Part One: The relationship between extraversion, agreeableness, and neuroticismCorrelation CoefficientTable 1 below presents the Pearson correlation coefficients depicting the relationship between each pair of variables.Table 1: Correlation MatrixCorrelation MatrixEXTRANEUROAGREEEXTRAPearson's rp-valueNEUROPearson's r-0.243*p-value0.015AGREEPearson's r0.299**-0.046p-value0.0030.649Note. * p .05, ** p .01, *** p .001 There is a statistically significant negative association between extraversion and neuroticism as shown by the Pearson correlation value of r = -0.243 and the p-value of .015, which is lower than the p=0.05 significance level. Negative correlation values range from 0 to -1, where -1 indicates an extremely strong negative association between the variables (Christmann & Badgett, 2009). The r value of -0.243 points to a weak negative association between extraversion and neuroticism (Christmann & Badgett, 2009). The implication is that a unit increase in the level of extraversion decreases neuroticism by 0.243 units. At the same time, the association between agreeableness and extraversion is positive…...
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References
Christmann, E. P, & Badgett, J. L. (2009). Interpreting assessment data: Statistical techniques you can use. NSTA Press.
Saliya, C. A. (2023). Doing social research and publishing results: A guide to non-native English speakers. Springer Nature.
Excel
"According to T-100 Domestic Market, the top seven airlines in the United States by domestic boarding in a recent year were Southwest Airlines with 81.1 million, Delta Airlines with 79.4 million, American Airlines with 72.6 million, United Airlines with 56.3 million, Northwest Airlines with 43.3 million, US Airways with 37.8 million, and Continental Airlines with 31.5 million. Using Excel, construct a pie chart and a bar graph to depict this information". ( Kaplan University, 2016 p 1).
Pie Chart
Bar Graph
Department of the Interior releases figures on mineral production. The following are the 15 leading states in nonfuel mineral production in the United States in 2008". ( Kaplan University, 2016 p 2).
State
Value ($Billon)
Arizona
Nevada
Florida
Utah
California
Texas
Minnesota
Alaska
Missouri
Colorado
Michigan
Wyoming
Georgia
New Mexico
Pennsylvania
1.68
Descriptive Statistics
Value ($Billion)
Mean
3,613333
Standard Error
0,496542
Median
2,98
Mode
#N/A
Standard Deviation
1,923099
Sample Variance
3,69831
Kurtosis
3,583069
Skewness
1,783901
ange
7,27
Minimum
1,68
Maximum
8,95
Sum
54,2
Count
15
Confidence Level (95,0%)
1,064977
The…...
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Reference
Kaplan University (2016). Using Data Analysis in Excel. USA
i) Part 1: Descriptive Variables Frequency Tables for Demographic VariablesTable 1.Variable 1: Agency where one WorksAGENCYFrequencyPercentValid PercentCumulative PercentValidAF333515.65.65.6AG433527.27.212.8AM1837.3.313.1A7000511.711.724.8BG526.1.124.9BO338.1.125.0CM207253.53.528.5CT476.1.128.5CU633.1.128.6DD308775.25.233.8DJ309785.25.239.0DL80751.41.440.3DN86241.41.441.8D1115.2.242.0ED2592.4.442.4EE1379.2.242.6EP79721.31.344.0FC594.1.144.1FQ470.1.144.1FT638.1.144.2GS71571.21.245.4HE430297.27.252.6HF412.1.152.7HS7389912.412.465.1HU4628.8.865.8IB829.1.166.0IN282904.74.770.7KS326.1.170.8NF940.2.270.9NL859.1.171.1NN115681.91.973.0NQ1743.3.373.3NU2308.4.473.7NV478828.08.081.7OM3069.5.582.2413.1.182.3SB1543.3.382.5SE3394.6.683.1SK355.1.183.1SN520.1.183.2ST72281.21.284.4SZ263184.44.488.8TD215523.63.692.4T420277.07.099.5XX3157.5.5100.0Total598003100.0100.0The Department of Health and Human Services (HHS) accounted for the greatest number of participants among the 85 participating agencies, at 12.4 percent, followed by the Department of Agriculture at 11.7 percent. The Department of Commerce accounted for 7 percent of participants in the survey.Table 2.Variable 2: SexDSEXFrequencyPercentValid PercentCumulative PercentValid7910013.213.213.2Male29301449.049.062.2Female22588937.837.8100.0Total598003100.0100.049 percent of the survey participants were male, 37.8 percent were female, and 13.2 percent preferred did not indicate their gender.Table 3.Variable 3: Education LevelDEDUCFrequencyPercentValid PercentCumulative PercentValidNo degree7738012.912.912.9Doctorate14916324.924.937.9Bachelors18297930.630.668.5Masters18848131.531.5100.0Total598003100.0100.024.9 percent of participants had a Doctorate degree, 31.5 percent had a masters degree, 30 percent had a Bachelors degree, and 12.9 percent had some college education with no degree.Table 4Variable 4: Tenure in the Federal AgencyDFEDTENFrequencyPercentValid PercentCumulative PercentValid1-3 years7307612.212.212.26 -10years22171237.137.149.311-20 years16263427.227.276.54 -5 years14058123.523.5100.0Total598003100.0100.037 percent of participants have been with the federal government for 6 to 10…...
