Jupyter notebooks with an introduction to the Design and Analysis of Experiments including simple python (Pandas) exercises. Authored with some reference to Field and Hole (2003) "How to design and report experiments", Sage publishing but expanding somewhat beyond the topic. The material is produced to be used for the BSc course for Medialogy students at Aalborg University, but I hope it will be useful also as a general intro /recap for others. It is also possible to run the code without specific installation using colab https://colab.research.google.com/github/sofiadahl/Design_and_Analysis_of_Experiments/blob/main/
The aim is to
- give an introduction to concepts and methods important in experimental design and statistical analysis of data
- be brief enough to be useful as a first reference point and re-cap to students of media technology
The topic is covered in the following notebooks:
DAE 0 START HERE
DAE 1 Variables
DAE 2 Designing Experiments
DAE 3 Measuring Validity and Reliability
DAE 4 Descriptive Statistics
DAE 5 Correlation and Linear Regression
DAE 6 Inferential Statistics
DAE 7 Parametric Data
DAE 8 Student t-test
DAE 9 ANOVA
DAE 10 Non-parametric tests
It is my hope that after going through the book and exercises, you should feel confident enough to pick up a book on statistics, and work out what type of measurement instruments and analysis that are appropriate to use for a particular evaluation.
It should be possible to follow and do most of the "think-and-do" try outs without particular skills in python or mathematics. For those that would like a brief introduction to Python please check out software-carpentry https://software-carpentry.org/lessons/ and the quick intro to pandas, https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html?highlight=randint.
Thanks to Prithvi Kantan for proofreading, helpful comments and suggestions.