Non-probability sampling refers to a group of sampling techniques where the selection of individuals or elements from a population is not based on a known and specified probability of each unit being included in the sample.
Unlike probability sampling, non-probability sampling methods do not guarantee that each member of the population has an equal chance of getting selected. Instead, individuals are chosen based on the researcher’s judgment or convenience.
Advantages of non-probability sampling include:
- Cost and time efficient: Non-probability sampling methods are often quicker and more cost-effective than probability sampling, making them suitable for exploratory research or studies with resource constraints.
- Practical: Non-probability sampling is practical when obtaining a complete population list is difficult or impossible.
- Specific objectives: Useful when the research objectives focus on particular perspectives, characteristics, or experiences.
Some limitations of non-probability sampling are:
- Lack of generalizability: Findings from non-probability samples may have limited generalizability to the broader population.
- Bias and subjectivity: Non-probability samples may be subject to bias and lack the randomness required for statistical inference.
- Limited control: The researcher has limited control over the sample’s composition, potentially leading to an unrepresentative sample.
List of recommended resources #
For a broad overview #
This blog entry on non-probability sampling by Statistics Canada gives an overview of the method of selecting units from a population using a subjective (i.e. non-random) method. It describes some of the commonly used non-probability sampling methods, along with crowdsourcing and web panels.
This article by Kassiani Nikolopoulou on Scribbr gives an overview of non-probability sampling, its various types and examples. It also details the advantages and disadvantages of non-probability sampling as well as differentiates it from the probability sampling method.
For in depth understanding #
Written by Sharon L. Lohr, this book explains the principles of sampling with examples from public opinion research, public health, social sciences, business, ecology and agriculture. It shows the readers how to design and analyze surveys to answer questions related to any field.
This book explores the increasingly scientific endeavor of surveys and their growing complexity, as different information sources and data collection modes are combined. Chapter 22 of this book, written by Vasja Vehovar, Vera Toepoel & Stephanie Steinmetz, deals particularly with non-probability sampling.
Case study #
This article by Mohamed Elfil and Ahmed Negida explains the different sampling methods in clinical research to get a better understanding of the generalizability of clinical research findings.
This report by Andrea Serra presents the results of a study which aims to understand the status of and challenges faced by Mozambican women’s small and medium enterprises (SME´s). The study was carried out through a quantitative survey and focus group discussions among a convenience sample of 70 business women.