Assignment Five: Data Analysis<\/p>\n
Name<\/p>\n
Institutional affiliation<\/p>\n
Course<\/p>\n
Instructor<\/p>\n
Date<\/p>\n
Introduction<\/p>\n
Data analysis ensures that information is generated from various data sets that provides insight on the topics of choice. In this case, the process of using the smart investment decisions required at private and public level analyzing insights requires intermediate analysis. Additionally, the processes allow for individuals to audit performance and inform communities on the progress made after specific interventions are made. The essay provides information on the specific community needs serving data analysis, gives a summary of the gap analysis for students in specific hard-to-reach demographics and recommendations on how to sustain performance in a competitive education sector.\u00a0<\/p>\n
Community Need<\/p>\n
Community needs define what services are offered by the government and those that do not exist in the local area (Bhise, Thorat, & Supekar, 2013). While some are perceived and others considered absolute, there are many needs within communities that are expressed to various agencies and nonprofit organizations that provide support to groups. In addition to establishing assessments, it is important for the larger community in the proposed area in Riverbend City, Illinois, to support students looking to improve their lives.\u00a0<\/p>\n
The second community need is to encourage parents to support children flexibly and ensure that they present a positive attitude towards education, since there is little assistance for this cause (Linneberg,& Korsgaard, 2019). Facilitating a proper learning climate for all is the third priority the community aspires to reach, to avoid closing out any children from minority groups from attending school from their formative years, to the more challenging high school ones.\u00a0<\/p>\n
Community involvement for a capable workforce is the final community mapped out in the data analysis and entails the identification of fewer specialized professionals in the city, where the number of young adults engaged in illegal activity is steadily increasing (Ibrahima,, Hassanb, & Usman, 2022). There is a need for prevention and rehabilitation strategies to be employed in the process of injecting new purpose to the community.\u00a0<\/p>\n
Data Choices for Analysis<\/p>\n
Since the data analysts are armed with the right tools to develop insight, the informed decisions on the school-going kids under review will be based on the best approaches that can improve decision-making on what the institutions they attend need to do to improve retention, performance, and access to tertiary institutions and make the industry better across the country. The three main forms of data analysis will be used to evaluate qualitative data provided, while quantitative analysis will be provided through comparisons and graphical presentations on Microsoft Excel. Content analysis will examine the completeness and quality of responses provided by students interviewed while discourse analysis will examine the relevance of responses to the research carried out (Weigand, Beermann, \u010ciampor, Costa, Csabai, Duarte, & Ekrem, 2019). Therefore, additional hypothesis analysis will give an honest examination of whether the specific analysis is true while content analysis carried out will compare the old data results provided in similar studies to the current data analysis process.\u00a0<\/p>\n
Gap Analysis\u00a0\u00a0<\/p>\n
Law enforcement agencies are responsible for putting away deviant youth engaging in crime.<\/p>\n
Proposed Questions for Review\u00a0<\/p>\n
What was the previous best approach to keep kids in school?<\/p>\n
The main purpose of this question is to determine why the data analysis is being carried out in the first place. Previous approaches have been used to evaluate the effectiveness of previous campaigns to resolve learning related issues in the community. Specifically, recidivism in schools remains a major problem, with more guidance required on long-term support to students trying to earn diplomas. Data analysis needs to determine the reasons why some students fall back on poor performance related to dropping out, crime, and other vices.\u00a0<\/p>\n
What are the major success drivers?<\/p>\n
This question is ideal in determining the classes and types of data required for the data analytical process to take place. The results of schools witnessing increased enrollment and less recidivism is the best possible outcome based on the data analysis process. The main objectives of the data analysis include ensuring that students are better engaged and exhibit intentions to graduate, regardless of their financial backgrounds.<\/p>\n
Hypothesis: If the institution implements the holistic Second Chance RBC program, then the most likely outcome is increased student retention in schools.\u00a0<\/p>\n
The main purpose of this question is to determine the data quality criteria that can shape future practices in schools. If the institutions are seen to better implement initiatives that will lead to donors supporting students\u2019 ambitions, then the probability of students graduating increases and they therefore follow the right paths towards better careers in the future.\u00a0\u00a0<\/p>\n
Research Methods Section<\/p>\n
There are infinite sources of data and uses that the data analysis can be employed in this case. The major purposes of selecting the right method to collect data is to help and protect information against fraudulent activity, inform the best choices for the respondents selected,\u00a0<\/p>\n
Data Collection Instruments<\/p>\n
