Purpose of Cluster Allocation and How It Functions - Research Paper Example

Task: Cluster Allocation Purpose of cluster allocation Cluster allocation is a procedure of selecting certain individuals orelements from the entire subject of study. The procedure randomizes social units or groups of units instead of units themselves. This procedure defines the particular group of subjects that are to be used in the analysis. The scheme treats the cluster as the sampling unit and conducts an analysis on the population of clusters. Consequently, the procedure reduces the cost of examination by increasing sampling efficiency. Clusters include geographical area and often the examiner treats various respondents or subjects within a local area as a cluster (Atzeni 40). Furthermore, the examiner increases the total sample size to establish equivalent accuracy in the estimators. The findings of the observation of any of the selected sample may not present an accurate highlight of the whole population, but they are mainly close to the actual behavior of the study subject. How cluster allocation functions The model is a sampling technique utilized when “natural” but uniform groupings are evident in a statistical population. In cluster allocation, the researcher assumes various steps in defining the sample population or constituents instead of selecting all subjects from the whole population. The examiner divides the entire population into various clusters from which he or she selects a random sample of groups (Karuri and Rainer 30). Consequently, the examiner gathers essential information from the random sample of elements in each selected group. One may evaluate every element in the selected groups or may select subsamples of fundamentals from each group. The procedure is motivated by the need of reducing the aggregate cost of the analysis. The scheme demands elements within a group to be heterogeneous while presenting homogeneity between group means. Furthermore, each cluster should be a subunit of the entire population. Clusters should also be mutually restricted and jointly exhaustive. This enhances systematic examination while minimizing sampling errors (Atzeni 37). The analyzer may utilize a single-stage cluster approach or two-stage cluster model in his or her analysis. In the single-stage scheme, one uses all elements from each selected group. However, in the two-stage cluster model, one conducts random sampling on the elements from each of the selected group. Often, cluster allocation is only applicable when groups are approximately of the same size. In situations where the clusters have varying sizes, the examiner may combine clusters to make them assume relatively similar sizes (Karuri and Rainer 32). Usefulness of cluster allocation Cluster allocation is useful in reducing the amount of funds used in the examinations. The cluster allocation procedure provides the examiner with the opportunity of concentrating resources on the few randomly selected groups instead of evaluating the entire population. This makes the examination procedure less costly, simple and fast. Particularly, the model reduces traveling and listing cost, which are the major finance consuming procedures in sampling. For example, compiling statistics about each household in a city would be challenging, while compiling statistics about various blocks of the city would be easier. In such a situation, the traveling and the listing efforts will be reduced considerably (Karuri and Rainer 53). The procedure is essentially useful in minimizing the potentially large estimation errors in diversification analysis (Geotzmann & Wachter 271). The procedure applies the concept of mean-variance in examining essential elements. The mean-variance model evaluates a set of subjects’ weights across assets, which establishes the highest probable return for each specific level of investor risk. Developing target groups enhance the accuracy of the procedure because one can conduct a detailed examination. Furthermore, the model provides an effective procedure of evaluating large populations (Geotzmann & Wachter 272). Often, large volumes of study elements may make applying other allocation procedures become problematic. Consequently, the cluster allocation approach is effective in situations where the examiner has difficulties in compiling an exhaustive list of elements that constitute the target population. This is because the model groups population elements into subpopulations providing the examiner with the opportunity of creating a list of essential indicators (Karuri and Rainer 71).The scheme is also effective in controlling “contaminations” across study subjects. Clusters act as independent units of examinations. Consequently, an evaluation error or bias experienced in a particular set of data may fail to be transferred to other sets. This increases the precision of an evaluation while enhancing chances of realizing an error that may be present within the examination. How cluster allocation differs with traditional portfolio allocation schemes Cluster allocation procedure is more effective than traditional systems for allocation. For example, cluster sampling mainly enrolls a larger sample size than other allocation procedures. The researcher has the opportunity of selecting more subjects that are readily available because he or she needs only to select a sample from a number of groups (Karuri and Rainer 17). However, the procedure uses a sample with least representatives of the subjects. Furthermore, the procedure may have an overrepresented or underrepresented sample because individuals within a cluster are likely to have similar characteristics. This may alter the results of an assessment leading to misguided deductions. The procedure also has high chances of establishing high sampling error. This emanates from the limited groups included in the sample, which excludes a considerable proportion of the population (Atzeni 19). Traditional portfolio allocation systems demand sampling frames of all the sampling units but in cluster allocation, analysis can be conducted without setting sampling frames. This is because the analyzer compiles the selected clusters into frames. Consequently, the evaluator conducts various statistical analyses from these frames and draws conclusions out of them. This enables the examiner evade the tedious procedure of setting sampling frames for all study units (Atzeni 24). Cluster allocation procedure differs significantly from traditional schemes such as stratified allocation. The main difference between these two models of allocation lies with the inclusion of the strata or cluster. The cluster allocation model treats group as a sampling unit while the stratified scheme analyses elements within the strata. The stratified technique divides a sample into stratums randomly (Karuri and Rainer 17). The model then creates different various stratums that allow the utilization of unlike sampling percentage of each stratum. These stratums are mainly simple clusters that comprise of various elements; however, each element is assigned single stratum. This approach mainly creates a weighted mean that has variability that is fewer than that of the arithmetic mean that is not the case in the cluster allocation model. Works Cited Atzeni, Paolo. Conceptual Modeling - Er 2004: 23rd International Conference on Conceptual Modeling, Shanghai, China, November 8-12, 2004: Proceedings. Berlin: Springer Link, 2004. Print. Geotzmann, William. & Wachter, Susan. Clustering Methods for Real Estate Solutions. Real Estate Economics, 1995 (23), 271-310 Karuri, Kingshuk, and Rainer Leupers. Application Analysis Tools for Asip Design: Application Profiling and Instruction-Set Customization. New York: Springer, 2011. Print Read More