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The role of security officer/manager and law enforcement

The role of security officer/manager and law enforcement officer have the same main goal being crime prevention, but the reasons for preforming the duties, target “audience”, and delivery method are quite different as we see on figure 7.1 in Private Security and Law by Charles Nemeth. First is the reason for crime prevention. Security companies need profits from clients to operate and provide crime prevention services when paid by a specific client for a specific job. Law enforcement agencies provide crime prevention services on the tax payers dollar and preform this task in order to keep the peace in the general area of their jurisdiction not just a specific client in a focused area. The target audience for security companies is whoever has them on their payroll and in contrast law enforcement agencies provide this to all citizens within their area of operation. When examining table 7.1 and 7.2 in Private Security and Law we also see that the importance of each task ranked by importance to each type of officer. In table 7.2 for the law enforcement officer we see that the arrest and prosecution of criminals is much higher on the list when compared to privatized security officers. A security officer will stay at his post and prepare for a known criminal to try to breach his layers of security and try to prevent this. A law enforcement officer may do the same at public events, but they will also go out into their communities and actively search for these criminals in a general area.
A security officers actions may not only effect himself but also the company he works for as made evident on page 156 of Private Security and Law. An employer will be held liable for any actions made by their employee. The videos we watched in week one come to mind again with security officers pulling out weapons unprovoked and then immediately being held responsible for their negligence. If someone had died the officer, security company, and business hiring the security company could be held liable.
We also see in Private Security and Law on page 233 that officers who restrain or detain an individual without a proper reason “…may be criminally liable for false imprisonment.” If a security officer impedes on a citizens rights knowing they haven’t actually done anything wrong or are they are the first aggressor in a violent act they will be held liable for the outcomes of those situations and put the companies employing them in a compromising position.

Sample Solution

ductor plays a major role in the determination of a sample size when there are items readily available or convenient to collect. However, there are more scientific ways to estimate a sample size. For instance, the experimenter could use a target for the power of the statistical test to be applied once the same is collected or he/she could use a confidence level which determines how accurate a result will turn out with lower chances of error. The requirement of a good sample is that the estimation should be based on these scientific forms and the means. One of those ways is using the standard error of the sample mean, that achievement with the corresponding formula: σ/√n , where n is the sample size and the σ2 the corresponding variance In addition to this, we express the 95% of the confidence interval with the form: (x̅-2σ/√n, x̅+2σ/√n), where x̅ is the sample mean with a Normal Distribution and defined using the Central Limit Theorem. Therefore, if we wish to have a confidence interval that is W units in width, we should calculate: n = 16σ2/W2. This means that the smaller in range we need the interval to be then the bigger the size of the sample. For example, if we are interested in estimating the amount by which a drug lowers a subject’s blood pressure with a confidence interval that is ten units wide, and we know that the standard deviation of blood pressure in the population is 20, then the required sample size is 64, [see 4, “Sample Size Determination”, para. 7]. A special case of the means is the estimation of a proportion. The estimator of a proportion is p̂=X/n, where X is the number of ‘positive’ observations. The correspondent formula for this case is: (p̂-2√(0.25/n), p̂+2√(0.25/n)) for the confidence interval. Consequently, if we wish to have a confidence interval that is W units in width, we should calculate: n = 4/W2 = 1/B2, where B is the error bound on the estimate. For instance, for B=10% then the required sample size is 100. This special case is often used for opinion polls, [see 4, “Sample Size Determination”, para. 6]. Source of errors There are many potential types of errors in survey sampling. According to Groves (1989)[see 1], the survey errors can be divided into two major groups: First, the errors of nonobservation where the sampled elements use only part of the target population, and the second one is the errors of observation, where the listed data deviate from the truth. Some examples of errors of nonobservation can be ascribed to sampling, coverage or nonresponse which is going to be analysed in the later part of this report. On the other hand, examples of errors of observation can be attributed to the interviewer, respondent or method of data collection. Both of our sources of obdurate errors can vigorously affect the accuracy of a survey. However, these errors cannot be eliminated from a survey but their effects can be reduced by caref

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