Sampling and Samples uk
A sample is a statistical representation of a larger or extensive population that has been defined through a particular procedure. Sampling is the procedure to select the sampled population. Sampling is a common practice in social sciences due to the diversity of the population under study. Social science is an area of study that focuses on people and their behavior in a particular society. Therefore, sampling offers the best method of collecting data from a large population to draw generalizable conclusions. In most cases, samples do not have a significant variation with the larger population. This paper aims at analyzing the major aspects of sampling as used in social sciences research.
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In many research scenarios, due to the diversity of the population characteristics, the researcher is bound to make an error. For instance, it would be impractical to assume that the mean height of trees in a forest is 25 meters when only a thousand out of one million trees have been sampled. Therefore, sampling error is defined as a variation between the sampled and the real characteristics of the population (Frankfort-Nachmias & Nachmias, 2007). Sampling error is a common occurrence in statistics for social sciences. The researcher must find a way to reduce the variation gap between the sample and the populaton. The researcher should evaluate all population characteristics and based on that choose to utilize the most effective sampling method.
Researchers use the data of a subset or a set to determine probability. The data analysis is valid, though the work may require complex probability calculations. The researchers base their confidence on the accuracy of the calculation to determine the effectiveness of the sample. The probabilistic models in social sciences usually reveal the error in the sample (Frankfort-Nachmias & Nachmias, 2007). Therefore, based on possible sampling errors the researchers can build confidence in their work through improvement. In addition, the researchers may repeat the sampling procedure across a number of samples. The use of larger number of samples in the population assures that the data analysis performed will determine the most valid and reliable sample.
Sampling is definitely a cheap and time saving technique of obtaining data from a population. Through mathematical calculations and repeat surveys, sampling can provide necessary data and build the confidence interval of the researcher. For instance, when a researcher seeks to estimate the average weight of persons in the U.S., he or she will need to do a repeat survey across all states. Information from onlyy one state may not justify the results and cannot be generalized to the population (Stopher, 1979). Similarly, if a researcher needs to find out the average height of high school students in the U.S, he/she must sample students from all high schools. The researcher would need to repeat the procedure on a number of students in a particular school, and collect data in all high schools. This kind of extensive sampling helps reduce statistical errors, and hence boost the confidence of the researcher.
In conclusion, sampling involves taking a subset of population to determine the average characteristics of the population. Sampling is a cost-effective and time-efficient method of research in social sciences. The use of mathematical calculations assists researchers in estimating the level of sampling error and a correction necessary to improve the research validity. The researchers can also overcome sampling errors by repeating the procedure of sampling across the target population. In addition, samples reflect the major characteristics of the population. Thus, sampling helps in boosting the confidence interval in population parameters. Therefore, the researcher must be knowledgeable in the field of mathematics and statistics to be able to estimate the level of error, and the possible changes to yield the valid and reliable results.
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