Sampling Strategies and their Advantages and Disadvantages

 

 

Type of Sampling

 

 

When to use it

 

Advantages

 

Disadvantages

Probability Strategies      

Simple Random Sampling

When the population members are similar to one another on important variables  

Ensures a high degree of representativeness

 

Time consuming and tedious

 

Systematic Sampling

When the population members are similar to one another on important variables Ensures a high degree of representativeness, and no need to use a table of random numbers  

Less random than simple random sampling

 

Stratified Random Sampling

When the population is heterogeneous and contains several different groups, some of which are related to the topic of the study  

Ensures a high degree of representativeness of all the strata or layers in the population

 

Time consuming and tedious

 

Cluster Sampling

When the population consists of units rather than individuals  

Easy and convenient

Possibly, members of units are different from one another, decreasing the techniques effectiveness
Non-Probability Sampling      
 

Convenience Sampling

When the members of the population are convenient to sample  

Convenience and inexpensive

 

Degree of generalizability is questionable

 

Quota Sampling

When strata are present and stratified sampling is not possible

 

Insures some degree of representativeness of all the strata in the population

 

 

Degree of generalizability is questionable

 

Notes:

  1. Reducing sampling error is the major goal of any selection technique.
  2. A sample should be big enough to answer the research question, but not so big that the process of sampling becomes uneconomical.
  3. Estimating sample size � in general, you need a larger sample to accurately represent the population when:
    1. The amount of variability within groups is greater, and
    2. The difference between the tw groups gets smaller.
  4. In general, the larger the sample, the smaller the sampling error and the better job you can do.
  5. If you are going to use several subgroups in your work (such as males and females who are both 10 years of age, and healthy and unhealthy urban residents), be sure your initial selection of subjects is large enough to account for the eventual breaking down of subject groups.
  6. If you are mailing out surveys or questionnaire, count on increasing your sample size by 40% to 50% to account for lost mail and uncooperative subjects.
  7. Remember that big is good, but appropriate is better.� Do not waste your hard-earned money or valuable time generating samples that are larger than you need� law of diminishing returns will set in!

UCLA Statistical Consulting Group. (2016). Introduction to Mplus: Featuring confirmatory factor

            analysis. Retrieved from http://tinyurl.com/jekut29

Sample size suggestion usually depend on the complexity of the specified model, but typically range between 5 to 20 questionnaires per observed variable, with overall sample size preferred to exceed n = 200 cases (UCLA Statistical Consulting Group, 2016).