Key Sampling Methods and Validity Concepts in Research
Essential Steps in the Research Sampling Process
Define the Population of Interest
A population is the entire group of people you want to draw conclusions about. For example, if Brooke wants to know how much stress college students experience during finals, her population is all college students.
Determine the Sampling Frame
A sampling frame is the specific group of individuals from which you will draw your sample. For example, Brooke might decide her sampling frame is every student at the university where she works.
Select Sampling Technique(s)
Choose the method(s) you will use to select your sample from the sampling frame. Techniques can be broadly categorized as random or non-random. Specific types are detailed below.
Determine the Sample Size
Decide how many individuals will be included in your study. In general, larger samples provide more reliable results but require more resources. For instance, analyzing 1,000 surveys yields stronger results than analyzing 10, but takes significantly more time. Researchers must balance obtaining good data with what is practical.
Execute the Sampling Process
Implement your chosen sampling method to select the participants based on your defined population, sampling frame, technique, and sample size.
Common Sampling Techniques
Simple Random Sampling
Individual judgment plays no part in the selection of the sample. Each element in the population has a known and equal probability of being selected. This implies that every element is selected independently of every other element.
Systematic Sampling
Items are selected from the population at a uniform interval defined in terms of time, order, or space. For example, an observation might be made every half hour, or every fourth person in a long queue might be selected.
Stratified Sampling
The entire population is divided into relatively homogeneous groups (strata), such as dividing students by gender (boys and girls). Then, a random sample is drawn independently from each group.
Cluster Sampling
The population is divided into groups or clusters (e.g., dividing a city into small localities). A sample of these clusters is then drawn, often using random sampling methods. Research based on well-designed cluster sampling can sometimes yield better results than simple random sampling for the same time and cost.
Understanding Measurement Validity in Research
Content Validity
Content validity addresses the match between test questions and the content or subject area they are intended to assess. It measures how accurately a measurement tool taps into the various aspects of the specific construct in question. Assessment often relies on the judgment of experts familiar with the construct.
Construct Validity
Construct validity refers to the degree to which a test or measure assesses the underlying theoretical construct it is supposed to measure (i.e., the test measures what it claims to measure). For example, consider a basic algebra test. If it’s designed to assess knowledge of rate, time, and distance relationships, but the questions involve long, complex reading passages, the test might inadvertently measure reading skills more than algebra knowledge.
Criterion-Related Validity
Criterion-related validity examines the relationship between test scores and a specific outcome (the criterion). For example, SAT™ scores are evaluated based on their ability to predict success in college, where first-year grade point average serves as the criterion. Analyzing the relationship between test scores and the criterion indicates how valid the test is for predicting that outcome.