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AAPOR
The leading association
of public opinion and
survey research professionals
American Association for Public Opinion Research

Nonprobability Samples 101

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Member Price: $133.00
Nonmember Price: $178.00

Student pricing available

Non-probability Sampling for Finite Population Inference
About This Course:

Although selecting a probability sample has been the standard for decades for making inferences from a sample to a finite population, incentives are increasing to use data obtained without a defined sampling mechanism, i.e., non-probability samples. In a world of “big data”, large amounts of data are readily available through methods that are faster and need fewer resources relative to most probability-based designs.  There are many ways of collecting data now without a pre-specified sampling design—volunteer web panels, tele-voting, expert selection, respondent-driven network sampling, and others—none of which require probability samples. 

Design-based inference, in which population values are estimated through the random sampling procedure specified by the sampler, cannot be used for non-probability samples. One alternative is quasi-randomization where pseudo-inclusion probabilities (referred to as propensity scores) are estimated from covariates available for both sample and nonsample units. Another estimation approach is superpopulation modeling; analytic variables collected on the sample units are used in a model to predict values for the nonsample units. Variances of estimators can be computed using replication methods or approaches derived using modelling. We include several simulation studies to illustrate the properties of these approaches and discuss the pros and cons of each.  


Learning Objectives:

  • Understand the different types of non-probability samples currently in use
  • Understand how non-probability samples can be affected by coverage errors, nonresponse, and measurement errors
  • Understand what methods of estimation can be used for non-probability samples and the arguments used to justify them


The Mixology of Samples; Blending Probable and Nonprobability Samples​
About This Course:

The times, they are a-changing! Subsequent to the webinar conducted in February on the fundamentals of nonprobability samples, this presentation provides concrete recommendations for producing inferences from surveys that rely on blended probability and nonprobability samples based on our years of hands-on experience. After a brief recap of the previous webinar, we will discuss robust weighting methodologies that can reduce some of the inherent biases of nonprobability samples, including extending weighting adjustments beyond standard geodemographic realignments. We then introduce an option for the optimal integration of surveys from different samples, and touch upon fusion of ancillary data from external sources. Next, we will provide recommendations for practical alternatives for approximating error margins from blended samples. We will end this webinar with some contemplations about the new directions in survey sampling in the digital age.

Learning Objectives:
  • Assessment of current practices for weighting nonprobability samples
  • Recommendations for optimal calibration and blending of survey data
  • Practical options for approximating error margins


A Practical Guide to Surveys Based on NonProbability Samples
About This Course:
The times, they are a-changing! Traditional probability-based sampling techniques require a number of fundamental, yet increasingly unattainable, assumptions. Briefly, without a complete sampling frame and a data collection protocol that can secure high response rates, the available inferential machinery can break down. These difficulties, along with the escalating costs of probability-based methods, have prompted many researchers to consider nonprobability samples as practical alternatives. This webinar provides concrete recommendations for working with different nonprobability samples based on our years of hands-on experience. We will begin with an overview of different types of nonprobability samples and discuss their pros and cons from a fitness for use perspective. Focusing on online surveys, we will cover best practices for sourcing and selection of such samples, including effective options for sample balancing, incentive options, and considerations for data quality given the vast and varied landscape of sample providers.   Lastly, we will lay the foundation for our next webinar on robust weighting and calibration methodologies that can reduce some of the inherent biases of such samples, as well as optimal methods for blending probability and nonprobability samples. We will end this webinar with some contemplations about the new directions in survey sampling in the digital age.

Learning Objectives:
  • Demystifying nonprobability samples and their pragmatic utilities
  • Reviewing best practices for nonprobability sample sourcing and selection methodologies
  • Identifying and dealing with the hidden hazards of nonprobability samples

Upcoming Webinars

Web & Multimode Survey Using Free/Open Source Tools
Presented on Thursday, February 17, 2022

Applications of Predictive Modeling to Survey Design & Operation in Address-based Samples
Presented on Thursday, March 17, 2022

Non-experimental study designs: The basics and recent advances
Presented on Thursday, April 7, 2022

Introduction to Machine Learning for Survey Research
Presented on Thursday, May 26, 2022

Introduction to Machine Learning for Survey Research
Presented on Thursday, May 26, 2022

A Beginners Guide to Publishing Methods Articles
Presented on Thursday, June 16, 2022

Ensuring Comparability and Measurement Equity: A Socio-Cultural Approach to Cognitive Interviewing
Presented on Thursday, July 21, 2022

Design Decisions for Survey Monitoring: From Tables to Dashboards
Presented on Thursday, August 18, 2022

Standards and guidelines for designing human-centered mobile surveys
Presented on Thursday, September 15, 2022

Video survey interviews: Recruiting, data quality, and respondent experience
Presented on Thursday, October 20, 2022

Data linkage -- A primer
Presented on Thursday, November 17, 2022

Systematic Visuo-Textual Analysis: a framework for analysing visual and textual data
Presented on Thursday, December 8, 2022

Past Webinar Recordings

Using Social Media for Web Survey Recruitment
Presented on Thursday, November 18, 2021

Short Course: Survey Weighting
Presented on Wednesday, October 13, 2021

Click here for a complete list of webinar recordings