Making Sense of Causes: Quantitative Tools for Real World Research

Description:

Introduction/Overview

Almost all the most interesting questions in research and policy rest on cause and effect relationships. Does social media harm young people’s mental health? Do smaller class sizes improve children’s educational outcomes? Did that policy we introduced have the desired effect?

This course will develop your understanding of they key quantitative tools we use to determine whether X really causes Y – including both experimental and non-experimental approaches. We will also address common obstacles and errors encountered when attempting to establish causation – and provide strategies for recognising and overcoming them.

After completing the course you will have the tools to both design effective analyses to address causal questions, and to recognise when we can and can’t safely conclude that X causes Y.

Learning Outcomes

By the end of the workshop participants will:

• Have a good understanding of the primary tools quantitative researchers use to determine causal relationships, including experimental methods and statistical methods for observational data.
• Be able to recognise when causal conclusions are supported or not supported by appropriate evidence.
• Be able to design simple, but effective experimental studies to test causal hypotheses.
• Be able to conduct and interpret statistical analyses to test causal hypotheses using observational data.

Topics

During the course we will cover:

• Experimental and non-experimental approaches to causal inference.
• Common factors undermining our ability to draw causal conclusions.
• Effective experimental design, and the interpretation of experimental results.
• Regression approaches to causal inference using observational data: including identifying appropriate control variables, mediators, and moderators and incorporating them into our analyses.

Who will benefit?

This course is intended primarily for participants who already have some experience with quantitative methods, but who wish to develop their skills further with respect to answering causal research questions. It will be of particular benefit to researchers and professionals who are involved in answering causal question – for example, in policy evaluation, public health, education, criminal justice, or other fields.

Course Tutor

Dr Robert de Vries is Senior Lecturer in Quantitative Sociology and Deputy Head of the School of Social Sciences at the University of Kent. His research explores on inequality and social mobility, with a strong commitment to conducting quantitative research with integrity – ensuring that the research conclusions are based on robust evidence. He is the author of Critical Statistics: Seeing Beyond the Headlines, an award-winning textbook that introduces essential statistical concepts through the lens of understanding the numbers that surround us in our everyday lives.

Cost:

£180 for SRA members, £235 for non-members

Website and registration:

Register for this course

Region:

International

Keywords:

Frameworks for Research and Research Designs, Data Collection, Data Quality and Data Management , Qualitative Data Handling and Data Analysis, Quantitative Data Handling and Data Analysis, Mixed Methods Data Handling and Data Analysis, ICT and Software, Research Management and Impact, Research Skills, Communication and Dissemination, AI and machine learning

Related publications and presentations from our eprints archive:

Frameworks for Research and Research Designs
Data Collection
Data Quality and Data Management
Qualitative Data Handling and Data Analysis
Quantitative Data Handling and Data Analysis
Mixed Methods Data Handling and Data Analysis
ICT and Software
Research Management and Impact
Research Skills, Communication and Dissemination
AI and machine learning

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