POL30660

Learning Outcomes:

Upon successful completion, students will be able to:
• understand the logic behind different data analysis methods in the social sciences
• prepare data for statistical analyses
• select appropriate analytical methods for various types of empirical questions, including description, prediction, and causation
• use data analysis tools, interpret the results, and present the findings to a wider audience

Indicative Module Content:

Descriptive analyses, e.g.
- Data visualisation
- Descriptive statistics

Predictive analyses, e.g.
- Linear regressions
- Logistic regressions

Causal analyses, e.g.
- Differences-in-differences
- Instrumental variables

Student Effort Hours:

Approaches to Teaching and Learning:

Teaching and learning approaches:

The seminars will consist of a mix of lecturing, group exercises, individual tasks, lab work, and student presentations.

Doing the mandatory readings before each class is crucial and your engagement with them will form part of your continuous assessment grade.

Students are provided with instructions on how to perform analyses during seminars and at home.

The mix of teaching and learning techniques is intended to allow students with diverse levels of prior training in statistics or programming to learn about a wide range of techniques and their application to questions in the social sciences.


Use of AI tools:

Artificial intelligence (AI) tools may be used to help your research process. However, note that they should be used with caution due to inaccuracies and other flaws. Students should thoroughly check and question any output that their AI tools provide and must provide manually verified sources other than AI tools' outputs. The nature and extent of the use of AI tools (if any) must be clearly declared and elaborated in any output. Students are ultimately responsible for their work, including any errors that were introduced by the AI tools they may use. AI tools may not be used to replace students' independent work process. If students are suspected of using AI to produce substantial portions of their work, instead of doing so themselves, this will be interpreted as a breach of academic norms and may result in serious consequences such as failing this module. In sum, this course's policy on the use of AI tools is "yellow" in the UCD traffic light system. Please refer to UCD's guide on the use of AI for further information: https://www.ucd.ie/artshumanities/study/aifutures/generativeaifaqs/

Requirements, Exclusions and Recommendations

Learning Recommendations:

Some familiarity with descriptive statistics and statistical modeling (e.g., regression analysis) will be helpful.

Module Requisites and Incompatibles

Equivalents:
Data Analytics for Soc Sci (POL30430)


 

Assessment Strategy

 

Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Feedback will be provided verbally and/or in writing to students on every assessment element as per university guidelines.

The syllabus will be distributed before seminars start. Readings indicated therein will be freely available, provided by the lecturer, and/or available in the UCD library.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.