Research Sampling Methods: Probability vs. Non-Probability Techniques
Probability and Non-Probability Sampling Methods
Probability (Random) Sampling
Probability sampling, also known as random sampling, is a method where the probability of being selected is known, meaning every member of the wider population has an equal chance to be included. The primary aim is for generalizability and wide representation.
Purpose and Example
- Purpose: To select a group of subjects representative of the larger population from which they are selected.
- Example: A university randomly selects 200 students from a complete list to study their eating habits.
Non-Probability (Purposive) Sampling
In non-probability sampling, the probability of being selected is unknown. Consequently, some members of the wider population will definitely be excluded, and others definitely included.
Purpose and Example
- Purpose: To select subjects who can be particularly informative about the research issues.
- Example: Twenty vegan students volunteer to talk about their eating habits and are selected for the study.
Detailed Look at Probability Sampling
Definition of Probability Sampling
Probability sampling is a method in which subjects are selected randomly from a population in such a way that the researcher knows the probability of selecting each subject.
For instance, in a sample of 10 from a population of 100, each subject has a 10% chance of being included in the sample.
Key Concepts: Population and Sampling Frame
- Population: A large group of individuals to whom the results of a study can be generalized.
- Sampling Frame (i.e., survey population or accessible population): The group to whom the researcher has access and from which the actual sample will be drawn.
Note that the sampling frame and the target population are often different:
- Example 1: The population could be all fourth graders in Madrid; the sampling frame is fourth graders in public schools in Madrid (i.e., excluding private school students due to their inaccessibility).
- Example 2: The population could be all graduate students at the Universidad Rey Juan Carlos. The sampling frame is all graduate students in the College of Education (i.e., excluding graduate students from all other colleges/departments).
Goal, Error, and Bias in Probability Sampling
The goal of probability sampling is to select a sample that is representative of the population from which it is selected.
- Sampling Error: The difference between the “true” result and the “observed” result that can be attributed to using samples rather than populations.
Examples of Sampling Error:
- Minimal Error: In a sample of 99 from a population of 100.
- High Error: In a sample of 2 from a population of 100.
- Sampling Bias: The difference between the “observed” and “true” results that is attributed to the sampling mistakes of the researcher.
Examples of Sampling Bias:
- Deliberately sampling subjects with certain attributes (e.g., positive attitudes, high self-esteem, high level of achievement, etc.).
- Using subjects from different populations and assigning them to different treatment groups (e.g., males to an experimental treatment group and females to a traditional treatment group).
Types of Probability Sampling Procedures
- Random Sampling
- A number is assigned to each subject in the population, and a table of random numbers or a computer is used to select subjects randomly from the population.
- Systematic Sampling
- A number is assigned to each subject in the population, and every nth member of the population is selected (e.g., 10, 20, 30, 40, etc.; 12, 22, 32, 42, etc.). The starting point for the selection is chosen at random.
- Stratified Sampling
- Similar to random sampling, but subjects are selected randomly from strata, or subgroups of the population (e.g., strata based on gender or age).
- Cluster Sampling
- Naturally occurring groups (clusters) are randomly selected first, and then subjects are randomly selected from the sampled groups.
Utility of Cluster Sampling
- Useful when it is impossible to identify all of the individuals in a population.
- Typical educational clusters are districts, schools, or classrooms.
Example: 27 of the 54 school districts were randomly selected, one secondary school in each district was randomly selected, and students randomly selected from each school were tested.
Steps in Selecting Probability Samples
- Define the target population and sampling frame.
- Determine the sample size.
- Select the sampling strategy (i.e., procedure).
- Select the sample.
Non-Probability Sampling Techniques
Definition and Rationale
Non-probability sampling is a method in which the probability of selecting a subject is unknown.
It is often not possible to use probability sampling techniques due to constraints such as access, time, resources, or finances. Furthermore, it is often desirable to select subjects who can be particularly informative about the research issues (e.g., if the researcher is trying to understand how teachers use manipulatives, it makes sense to select teachers who currently use these in their classes).
The goal of non-probability sampling is to identify information-rich participants.
Application of Sampling in Research Studies
Quantitative vs. Qualitative Studies
- Quantitative Studies
- The desired approach is the use of probability sampling due to the ability to generalize the results to the larger population.
- However, there is frequent use of non-probability techniques—particularly convenience sampling—due to access, time, resource, or financial constraints.
- Qualitative Studies
- Almost exclusive reliance on non-probability techniques—particularly purposeful sampling.