it details the specific analyses that will be conducted to explore each of the research hypotheses or study aims. The plan includes phases of data cleaning, addresses the assumptions inherent in the analyses, and outlines the selection of appropriate statistical tests.

Statistics Solutions is dedicated to assisting you in developing a robust quantitative data analysis plan. Our services include:

• Refining Research Questions and Hypotheses: We edit your research questions and both null and alternative hypotheses to ensure clarity and relevance.
• Crafting Your Data Analysis Plan: We meticulously draft your data analysis plan, specifying the appropriate statistics for addressing the research questions, delineating the assumptions of these statistics, and justifying their selection with scholarly references.
• Sample Size and Power Analysis Justification: We provide a detailed justification for your sample size and power analysis, complete with supporting references.
• Plan Explanation: We explain your data analysis plan in detail, ensuring you are comfortable and confident with its components.

In Practice: Quantitative Approaches

Data analysis plans are integral to the methodology section and are crucial for the IRB approval process. They articulate the procedures for analyzing quantitative data, enhancing the study’s scientific rigor and ensuring reproducibility by other researchers. The choice of statistical tests is influenced by how the research questions are phrased and the measurement level of the data involved.

Here are examples of how research questions might influence the selection of statistical tests, using Engagement Life Scores (0-100) and Meditation group participation (yes vs. no) as variables:

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• Differences: “Is there a difference in Engagement scores by Meditation group?” This question suggests using an independent t-test, as it investigates differences in a scale variable by a dichotomous independent variable. Generally, questions about differences lead to t-tests, ANOVAs, and MANOVAs.
• Prediction: “Does participation in a Meditation group predict Engagement scores?” This predictive question typically requires a linear regression, as it explores the impact of a dichotomous variable on a scale variable.
• Relationships: “Is there a relationship between Engagement scores and Meditation group participation?” This type of question, examining the relationship between a scale variable and a dichotomous nominal variable, is best addressed using a point-biserial correlation. Relationship inquiries often lead to various correlations (Pearson, Spearman, point-biserial) or chi-square analysis for nominal variables.

These examples demonstrate how the phrasing of research questions and the data’s measurement levels drive the statistical methods selected, ensuring that your research is methodologically sound and statistically valid.