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Capella University — Doctor of Business Administration

DB9802: Data Analysis Practice and IRB Approval

A complete guide to Capella's DB9802. This second course in the DBA project sequence covers finalizing the analysis plan for the approved proposal and successfully navigating the IRB approval process before data collection begins.

DoctoralIRB ApprovalData Analysis PlanningAPA 7th Edition

DB9802 bridges the approved proposal and actual data collection — finalizing exactly how the data will be analyzed once collected, and securing the ethical approval required before any data collection can legally and ethically begin.

Finalizing the data analysis plan

DB9802 requires students to specify their analysis plan in detail before data collection begins — the specific statistical tests for quantitative studies, or the specific coding and thematic analysis approach for qualitative studies — rather than deciding on analysis methods informally after the data is already in hand. Planning analysis in advance also helps surface any mismatch between the proposed data collection instruments and what the intended analysis actually requires.

Navigating IRB submission and approval

The course covers the practical process of preparing and submitting a complete IRB application — informed consent documents, data security plans, and risk/benefit analysis — and responding to IRB reviewer feedback, which frequently requires revision before final approval. Students learn that IRB review exists to protect research participants, not as a bureaucratic formality to rush through.

Key topics in DB9802

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Worked example: catching an instrument-analysis mismatch before data collection

  • Planned analysis: A regression analysis testing whether specific leadership behaviors predict team performance
  • Instrument review: The planned survey instrument only captures leadership style categorically (transformational vs. transactional), not the specific behavioral variables the regression model requires
  • Correction: Instrument is revised to include a validated behavioral leadership scale with the granularity the analysis plan actually needs
  • Lesson: Finalizing the analysis plan before data collection surfaces exactly this kind of costly mismatch while it's still cheap to fix

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Frequently asked questions

Why must a detailed data analysis plan be finalized before data collection begins, rather than deciding on analysis methods afterward?

Deciding on analysis methods only after data has already been collected risks discovering that the collected data doesn't actually support the intended analysis — for example, a planned statistical test might require a specific type of measurement scale the collection instrument doesn't actually provide, or a planned qualitative coding approach might require types of interview probing questions that weren't included in the original interview protocol. DB9802 requires finalizing the analysis plan in advance specifically to surface and correct this kind of mismatch while it's still inexpensive to fix — revising a data collection instrument before any data has been gathered is straightforward, while discovering after the fact that already-collected data can't answer the intended research question is a far more costly and sometimes unrecoverable problem.

Why does IRB review sometimes require multiple rounds of revision before approval, and why shouldn't this be seen as a bureaucratic obstacle?

IRB reviewers are specifically tasked with evaluating whether a research study adequately protects the rights, privacy, and wellbeing of the people who will participate in it — reviewing informed consent language for clarity, evaluating whether data security plans genuinely protect sensitive information, and assessing whether the study's potential benefits justify any risks or burdens placed on participants. DB9802 teaches that IRB feedback requiring revision often reflects a legitimate gap the reviewer identified — perhaps consent language wasn't clear enough for a lay audience, or a data storage plan didn't adequately protect participant confidentiality — and treating this feedback as a bureaucratic hurdle to minimize or work around, rather than substantive protection to genuinely address, reflects a misunderstanding of the IRB's actual purpose: protecting real people who will trust the researcher with their time, information, and sometimes vulnerable disclosures.