Have you ever needed to analyse hundreds of documents, spent days going through only a fraction of them, and then thrown your pencil up in despair as you scream “there must be a better way!” If so, quantitative text analysis may be for you.
When writing code you’ll probably, at some point, want to reuse that code and maybe have someone else be able to read it and use it. So it is important to make sure that the code is readable for both yourself and others. You can achieve this by bearing in mind the 3 rules in this blog post.
To investigate data and tell a story with it requires a process and a reliable set of tools. Martin Hadley, course instructor for Interactive Visualization with R, explains how 3 interactive viz tools can be applied to social science research.
At SAGE Campus we like to speak to, and learn from, the people that take our courses.
We’re fascinated to hear about our learners’ backgrounds, and how they intend to use their data science skills in the future. This week, we spoke to Ariel Quinio, a learner on Practical Data Management with R for Social Scientists.
Interactive visualization is particularly useful for social science as we usually look for variation or trends over time, which are easy to spot in visualizations. Here are 3 benefits of using interactive data visualization for your research.
At SAGE Campus we’re always keen to hear how researchers are using computational methods. We spoke to Nicole Rae Baerg one of our social science experts on Fundamentals of Quantitative Text Analysis, and asked her a few questions about her work, and which tools she recommends to others.
Quantitative Text Analysis is the automated, systematic method for processing large amounts of text. This means we can easily carry out tasks such as extracting policy positions from election manifestos or speeches, or even study attitudes or emotion in newspapers. The common focus across all methods used in QTA is that they can be reduced to three basic steps. Find out what the 3 steps of QTA are in this blog post.
We asked Phillip Brooker, an interdisciplinary researcher in the field of social media analytics, and social science expert on Introduction to Python for Social Scientists, for his advice on using data science methods in social science research.
Phillip has background in sociology and sociological research methods, and co-convenes the Programming-as-Social-Science (PaSS) network which explores computer programming as a subject and methodological tool for social research and teaching. So if you’re looking into computational social science, listen up, you’re in good hands!
This blog post talks about how online learning can benefit learners and in particular how it allows learners to fail.
The internet represents a vast and ever expanding source of social science data. Some of these data are well curated and easily downloadable, but much of these data are “hidden in plain sight”. An increasingly important tool in the social scientist’s toolkit is the ability to automatically collect data from the internet – a process commonly referred to as web scraping. Here’s a bitesize look at web scraping.
If you are just getting started with R, and coming to it from a background using statistical analysis software like Excel, SAS, Stata, or SPSS, then one of the first things you will have to get used to is the concept of a data structure. In all of the aforementioned software, you read in your data as a spreadsheet and then only operate on that one spreadsheet (with some exceptions). In R you can represent data in many more formats than just a spreadsheet, and you can hold all of these objects in memory at the same time. This is a very powerful concept, and one that allows R to perform many data management tasks that would simply be impossible in the programs named above. Here I will provide a brief conceptual overview of five of the most commonly used data structures in R.
Is the news all bad when it comes to big data and its potential uses? How can we effectively utilise the power of big data in the social sciences? These are just two of the questions that were up for discussion at the ESRC Festival of Social Science panel entitled "Putting big data to good use" at the British Academy in London. In this post, Katie Metzler introduces us to the panelists and some of the topics that were discussed.
Today, in an age of big data and new technologies, researchers within the social sciences have opportunities to analyze information in ways that were previously thought impossible. Yet this has also posed challenges as not everyone is equipped with the skills or knowledge to be able to use this data in the most effective way. This is where the SAGE Campus team comes in: earlier this year, we launched a series of online courses to teach data science skills to social sciences. My name is Katie Metzler, Head of Methods Innovation at SAGE, and I’m delighted to be able to introduce you to the team.
The digital age has made huge amounts of data available for analysis in the form of newspapers, blogs, social media feeds, government documents, the list goes on!
In this post we consider some of the challenges of working with such vast amounts of data and the role that QTA plays.
The big data revolution offers huge potential for social scientists. However, the successful collection and rigorous analysis of this data require new skills, new collaborations, new research methods, and new computational tools. Learning data science skills may seem daunting, but there are many reasons why learning to program will benefit both you and your field of study. Find out why here.
Learner Story - Dr Jason Jackson, Introduction to Python