The Organizational Assessment Study leader should implement sampling strategies which Iwamoto, Crews, & Coe Company could use to collect the data they require. The data sampling strategies include the following:
Probability Based Sampling Strategies
Simple Random Sampling Methods
In simple random sampling techniques, each component of the population is given an equal opportunity or probability of being selected into the sample. There are several methods to carry out a simple random sampling. One of them is by balloting which entails using pieces of paper to carry out a voting exercise or a selection process. Another method of simple random sampling is the use of lottery. This entails using numbered balls, simulation exercise or IT technologies for example in the sweepstake lottery. The third method of simple random sampling is by using the random numbers design. This is not a deterministic procedure and it is a computer or a calculator that is used to operate the random numbers (Neuman, 2011).
Complex Random Sampling Methods
Before establishing which sampling strategy would be the most efficient to use, the researcher should determine which form of study is being carried out. There are usually two types of studies which include population and process studies. In the population study, the researcher is concerned with approximating or illustrating a feature of the population in regards to inferential statistics. In the process study, the researcher is concerned with forecasting a process feature or transformation over a certain period (Neuman, 2011).
Random Sampling
The random sampling technique is utilized when the population components are identical to each other on essential variables such as in historical or group data. The advantage of using the random sampling technique is that it guarantees a high scale of representation and every element in the population has an equivalent probability of being chosen in the sample. The disadvantage of using random sampling technique is that it is time consuming and tiresome (Kiess & Green, 2010).
Stratified Random Sampling
The stratified random sampling technique is utilized when the population is assorted and includes various diverse and independent sample collections from every group. The advantage of using the stratified sampling technique is that it guarantees a high level of representation of all the strata in the population. The disadvantage of using the stratified sampling technique is that it time consuming and tiresome (Struwig & Stead, 2001).
Systematic Sampling
The systematic sampling technique is used when data is collected in actual instants during frequency procedures and when components of the population are identical to each other on significant variables. The advantage of using the systematic sampling technique is that it guarantees a high level of representation and hence it is not necessary to use a chart of random figures. The disadvantage of using systematic sampling technique is that it is prone to bias and simple random sampling methods are better to use than the systematic sampling (Neuman, 2011).
Cluster Sampling
The cluster sampling technique is used when the population is comprised of units instead of people. The advantage of using the cluster sampling technique is that it is straightforward and convenient. The disadvantage of using the cluster sampling technique is that there is a high probability that the constituents of the units are dissimilar to each other which will diminish the efficiency of the method (Kiess & Green, 2010).
Non Probability Based Sampling Strategies
Convenience or Accidental Sampling
The convenience or accidental sampling technique includes any component which is conveniently found and recruited into the sample. The advantage of using the accidental sampling technique is that it is convenient and economical. The disadvantage of using the convenience sampling technique is that the extent of generalization makes it uncertain to utilize (Aron & Coups, 2011).
Purposive Sampling
The purposive sampling technique includes methods like judgment which entails the use of experience or some criteria. The advantage of using the purposive sampling technique is that there is an extensive variety of qualitative research methods that researchers can present on. The disadvantage of using the purposive sampling technique is that it is highly susceptible to bias from the researcher and it is hard to guarantee people that the method utilized to decide on the units of study was suitable (Aron & Coups, 2011).
Quota Sampling
The quota sampling technique takes care of the representation of characteristics when there are layers and stratified sampling is not achievable. The advantage of using the quota sampling technique is that it guarantees an extent of representation of all the strata in the population. The disadvantage of using the quota sampling technique is that the extent of generalization is uncertain (Aron & Coups, 2011).
Snowball Sampling
The snowball sampling technique is applicable in cases where the subjects or members are difficult to identify or sample for example drug users and gangs. To carry it out, one requires a reference to increase their numbers. The advantage of using the snowball sampling technique is that the design is inexpensive, straightforward and requires less preparation and fewer personnel. The disadvantages of using the snowball sampling technique is that it is susceptible to bias, the researcher has less control over the sampling design, and there is no guarantee on the representation of the method (Struwig & Stead, 2001).
References
Aron, A., & Coups, E. J. (2011). Statistics for the behavioral & social sciences: A brief course. 5th ed. Upper Saddle River, NJ: Prentice Hall.
Kiess, H. O., & Green, B. A. (2010). Statistical concepts for the behavioral sciences. 4th ed. Boston, MA: Allyn and Bacon.
Neuman, W. L. (2011). Social research methods: Qualitative and quantitative approaches. 7th ed. Boston, MA: Allyn and Bacon.
Struwig, F. W., & Stead, G. B. (2001). Planning, designing, and reporting research. Cape Town: Pearson Education South Africa.