Data Analysis Framework

The objective is to provide an easy to follow framework for analysis that allows the analyst or PhD student to walk through their entire approach in a step-by-step manner with their colleagues or PhD supervisors.

In each of the 6-steps, the researcher is to provide their code, outputs for the code, and any comments/thoughts that may help the reader interpret the approach that was taken.

Step 1: Research Question/Problem Statement

  1. Research Question/Problem Statement #1

  2. Research Question/Problem Statement #2


Provide any additional thoughts around the research question

  • Hypothesis
  • Potential issues or challenges
  • Potential limitations
  • Etc.

Step 2: Data Collection/Measurement Strategy

  1. What type of data is required
  • Data sources (database, websites, data collection, etc.)
  • Structure of the data
  • Data issues (missing data, messy data, etc.)
  1. Collection/Measurement
  • If data needs to be collected, what are the measurements being taken (clearly define the procedures and standardization)?
  • Are the measurements valid and reliable (is there potentially a need to add a research step here and conducting your own validity/reliability study before proceeding)?
  1. Data Cleaning
  • What pre-processing steps were taken?
  • Clearly walk through the data cleanning process.
  • Is any data missing, if so how much?
  • Describe any imputation process for missing data.
  • If any data was removed prior to analysis explain why.

Step 3: Visualize & Summarize Data

  • Once data has been collected and cleaned, provide an overview of the data using summarize statistics and visuals.

  • Offer interpretation of visuals that may help guide the model building process or generate discussion about any underlying trends in the data specific to the research question.

Step 4: Model Development/Interpretation

  • Iteratively build models (simple to complex).
  • Interpret the results of each model to explain why a more complex model or different modelling strategy may be required.

Step 5: Model Evaluation

  • Evaluation the final model(s), describing model errors, model accuracy, residuals, assumptions, etc.

Step 6: Communication of Results

  • Communicate the results of the final model(s) in a clear manner using visualizations and language that is understandable to the end user.
  • Explain whether or not the research question has been answered.
  • Clearly discuss any limitations of the analysis.
  • Offer suggestions for future analysis or perhaps other data sets that may be incorporated to provide a more contextual answer to the research question.