Effective Sampling Techniques and Validity in Research
Sampling Techniques and Validity in Research
Defining the Population of Interest
A population is the group of people that you want to make assumptions about. For example, if you want to know how much stress college students experience during finals, your population is college students.
Determining the Sampling Frame
A sampling frame is the group of people from which you will draw your sample. For example, your sampling frame might be every student at the university where you work.
Selecting a Sampling Technique
Sampling can be done randomly or non-randomly.
Determining the Sample Size
In general, larger samples are better, but they also require more time and effort to manage. Researchers must balance the need for good data with practical considerations.
Executing the Sampling Process
Once you know your population, sampling frame, sampling method, and sample size, you can use all that information to choose your sample.
Types of Sampling
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 selection. This implies that every element is selected independently of every other element.
Systematic Sampling
The items are selected from the population at a uniform interval defined in terms of time, order, or space. For example, an observation may be made every half an hour, or from a long queue of people every fourth person may be selected.
Stratified Sampling
The entire population is divided into relatively homogeneous groups. For example, all the students of a school may be divided into groups of boys and girls. Once this is done, a random sample from each of such groups is drawn independently.
Cluster Sampling
The population is divided into groups or clusters, and a sample of these clusters may be drawn. For example, a city may be divided into a cluster of small localities, and a sample of these localities may be drawn using random sampling methods. A research based on a well-designed cluster sampling can often give better results than a research based on simple random sample with the same time and cost of research.
Validity in Research
Content Validity
Addresses the match between test questions and the content or subject area they are intended to assess, mostly measured by relying on the knowledge of people who are familiar with the construct being measured. It assesses how accurately a measurement tool taps into various aspects of the specific construct in question.
Construct Validity
Refers to the degree to which a test or other measure assesses the underlying theoretical construct it is supposed to measure (i.e., the test is measuring what it is purported to measure). As an example, think about a general knowledge test of basic algebra. If a test is designed to assess knowledge of facts concerning rate, time, distance, and their interrelationship with one another, but test questions are phrased in long and complex reading passages, then perhaps reading skills are inadvertently being measured instead of factual knowledge of basic algebra.
Criterion-Related Validity
Looks at the relationship between a test score and an outcome. For example, SAT™ scores are used to determine whether a student will be successful in college. First-year grade point average becomes the criterion for success. Looking at the relationship between test scores and the criterion can tell you how valid the test is for determining success in college.