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References Hair, J. F., Hult, G. T., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural modeling (3rd ed.). SAGE.
Data Warehousing: A Strategic Weapon of an Organization.
Within Chapter One, an introduction to the study will be provided. Initially, the overall aims of the research proposal will be discussed. This will be followed by a presentation of the overall objectives of the study will be delineated. After this, the significance of the research will be discussed, including a justification and rationale for the investigation.
The aims of the study are to further establish the degree to which data warehousing has been used by organizations in achieving greater competitive advantage within the industries and markets in which they operate. In a recent report in the Harvard Business eview (2003), it was suggested that companies faced with the harsh realities of the current economy want to have a better sense of how they are performing. With growing volumes of data available and increased efforts to transform that data into meaningful knowledge that can…...
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References
Agosta, L. (2003). Ask the Expert. Harvard Business Review, 81(6), 1.
Beitler, S.S., & Lean, R. (1997). Sears' EPIC Transformation: Converting from Mainframe Legacy Systems to Online Analytical Processing (OLAP). Journal of Data Warehousing (2:2), 5-16.
Scores
Generally speaking, a set of test scores such as those provided would not be considered continuous data, as in most test scenarios there are a finite number of scores possible. If a test has 100 questions and is worth one point per question, then the only possible scores are those whole numbers from 0 to 100 -- a finite set of responses rather than an infinitely reducible continuum. In order for this data set to be considered continuous, it would need to be demonstrated that partial points were possible to a degree that enabled a full continuum of scores to be presented.
As the data set represents test scores, it must certainly be considered at least an interval scale, with the two point difference between 79 and 81 the same interval as the two points between 97 and 99. It can also be inferred that these are percentages, and that…...
epidemiological data, and then exploring possibility of a causal connection between lack of government funding for community-based treatments and increase in HIV incidence in queer male communities.
Both statistical descriptive and inferential tests will be employed.
The descriptive tests summarize and describe the data. These would include frequency analysis e.g. Of the amount of men diagnosed as queer, and frequency that the participants experienced homophobia. Univariate analysis would focus on one variable, e.g. frequency of homophobia, by analyzing the mean, the distribution, the central tendency, and the dispersion of the occurrence.
The distribution would provide some assumption of the pattern of the range: whether normal or skewed.
The central tendency would, in this case, measure the mean of the data, i.e. average number of males that experienced discriminatory treatment. Dispersion would be another descriptive tool that measures the spread of values around the central tendency, i.e. range and standard deviation.
A full data set that has been converted thusly is a standardized normal distribution.
Assuming z follows a standard normal distribution, if a = 1 than P (z...
Tailor data analysis to organizational and employee needs.
Quantitative Data Analysis
(Cohen & Crabtree, 2006)
Quantitative data is tabulated so according to the results for different variables in the data set. This provides a comprehensive picture of the data and assists in the process of identifying patterns.
A common way of displaying the data to facilitate the analysis is by using a frequency distribution, which is an organized tabulation of the number of the responses or scores according to each variable category. Tabulation provides a structured way to determine data accuracy, identify data outliers, gauge the spread of the scores or responses, and observe categorical frequency
Quantitative data analysis using interval data that are continuous, that has a logical order with standardized differences between values, but that does not have a natural zero. Items on a Likert scale are a good example of interval data.
