An Examination of Means of Application and Core Function in Predicting Assignment

An Examination of Means of Application and Core Function in Predicting Crime - Assignment Example Using the example of the New York City Police Department, given in the prompt for this essay, one understands that the key inputs that the police department interfaces with in regards to building the outputs and feedbacks that are generated is made available through a weekly process of data input. In this way, personnel from New York City Police Department’s precincts, service areas, and transit districts compile a statistical summary or overview of the week’s crime data. In this way, crime patterns, arrests, complaints, and other verifiable and measurable data of significance are collated and entered into a database. This process represents the input phase, or data gathering phase that forms the very backbone of the process of CompStat. It is important to note that this process itself is the most important as it is the only stage in the process where human inputs can directly affect and oftentimes skew the results that are reported. For this reason, an individual depar tment has been set up within the Chief of Police’s office; labeled as the CompStat Unit. As such, the unit is directly responsible for winnowing, sorting, and inputting in the correct fashion, any and all pertinent information that could be of use with reference to developing a type of predictive policing to ultimately reduce the level of crime displayed within the city’s regions and precincts. ... For this reason, an individual department has been set up within the Chief of Police’s office; labeled as the CompStat Unit (Willis et al 2012). As such, the unit is directly responsible for winnowing, sorting, and inputting in the correct fashion, any and all pertinent information that could be of use with reference to developing a type of predictive policing to ultimately reduce the level of crime displayed within the city’s regions and precincts. Once the inputs have been entered into the system, the second phase of analysis then takes place; that of processing of the determinant data. This stage is reliant not upon the level of analysis that individual humans would otherwise perform; rather, it is entirely dependent upon the algorithms and likelihood matrixes that CompStat itself can infer from the inputs it has been given. As with the old cliche with regards to a computer program only being as good as the programmers that programmed the code, the same is true with relation to CompStat as it is but a tool and should not be seen as a perfect representation truth or overall infallibility. As has been stated with the input stage that has previously been discussed, the processing of the data and the outputs that it ultimately yield is solely reliant on the quality and voracity of the information that is loaded into the system. In other words, only a quality level of inputs will be processed and related into a quality level of outputs from which a degree of reasonable and actionable inference could foreseeably be drawn. The heart of the CompStat process therefore relies on each step of the process; however, for purposes of evaluation, the third step, or the outputs, is of perhaps the most interest. Within this step, a team of

Job Satisfaction Perceived Efficacy Correlation Dissertation

Job Satisfaction Perceived Efficacy Correlation - Dissertation Example Population and Sample The population for this study included all 139 South Dakota public school special education administrators, including full-time, part-time, and mixed responsibility special education administrators. The information used in the study was gathered from the South Dakota Department of Education 2010-2011 Educational Directory. Data Collection Data collection included the use of three separate surveys. Paul Spector’s Job Satisfaction Survey was used to collect job satisfaction data. Chen, Gully, and Eden’s New General Self-Efficacy Scale was used to collect perceived self-efficacy data. Finally, a researcher-developed demographic survey was utilized to collect demographic data. The survey was posted electronically and all 139 public school special education administrators in the state of South Dakota were invited to participate via email. Names and email addresses of special education administrators were gathered from the South Dakota Department of Educ ation 2010-2011 Educational Directory. A cover letter (see Appendix G) with instructions on how to access the electronic survey was sentto all public school special education administrators in the state of South Dakota, via email. ... The invitees completed 35 useable surveys, accounting for a study response rate of 25.2 percent. Demographics Selected demographic characteristics of the study respondents are presented in Table 4.1. The two largest groups of respondents were aged 36-50 and 50 and over, with each category making up 40 percent of the respondents, while those aged 35 or younger represented 20 percent of the respondents. The majority of respondents were female, representing 71.4 percent. It is important to note that all demographics were computed using the responses provided by respondents. Not all respondents completed all questions. Masters Degrees represented the highest level of education for 40 percent of respondents, which was the largest group. Those holding a Bachelors Degree accounted for 22.9 percent of the respondents. Both the Education Specialist and Doctorate degree each represented 20 percent of the respondents. The current certification question allowed respondents to check all that appl ied so that some respondents selected multiple current certifications. The largest number of respondents, 57.6 percent, responded that their current certification was Special Education Director. Pre-K-8 Principals accounted for 11.4, while 7-12 Secondary Principals comprised 6 percent of respondents. Similarly superintendents also accounted for 6 percent of respondents. One respondent selected Superintendent and Special Education Director, one respondent chose PK-12 Principal, Superintendent, and Special Education Director as his or her current level of certification, one respondent chose PK-8 Principal and Superintendent as his or her current level of certification, and one respondent selected PK-8 Principal, Superintendent and