Type Sampling approaches in research 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.

Where voting is not compulsory, there is no way to identify which people will actually vote at a forthcoming election in advance of the election. Heterogeneity Sampling We sample for heterogeneity when we want to include all opinions or views, and we aren't concerned about representing these views proportionately.

Probability methods include random sampling, systematic sampling, and stratified sampling. What if religion or ethnicity is an important discriminator? Every element has a known nonzero probability of being sampled and involves random selection at some point.

Similar considerations arise when taking repeated measurements of some physical characteristic such as the electrical conductivity of copper. Hence, because the selection of elements is nonrandom, nonprobability sampling does not allow the estimation of sampling errors.

Simple random sampling A visual representation of selecting a simple random sample In a simple random sample SRS of a given size, all such subsets of the frame are given an equal probability.

Although the population of interest often consists of physical objects, sometimes we need to sample over time, space, or some combination of these dimensions. Descriptive statistics were used to describe admission patterns.

One option is to use the auxiliary variable as a basis for stratification, as discussed above. Finally, in some cases such as designs with a large number of strata, or those with a specified minimum sample size per groupstratified sampling can potentially require a larger sample than would other methods although in most cases, the required sample size would be no larger than would be required for simple random sampling.

In your textbook, the two types of non-probability samples listed above are called "sampling disasters. Randomization occurs when all members of the sampling frame have an equal opportunity of being selected for the study.

Because there is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample or subset of that population. Advantages over other sampling methods Focuses on important subpopulations and ignores irrelevant ones.

For example, a manufacturer needs to decide whether a batch of material from production is of high enough quality to be released to the customer, or should be sentenced for scrap or rework due to poor quality.

Multistage sampling involves selecting a sample in two or more successive stages. Non-probability Sampling The following sampling methods that are listed in your text are types of non-probability sampling that should be avoided: Factors commonly influencing the choice between these designs include: Instead, you simply want to have enough to assure that you will be able to talk about even small groups in the population.Patton, M.

(). Qualitative evaluation and research methods (pp. ).

Beverly Hills, CA: Sage. Designing Qualitative Studies PURPOSEFUL SAMPLING. Even if a stratified sampling approach does not lead to increased statistical efficiency, such a tactic will not result in less efficiency than would simple random sampling, provided that each stratum is proportional to the group's size in the population.

Research Methods for Everyday Life.

Research Methods for Everyday Life is a fresh and engagingintroduction to the process of social research and the variety ofresearch methods, highlighting quantitative and qualitative methodsand how to combine them.

Sampling Methods in Research Sampling is that part of statistical practice concerned with the selection of an unbiased or random subset of individual observations within a population of individuals intended to yield some knowledge about the population of concern, especially for the purposes of making predictions based on statistical inference.

The difference between nonprobability and probability sampling is that nonprobability sampling does not involve random selection and probability sampling does.

Does that mean that nonprobability samples aren't representative of the population?

Not necessarily. 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. Probability methods. This is the best overall group of methods to use as you can subsequently use the most powerful statistical analyses on the results.

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