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DQ1- Sampling theory has been known to assist the criminal justice system. The criminal justice system has worked diligently in the effort to reduce and mitigate criminal inclined individuals coupled with crime control programs. Sampling, in essence may specify the connections between certain samples used based on population break downs. Sampling methods are utilized in many studies and investigations by understanding important correlations related to the characteristics of a population. These types of research may focus on subcategories of a population due to the knowledge that the study will bring forth. The information gather will be used to understand subcultures and the type of occupants in the surrounding geographical areas. According to Ligita et al. (2019The findings can be used to initiate new observations by drawing statistical inference.
Titan Ligita, Nichole Harvey, Kristin Wicking, Intansari Nurjannah, & Karen Francis. (2019). A practical example of using theoretical sampling throughout a grounded theory study : A methodological paper. Qualitative Research Journal, 20(1), 116–126. https://doi-org.lopes.idm.oclc.org/10.1108/QRJ-07-…
DQ2- Sampling theory can be explained as a field of statistics that is involved with the collection, analysis and the interpretation of the data that has been gathered or being gathered. This theory is helpful when providing an outcome when testing a larger population or size of research; along with applying to random sampling as well. Sampling theory works well with law enforcement and criminal justice research as well. For example, this theory was used in a random sampling study that involved investigations of child abuse material in Washington DC. The Digital Forensic Research Conference (n.d.) has concluded the process of the added advantage of reducing staff exposure to child abuse material and providing the courts with an easily interpreted report.
Wilkinson, M., Jones, B., & Pleno, S. (n.d.). The Use of Random Sampling in Investigations Involving Child Abuse Material. DC, Washington: DIGITAL FORENSIC RESEARCH CONFERENCE. doi:https://dfrws.org/sites/default/files/session-file…
DQ3- Probability sampling may apply to random selections of a studied population. There are four concepts and types of probability samples which are simple random sampling; These sampling methods introduce equal chances of every member of the inhabitants alike as probability of being handpicked with a variation of random numbers may be prevelant. Systematic sampling is quite the same, however, every member of the population is tagged with a designated number or labeled. Stratified sampling is useful with inhabitants/population, research may come with variegation features. Therefore, every research design will anticipate every feature or attribute is comparable and equally constituted when divided into units. Cluster sampling will divide the inhabitants in units that have similar characteristics. It is the sampling often utilized by planning the complete population. In sum, many of this sampling procedures are a complete random method of selecting subject matters related to the topic which is being investigated and assessed (Ong, Lee & Low, 2020).
Ong, S.-H., Lee, W.-J., & Low, Y.-C. (2020). A general method of computing mixed Poisson probabilities by Monte Carlo sampling. Mathematics and Computers in Simulation, 170, 98–106. https://doi-org.lopes.idm.oclc.org/10.1016/j.matco…
DQ4- Hey class, The four types of probability sampling techniques or simple random sampling cluster sampling systematic sampling and stratified random sampling. With simple random sample ling it helps you to save time and resources where it allows you to obtain information from members chosen randomly out of that population where no one knows who will be paid until they are pitch in so that could keep it exciting because they are not picking out they are just turning over a cord and seeing who’s name is on it so if you have let’s say 700 people there or in your organization and your team leaders wants to conduct team building activities. It is going to be hard for them to just take people out of that 700 and so to make it easier drawing their names out of a bowl might be more convenient. And with cluster sampling the population is divided into sections or clusters in these clusters or sections are representing that population these clusters or sections can be based on Sex, location, age etc. and example of this would be let’s say you are trying to figure out how many old people Was in a certain job field and so in order to find out that information you may divide them into clusters based on states that way when you are conducting the survey it will be more affective and organized where you can see from what state oh people worked at a certain job how many. With Stratified random sample ling populations are divided into smaller groups that are not able to overlap and still be able to represent that large population where when they are put into the small groups they are organized and then they take samples from each group separately in order to get that information. stratified random sample ling populations are divided into smaller groups that are not able to overlap and still be able to represent that large population where when they are put into the small groups they are organized and then they take samples from each group separately in order to get that information. Where systematic random sampling There can be a list of every single person that is in that population and so they randomly selected At regular intervals having pre-defined range.
Etikan, I. (2017). Combination of Probability Random Sampling Method with Non-Probability Random Sampling Method (Sampling Versus Sampling Methods). Biometrics & Biostatistics International Journal, 5(6). DOI:10.15406/bbij.2017.05.00148