SGIA40G15: Full Stack Research Design

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• To illustrate differences in research questions, concepts and theories between social science and other disciplines.
• To facilitate understanding of different types of data and their relevance to social science research.
• To teach research design and measurement methods with practical examples from the social sciences.
• To introduce state of the art applications of computational methods in substantive areas of social science.

• Indicative content is listed below and will be taught with reference to problems/scenarios within the social sciences:
• Questions, concepts and theories Data types, sources and their relevance;
• Computational measurement designs;
• Sampling and surveys;
• Causal inference in experimental and observational studies;
• Application of computational methods.

Subject-specific Knowledge:

• By the end of the module students will have a working knowledge and understanding of the following areas:
• The use of theories and concepts in the social sciences;
• Data types and sources;
• Types of research designs;
• Strengths and limitations of different computational methods in social science.

Subject-specific Skills:

• Ability to navigate social science concepts and methods;
• Leveraging types and sources of data in social science;
• Designing specialist research in social science;
• Critically appraising the state of the art research in social science.

Key Skills:

• These include:
• Critical appraisal of concepts, theories, and evidence in social science;
• Application of computational measurement approaches, including in complex settings;
• Application of research designs for exploration, description, forecasting, and causal inferences;
• Ability to interpret and communicate findings from social science research to different audiences, including specialised and wider audiences;

• There are 10 Lecture-Seminar pairs with the seminar taking place later than its paired lecture.
• Lectures will demonstrate the conceptual foundations of applications of computational methods in social science. There are 10 lectures, 1-hour long.
• Seminars: enable students to explore and evaluate some of the key concepts discussed in the module. There are weekly seminars of 1-hour duration.
• Independent Reading: provides students with the opportunities to read widely, particularly in preparation for formative and summative assessments. Independent reading enables students to engage in debates within scholarly journals and research monographs, in ways that enhance a critical understanding and engagement with key issues in social data science.
• The formative essay requires students to discuss a social science topic and outline how they could contribute to the knowledge on this topic with an original research project.
• Summative research design proposal is designed to test the acquisition and articulation of knowledge and critical understanding, and skills of implementation and interpretation of classical and computational methods as applied to both synthetic and real social science data-related problems.

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