Sampling and Date Collection

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A sample is drawn from a population, which refers to all possible cases of what the researcher is interested in studying. A sample consists of one or two elements selected from a population. To select a good sample we have to clearly define the population from which we will be drawing a sample. Failure to do so can result in inaccurate conclusions. In probability sampling each element has an equal chance of inclusion. The simplest technique for probability samples is Simple Random Sampling. Simple random sampling treats the target population as a unitary whole. We might start off with a sampling frame which has a list of the whole population—or as much information as we can obtain. Next we would number the elements in the sampling frame sequentially and select elements from the list randomly. We would not know what the outcome would be, but each has an equal shot at being chosen. We can also program the computer to choose a random sample. Researchers may also use non probability sampling. In this type of sampling the researcher does not know the probability of each population’s elements inclusion in the sample (Dejong, 2011).

Data Collection
There are many different ways to collect data for research. Measures differ from one another in terms called levels of measurement. Nominal measures classify observations into mutually exclusive and exhaustive categories. They represent nominal variables such as sex, ethnicity, religion, and political party preferences. For the purpose of data collection a number might be assigned to each category. Ordinal measures are at a higher value then nominal numbers because categories have a fixed order. Interval measures share the characteristics of ordinal scales, mutually exclusive and exhaustive categories and an inherit order, but have equal spacing between the categories.…...

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