Job opening: 
Research Officer in Quantitative Text Analysis

Duration: 24 months
Start Date: 1 March 2012 or as soon as possible thereafter
Salary: £31,998 – £38,737 p.a. incl.

Applications are invited for the post of Research Officer, to assist with a principal research officer for the European Research Council funded grant Quantitative Text Analysis for the Social Sciences (QUANTESS).

The research officer’s general duties and responsibilities will be to work with text and the computer organization, storage, processing, and analysis of text. These tasks will involve a combination of programming, database work, and statistical computation. The texts will be drawn from social, political, legal, and commercial examples, and will have a primarily social science focus. The research officer will be expected to work with existing tools for text analysis, actively participate in the development of new tools, and participate in the application of these tools to the social scientific analysis of textual data.

The successful applicant will be expected to possess advanced skills and experience with computer programming, especially the ability use a language used in text processing such as Python; familiarity with SQL; and experience with the R statistical package or the ability to learn R.

The successful candidate should have a postgraduate degree in computer science, computational linguistics, or possibly a cognate social science discipline and have an interest in text analysis and quantitative linguistics, have a knowledge of social science statistics and have worked in a research environment previously.

To apply for this post please go to www.lse.ac.uk/JobsatLSE and select “Visit the ONLINE RECRUITMENT SYSTEM web page”. If you have any queries about applying on the online system, please call 020 7955 6656 or email hr.jobs@lse.ac.uk quoting reference 1223243.

Closing date for receipt of applications is: 31 January 2012 by 23.59 (UK time).

Course handout (syllabus)

Day 1: Introduction to Computer-Assisted Text Analysis

Day 2: Textual Data, Sampling, and Working with Texts

• Class Assignment 1 and the files you need for it.

Day 3: Descriptive Inference from Text

• Class Assignment 2
• R code solution for Assignment 2
• Stata code solution for Assignment 2

Day 4: Research Design issues in textual studies

Day 5: Classical Quantitative Content Analysis

• Class Assignment 3
• French manifestos for Assignment 3
• UK Manifestos for Assignment 3

Day 6: Automated dictionary-based approaches

• Class Assignment 4
• The file LaverGarryAJPS.ykd used in Assignment 4.

Day 7: Dictionary Construction; Words as Data Approaches

• Class Assignment 5
• The file iebudget2010.zip used in Assignment 5.

Day 8: Text Scaling Models I

• Class Assignment 6
• The file LBGexample.csv used in Assignment 6.
• Assignment 6 solution in Excel and SPSS.

Day 9: Text Scaling Models II

• Class Assignment 7

Day 10: Additional Scaling Issues

• No lab assignment but we will go through the budget speech example using this code: Assignment_8.r.

European University Institute, May 23–27, 2011
Professor Kenneth Benoit
Methodology Institute, London School of Economics
http://www.kenbenoit.net/mlm/

Course handout here.

Readings are available from Mark Franklin’s Dropbox account for this course. If you are not yet subscribed, then email mark at Mark.Franklin@EUI.eu.

Day 1: Introduction and Motivation for multi-level models.

• Required Reading: Rabe-Hesketh & Skrondal (2008, Chs. 1–2); Stata manual for reshape.
• Recommended Reading: Franzese (2005); Gelman (2006); Austin, Goel & van Walraven (2001).
• Homework 1 and Homework 1 Answer code.

Day 2: Estimating models with multi-level data.

• Required Reading: Continue with Rabe-Hesketh & Skrondal (2008, Chs. 1–2) and Stata [XT] manual.
• Recommended Reading: Steenbergen & Jones (2002); Austin, Goel & van Walraven (2001); Snijders & Bosker (1999); Goldstein (2003).
• Homework 2: Question 2.3 from p87 of Rabe-Hesketh & Skrondal. Use xtmixed for part 2.3.2. Homework 2 Answer code.

Day 3: Random-intercept models.

• Required Reading: Rabe-Hesketh & Skrondal (2008, Ch. 3).
• Recommended Reading: Austin, Goel & van Walraven (2001); Snijders & Bosker (1999).
• Homework 3: Question 3.2 from pp133-4 of R-H&S, plus:
  5. Compare a “random effects”, “fixed effects” (using the “fe” option to xtreg), and “between effects” regression by running them and discussing the differences on the estimated coefficient on the “deprive” variable, including the student-level covariates as in part 3 of the question.
  Homework 3 Answer code.

Day 4: Random-coefficient models.

