General rule - as large as possible to increase the representativeness of the sample Increased size decreases sampling error Relatively small samples in qualitative, exploratory, case studies, experimental and quasi-experimental studies Descriptive studies need large samples; e. Type I and Type II errors Type I error Based on the statistical analysis of data, the researcher wrongly rejects a true null hypothesis; and therefore, accepts a false alternative hypothesis Probability of committing a type I error is controlled by the researcher with the level of significance, alpha. True In the real world, the actual situations is that the null hypothesis is: False Based on statistical analysis, the researcher concludes that:

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:A well chosen sample will contain most of the information about a particular population parameter but the relation between the sample and the population must be such as to allow true inferences to be made about a population from that sample.

Stratified sample: a researcher divides the population into groups based on characteristics, and then the researcher randomly selects from each group based on its size. Sampling Methods for Quantitative Research.

Sampling Methods. Sampling and types of sampling methods commonly used in quantitative research are discussed in the following module.

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. Non-probability population sampling method is useful for pilot studies, case studies, qualitative research, and for hypothesis development.

This sampling method is usually employed in studies that are not interested in the parameters of the entire population. Some researchers prefer this sampling technique because it is cheap, quick and easy. 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 . Non-probability population sampling method is useful for pilot studies, case studies, qualitative research, and for hypothesis development.

This sampling method is usually employed in studies that are not interested in the parameters of the entire population.

Some researchers prefer this sampling technique because it is cheap, quick and easy.

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Research Population - The Focus Group of a Scientific Query