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Overview


Fundamentals of Quantitative Text Analysis for Social Scientists

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Overview


Fundamentals of Quantitative Text Analysis for Social Scientists

Next course runs from [[date]]

Course description

Do you want to analyze the political implications of thousands of social media comments? Or perhaps determine newspaper coverage of pollution in our cities? 

If you’re looking to analyze large amounts of textual data you will find quantitative text analysis with R an efficient, powerful and easy-to-use method.
 

Learning outcomes

By taking this course you will be able to:

  • Understand the theoretical basis for quantitative text analysis
  • Survey methods for systematically extracting quantitative information from text for social scientific purposes
  • Identify texts and units of texts for analysis
  • Convert texts into matrices for quantitative analysis
  • Analyze these matrices in order to generate inferences using quantitative or statistical methods

 

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Fundamentals of Quantitative Text Analysis for Social Scientists

Effort
This course is approximately 10 hours of learning including activities. It is recommended that you complete one module a week for 4 weeks.
Prerequisites
While our R scripting will be at a fairly basic level, you should have some familiarity with R. Familiarity should include:
  • an understanding of basic R objects, functions, and data structures (e.g vectors, matrices, lists, data frames and factors)
  • Understanding the difference between a string and a numeric variable
A basic knowledge of statistics would also be helpful. Most of the work in the course will be with simple summary statistics (mean, median, mode, standard deviation) but familiarity with inferential statistics and a basic knowledge of OLS regression would be helpful.
Instructors
Professor Jonathan Slapin
In association with
Essex Summer School of Social Science Data Analysis, University of Essex
Language
English
 
from 299.00
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Course Instructors


Course Instructors


Course Instructors

 
 

How it works


How it works


HOW IT WORKS

The course is organized into a set of four interactive learning modules, and you should work through the modules sequentially.
 
The interactive learning modules contain a number of topic pages. Each topic page has a video to walk you through the concept and interactive text to reinforce what was covered in the video, quick questions and knowledge checks.
 
There are three additional types of activity in your course to facilitate deeper learning. These are presented in the relevant topic pages.

  1. Match: These activities require you to have a go at a task offline, then select the correct solution
  2. Guided: These are multi-part match activities so you do a part of the task then submit your solution, which unlocks feedback on your attempt and the next part of the task
  3. Structured: This is a more complex offline task and to see the Tutor’s solution you need to share your attempt at the task and your reasoning. You also get to see other participants' attempts and are encouraged to engage in discussion. The Tutor will then share further feedback

The vast majority of topics in the course are fundamentally practical. You are strongly encouraged to recreate and run the code as you work through them, and complete knowledge checks and activities.

 
You have 3 months' access to this course.
During the first 4 weeks of your course you will receive learning support from Nicole, your SME (subject matter expert).  We recommend completing your course in these initial 4 weeks as Nicole will be on hand to answer any questions, or help you if you get stuck.

After the learning support period, you’ll still have access to the course materials but you won’t receive support from the instructor. SAGE Campus will help you with any IT or platform issues you might have throughout the course. 

Syllabus


Syllabus


Syllabus 

Module 1

In this Module, you’ll cover:

  • Introduction explaining course purpose: goals and objectives
  • Conceptual foundations of text analysis
  • Quantitative text analysis as a field and the development of the field
  • Logistics and software - required setup and work files
  • A basic example of performing a text analysis

Module 2

In this Module, you’ll cover:

  • Where to obtain textual data
  • Formatting and working with text files
  • Practical considerations of indexing and metadata
  • Units of analysis: strategies for selecting units of analysis
  • Overview and examination of complexity and readability measures

Module 3

In this Module, you’ll cover:

  • Keywords in context Coverage and examples of KWIC
  • Consideration of concordance and dictionaries
  • Detecting and identifying collocations
  • Stemming: An in-depth discussion of text types, tokens, and equivalencies
  • Stop words and feature weighting: An in-depth discussion of text types, tokens, and equivalencies

Module 4

In this Module, you’ll cover:

  • Euclidean distance and its use in comparing texts
  • Cosine similarity and its use in comparing texts
  • General principles and rationale for dictionaries
  • External dictionaries: How to add a third party dictionary
  • How to create your own dictionary
  • Overview of wordscores
  • Implementing in R a basic model

In association with


In association with


developed in association witH

This course is a modified version of a course taught at the Essex Summer School in Social Science Data Analysis for the previous two summers. It brings together research and teaching interests in quantitative text analysis to more fully understand social phenomena.
 

FAQs


FAQs


Frequently Asked Questions

Please see below answers to some of the most frequent questions we get about this course

What software do I need for this course?

You will need to have R installed to work through this course and it is essential that your version is 3.4.1 or above.

You will need to install the quanteda package and the quantedaData package. Quanteda can be downloaded from CRAN and should be version 9.9.6.5 or above. The quantedaData package can't be installed from CRAN and you will need to install the devtools package from CRAN and then install quantedaData from github. You can use the code below to do this.

install.packages("devtools") devtools::install_github("kbenoit/quantedaData")

You should also install the readtext package from CRAN as we will be using that to read text files into R.

install.packages("readtext")

Do I need to buy any of this software?

No, they are either open source or have community (free) versions.

What do I need to participate on this course?

A computer or laptop with the suggested software and a modern browser e.g. Internet Explorer 10+ or the latest versions of Chrome and Firefox.

Can I do this course on my mobile device?

While you can access the course on your mobile device, go through the content and answer questions, you will need a desktop or laptop computer to practice and complete the activities that require you to write and/or test code.

How long will I have access to the course for?

The course will be run over 4 weeks, during which you will have access to learning support provided by the course instructor. After the 4 weeks, you will still have access to the course materials for another 2 months, but you will not be able to receive learning support from the instructor, and if there is a course forum, you will not be able to ask any questions.

Do learners get a certificate?

All of our courses offer a certificate of completion signed by your instructor. You will be able to download this certificate, from the Learning Platform, when you complete the course.

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Fundamentals of Quantitative Text Analysis for Social Scientists
from 299.00
Start date:
Enroll