Research Methods and Statistics Lecture 4-I Sampling Research Methods and Statistics

Sampling Why is sampling important? - The quality of a research study stands or falls in part by the appropriateness and suitability of the sampling strategy used. Purpose of sampling - Reduced costs - Speed - Can provide more highly accurate measures than including an entire population in a study.

Guidelines for Making Decisions about Sampling Your Sampling Scheme - Should allow for a potentially valid means by which to answer your research question. - Should be feasible - Should be large enough to allow for the possibility of providing credible explanations and/or drawing clear inferences. - Must be ethical.

Sampling: Defining a Population Defining the population of interest - In what group exactly are you interested? - To whom do you want the result of the study to apply? Characteristics of a population - Can be any size - Includes all of the individuals who possess a certain characteristic or set of characteristics

Determining Sample Size How large should a sample be for a study? - No clear-cut answers, except that it should be “large enough.” - An adequate sample size depends on the purpose of the study and the nature of the population under investigation. Rules of thumb for determining a sample size - A sample of 30 as the minimum needed for statistical analyses - The more heterogeneous the population, the larger the sample should be. - For smaller populations, include the entire population.

Sample Size Desired sample size/Estimated percentage of people likely to participate = Number of people to include in your original sample Some tips for sample size: Response rate - The final sample size < the number people who participate in your study (response rate). - Always should over-sample to control for possible lower- than-expected response rates.

Probability Sampling: Simple Random Sampling C D E F G H I J K L M N O P Q R S T U V W X Y Z Population Sample (Random Selection) Advantage - Good for obtaining a sample that is representative of individuals in a population. Disadvantages - May be difficult to reach all individuals selected in the sample, particularly if they are dispersed geographically. - May not get good representation of subgroups in a population.

Probability Sampling: Systematic Random Sampling Population Sample T O J E Y A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Every kth unit Random starting point Advantage - Sample selection method is simple, and is especially useful if the population is very large. Disadvantages - Periodicity(pattern): If individuals on sampling frame are not randomly ordered, sample you draw may not be representative of population.

Probability Sampling: Stratified Random Sampling Population Sample A B C D E P Q R S T F G H I J K L M N O (25%) (50%) F M J O P S B D F M O (33%) P S T B D E Proportional Disproportional (Random Selection) Advantages - Increases likelihood of representation of key variable(s) for a particular study Useful for examining group differences Disadvantage - Need to have information on variable of interest for each individual in the population

Probability Sampling: Cluster Sampling Population Sample HI A B C D L M J K S T U E F G Q R (Random Selection) Advantages - Efficient sampling method when population is large or individuals are dispersed over wide geographic area. - Doesn’t require detailed information on individuals in a population Disadvantage - Reduction in the representativeness of the population, as similar people tend to naturally “cluster” together.

Probability Sampling: Two-Stage Cluster Sampling Population Sample Q R C D E F G A B L M J K S T U HI D F Q (Random Selection) Advantages - Smaller sample size than single- stage cluster sampling Disadvantage - If cluster are not approximately the same size, must use probability proportional to size (PPS) method to increase representativeness of the sample.