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Population is the entire group of people, objects or events which one wants to research. However, it not always feasible or possible to study all member of a population. Therefore, it is more effective to study a subset of the population. A sample is a subset of a group of people, objects or events from a larger population that one collects and study to make a conclusion. To ensure that the population is adequately represented the sample data must be random and must include a large amount of the population being study. An example, one could count the number of children with strawberry birthmarks in a random sample then use a hypothesis test to estimate the percentage of all children with strawberry birthmarks.

There are a number of different methods of random sampling. These include simple, stratified, cluster, systemic, and multi-stage sampling. The first type of random sampling is called simple sampling Simple sampling is the most basic of the random sampling. With simple sampling everyone has an equal chance of being part of the sample. The 2nd method of random sampling is stratified sampling. Stratified sampling is when the population is divided into subgroups that do not overlapped. Theses non-overlapping subgroup called strata. Simple random samples are chosen from each stratum. Strata are group that are similar due to some of the characteristic of the group members. The next method of random sampling is cluster sampling. With cluster sampling the populate is divided I into strata called cluster. Clusters are randomly select and them all the individuals in the cluster are contained in the sample. An example of cluster sampling would be to select a sample of student to answer a survey on the policy of late assignment. One way to accomplish this would be to randomly select 4 classes during the winter session. Surveying all the student in the classes would be cluster sampling.

The next method of random sampling is systematic sample. Systematic sample selects a random starting point from the population. Then a sample is taken from a regular set interval of the population. An example would be a local Head Start program wants to form a systematic sample of 400 volunteers from a population of 4000. By selecting of every 10th person in the population would be a systematic sample. The final of random sampling is Multi-stage sampling. Multi-stage sampling can include of different stages of one kind of sampling. It can however it can be a combination of sampling methods.

The non-random sample are convenience sampling and volunteer sampling. Convenience sample is using research that is already available. An example would be the researcher asking people at the mall taking a poll. Volunteer sample is based on individual’s decision to volunteer or not.

The next top this paper will explore is variables and measurement. Variables is what you want to study. Anything can be considered as a variable. A simple way to look at variable is by their qualitative or quantitative properties. Qualitative data is the conclusion of describing or classifying qualities of a population that is neither measure nor counted. Whereas, quantitative data is conclusion base on counting or measuring qualities of a population.

The two main variable in a research is independent and dependent. An independent variable is the variable that is controlled or changes in research to test the properties of the dependent variable. The dependent variable is the one being tested a measure in the research. In other words, the dependent relies on the independent variable.

The different kinds of variables are measure in different ways. There are four scales of measurement. These four scales of measurement are: nominal scales, ordinal scales, interval scales and ratio scales. Nominal scales have no quantitative value and are used for labeling variables. Ordinal scales the important thing is the order of the values. However, the difference between each value is not known. Interval scales are numeric both the order and the exact different between the values are known. Ratio scales tell the order, the exact value between the elements and they have an absolute zero.

References:

Carruthers, M. W., Maggard, M. (2019). Smart Lab: A Statistics Primer. San Diego, CA: Bridgepoint Education, Inc.