Some of the instruments that will be used include primary and secondary data collection tools, which come from direct observations, surveys, and peer-reviewed documents.\u00a0 Each of these tools feed unique insights into the results model that will provide decision-makers with the right information about students.\u00a0<\/p>\n
Primary Data Sources<\/p>\n
Observation<\/p>\n
One of the key activities that will take place in the data collection process is collection through observation. For example, it would be ideal for the data collectors to take a monthly set of data from the schools in Riverbend, where the number of students in the Latin-speaking, white, and black categories will be used to compute daily attendance for classes.\u00a0 The findings will show the significance of the research in determining the rate of class participation as well, with an emphasis on the achievements shown in the program. Additionally, the categorization of kids with disciplinary issues, those near graduation, those attending alternative schools will all be verifiable variables that can be confirmed. Therefore, observation will play a crucial role in determining the data authenticity for primary sources.\u00a0<\/p>\n
Surveys<\/p>\n
Surveys will also be used to ensure that the numerous respondents who are not contacted during class sessions obtain the opportunity to share their feedback and opinions regarding the program. Since the second chance program has been running for a significant period of time, its contribution to the performance of children in the community will inform the data collectors on the probability of the scheme continuing to support the community. Therefore, details regarding long term student endorsement for younger counterparts in need of support.\u00a0<\/p>\n
Secondary Data<\/p>\n
There are numerous contributors to the studies on the student second chance program and other similar initiatives. They shared their takes on various aspects of data collection and analysis in learning institutions, through their publications online and in libraries across the globe (Grimm, Ramos, Maloney, Abresch, Tibbits, Lyons, & Palm, 2021). Since the rate of crime only changes each year but remains prevalent everywhere, it is simpler to review findings to ensure that data can be compared with that from different regions. Additionally, this information can support future studies that can be used to explore the community support endeavors by both government and private sector players.\u00a0<\/p>\n
Peer reviewed articles and journals are two important sources of information on data analysis that will be used in this section. Journals provide a reliable source of time-series data that will be ideal in gauging answers to questions that were asked in previous studies. Articles will have the static results from older studies ranging from 2018 onwards, in a bid to ensure that recent trends are examined and recorded appropriately to feed the massive information pool required for decision-making. When compared to peer-reviewed articles, it is important to note that the completeness of data varies from one group of data analysts to another. Therefore, the scope of the study shall not be fully covered.\u00a0<\/p>\n
Ethical Concerns<\/p>\n
There were numerous concerns that arose in each step of data collection and processing that came to the attention of the analysts. For instance, during the collection phase, it was unclear whether the students being interviewed understood their participation in the study would be used to gather vital data for comparisons (Grimm, Ramos, Maloney, Abresch, Tibbits, Lyons, & Palm, 2021). Additionally, the data was also going to be quite personal and not many would be pleased with their details being shared with the public later on. Therefore, young respondents were quick to either agree or disagree with participating in the activities of the research due to these reasons.\u00a0<\/p>\n
The second issue that arose was the treatment of detected omissions which were seen in the tabulation process. In some cases, this was seen to influence the rate of errors in the collection process (Linneberg, & Korsgaard, 2019).\u00a0 The accuracy and consistent nature of the data also came into question due to the different data collectors who experienced challenges engaging learners to participate in the study. It is recommended that a standard approach is always used to carry out studies for higher levels of completeness so that classification is simpler. The challenge was not completely addressed during the study but additional work was carried out during classification into homogenous groups for better interpretation. Moreover, the tabulation phase was also subject to ethical concerns regarding the best and most impartial way to present statistical data, for better understanding. Therefore, the challenge was to ensure that the categories were selected in the best possible way without guiding readers to specific conclusions regarding recidivism.\u00a0<\/p>\n
Personal identity issued by students needed to be stored safely to protect respondents. The main challenge wit this was to do with the numerous data handlers who were vulnerable to data loss through online tampering and unintended physical exposure of respondent\u2019s details. The use of survey-based study and data collection strategy created the issue of the respondent’s confidentiality and need for anonymity. In addition to the promise of participant confidentiality, the survey employed also confirmed the special treatment of the data and use of the information to specifically meet the study objectives. This was the same as the informed consent requirement where the participants were expected to accept to participate and give personal information while being engaged in the study. Acknowledging participating in the study proved that the respondent had a full understanding of the study goals, deployment, and best way to achieve mutual benefit.\u00a0<\/p>\n
