Study Design and Sampling Study Design Cross-sectional studies are simple in design and are aimed at finding out the prevalence of a phenomenon, problem, attitude or issue by taking a snap-shot or cross-section of the population.
However, there are obviously times when one sampling method is preferred over the other. The following explanations add some clarification about when to use which method.
Stratified sampling would be preferred over cluster sampling, particularly if the questions of interest are affected by time zone. For example the percentage of people watching a live sporting event on television might be highly affected by the time zone they are in. Cluster sampling really works best when there are a reasonable number of clusters relative to the entire population.
In this case, selecting 2 clusters from 4 possible clusters really does not provide much advantage over simple random sampling. Either stratified sampling or cluster sampling could be used.
It would depend on what questions are being asked. For instance, consider the question "Do you agree or disagree that you receive adequate attention from the team of doctors at the Sports Medicine Clinic when injured? In contrast, if the question of interest is "Do you agree or disagree that weather affects your performance during an athletic event?
Consequently, stratified sampling would be preferred. Cluster sampling would probably be better than stratified sampling if each individual elementary school appropriately represents the entire population as in aschool district where students from throughout the district can attend any school.
Stratified sampling could be used if the elementary schools had very different locations and served only their local neighborhood i.
Again, the questions of interest would affect which sampling method should be used. The most common method of carrying out a poll today is using Random Digit Dialing in which a machine random dials phone numbers.
Some polls go even farther and have a machine conduct the interview itself rather than just dialing the number! Since the people who have landline phone service tend to be older than people who have cell phone service only, another potential source of bias is introduced.
National polling organizations that use random digit dialing in conducting interviewer based polls are very careful to match the number of landline versus cell phones to the population they are trying to survey.
Non-probability Sampling The following sampling methods that are listed in your text are types of non-probability sampling that should be avoided: In your textbook, the two types of non-probability samples listed above are called "sampling disasters.
The article provides great insight into how major polls are conducted. When you are finished reading this article you may want to go to the Gallup Poll Web site, https: It is important to be mindful of margin or error as discussed in this article.
We all need to remember that public opinion on a given topic cannot be appropriately measured with one question that is only asked on one poll. Such results only provide a snapshot at that moment under certain conditions. The concept of repeating procedures over different conditions and times leads to more valuable and durable results.
Within this section of the Gallup article, there is also an error: In 5 of those surveys, the confidence interval would not contain the population percent.Sampling methods are classified as either probability or nonprobability.
In probability samples, each member of the population has a known non-zero probability of being selected. In probability samples, each member of the population has a known non-zero probability of being selected.
Research philosophy is a vast topic and here we will not be discussing this topic in great details. In business and economics dissertations at Bachelor’s level, you are not expected to discuss research philosophy in a great level of depth, and about one page in methodology chapter devoted to research philosophy usually suffices.
The Knowledge Base An online hypertext textbook on applied social research methods that covers everything you want to know about defining a research question, sampling, measurement, research design and data analysis.
What is Respondent Driven Sampling? Respondent-driven sampling (RDS), combines "snowball sampling" with a mathematical model that weights the sample to compensate for the fact that the sample was collected in a non-random way. There are many methods of sampling when doing research. This guide can help you choose which method to use.
Simple random sampling is the ideal, but researchers seldom have the luxury of time or money to access the whole population, so many compromises often have to be made.
While choosing one of these methods could result in biased data or a limited ability to make general inferences based on the findings, there are also many situations in which choosing this kind of sampling technique is the best choice for the particular research question or the stage of research.