Sampling techniques for quantitative research
Finally, the researcher selects subjects from the various subgroups while taking into consideration the proportions noted in the previous step.
This process is represented in Figure 7. Random or systematic samples of a predetermined size will then have to be obtained from each group stratum.
In this case, your selection interval, or k, is 4. Researchers use convenience sampling not just because it is easy to use, but because it also has other research advantages.
Probability sampling includes simple random sampling, systematic sampling, stratified sampling, cluster sampling and disproportional sampling The advantage of probability sampling is that sampling error can be calculated.
In systematic random sampling, the researcher first randomly picks the first item or subject from the population.
Convenience sampling in quantitative research
The researcher must be certain that the chosen constant interval between subjects do not reflect a certain pattern of traits present in the population. For each village select 10 households. This is the type of sampling that is used in lotteries and raffles. It can also be used to estimate the population parameters since it is representative of the entire population. There are several possible sources for obtaining a random number table. For example: Random selection of 20 students from class of 50 student. Types of Stratified Sampling A-Proportionate Stratified Random Sampling The sample size of each stratum in this technique is proportionate to the population size of the stratum when viewed against the entire population. In such cases, it is also necessary for the researcher to use this type of sampling technique.While there are certainly instances when quantitative researchers rely on nonprobability samples e. The researcher selects the sample based on judgment. In non-probability sampling, the degree to which the sample differs from the population remains unknown Convenience Sampling In all forms of research, it would be ideal to test the entire population, but in most cases, the population is just too large that it is impossible to include every individual.