The final ethical consideration was the issue of identifying the survey objectives and drives to engage the public. With the assumption that different survey results have varied implications and use for the engagement, the participants should be made aware of the three critical ethical areas of consideration. These include but are not limited to the purpose, sponsorship, and content of the study. The identification of critical ethical considerations and promise of withholding these issues increases the participant’s trust, while any assurance by the engaging party must be respected and upheld to achieve mutual benefit. This should also be the same in the case of study limitations and possible side effects that can impact the participant or the community. With the expectation of minimal or no negative impact on the engagement, the study should also strive to positively impact the participants and promote wellbeing.\u00a0<\/p>\n
Methods of Data Collection<\/p>\n
The methods used included surveys, door-to-door interviews during the weekend. Additionally, the use of mobile phones by an overwhelming majority simplified the process of determining the best approaches to securing data and protecting information shared by respondents. The reason for this was its ability to provide respondents with the opportunity to provide consent over their information being used. The mobile phone platforms also provided a feeling of anonymity which was important for some young respondents offering sensitive details regarding their pasts and the educational experiences they preferred. When used, the surveys granted real-time data collection that was appropriate for saving information on the program and its actual contributions to the community. Therefore, this was considered the most convenient data collection tool during the entire process.\u00a0<\/p>\n
Social Media Polls<\/p>\n
Social media polls that were used to determine the sentiments of students were quick, free, and based on the most preferred platforms used by students in the city (Phadermrod, Crowder,\u00a0 & Wills, 2019). The platforms included Facebook, Twitter, Instagram and TikTok. Here, simple alternatives to set questions were used to obtain insight into the effectiveness of the program in the country.\u00a0<\/p>\n
Door-to-Door Interviews<\/p>\n
These were carried out in areas where respondents were few, due to the logistical challenges associated with school-going kids in hostile parts of the city. These responses were gathered through surveys and simple recorded discussions that gave researchers more information on the causes of recidivism and some of the major consequences of dropping out of school for some respondents.\u00a0 The most appealing part of these forms of interviews were the honest descriptions provided regarding the city, challenges that almost all kids face, and the perspectives that parents had regarding learning.<\/p>\n
Summary of the Data Analysis Process: SWOT Analysis<\/p>\n
The strengths of the data analysis process included the availability of vast volumes of data that could be used to generate meaningful information on the trends of the second chance program, especially with the interest exhibited by respondents. The second strength was the sufficient experience by analysts to systematically process the collected data for quantitative and qualitative results that were required for decision making.\u00a0\u00a0<\/p>\n
The weakness of the process was time restrictions, limiting further data mining to give information on the exact number of students who regressed into old criminal behavior for all genders. This would have further informed the crime rates and preventive measures that could have been recommended to rehabilitation centers and law enforcement agencies (Kalirajan, 1991). The second weakness of the data analysis process was the restrictive nature of the data, which kept evolving with each unique response provided. As such, it was a time-consuming and complicated process for the data analysts.\u00a0<\/p>\n
There are various opportunities for the data analysts in this investigation, including requesting for more chances to have interviews with focus groups through discussions that can better inform the peer perceptions regarding recidivism, crime, education, and life after school. These topics would guide the second chance program on aspects of the issues that are yet to be better addressed. The second opportunity for the analytical process is further processing through statistical software that would provide informative presentation in table and graph format, for related aspects of the second chance program in the city.<\/p>\n
There are some threats to the research, including the disinterest and apathy of students who are afraid of providing information to researchers. Additionally, there is also major concern regarding the viability of the second chance program in deeper parts of the city and similar environments.\u00a0<\/p>\n
Sample Section<\/p>\n
The following graph summarizes the respondents who took part in the process of gathering information for the second chance program.\u00a0<\/p>\n
Demographic Overview<\/p>\n
