Foundation/Year Zero: Level 3
Thursday 08 October 2020
Friday 02 July 2021
29 May 2020
Requisites for this module
BA M903 Criminology (Including Foundation Year),
BA L250 International Relations (Including Foundation Year),
BA P300 Communications and Digital Culture (Including Foundation Year),
BA L202 Politics (Including Foundation Year),
BA L2CH Social Sciences,
BA LFCH Social Sciences,
BA L304 Sociology (Including Foundation Year),
BA LMHX Sociology and Criminology (Including Foundation Year),
BSC L313 Sociology with Data Science (including foundation Year),
BA Q121 Linguistics with Data Science (Including Foundation Year),
BA R111 International Relations and Language Studies (Including Foundation Year)
This module is designed to equip students with practical and analytical skills to understand, generate, analyse, interpret and present data, to draw valid conclusions from data and to critically assess examples of data use. Although these skills are applicable across disciplines, they will be taught in the context of social sciences using examples of political and social data from a range of sources including academic articles, newspaper reports, data archives, and Government statistics.
The module will also give students practical research and employability skills and prepare them for studying further data and research-based modules later in their academic careers, in particular GV110: Scientific Reasoning for the Social Sciences, GV112: Comparative Political Analysis and SC101: Researching Social Life I.
Learning on the module will be driven by practical applications and direct experience of working with data and will be underpinned by a basic introduction to theoretical ideas from statistics, comparative political analysis and social research.
The emphasis of the module will be on gaining competence and confidence in working with data and gaining an informal understanding of the scientific method and the logic behind working with data; mathematical content will be kept to a minimum and the module will be accessible to students with limited mathematical backgrounds as well as to those with more advanced maths skills.
The module aims to:
- Equip students with skills for collecting, understanding and using data to gain knowledge and insights into social scientific issues.
- Give students the competence and confidence to read, understand and critically assess examples of how data is used and reported.
- Introduce students to key theoretical ideas and concepts in undertaking data-based research in social sciences.
- Provide students with a strong set of transferrable skills for using statistical software to investigate, analyse, summarise and present data.
- Prepare students for undertaking further data and research-based modules later in their degree programmes.
- Support students to develop key communication, analytical, research and employability skills.
It is expected that students who successfully complete this module will be able to:
1. Understand the importance of data in social sciences.
2. Recognise different types of data and understand their role in gaining knowledge and understanding about key social scientific topics.
3. Formulate workable hypotheses to answer research questions
4. Collect or gather data and identify the importance of and methods for obtaining reliable, efficient and unbiased data.
5. Undertake a range of basic data analyses to gain insight into relevant research questions and to provide evidence for testing hypotheses.
6. Understand how to interpret and apply the output from basic statistical analyses.
7. Understand the principles of good data presentation and have the ability to select and use appropriate data presentation techniques.
8. Critically assess data reports, identify examples of biased sampling and misleading presentations of data and evaluate the validity of conclusions from data analyses.
9. Extensively use Microsoft Excel to summarise, analyse and present data.
Introduction to Data: What is data and where does it come from? How is data used, and how is it misused? What can we do with data? Why is data important in social sciences? What can we discover from data?
Asking Questions: What do we want to know or learn about the political or social world? What can data tell us about these things? What can data not tell us? What questions do we want to answer with data, and how can we formulate those questions into statements that can be tested by data?
Collecting Data: What data is already available to help us answer our questions? Have our questions already been answered? If not, how can we collect data? Different types of data collection methods. Sampling methods and sampling errors. Hard data (statistics, numbers, facts) and soft data (opinions, observations).
Working with Data: Data input and manipulation. Cleaning data. The importance of unbiased and efficient data (rubbish in – rubbish out).
Looking at Data: Using graphs and tables to summarise our data. What does our data tell us about our research topic? Selecting the most appropriate presentation, and how different graphs can give different insights into our data.
Measuring Data: Using summary statistics to understand, compare and communicate key features of our data. Using averages to measure the size, and measures of dispersion to measure the consistency of the things we are measuring.
Gathering Evidence: Does the data support our ideas and theories about the political and social world? Using statistical tests and assessing the strength of the evidence for or against our hypotheses.
Interpretation: What can we conclude from our investigation? What have we learned about our research questions and how confident are we in our findings? Identifying relationships and patterns and considering cause and effect.
Presenting Data: How to communicate our results for different audiences. How to tell the truth with data and recognising how data can be (and sometime is!) used to not tell the truth. Principles of good data presentation – communicating complex ideas simply and effectively.
Formative assessment will take place throughout the module in lab classes and tutorial classes. During lab classes students will undertake activities which cover the same learning outcomes as those in the summative assessments and receive feedback applicable to the assignments and tests. During tutorial classes students will complete worksheets which will form the basis for their assignments. Feedback will be given during each tutorial class to be incorporated into the submitted assignments.
* Assignment 1 (20%): Investigate a secondary source of data, identify the type of data and variables used, discuss how the data could be used to answer social scientific questions, formulate a relevant hypothesis, use Excel to undertake a basic analysis to test the hypothesis and present the findings in the form of graphs, tables and a discussion of findings.
* Take-home test (20%)
Assess case studies of biased data collection, misleading or poor data presentation and invalid interpretation of data analysis. Provide suggestions for how the relevant activity could have been done better.
* Assignment 2 (20%)
Collect data and create a dataset that can be used to explore relationships between variables.
* Participation (5%): Complete 3 online data collection activities during the autumn term.
* Final Assignment (35%): Undertake a small research project. Formulate a research question and relevant hypotheses. Identify suitable sources of data. Collect data, input into Excel and clean. Undertake an analysis of the data using a range of analysis techniques. Interpret results of analysis and present findings.
Resubmit a piece of coursework (1,500 words) which will become the new module mark. The reassessment task will enable the relevant learning outcomes to be met.
The module is delivered via 1 x 2-hour lab, 1 x 1-hour lecture, a post-lecture discussion and 1 x 1-hour tutorial each week.
Lab Classes: 2 hours per week. Students will undertake practical exercises working with and gaining understanding of data. These will include researching data sources, sourcing and critically assessing examples of data reports, data collection, designing questionnaires and using Excel to input, manipulate, analyse and present data. These classes will be delivered in classrooms for on-campus students, and online for students studying remotely.
Lectures: 1 hour per week. Lectures will introduce basic social research techniques, concepts and theories that will underpin the practical work done in Lab Classes and prepare students for studying future data and research-based modules. Lectures will be pre-recorded and available online.
Post-Lecture Discussion: An opportunity for students to seek clarification and ask questions regarding the content covered in each week's lecture. This will include timetabled online discussions, and a module forum.
Tutorial Classes: 1 hour per week. Students will use these classes to apply the ideas from the lectures to the practical work they have undertaken and to use this to reflect upon, refine and improve the output of their practical work. These classes will be delivered in classrooms for on-campus students, and online for students studying remotely.
This module does not appear to have any essential texts. To see non-essential items, please refer to the module's reading list.
Assessment items, weightings and deadlines
|Coursework / exam
||IA129 - Participation
||IA129 - Assignment 1
||IA129 - Assignment 2
||IA129 - Final Assignment
||IA129 - In-class test
Module supervisor and teaching staff
Dr James O'Geran, email: firstname.lastname@example.org.
Dr James O’Geran
Becky Humphreys (email@example.com or 01206 872217)
No external examiner information available for this module.
Available via Moodle
Of 201 hours, 18 (9%) hours available to students:
183 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.
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