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How can you ensure statistical analysis transparency in quantitative research projects?

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Statistical analysis transparency is essential for ensuring the validity, reliability, and reproducibility of quantitative research projects. It refers to the clear and comprehensive disclosure of the data, methods, and results of the statistical procedures used to test hypotheses, answer research questions, and draw conclusions. However, achieving statistical analysis transparency can be challenging, especially when collaborating with multiple researchers, using complex or novel techniques, or dealing with sensitive or incomplete data. In this article, you will learn some practical tips to enhance the transparency of your statistical analysis in quantitative research projects.

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Rahul Patel

Full Stack?Operations Research?Machine Learning

1 Choose appropriate methods

One of the first steps to ensure statistical analysis transparency is to choose appropriate methods that match your research objectives, design, and data characteristics. You should justify your choice of methods based on theoretical and empirical grounds, and explain how they address your research questions or hypotheses. You should also acknowledge the limitations and assumptions of your methods, and how they may affect your results and interpretations. For example, if you use regression analysis, you should state the type of regression model, the variables, the coefficients, the fit measures, and the assumptions you checked.

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2 Document your workflow

Another important step to ensure statistical analysis transparency is to document your workflow, which is the sequence of steps and decisions you made from data collection to data analysis. You should keep a detailed record of your data sources, sampling methods, data cleaning and preparation procedures, variable definitions and transformations, missing data handling, outliers detection and treatment, exploratory analysis, hypothesis testing, model building and selection, and results presentation. You should also note any changes, errors, or deviations from your original plan, and how you resolved them. You can use tools such as notebooks, scripts, or logs to document your workflow.

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3 Share your code and data

A third step to ensure statistical analysis transparency is to share your code and data with other researchers, reviewers, or audiences who may want to verify, replicate, or extend your analysis. You should make your code and data accessible, readable, and reusable, by following good practices such as using consistent and descriptive names, adding comments and annotations, organizing files and folders, and applying formatting and coding standards. You should also provide metadata and documentation that describe the content, structure, and origin of your code and data. You can use platforms such as GitHub, Zenodo, or Figshare to share your code and data.

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4 Reproduce your results

A fourth step to ensure statistical analysis transparency is to reproduce your results, which means to obtain the same results using the same code and data. You should check that your code runs without errors or warnings, and that your results are consistent and accurate. You should also ensure that your results are reproducible across different environments, such as software versions, operating systems, or hardware configurations. You can use tools such as Docker, Binder, or RStudio to create reproducible environments for your analysis.

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Rahul Patel

Full Stack?Operations Research?Machine Learning
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Reproducibility for many methods is achievable but there are some models that have a randomness built into the algorithm. For that it is also very important to fix the random seed in your code to ensure the results are consistently reproducible.

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5 Report your results

A fifth step to ensure statistical analysis transparency is to report your results in a clear and comprehensive manner. You should present your results in a logical and coherent order, and use appropriate tables, graphs, or figures to illustrate them. You should also provide sufficient information and details about your results, such as summary statistics, effect sizes, confidence intervals, p-values, or model diagnostics. You should also interpret your results in relation to your research questions or hypotheses, and discuss their implications, limitations, and uncertainties. You can use guidelines such as APA, CONSORT, or STROBE to report your results.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you ensure statistical analysis transparency in quantitative research projects?

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1 Choose appropriate methods

2 Document your workflow

3 Share your code and data

4 Reproduce your results

5 Report your results

6 Here’s what else to consider

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