The students who participated in the study were from the three major races and the following results regarding the rate of recidivism and education exposure showed that the Hispanic students struggled most when it came to the second chance program because of the gang related behavior in the city (Miller, 2007). Additionally, the female students of Hispanic origin suffered too, due to the witnessed level of stress perceived by them in the city. This performance was similar to that of black male and female students interviewed and aware of the second chance program. They also faced similar challenges including poverty and the pressure to support their families. This led to the significantly high dropout rate witnessed. Their white female and male colleagues faced similar but not overwhelming levels of recidivism when interviewed.\u00a0<\/p>\n
At least 50% of all respondents in the data analysis process were found to be women and the other were either male, transgender, or did not wish to disclose their sexual orientation, as is common in some studies (Phadermrod, Crowder, & Wills, 2019).\u00a0 However, the rates were found to be just as impactful when examined carefully. The range of age for the respondents was between 12 and 19 years old in this study. This was restricted to the inclusive ages for the male and female respondents, who all shared their experiences and knowledge levels of the second chance program.\u00a0\u00a0<\/p>\n
Three races were selected for the study, due to the various factors such as willingness to participate in the study and availability of consent from guardians, parents, and teachers aware of the data analysts performing the exercise (Miller, 2007). The religious background of the respondents was distributed as indicated in the graph below, with a majority of students being Catholics. The socioeconomic status of the group was diverse. The majority of the respondents were also from lower middle-class families while the minorities were working middle class family members.\u00a0<\/p>\n
Recommendations and Future Implications\u00a0<\/p>\n
The success of the student engagement and improvement program depended on the successful engagement of the critical community members and facilitators, including parents, school, and the environment setting. The need for support resources for effective engagement necessitated adequate financing and continuous evaluation to ensure the sustainability of the program. This was due to the high risk and probability of unforeseen program hazards and shortcomings that increased the chance of the intervention failure or incapacity to achieve change. Therefore, critical resource areas included support from parents and the education program, as well as the inclusion of the whole community to deliver a wholesome intervention model.\u00a0<\/p>\n
In the same way, data proved that the education exposure and recidivism rates among the respondents were moderately distributed across all the communities and racial settings. This proved that the intervention program should be adopted for all racial groups to achieve improved student engagement and performance. With the assumption that all racial settings are affected by the program, the government should not target specific social-economic settings. Instead, the development and engagement program should be distributed across men and women, with data showing the minimal difference between gender and racial settings. Here, data inclusion is critical for the achievement of the different evaluation processes and intervention modeling, allowing identification of affected areas and improvement alternatives.\u00a0<\/p>\n
Conclusion<\/p>\n
The Second Chance Program is likely to endure into the future for schools that require support in addressing issues such as recidivism, which influence the success of students in the minority races. It is therefore important that the data analysts present the case of the program to support the lower income bracket and also promote the efforts by parents to care for children already engaging in deviant behavior. Further, it is likely that the program will lead to the improvement of the performance of the city overall.\u00a0<\/p>\n
References<\/p>\n
Bhise, R. B., Thorat, S. S., & Supekar, A. K. (2013). Importance of data mining in higher education system. IOSR Journal Of Humanities And Social Science (IOSR-JHSS), 6(6), 18-21.<\/p>\n
Ibrahima, A. M., Hassanb, M. S., & Usman, U. M. (2022). Online Support Communities and User Personal Information Disclosure Behaviour: Investigating the Importance of Anonymity in Online Support Community When Disclosing Sensitive Support Needs.<\/p>\n
Grimm, B., Ramos, A. K., Maloney, S., Abresch, C., Tibbits, M., Lyons, K., & Palm, D. (2021). The Most Important Skills Required by Local Public Health Departments for Responding to Community Needs and Improving Health Outcomes. Journal of community health, 1-8.<\/p>\n
Linneberg, M. S., & Korsgaard, S. (2019). Coding qualitative data: A synthesis guiding the novice. Qualitative research journal.<\/p>\n
Kalirajan, K. P. (1991). The importance of efficient use in the adoption of technology: a micro panel data analysis. Journal of Productivity Analysis, 2(2), 113-126.<\/p>\n
Miller, M. G. (2007). Environmental metabolomics: a SWOT analysis (strengths, weaknesses, opportunities, and threats). Journal of proteome research, 6(2), 540-545.<\/p>\n
Phadermrod, B., Crowder, R. M., & Wills, G. B. (2019). Importance-performance analysis based SWOT analysis. International Journal of Information Management, 44, 194-203.<\/p>\n
Weigand, H., Beermann, A. J., \u010ciampor, F., Costa, F. O., Csabai, Z., Duarte, S., … & Ekrem, T. (2019). DNA barcode reference libraries for the monitoring of aquatic biota in Europe: Gap-analysis and recommendations for future work. Science of the Total Environment, 678, 499-524.<\/p>\n","protected":false},"excerpt":{"rendered":"
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