This repository aims to assemble a set of methods/ for our statistical analysis. We use several statistical models (e.g.: ANOVA, Kruskal-Wallis One-Way Analysis of Variance, Mann-Whitney U test, etc...) to analyse our data and deeper understanding it. For instance, we used the Kruskal-Wallis One-Way Analysis of Variance for our ISS'17 Publications (Paper & Poster) for the Results analysis of our data. The hereby repository is dependent from the sheet-reader repository, so please first of all clone that to your machine.
To run the various methods available on the src/methods/ directory, it is needed:
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Python 2.6 or latest;
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The pip package management tool;
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The
sheet-reader(>= v1.2.1 version) repository;
The instructions are as follows. We assume that you already have knowledge over Git and GitHub. If not, please follow this support information. Any need for support, just open a New issue.
To clone the hereby repository follow the guidelines. It is easy as that.
1.1. Please clone the repository by typing the command:
git clone https://github.com/MIMBCD-UI/statistical-analysis.git
1.2. Get inside of the repository directory:
cd statistical-analysis/
1.3. For the installation and running of the source code, follow the next steps;
The installation guidelines are as follows. Please, be sure that you follow it correctly.
2.1. Run the following command to install the library using pip:
pip install --upgrade google-api-python-client
2.2. Follow the next step;
The running guidelines are as follows. Please, be sure that you follow it correctly.
3.1. Run the sample using the following command:
python3 src/methods/anova.py
3.2. Enjoy our source code!
You can also run a Notebook to watch some of our methods chart plots. For this goal we are using the well known Jupyter Notebook web application. To run the Jupyter Notebook just follow the steps.
4.1. Get inside our project directory:
cd statistical-analysis/
4.2. Run Jupyter Notebook application by typing:
jupyter notebook
If you have any question regarding the Jupyter Notebook just follow their Documentation. You can also ask for help close to the Community.
As far as we have to do several statictical analysis over our users, we need to address their results by calculating a set of measures. This measures will gave us a better understanding regarding how users are aiming to interact with our systems. Therefore it is of chief importance to scale this solution for a spreadsheet template-like where we can duplicate the same document and apply this group of source code to consume our data each time we need it.
For the User Test Analysis 4 (UTA4) of this project we generated a combination of interesting datasets. To publish our datasets we used a well known platform called Kaggle. To access our project's Profile Page just follow the link. We are also working on several other User Research studies, while this repository is being an important asset to them.
We would like to convey Google from their Google Sheets API Documentation. This repository source code is based on Google's Python Quickstart guide.
