SPOL10030

Learning Outcomes:

What will I learn?
On completion of this module, you should be able to:
1. Identify the key social policy data sources that are available online for Ireland and the EU;
2. Understand what kinds of information those sources contain;
3. Access these sources and locate information within them on particular social policy topics;
4.Extract and download data on these topics;
5. Use Excel spreadsheets to organise the data and present them in graphs and tables;
6. Apply basic descriptive statistics in Excel;
7. Write short descriptive accounts of what the data reveal;
8. Work effectively in groups.

How will I learn?
The course delivery is a mix of lectures and hands-on computer workshops in small groups. Additional videos and step-by-step guides will assist your learning.
The first assignment is a group work to encourage debate, mutual support and effective collaboration.

Indicative Module Content:

The key aim of the module is to introduce students to social science data analysis. Throughout the module the main examples are drawn from analysing the labour market which includes unemployment, education level, gender pay gap, occupational segregation, female managers and the effects of Covid-19 on employment levels. With a focus on Ireland all data analysis compares European countries and contextualises Ireland within the European Union.

Student Effort Hours:

Approaches to Teaching and Learning:

The module consists of lectures and data analysis workshops.
1. The lectures will introduce the module and examine basic aspects of social policy data, with reference especially to data on unemployment. The lectures also discuss the practical data analysis and introduce you to critically assess tables, graphs and results.
2. In six weeks of data analysis workshops you will learn how to put into practice some of what you have learned in the lectures. The class will be divided into small groups for these workshops. Each student will have one workshop per week for this six-week period. The computer labs are practical and hands-on sessions where you can explore and familiarise yourself with social data.
3. The first assignment is conducted as group work to develop effective group working skills, provide mutual support and form the basis for critical debating skills among students.

Artificial Intelligence (AI) is not taught nor used in this module. In line with the UCD Academic Integrity Policy generative AI may only be used in assignments if transparently reported for which tasks AI was used.

Requirements, Exclusions and Recommendations

Learning Recommendations:

To learn about the power of stats watch this video:
https://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen

Module Requisites and Incompatibles

Equivalents:
Social Policy Research (SPOL1903)


 

Assessment Strategy

 

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

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

The mid-term assignment will develop your analytical and writing skills with detailed feedback to improve data analysis. The final assignment asks for a more comprehensive and advanced data analysis and the previous feedback should be considered. In the data analysis workshops oral feedback is provided, but there will be no feedback on the submission of workshop outcomes.

Quirk, Thomas J. (2016) Excel 2016 for Social Science Statistics. A Guide to Solving Practical Problems. Cham: Springer. DOI:10.1007/978-3-319-39711-5
Guerrero, Hector (2014 or 2019) Excel Data Analysis. Modeling and Simulation. Heidelberg: Springer. DOI:10.1007/978-3-642-10835-8

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