Avoiding Data Manipulation when Sampling - Essay Example

Avoiding Data Manipulation When Sampling One of the many s discussed that caught my attention was the sampling method as a recognized research and data collection technique. To be more specific, what I found most amazing was not the definition of sampling, but rather the actuality of sample manipulation to foster a desired/planned result. Sampling seems to me to be an extraordinary means in which to gather data, but how can I ensure that data gathered is not subject to manipulation? Through this brief essay I will attempt to identify a few key ideas in which can be put in place to help ensure the data collected during a sample is unbiased and free of manipulation.

One of the important features in avoiding data manipulation when sampling is to be aware of the imperfections in the survey. There are an infinite number of means by which sampling exhibits imperfect features, and indeed, the idea of a perfect sample is a misnomer. Edwards (2010) indicates that even the color of a survey can alter sampling response. There are a variety of specific means by which researchers and statisticians seek to avoid these data manipulations in sampling. An overarching approach is to ensure that the survey method is designed in as clear and unbiased as possible.

In these regards, Edwards (2010, pg. 26-27) identifies a number of key features to avoid, “failure of the questionnaire.failure to recognize differences between various kinds and degrees of canvass and to fit the questions to the type of canvass.failure to define the universe with enough precision and to provide a satisfactory frame.” Adhering to these methods greatly aids the research avoid data manipulation. In addition to these areas that must be considered when constructing sampling data, there are features that must be considered to avoid sampling bias.

As a means of avoiding data manipulation in sampling its generally recommended that researchers implement random sampling as an overarching model. A random sample consists of a random sampling of survey participants and can be conducted through a number of means. Among these involve a simple random sample, stratified sampling, and cluster sampling, with their distinguishing features based on the means by which they select the sample group (Thompson 2002). When examining sample data from the random sample its important to consider the possible statistical error that can be included, as such errors can be utilized in data manipulation when in actuality they are necessary elements of the research.

In these regards, human failure must be avoided as it oftentimes is one of the prevailing elements that results in sample bias and data manipulation. In these regards, its been indicated that researchers must guard against, “getting into the wrong areas, going beyond bounds, partially or wholly omitting an occasional sample area or household that prevents difficulties” (Thompson 2002, pg. 110). Within this paradigm, humans will oftentimes give preferential treatment to specific areas or individuals, as a result its necessary to become aware of these biases and implement systems of checks and balances to guard against them.

In conclusion, its clear that are a number of elements that must be considered to avoid data manipulation when sampling. For instance, one must be aware of the survey instrument itself, as the varying elements relating to it can greatly effect the data. In these regards, a number of features relating to ensuring valid sampling are articulated. In addition to these methods there exists a number of elements researchers must be aware of to avoid sample bias. The overarching element is that of random sampling, which ensures that the same group is not subject to researcher preference or bias.

One must also guard against human error and preference. References Edwards, William. (2010). Some Theory of Sampling. Massachusetts: Dover. Thompson, Steven. (2002). Sampling. New York: Wiley-Interscience.