statistics GCU
WEEK FOUR
DQ1
Explain the importance of random sampling. What problems/limitations could prevent a truly random sampling and how can they be prevented?
Probability sampling, also known as random sampling, requires that every member of the study population have an equal opportunity to be chosen as a study subject. For each member of the population to have an equal opportunity to be chosen, the sampling method must select members randomly. Probability sampling allows every facet of the study population to be represented without researcher bias. Four common sampling designs have been developed for selection of a random sample: simple random sampling, stratified random sampling, cluster sampling, and systematic sampling (Burns & Grove, 2007). Simple random sampling is achieved by random selection of members from the sampling frame. The random selection can be accomplished many different ways, but the most common is using a computer program to randomly select the sample. Another example would be to assign each potential subject a number, and then randomly select numbers from a random numbers table to fulfill the required number of subjects for the sample. Stratified random sampling is used when the researcher knows some of the variables within a population that will affect the representativeness of the sample. Some examples of variables include age, gender, ethnicity, and medical diagnosis. Thus, subjects are selected randomly on the basis of their classification into the selected stratum. The strata ensure that all levels of the variable(s) are represented in the sample. For example, age could be the variable, and after stratification, the sample might include equal numbers of subjects in the established age ranges of 20–39, 40–59, 60–79, and over 80. Researchers use cluster sampling in two different situations: (1) when the time and travel necessary to use simple random sampling would be prohibitive, and (2) when the specific elements of a population are
DQ1
Explain the importance of random sampling. What problems/limitations could prevent a truly random sampling and how can they be prevented?
Probability sampling, also known as random sampling, requires that every member of the study population have an equal opportunity to be chosen as a study subject. For each member of the population to have an equal opportunity to be chosen, the sampling method must select members randomly. Probability sampling allows every facet of the study population to be represented without researcher bias. Four common sampling designs have been developed for selection of a random sample: simple random sampling, stratified random sampling, cluster sampling, and systematic sampling (Burns & Grove, 2007). Simple random sampling is achieved by random selection of members from the sampling frame. The random selection can be accomplished many different ways, but the most common is using a computer program to randomly select the sample. Another example would be to assign each potential subject a number, and then randomly select numbers from a random numbers table to fulfill the required number of subjects for the sample. Stratified random sampling is used when the researcher knows some of the variables within a population that will affect the representativeness of the sample. Some examples of variables include age, gender, ethnicity, and medical diagnosis. Thus, subjects are selected randomly on the basis of their classification into the selected stratum. The strata ensure that all levels of the variable(s) are represented in the sample. For example, age could be the variable, and after stratification, the sample might include equal numbers of subjects in the established age ranges of 20–39, 40–59, 60–79, and over 80. Researchers use cluster sampling in two different situations: (1) when the time and travel necessary to use simple random sampling would be prohibitive, and (2) when the specific elements of a population are