• Skip to secondary menu
  • Skip to main content
  • Skip to primary sidebar
  • My Store
  • Glossary
  • Home
  • About Me
  • Contact Me

Statistics By Jim

Making statistics intuitive

  • Graphs
  • Basics
  • Hypothesis Testing
  • Regression
  • ANOVA
  • Probability
  • Time Series
  • Fun

Convenience Sampling: Definition & Examples

By Jim Frost 1 Comment

What is Convenience Sampling?

Convenience sampling is a non-probability sampling method where researchers use subjects who are easy to contact and obtain their participation. Researchers find participants in the most accessible places, and they impose no inclusion requirements. Convenience sampling is also known as opportunity or availability sampling.

Examples of convenience sampling include online and social media surveys, asking acquaintances, and surveying people in a mall, on the street, and in other crowded locations.

Photograph of relaxing thanks to convenience sampling.
Taking it easy thanks to convenience sampling!

While the subjects are easy to access, the researchers are unlikely to obtain a sample representing the population accurately. Sampling bias is likely to be high. You cannot generalize the sample results to a population. In some cases, you might not even be fully aware of the populations from which you’re sampling. Who’s answering your online surveys? In short, the results you obtain using this approach apply only to your sample.

Convenience samples serve as a sharp contrast to representative samples in terms of being able to generalize the results. Learn more about representative samples.

Statisticians rarely recommend this method because being unable to generalize your results beyond the sample is a huge limitation. Your results apply to your sample alone. Despite this weakness, there are a few situations where this method is warranted.

Learn more about Types of Sampling Methods in Research.

When to Use Convenience Sampling

Convenience sampling is most useful for pilot testing. Use it when you’re testing your survey instrument and other research protocols. It’s an inexpensive way to work out any problems with your study before committing more resources to obtain a representative sample.

This method can also provide initial ballpark estimates in the exploratory stages of research. For example, a company might want some quick feedback about new logo candidates and obtain a quick sample for that purpose. At the very least, it expands the feedback beyond those directly involved in the process.

In other cases, this approach might be the only viable approach. Researchers might not have the resources to conduct representative sampling methods, such as simple random, systematic, stratified, or cluster sampling. These methods entail more time and resources and often require a complete list of population members. Consequently, student projects often use convenience samples for this reason. In these cases, the preliminary results can serve as a call for more rigorous studies in the area.

If you know that subjects with particular characteristics are especially helpful for your study, you might use Purposive Sampling.

Conversely, if your subjects belong to a hard-to-find population (i.e., it’s inconvenient finding any of them), consider Snowball Sampling.

Convenience Sampling Example

A classic example of convenience sampling is the Pepsi Challenge. Originally, the Pepsi Challenge was a blind taste test conducted at shopping malls, stores, and other public venues. Participants taste unmarked cups containing Coca-Cola and Pepsi and then indicate their preference.

The Pepsis Challenge has all the hallmarks of this method, including the use of crowded areas facilitated the easy acquisition of participants and the lack of requirements for participating.

Advantages and Disadvantages of Convenience Sampling

The advantages of convenience sampling are the following:

  • Quick, easy, and inexpensive data collection.
  • It can help work out problems with the design in a pilot study.
  • Obtain initial data for the exploratory phase.
  • It can be the only viable method for low resource studies.

Its disadvantages are the following:

  • Nonrepresentative samples with high sampling error.
  • Cannot generalize the results beyond the sample.
  • Results have minimal usefulness.

If you want to perform convenience sampling but control representation in the sample, consider using Quota Sampling.

Reference

Sampling in Developmental Science: Situations, Shortcomings, Solutions, and Standards (nih.gov)

Share this:

  • Tweet

Related

Filed Under: Basics Tagged With: conceptual, experimental design, sampling methods

Reader Interactions

Comments

  1. Ummy hemed says

    December 23, 2022 at 3:15 am

    How does geographical proximity in hance convenience sample

    Reply

Comments and Questions Cancel reply

Primary Sidebar

Meet Jim

I’ll help you intuitively understand statistics by focusing on concepts and using plain English so you can concentrate on understanding your results.

Read More...

Buy My Introduction to Statistics Book!

Cover of my Introduction to Statistics: An Intuitive Guide ebook.

Buy My Hypothesis Testing Book!

Cover image of my Hypothesis Testing: An Intuitive Guide ebook.

Buy My Regression Book!

Cover for my ebook, Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models.

Subscribe by Email

Enter your email address to receive notifications of new posts by email.

    I won't send you spam. Unsubscribe at any time.

    Follow Me

    • FacebookFacebook
    • RSS FeedRSS Feed
    • TwitterTwitter

    Top Posts

    • How to Interpret P-values and Coefficients in Regression Analysis
    • How To Interpret R-squared in Regression Analysis
    • Mean, Median, and Mode: Measures of Central Tendency
    • Multicollinearity in Regression Analysis: Problems, Detection, and Solutions
    • How to Interpret the F-test of Overall Significance in Regression Analysis
    • Choosing the Correct Type of Regression Analysis
    • How to Find the P value: Process and Calculations
    • Interpreting Correlation Coefficients
    • How to do t-Tests in Excel
    • Z-table

    Recent Posts

    • Fishers Exact Test: Using & Interpreting
    • Percent Change: Formula and Calculation Steps
    • X and Y Axis in Graphs
    • Simpsons Paradox Explained
    • Covariates: Definition & Uses
    • Weighted Average: Formula & Calculation Examples

    Recent Comments

    • Dave on Control Variables: Definition, Uses & Examples
    • Jim Frost on How High Does R-squared Need to Be?
    • Mark Solomons on How High Does R-squared Need to Be?
    • John Grenci on Normal Distribution in Statistics
    • Jim Frost on Normal Distribution in Statistics

    Copyright © 2023 · Jim Frost · Privacy Policy