Data are frequently displayed in a manner that condenses the…...
Decision Analysis System Modeling
Using Spreadsheets"
"DATA DECISION ANALYSIS SYSTEM MODELING USING SPEADSHEETS"
"Data Decision Analysis System Modeling Using Spreadsheets."
Spreadsheet is one of the most popular software packages on the planet. Daily, million of business people, students and individuals use spreadsheet program to build models to assist in solving decision problems they face on their work activities. Thus, employers generally look for individuals having experience and skills with spreadsheets. Typically, spreadsheets assist in developing varieties of management techniques in modeling environment. More importantly, spreadsheets assist in developing models and make decision within a business environment.
Within the present contemporary business environment, many business managers face daunting tasks to make effective decisions. With the fast-paced and dynamic changes within a competitive business environment, business people are often faced with extremely complex business alternatives. Evaluation of the alternatives and choosing the best option from these alternatives has become a daunting task for business people. Thus,…...
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References
Evans, M.H. (2010). Course 3: Capital Budgeting Analysis. Continuing Professional Education
Lin, G.C.I. & Nagalingam, S.V. (2000). CIM justification and optimization. London: Taylor & Francis.
Ragsdale, C.T.(2010). Decision Analysis and Spreadsheet Modeling. A Practical Introduction to Management Science (4th edition). Cengage Learning.
Coding and Data Analysis Process in Qualitative esearch
"Coding is the process of combing the data for themes, ideas and categories and then marking similar passages of text with a code label so that they can easily be retrieved at a later stage for further comparison and analysis. Coding the data makes it easier to search the data, to make comparisons and to identify any patterns that require further investigation" (Taylor & Gibbs 2010). Coding is a critical aspect of qualitative research as without effective coding there is nothing but a sea of potentially interesting but not necessarily useable data. It could also be argued that during the coding process analysis is already taking place, given that qualitative research is empirical and proceeds 'from the ground up:' during the coding process, the theory or ideas about the subject matter are only just beginning to take shape. Some codes before the…...
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References
Chapter 17: Qualitative data analysis. Retrieved from:
When leaders in the field of criminal justice are going to develop, change or implement policies within their field, it is always important that these developments, changes and implementations are grounded in evidence. Evidence-based practice is universally recognized as essential to good decision making (Noe, Hollenbeck, Gerhart & Wright, 2003). In order to use the evidence, one has to obtain the evidence—and that happens by way of statistical analysis and research. Researchers who gather, assess and use statistical data to understand an issue and devise a solution to a problem are grounding their work in evidence that can be quantified. When evidence can be quantified—i.e., statistically measured—it is easier to see when policies are working and when they are not. For example, in criminal justice policy making, leaders might want to institute a new way method for police to report internally on abuses in the workplace. The method they choose,…...
popular and highly common method of data collection used in all manner of research. The survey can take a wide array of forms and typically requires the respondent to answer a series of questions or items. The effective survey instrument will be one that is objective and which offers questions that are presented in an unbiased manner.
Practical Application
The practical applications of the survey are manifold. One practical application of the survey is in ts capacity to yield first-hand accounts which may be protected by anonymity and which enable researchers to formulate data analyses that are not influenced by personal perceptions of subjects. This benefit is supplemented by the ability which the survey offers researchers to craft data-gathering questions according to the needs of the study. With this method of qualitative data accumulation, questions can be shaped to cast a broad reach in terms of that which can be utilized…...
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References
Trochim, W. & Donnelly, J.P. (2008). The Research Methods Knowledge Base, 3e.
Crime and the Depenalization of Cannabis Possession: Evidence from a Policing Experiment" -- published in the Journal of Political Economy, a bimonthly peer-reviewed journal issued by the University of Chicago -- is a fascinating quantitative analysis on the impact of a localized policing "experiment" conducted over the course of seven and a half years. Between April 1998 and January 2006, the south London borough of Lambeth instituted a policy of "depenalization" for cannabis possession and consumption, the LCWS or Lambeth Cannabis Warning Scheme (Adda McConnell asul 2014, 1133). Although marijuana still remained illegal, the police in Lambeth made it known that "(i) the possession of small quantities of cannabis for personal consumption was still a recordable offence, but would no longer lead to the individual being arrested; (ii) the primary motivation was to free up police time and other resources to focus on crimes related to other drugs or…...
