Sampling in Research: Population, Types, and Errors

Population and Sample

• A population is a group or class of subjects, variables, concepts, or phenomena.
• The goal of scientific research is to describe the nature of a population.
• A census is the process of examining every member of a population, but it is not possible to conduct a census every time due to time and resource constraints.
• A sample is a subset of the population that is representative of the entire population.
• Sample representation is directly affected by three factors: sampling procedure, sample size, and participation rate.
• If a sample is sufficiently large and representative of the population, the results from a study using the sample can be generalized to the population.

Types of Sampling Procedures

• There are two broad types of sampling methods: probability and non-probability sampling.
• Probability sampling:
  • A sample that is selected according to mathematical guidelines.
  • Each target population element's chance for selection is known.
  • Allows researchers to calculate the amount of sampling error and make inferences about the population.
  • Types of probability sampling: simple random, systematic, stratified, and cluster.
• Non-probability sampling:
  • A sample that relies on personal judgment in the element selection process.
  • Neither sampling error nor the margin of sampling error can be estimated or calculated.
  • Inferences are limited to the sample.
  • Types of non-probability sampling: convenience, judgment, snowball, and quota.

Probability Sampling Methods

• Simple random sampling: each subject, element, event, or unit in the population has an equal chance of being selected.
• Stratified sampling:
  • The population is divided into mutually exclusive and exhaustive subsets.
  • A simple random sample of elements is chosen independently from each group/subset.
  • Used when researchers wish to ensure that a specific subsample is adequately represented.
• Systematic sampling: consistently produces incorrect results in the same direction, therefore predictable.

Research Error

• Research error can be classified into two types: sampling error and non-sampling error.
• Sampling error: occurs when the sample is not representative of the population.
• Non-sampling error: occurs when the data is not accurate or reliable.
• Systematic error: consistently produces incorrect results in the same direction, therefore predictable.
• Non-response error: occurs when an individual fails to provide information or responds inaccurately.
• Response error: occurs when an individual provides inaccurate information due to various reasons.

Sample Size

• The sample size depends on the research design, the amount of acceptable error, time constraints, and the quality of data required.
• Generally, the larger the sample, the better, but a large unrepresentative sample is as meaningless as a small unrepresentative sample.
• Sample quality is more important than mere size.
• Factors to consider when determining sample size: research design, amount of acceptable error, time constraints, and the quality of data required.

This lecture covers the basics of sampling in research, including the difference between population and sample, types of sampling procedures, and research error. It also discusses sample size and its importance.