Introduction to Sampling for Social Surveys

Description:

Introduction/Overview

Making inferences about a population of interest is often essential for scientific inquiry. This can be important both in traditional social science research, for example when using probability surveys, as well as in new forms of data, for example sampling from large volumes of data. Furthermore, understanding sampling can enable researchers to evaluate the strengths and limitations of research designs and guide them towards more valid and robust ways of collecting and analysing data.

This course provides an overview of sampling techniques frequently used in survey designs. In particular, it focuses on the principles of designing and selecting samples of individuals. These principles are also discussed in terms of the effects on inference to the population of interest, the key goal of survey research.

Course objectives

By the end of the workshop the participants will:

• Learn about the main types of probability and non-probability sample designs
• Will learn about sampling frames
• Will understand how sampling error is related with standard errors and confidence intervals
• Will understand the concept of design effects and effective sample size

Topics

• Introduction to probability and non-probability sampling
• Sampling frames
• Sampling error and confidence intervals
• Design effects and sample size

Who will benefit

Sampling is an essential part of research in the social sciences. This course will benefit anyone working with social data or planning to collect their own data. Knowing the basics of sampling will enable practitioners to understand the strengths and limitations of the data they use or want to collect.

Learning outcomes

• Know the main types of sampling approaches in survey research
• Understand how probability sampling can lead to inferences about populations of interest
• Understand the relationship between sampling, sampling error and design effects
• Understand the relationship between sample sizes, design effects and sampling strategies

Course Tutor

Alexandru Cernat is a lecturer in social statistics at the University of Manchester since 2016. He received a PhD in survey methodology from the University of Essex and was a post-doc at the National Centre for Research Methods.

His expertise covers: latent variable modelling, non-response, new forms of data, longitudinal data design, longitudinal analysis.

He has taught from multiple organizations such as the National Centre for Research Methods, European Survey Research Association, International Program in Data Science and the African Institute of Mathematical science.

You can find more about his research and activities at www.alexcernat.com

Cost:

£165 for SRA members and £220 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 , Quantitative Data Handling and Data Analysis, Research Management and Impact, Research Skills, Communication and Dissemination

Related publications and presentations from our eprints archive:

Frameworks for Research and Research Designs
Data Collection
Data Quality and Data Management
Quantitative Data Handling and Data Analysis
Research Management and Impact
Research Skills, Communication and Dissemination

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