• Required Reading: Rabe-Hesketh & Skrondal (2008, Ch. 4)
• Recommended Reading: Austin, Goel & van Walraven (2001); Snijders & Bosker (1999)
• Homework 4: No pre-assigned homework today, although we will go through an example together in class that I will post on-line on the morning of Day 5.

Day 5: Extensions of the multi-level model

• Random intercept (xtlogit) example from class (.do file).
• Required Reading: Continue with Rabe-Hesketh & Skrondal (2008, Chs. 6, 9)
• Recommended Reading: McMahon & Heath (1992)

• The course handout (syllabus) is available here.
• The readings are now available in a zip file of pdfs from http://www.kenbenoit.net/files/CEU_CTA_Benoit_readings.zip.

Day 1: Introduction and Overview

• Slides for Day 1.
• Class exercise for Day 1 here, and the files you need for it here.

Day 2: Descriptive Inference From Text

• Slides for Day 2.
• Class exercise for Day 2 here.

Day 3: Classical Content Analysis

• Slides for Day 3.
• Class exercise for Day 3 here.

Day 4: Dictionary-Based Coding

• Slides for Day 4.
• Class exercise for Day 4 here.

Day 5: Text As Data (and Wordscores)

• Slides for Day 5.
• Class exercise for Day 5 here.

Day 6: Text Scaling

• Slides for Day 6.
• No class exercise for Day 6.
• R code from class example.

In brief: This is designed to be a shorter version of your final assignment research proposal, providing a more summary version of the project will propose in fuller form at the end of the term. It will include a research question, an indication of the key causal explanation, a discussion of the key observable implications of that causal explanation, and a justification for the project. To make things even more clear, we have suggested explicit guidelines below.

Note:

• YOUR NAME needs to be on all of your written work please!
• All short proposals must be submitted through turnitin.com.
• This outline will be graded and counts for 20% of the final course grade.

Suggested contents for your outline:

1. Title.
2. Overview.
   1. What is the main problem you wish to address? Summarize the project and its motivation in no more than a page.  Focus on what general theme the project addresses (e.g. “do campaigns make a difference”) and describe the specific and concrete way that your research will investigate this theme (e.g. to study the association between spending recorded in Irish election campaigns and relative vote share received in the election, at the constituency level by candidates).
   2. What contribution will your study make, even if very small, to our understanding of the problem? this section can be short.
3. Theory and/or Hypotheses. Every project will be different with regard to this section, with some being more aimed at testing “causal relationships” than others. But in this part, probably between 0.5-1.5 pages depending on what your paper is about, you will describe what you hope to test or establish or discover. For instance, you might be interested in examining how the choice of executive institutions influences post-conflict political systems (a version of the presidential versus parliamentary system debate). Or you might be interested in explaining why in a specific case it appeared that two democracies went to war with another, despite the widespread belief that this never happens.
4. Data. This section needs to clearly identify what the main unit of analysis is and how you will observe it. This section needs therefore to be both conceptual and practical. Sometimes, these two levels point to different units, for instance: say you are interested in why voters in two-ballot (or “mixed member”) electoral systems split their ticket by voting for different parties on different ballots. Your conceptual unit of analysis, following from your theory about why and how voters split tickets, is at the individual level. But your practical unit of observation may be at the level of aggregated election returns, say party totals for each ballot, in an electoral district. This is because no record is published by the election authorities linking individual votes on the two ballots. Alternatively, if you had access to an election study (a detailed survey of individual respondents), then your unit of observation would be an individual survey respondent.
5. Methods. Here you need to be very clear as to what methodology you plan to use to analyze your data, linking it to theory. Don’t be overly vague, for instance by stating “I will use qualitative and quantitative methods to analyze my dataset. Instead, state: “I will use macro-statistical methods to determine the association between the level of democracy and the rate of foreign war involvement, at the country-year level. I will also analyze the three cases of the X-Y 1970 conflict, the Y-Z 1973 conflict, and the ABCD 1980 conflict in detail using historical accounts.”
6. Expected and/or potential findings. Here you should also point out what sort of results from your data analysis would lead you to specific conclusions. Conversely you should also point out what sort of evidence would lead to to reject those conclusions. This acts as a good test that your theory and/or hypotheses are potentially falsifiable. Note that if your paper is more of an investigative report on something that has received little systematic study, then your findings will be more of a descriptive nature and hence the falsifiability criterion will apply less to your project than to others. But it should still be easily possible to describe what you might or might not find following a full investigation of your data.

Information on the conference and the online proposal system can be found here, and the EPSA website accepts discounted early membership registration and payment here.

EPSA is also on Facebook and Twitter (@europsa) if you fancy those.