Peacebuilding Data Analysis

Participant Learning Objectives

Participants who complete this series will:

• Apply new/improved conceptual data management, analysis, visualization, and reporting skills to their work.

• Apply new/improved technological data management, analysis, visualization, and reporting skills.

• Be more familiar with the broad array of data management, analysis, visualization, and reporting software available.

Prerequisites

• These prerequisite items must have been completed and materials downloaded prior to beginning this series. If you want to follow along, you should... 

• Have a quality internet connection

• Have Microsoft Excel 2016 (or higher) installed on your computer

• Download and install OpenRefine.

Section 1: Reinforcing the Basics

The collection of videos has been selected to refresh your memory of key research and data concepts, which the following sections will build upon. Take your time watching these videos, writing down questions/ notes, and practicing. The quiz at the end of the section will give you an opportunity to test your knowledge of the concepts provided. 

1.1 Reinforcing the Basics - Qualitative & Quantitative Research/Data

This section outlines the differences between qualitative and quantitative research. These two videos explore important research concepts that will appear in the quiz for this section. 

1.2 Data Concepts & Variable Types

This section provides an overview of key data concepts that will appear on the quiz at the end of this section.

1.3 Excel Basics

This series of videos from "Technology for Teachers and Students" is a great way for advanced and intermediate Excel users to learn new tips and tricks. For beginners, this series will be very valuable. There are ten videos below. Watch them all!

Section 2 Overview: Qualitative Data Analysis

There is no magical software for qualitative data analysis. Good qualitative research and data analysis start with being organized and creating a research design that will benefit you from begining to end. This section of the course outlines some research design strategies and ties them to strategies for qualitative data collection and analysis. This section also covers qualitative data concepts more broadly. 

Section 2.1 Inductive & Deductive Research Design

Section 2.2 Backplanning

Monitoring and evaluation efforts are inherently deductive in nature, because we are testing the theories of change used to design our programs. During the design phase of our programs at Search, our program teams base their activities on theoretical understandings of how a given intervention will lead to an expected change. The data we collect during the evaluation process either supports or does not support those theories. The next video briefly outlines an approach for using qualitative data in deductive evaluations and research. 

2.3 Inductive Approaches to Qualitative Data Analysis

Here are a few (unbiased) sites that compare QDA softwares:

https://www.bu.edu/tech/services/cccs/desktop/distribution/nvivo/comparison/

https://guides.nyu.edu/QDA/comparison

Caution: there are many software comparison sites out there that are "pay-to-play" meaning that these companies can pay for a better rating. This is why I've chosen these two university sites. There are also many software companies that will claim to be "free," but they ultimately ask for payment. 

Unfortunately the RQDA Package is no longer maintained on the CRAN library, If you're interesting in learning more about RQDA, read this page: http://rqda.r-forge.r-project.org/

3.1 Data Management Concepts Explored

3.2 Data Cleaning with Open Refine

In this section of the course, we'll be looking at ways to transform and clean messy data. Our primary tool will be OpenRefine (download here: http://openrefine.org). The videos below provide a brief introduction to the tool and its use cases. These videos can also be found on the OpenRefine website 

3.3 Respondent to Analyst: Data Pipelines

This video examines the idea of a data pipeline and how digital technology can help us improve data collection efforts and data quality in our work.

3.4 More on Data Collection Tools

Section 4: Quantitative Data Analysis

4.1 Intermediate Excel Skills

4.2 Diving into Quantitative Analysis of Conflict Data with Excel

This video reviews some data analysis principles that will take your data analysis skills further. It walks you through how to use pivot tables to analyze ACLED data. Before watching this video make sure you are familiar with pivot tables; videos in the last module should have helped with that.

If you'd like to do a similar exercise as seen in the video, using your own ACLED data from a country of your choosing. A more detailed description of the assignment can be found in the following module. Important: ACLED now requires that all users register a free account to download and use data.

4.3 Working with Ordinal Variables

Section 5: Reporting, Data Visualizations & Dashboards

All data visualization should begin with understanding WHY and FOR WHOM you are visualizing your data. This section of the course provides a breif introduction to audience analysis. It offers some examples of what not to do in terms of data visualization. Then, the course provides insights on how to build interactive visualizations and dashboards in Excel. Finally, this module will provide some ideas for how you can go beyond data vizualization with Excel using additional tools and Search resources. 

5.1 Audience Analysis

5.2 Confusing and Bad Visualizations

5.3 Dashboards in Excel

5.4 More on Data Visualization

In this video, the presenter covers a simple method for incorporating pictures and icons into your Excel graphs. 

This video provides some really innovative ways to improve basic graphs in Excel with shapes and dynamic formatting. This ideas would be great for dashboards.