Population definition[ edit ] Successful statistical practice is based on focused problem definition. In sampling, this includes defining the population from which our sample is drawn.
Sampling Methods for Quantitative Research Sampling Methods Sampling and types of sampling methods commonly used in quantitative research are discussed in the following module.
Explain probability and non-probability sampling and describes the different types of each. 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: Random sampling — every member has an equal chance Stratified sampling — population divided into subgroups strata and members are randomly selected from each group Systematic sampling — uses a specific system to select members such as every 10th person on an alphabetized list Cluster random sampling — divides the population into clusters, clusters are randomly selected and all members of the cluster selected are sampled Multi-stage random sampling — a combination of one or more of the above methods 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. Bias is more of a concern with this type of sampling. The different types of non-probability sampling are as follows:CHAPTER 3 Research design, research method and population INTRODUCTION Chapter 3 outlines the research design, the research method, the population under study, the sampling procedure, and the method that was used to collect data.
The reliability and validity of the research. 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.
Once you know your population, sampling frame, sampling method, and sample size, you can use all that information to choose your sample.
Importance As you can see, choosing a . Population and sample. Sampling techniques Let us extend in this chapter what we have already presented in the beginning of Descriptive Statistics, including now the deﬁnition of some sampling techniques and concepts in order to be able to decide which is the appropriate sampling technique for each situation.
The following Slideshare presentation, Sampling in Quantitative and Qualitative Research – A practical how to, offers an overview of sampling methods for quantitative research and contrasts them with qualitative method for further understanding.
Relationship of Sample and Population in Research. A sample is simply a subset of the population. The concept of sample arises from the inability of the researchers to test all the individuals in a given population. The sample must be representative of the population from which it was drawn and it must have good size to warrant statistical analysis.