Data Analysis Planning
For Data Analysts, effective planning ensures that data analysis projects are ethical, reproducible, and aligned with stakeholder needs. A structured approach reduces errors, mitigates bias, and ensures that chosen methods are appropriate for the research objectives and data characteristics (Simpson, 2015).
Technique Overview
Data Analysis Planning Definition
Data analysis planning is the structured process of engaging stakeholders, defining objectives, assessing available data, selecting appropriate methodologies, allocating resources, and establishing timelines, validation protocols, and documentation procedures before analysis begins. This ensures that work is credible, valid, reproducible, and aligned with project goals (Sandve et al., 2013; Vandenbroucke et al., 2007; Hubbard & Carriquiry, 2019).
Data Analysis Planning Description *
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Business Evidence
Strengths, weaknesses and examples of Data Analysis Planning *
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Business Application
Implementation, success factors and measures of Data Analysis Planning *
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Professional Tools
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Further Reading
Data Analysis Planning web and print resources *
Data Analysis Planning references (4 of up to 20) *
- Broman, K.W. and Woo, K.H. (2018). Data Organization in Spreadsheets. The American Statistician, [online] 72(1), pp.2–10. doi: https://doi.org/10.1080/00031305.2017.1375989
- Ioannidis, J.P.A. (2005). Why Most Published Research Findings Are False. PLoS Medicine, [online] 2(8). doi: https://doi.org/10.1371/journal.pmed.0020124
- Simpson, S.H. (2015). Creating a Data Analysis Plan: What to Consider When Choosing Statistics for a Study. The Canadian Journal of Hospital Pharmacy, [online] 68(4), pp.311–317. doi: https://doi.org/10.4212/cjhp.v68i4.1471
- Hubbard, D.W. and Carriquiry, A.L. (2019). Quality Control for Scientific Research: Addressing Reproducibility, Responsiveness, and Relevance. The American Statistician, 73(sup1), pp.46-55. doi: http://dx.doi.org/10.1080/00031305.2018.1543138
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