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! Such " robo call polls " can be very biased because they have extremely low response rates most people don't like speaking to a machine and because federal law prevents such calls to cell phones.
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. The following sampling methods that are listed in your text are types of non-probability sampling that should be avoided:.
Since such non-probability sampling methods are based on human choice rather than random selection, statistical theory cannot explain how they might behave and potential sources of bias are rampant.
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.
Eberly College of Science. Printer-friendly version Sampling Methods can be classified into one of two categories: Sample has a known probability of being selected Non-probability Sampling: Sample does not have known probability of being selected as in convenience or voluntary response surveys Probability Sampling In probability sampling it is possible to both determine which sampling units belong to which sample and the probability that each sample will be selected.
Simple Random Sampling SRS Stratified Sampling Cluster Sampling Systematic Sampling Multistage Sampling in which some of the methods above are combined in stages Of the five methods listed above, students have the most trouble distinguishing between stratified sampling and cluster sampling.
With stratified sampling one should: With cluster sampling one should divide the population into groups clusters. 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.
Sampling and types of sampling methods commonly used in quantitative research are discussed in the following module. Researchers commonly examine traits or characteristics parameters of populations in their studies. A population is a group of individual units with some commonality. For example, a researcher may want to study characteristics of female smokers in the United States.
This would be the population being analyzed in the study, but it would be impossible to collect information from all female smokers in the U. Therefore, the researcher would select individuals from which to collect the data. This is called sampling. The group from which the data is drawn is a representative sample of the population the results of the study can be generalized to the population as a whole. The sample will be representative of the population if the researcher uses a random selection procedure to choose participants.
The group of units or individuals who have a legitimate chance of being selected are sometimes referred to as the sampling frame. If a researcher studied developmental milestones of preschool children and target licensed preschools to collect the data, the sampling frame would be all preschool aged children in those preschools. Students in those preschools could then be selected at random through a systematic method to participate in the study.
This does, however, lead to a discussion of biases in research. For example, low-income children may be less likely to be enrolled in preschool and therefore, may be excluded from the study.
Extra care has to be taken to control biases when determining sampling techniques. There are two main types of sampling: The difference between the two types is whether or not the sampling selection involves randomization. Randomization occurs when all members of the sampling frame have an equal opportunity of being selected for the study. Following is a discussion of probability and non-probability sampling and the different types of each.
Probability Sampling — Uses randomization and takes steps to ensure all members of a population have a chance of being selected. There are several variations on this type of sampling and following is a list of ways probability sampling may occur:. Non-probability Sampling — Does not rely on the use of randomization techniques to select members.
This is typically done in studies where randomization is not possible in order to obtain a representative sample.
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.
Sampling and types of sampling methods commonly used in quantitative research are discussed in the following module. Learning Objectives: Define sampling and randomization. Explain probability and non-probability sampling and describes the different types of each.
Sampling Methods can be classified into one of two categories: Probability Sampling: Sample has a known probability of being selected. Non-probability Sampling: Sample does not have known probability of being selected as in convenience or voluntary response surveys. RESEARCH METHOD - SAMPLING 1. Sampling Techniques & Samples Types. 2. Outlines Sample definition Purpose of sampling Stages in the selection of 3. The process of selecting a number of individuals for a study in such a way 6. Population the larger group from which individuals are selected to participate in a study.
Types of Sampling Methods and Techniques in Research The main goal of any marketing or statistical research is to provide quality results that are a reliable basis for decision-making. That is why the different types of sampling methods and techniques have a crucial role in research methodology and statistics. Video: What is Sampling in Research? - Definition, Methods & Importance - Definition, Methods & Importance The sample of a study can have a profound impact on the outcome of a study.