DeliberativePolling 0.0.3

A package for analyzing survey data from Deliberative Polling experiments.

How To

Objective

This guide is designed to assist professionals in efficiently leveraging a Python package tailored for the analysis of survey data in Deliberative Polling experiments.

1. Preparation of Data

• Step 1.1: Ensure your data is structured and saved as an .SAV file format, primarily associated with IBM SPSS software.

2. Variable Classification

Variables within survey data can generally be classified into three major categories. It's imperative to identify and label each correctly for precise analysis.

• Step 2.1 Nominal Variables

  • Definition: These are categorical variables that do not have an intrinsic order.
  • Example: Gender, where categories like male, female, and non-binary do not have a specific sequence.

• Step 2.2 Ordinal Variables

  • Definition: These are categorical variables with a clear, definable order.
  • Example: Data derived from a Likert scale ranging from 0 to 10. The values indicate a progression from least to most favorable (or vice versa).

• Step 2.3 Scale Variables

  • Definition: Variables not classified as either Nominal or Ordinal are listed under this category. These can be continuous or discrete variables.
  • Examples: Critical variables such as Time, Group, and ID fall under this category.

3. Conclusion

With the data appropriately classified and organized, you are now poised to employ the Python package for rigorous analysis of your Deliberative Polling experimental data.