Sampling refers to a statistical process where part of a given data from a target population is selected and used as a representative of the whole population. There are various types of sampling processes including random, systematic, cluster and so forth. In regards to random sampling, each element of the target population bears equal likelihood of occurrence during selection. This is the most preferred method of sampling amongst all the other methods. Additionally, it requires minimal knowledge about the target population. Despite its preference over other sampling methods, it poses difficulty in carrying it out thus inefficient. This is attributed to the fact that it requires each and every element of the target population to be obtained. Therefore, the investigator may not capture certain information of interest. In addition to the various types of sampling, there are several key terms used in a sampling process. These terms are; analysis unit, population, sampling frame, sampling element, sampling unit, observation unit, sampling design, sampling error, sampling bias, parameters, and statistics (Shi, 2008).
Relative to analysis unit or sample, it is the object upon which an investigator uses to make inferences about the study. Analysis units include individuals, groups, institutions, events amongst many others. Next, population is the target complete set upon which an investigator draws the study results. The target population usually bears similarity in something. Sampling frame refers to the list of population from where the analysis units will be obtained. Further, sampling element is the analysis unit to be surveyed and is usually specified before the sampling process. On the other hand, sampling unit is the specific analysis unit to be considered for the survey. Likewise, observation unit is actual unit but from which data is to be collected. As for sampling design, it is a procedure/plan stipulated prior to collection of any data meant fro obtaining samples from the target population (Shi, 2008).
In making inferences using samples collected from a target population, errors are prone to occur. These errors are defined to as the sampling errors. Moreover, sampling errors are of two types; random and bias. Random errors seem to cancel out thus the outcome obtained is still accurate. On the contrary, sampling bias are serious, consistent and one sided hence do not cancel one another. On the final bit, parameter is the unknown value characterizing the target population whereas statistics is the value and estimator from the sample that gives information about the parameter (Shi, 2008).
As far as the research outcome is concerned, valid outcomes are dependent on the response rate. The response rate is determined by the number of sampling units say people who responded to the survey over the total of the target population. Most notably, a higher response rate means good and valid results representative of the target population and vice versa. This in turn indicates the return on investment about the survey. Preferably, high response is crucial and necessary. High response can be facilitated through the following. First is making a pre-contact with the respondents either by phoning, emailing, faxing, sending a letter or setting up an interview. Secondly design questionnaires with a simplified format, clear instructions and unambiguous questions. Another way is offering incentives such as gift/prices to respondents to enhance their correspondence. Furthermore, a proper method of implementing the survey and good timing is a necessity. Finally, above the pre-contact, follow up on the respondents is significant and relevant. This may work to remind and assist them in cases of any difficulties (Shi, 2008).
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