Self-selection sampling, also known as voluntary response sampling, is a non-probability sampling method where individuals choose to participate in a study on their own accord. In this method, participants actively decide to be part of the sample, often in response to an open invitation, advertisement, or call for volunteers.
Unlike probability sampling methods, self-selection sampling does not involve random selection, and the sample comprises individuals willing and motivated to participate.
Advantages of self-selection sampling include:
- Ease of implementation: This method is easy to implement, especially when researchers may not have direct access to the entire population.
- Cost-effective: It can be cost-effective, as it relies on voluntary participation without extensive recruitment efforts.
- Quick data collection: Researchers can quickly collect data by allowing individuals to self-select into the sample.
Some challenges that may arise while conducting self-selection sampling include:
- Selection bias: Self-selection sampling introduces a risk of selection bias, as individuals who choose to participate may have characteristics that differ systematically from those who do not participate.
- Lack of representativeness: The sample may not represent the broader population, especially if certain groups are more likely to self-select.
- Overrepresentation of certain views: Individuals with strong opinions or specific views may be more motivated to participate, leading to overrepresenting certain perspectives.
- Difficulty generalizing findings: Findings from self-selection samples may not be generalizable to the wider population due to the non-random nature of the sample.
Self-selection sampling is commonly used in online surveys, questionnaires, social media polls, public opinion forums, and studies that rely on participants responding to public invitations.
List of recommended resources #
For a broad overview #
This blog on QuestionPro gives an overview of voluntary response sample or self-selection sample, a type of non-probability sampling method, along with its uses and advantages.
This article by Kassiani Nikolopoulou on Scribbr gives an overview of non-probability sampling, its various types and examples, including self-selection sampling. It also details the advantages and disadvantages of non-probability sampling as well as differentiates it from probability sampling methods.
For in depth understanding #
This article by Arkadiusz Wiśniowski, Joseph W Sakshaug, Diego Andres Perez Ruiz and Annelies G Blom evaluates supplementing inferences based on small probability samples with prior distributions derived from non-probability data. It further demonstrates that informative priors based on non-probability data can lead to reductions in variances and mean squared errors for linear model coefficients.
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 study by Vivian Hoffmann, Miki Khanh Doan and Tomoko Harigaya assesses farmers’ participation in a farm business training activity before the agronomy training intervention as a sample identification mechanism. The study finds that using a self-selected sample reduces the minimum detectable effect of agronomy training on coffee yield to 15.83%, compared to 38% if population-based sampling were used.
This research article by Michael Meyer, Michaela Neumayr and Paul Rameder investigates whether effects of voluntary service programs are caused by service experience or by prior self-selection. The study uses data from a pre–post quasi-experimental design conducted at a public university in Europe and takes students’ socioeconomic background into account.