Introduction

  • Determine whether the type and volume of your research data, as well as your research question, justify the use of quantitative data analysis. Avoid performing quantitative data analysis merely for the sake of having done something “digital”!

  • If you choose to pursue quantitative data analysis, consider what insights you want to extract from your data and how you can achieve this using Python programming.

Analyzing Tabular Data

  • Formulate appropriate research questions when working with tabular data.
  • Identify the quantitative analysis methods best suited to answering these questions.
  • Break down the analysis into smaller tasks, translate them into computer logic using pseudocode, and implement them in Python code.
  • Learn about Python functions and methods.
  • Learn about histograms.
  • Use pandas for counting and searching values in tabular datasets.
  • Use plotly.express for visualizing tabular data.

Analyzing Text Data

  • Formulate appropriate quantitative research questions when working with data composed of literary texts.
  • Learn about dictionaries, lists, for loops, and f-strings in Python.
  • Get to know and use the Python library spaCy.
  • Perform word frequency analysis using spaCy.
  • Perform collocation analysis using spaCy.
  • Measure lexical diversity in a body of text.