Module Directory

This module looks at how social scientists research and investigate the social and political world.  We learn that there are two main approaches to undertaking social scientific research – quantitative and qualitative – and that these represent different ways of viewing and interpreting the world.  We explore the main methods used in each approach for collecting and analysing data to develop robust theories, and apply what we learn in computer labs where we work with data and undertake research activities.

The aims of this module are:

• To equip students with skills for collecting, understanding and using data to gain knowledge and insights into social scientific issues.


• To give students the competence and confidence to read, understand and critically assess examples of how data is used and reported.


• To introduce students to key theoretical ideas and concepts in undertaking data-based research in social sciences.


• To provide students with a strong set of transferrable skills for using statistical software to investigate, analyse, summarise and present data.


• To prepare students for undertaking further data and research-based modules later in their degree programmes.


• To support students to develop key communication, analytical, research and employability skills.

By the end of this module, students will be expected to 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, both quantitative and qualitative, to gain insight into relevant research questions.


6. Understand how to interpret and apply the output from basic quantitative and qualitative 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.

Skills for your professional life (Transferable Skills)

By the end of this module, you will have been offered opportunities:

1. To improve your data analysis and numeracy skills.


2. To develop your IT skills by learning to work with data in Excel to analyse political and social datasets.


3. To improve your knowledge of data collection and analysis.


4. To enhance your communication skills in class discussions.


5. To develop critical thinking skills.


6. To develop your personal plan of setting targets and time management to undertake coursework and tests.

Syllabus

• 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, quantitative and qualitative. 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. Understand and apply qualitative analysis techniques.


• 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 to different audiences. How to tell the truth with data and recognise how data can be (and sometimes is!) used to not tell the truth. Principles of good data presentation – communicating complex ideas simply and effectively.

In the Autumn and Spring Terms, this module will be delivered via:

• One 1-hour lecture per week.
• Two 1-hour laboratory sessions per week.
• One 1-hour tutorial per week.

In the Summer term, there will be two 2-hour labs for the first two weeks where students can receive support and feedback for reviewing and completing the final assignment

Teaching and learning on Essex Pathways modules offers students the ability to develop the foundation knowledge, skills, and competencies to study at the undergraduate level, through a curriculum that is purposely designed to provide an exceptional learning experience. All teaching, learning and assessment materials will be available via Moodle in a consistent and user-friendly manner.

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.

Laboratory sessions: Students will undertake practical exercises working with and gaining an 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.

Tutorials: 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.

Academic Support Hour: An opportunity for students to seek clarification and ask questions regarding the content covered in each week's lecture or support with assignments.