# Sampling Methods for Quantitative Research

**Sampling Methods for Quantitative Research**

Sampling and types of sampling methods commonly used in quantitative research are discussed in the following module.

**Learning Objectives**

- Define sampling and randomization
- Explain probability and non-probability sampling and describes the different types of each

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Researchers commonly examine traits or characteristics (**parameters**) of populations in their studies. A **population** is a group of individual units with some commonality. For example, a researcher may want to study characteristics of female smokers in the United States. This would be the population being analyzed in the study, but it would be impossible to collect information from all female smokers in the U.S. Therefore, the researcher would select individuals from which to collect the data. This is called **sampling.** The group from which the data is drawn is a **representative sample** of the population the results of the study can be **generalized to the population as a whole**.

The sample will be representative of the population if the researcher uses a **random selection procedure** to choose participants. The group of units or individuals who have a legitimate chance of being selected are sometimes referred to as the **sampling frame**. If a researcher studied developmental milestones of preschool children and target licensed preschools to collect the data, the sampling frame would be all preschool aged children in those preschools. Students in those preschools could then be selected at random through a systematic method to participate in the study. This does, however, lead to a discussion of **biases** in research. For example, low-income children may be less likely to be enrolled in preschool and therefore, may be excluded from the study. Extra care has to be taken to control biases when determining sampling techniques.

There are two main types of sampling: **probability and non-probability sampling**. The difference between the two types is whether or not the sampling selection involves randomization. Randomization occurs when all members of the sampling frame have an equal opportunity of being selected for the study. Following is a discussion of probability and non-probability sampling and the different types of each.

**Probability Sampling** – Uses randomization and takes steps to ensure all members of a population have a chance of being selected. There are several variations on this type of sampling and following is a list of ways probability sampling may occur:

- Random sampling – every member has an equal chance
- Stratified sampling – population divided into subgroups (strata) and members are randomly selected from each group
- Systematic sampling – uses a specific system to select members such as every 10
^{th}person on an alphabetized list - Cluster random sampling – divides the population into clusters, clusters are randomly selected and all members of the cluster selected are sampled
- Multi-stage random sampling – a combination of one or more of the above methods

**Non-probability Sampling** – Does not rely on the use of randomization techniques to select members. This is typically done in studies where randomization is not possible in order to obtain a representative sample. Bias is more of a concern with this type of sampling. The different types of non-probability sampling are as follows:

- Convenience or accidental sampling – members or units are selected based on availability
- Purposive sampling – members of a particular group are purposefully sought after
- Modal instance sampling – members or units are the most common within a defined group and therefore are sought after
- Expert sampling – members considered to be of high quality are chosen for participation
- Proportional and non-proportional quota sampling – members are sampled until exact proportions of certain types of data are obtained or until sufficient data in different categories is collected
- Diversity sampling – members are selected intentionally across the possible types of responses to capture all possibilities
- Snowball sampling – members are sampled and then asked to help identify other members to sample and this process continues until enough samples are collected

The following Slideshare presentation, Sampling in Quantitative and Qualitative Research – A practical how to, offers an overview of sampling methods for quantitative research and contrasts them with qualitative method for further understanding.

**Suggested Readings**

Bryman, A., & Cramer, D. (1994). *Quantitative data analysis for social scientists (rev*. Taylor & Frances/Routledge.

Creswell, J. W. (2002).

*Educational research: Planning, conducting, and evaluating quantitative*. Prentice Hall.

Creswell, J. W. (2013).

*Research design: Qualitative, quantitative, and mixed methods approaches*. Sage Publications, Incorporated.

Gall, M. D., Borg, W. R., Gall, J. P. (2003).

*Educational research: An introduction*. (7

^{th}Edition). White Plains, New York: Longman.

Neuman, W. L., & Robson, K. (2004).

*Basics of social research*. Pearson.

Neuman, W. L., & Neuman, W. L. (2006). Social research methods: Qualitative and quantitative approaches.

Robson, C. (2002).

*Real world research*(Vol. 2). Oxford: Blackwell publishers.

Trochim, W. M., & Donnelly, J. P. (2001). Research methods knowledge base.

- Quantitative Research
- Overview of Quantitative Research
- When to Use Quantitative Methods
- Writing Research Questions & Hypotheses
- Variables & Operational Definitions
- Key Issues in Quantitative Research
- Quantitative Approaches
- Quantitative Scales of Measurement
- Quantitative Data
- Sampling Methods for Quantitative Data
- Analyzing Quantitative Data
- Ethics & Quantitative Research
- Introduction to Quantitative Software
- Extra Quantitative Research Links
- More

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