Network Analysis – Depict Data Studio https://depictdatastudio.com Tue, 08 Feb 2022 20:41:15 +0000 en-US hourly 1 https://wordpress.org/?v=7.0 How to Make a Social Network Map with NodeXL https://depictdatastudio.com/how-to-make-a-social-network-map-with-nodexl/ https://depictdatastudio.com/how-to-make-a-social-network-map-with-nodexl/#comments Fri, 01 Nov 2013 15:08:11 +0000 http://emeryevaluation.com/?p=3104
Art image of green, red, blue and purple circles connected by lines.

Note from Ann: Today’s guest post is from Johanna Morariu, Director of Innovation Network, AEA DVRTIG Chair, and dataviz aficionado.

Basic social network analysis is something EVERYONE can do. So let’s try out one social network analysis tool, NodeXL, and take a peek at the Twitter hashtag #eval13.

Using NodeXL (a free Excel plug-in) I will demonstrate step-by-step how to do a basic social network analysis (SNA).

What is Social Network Analysis?

SNA is a dataviz approach for data collection, analysis, and reporting. Networks are made up of nodes (often people or organizations) and edges (the relationships or exchanges between nodes).

The set of nodes and edges that make up a network form the dataset for SNA.

Like other types of data, there are quantitative metrics about networks, for example, the overall size and density of the network.

How to Create a Social Network Map in NodeXL

There are four basic steps to creating a social network map in NodeXL:

  1. Get NodeXL.
  2. Open NodeXL.
  3. Import data.
  4. Visualize the data.

View a Sample Dataset

Do you want to explore the #eval13 social network data? Download it here.

Why Social Network Analysis is Valuable

Here’s where SNA gets fun– there is a lot of value in visually analyzing the network.

Yes, your brain can provide incredible insight to the analysis process.

In my evaluation consulting experience, the partners I have worked with have consistently benefited more from the exploratory, visual analysis than they have benefited from reviewing the quantitative metrics.

Sure, it is important to know things like how many people are in the network, how dense the relationships are, and other key stats.

But for real-world applications, it is often more important to examine how pivotal players relate to each other relative to the overall goals they are trying to achieve.

Your Turn

So here’s your challenge– what do you learn from analyzing the #eval13 social network data?

Share your visualizations and your findings!

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