Undergraduate Course: Using pre-existing data for your own research (SSPS10030)
|School||School of Social and Political Science
||College||College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)||SCQF Level 10 (Year 3 Undergraduate)
||Availability||Available to all students
|Summary||This course teaches students the data and research skills they need to search, access download, manage, process, analyse and present data as empirical research evidence. It assumes that they have had some general introduction to quantitative methods and perhaps some familiarity with SPSS or another statistics analysis package. It will reinforce their existing skill in regression, or develop them for those without much previous exposure to it.
Become familiar with and develop a deeper understanding of the core concepts of quantitative data analysis through the practice of carrying it out.
Learn how to use online analysis tools such as Nesstar to explore data.
Learn how to search, locate download and manage data from data archives, respecting the confidentiality or other obligations set by data providers and maintaining the security and integrity of the data they use.
Learn good housekeeping rules for effective data storage retrieval and administration.
Learn how to manipulate and process data to make it suitable for analysis, including
Learning how to search data documentation for key information about any dataset
Cleaning data and checking for errors and outliers
Identifying variables and linking derived variables to their origins in source questionnaires, showcards and other material
Dealing with different types of missing values
Dealing with weights
Recoding variables, including the standardisation of measurements, creation of scales, dummy variables, or dealing with multiple coding.
Restructuring data to move between values, variables and cases
Dealing with hierarchical data and household grids
Merging and splitting data files
Learning SPSS syntax
Learn how to formulate researchable questions with data availability in mind.
Distinguish between data exploration and hypothesis testing
Recognise the importance of replication in research.
Learn how to carry out simple analysis of continuous and categorical data using descriptive and inferential statistics, including commands such as means, frequency and crosstabs, correlation, linear and logistic regression.
Learn how to use and interpret diagnostic statistics
Learn how to use graphing and visualisation as an aid both to analysis and the presentation of results
Consider how best to present results, editing output in SPSS or exporting for processing to other applications.
Information for Visiting Students
|Pre-requisites||Visiting students must have taken an introductory statistics course.
|High Demand Course?
Course Delivery Information
|Not being delivered|
On completion of this course, the student will be able to:
- Develop a critical understanding of the main challenges for secondary data analysis
- Become familiar with and develop a deeper understanding of the course concepts of quantitative data analysis through the practice of carrying it out.
- Learn how to search, locate download and manage data from data archives, respecting the confidentiality or other obligations set by data providers and maintaining the security and integrity of the data they use.
- Learn good housekeeping rules for effective data storage retrieval and administration.
- Use a range of research skills to plan and execute a research project using secondary data
|MacInnes, J. An Introduction to Secondary Data Analysis with IBM SPSS Statistics Sage 2017.|
Blasius, J & Thiessen, V. Assessing the Quality of Survey Data (Research Methods for Social Scientists) Sage 2012.
De Vaus, D. Surveys in Social Research (Social Research Today), Routledge 2013.
|Graduate Attributes and Skills
||Being able to use a range of research skills to plan and execute a research project using secondary data.
|Course organiser||Dr John MacInnes
Tel: (0131 6)50 3867
|Course secretary||Miss Katarzyna Pietrzak
Tel: (0131 6)51 3162