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References
Adda, J., McConnell, B., Rasul, I. (2014) Crime and the depenalization of cannabis possession: Evidence from a policing experiment. Journal of Political Economy 122(5), 1130-1202. http://www.journals.uchicago.edu/doi/10.1086/676932
Data mining is one of the ways that a company can check into this consideration, but it is far from the only way. Companies that over-utilize it as a means to solve all of their problems are usually the companies that fail or struggle because they start making bad business choices.
These can be corrected but it takes time, and companies that do not learn what they did wrong will often continue data mining in an effort to discover where they went wrong. This can compound the problem and allow data miners to truly overwhelm the organization with facts, figures, calculations, and ideas for change. Some of these will certainly be good ideas, but they will get lost in the shuffle, and if a business implements one good idea and three bad ideas, that good idea will probably not be recognized because sales will continue to fall. Companies that are…...
DOCTORAL SUCCESS: QUALITATIVE DATA ANALYSIS DOCTORAL SUCCESS: QUALITATIVE DATA ANALYSIS 11Doctoral Success: A Qualitative Data AnalysisStudent NameInstitutionIntroductionStatement of the ProblemGetting accepted into a doctoral program is only half the battle. Completing the program is not a given, and current doctoral completion rates are only fifty-seven percent: meaning that almost half of all doctoral students do not complete the program as planned (Johnson, 2015). Those rates are substantially lower for minorities and women. Although women outnumber men at the time of enrolment in masters and doctoral programs, a greater percentage of men go on to complete their degrees (Castro, Garcia, Cavazos, et al, 2011). Understanding why students are not completing degrees is important because it will help reveal which student needs are not being met.Given the tremendous challenges of simply reaching the stage of beginning ones doctoral degree program, motivation and aptitude would not seem to be variables that impact doctoral…...
Modern newsgathering technologies have significantly increased the efficiency and effectiveness of gathering news. These technologies have revolutionized the way journalists collect information, process data, and report news stories. Some of the key benefits of modern newsgathering technologies include:
1. Real-time reporting: With tools like social media platforms, live streaming services, and mobile journalism apps, journalists can report news as it happens, providing timely and up-to-date information to the audience.
2. Enhanced multimedia capabilities: Modern newsgathering technologies enable journalists to capture high-quality photos, videos, and audio recordings, enhancing the visual and auditory elements of news stories.
3. Improved accessibility: With the internet and digital....
Effectiveness of Modern Newsgathering Technologies
Modern newsgathering technologies have revolutionized the way journalists gather and report news. These technologies have brought about numerous benefits, enabling journalists to work more efficiently, effectively, and reach a wider audience.
Real-Time Reporting and Breaking News Coverage
One of the most significant advantages of modern newsgathering technologies is the ability to report breaking news events in real-time. Social media platforms, such as Twitter and Facebook, allow journalists to share updates, videos, and photos from the scene of an event almost instantly. Live streaming services enable news organizations to broadcast live video footage, providing viewers with an immersive....
The autonomous revolution-driven vehicles are poised to transform the transportation landscape, offering promising benefits such as reduced traffic congestion, improved road safety, and increased accessibility for people with disabilities. However, the advent of self-driving cars also raises a myriad of ethical concerns that demand careful consideration.
1. Safety and Liability
Self-driving cars rely on complex algorithms and sensors to navigate roads. While these technologies promise to enhance safety by eliminating human error, they also introduce new challenges. Unforeseen scenarios, such as unexpected road conditions or obstacles, could lead to accidents. Determining liability in such cases becomes complex. Who....
A study conducted by Guo et al. (2018) investigated the performance of the ARIMAX-ANN hybrid forecasting model in predicting rice yield compared to the traditional ARIMAX and Artificial Neural Network (ANN) models. The researchers found that the ARIMAX-ANN hybrid model outperformed both the ARIMAX and ANN models in terms of forecasting accuracy and efficiency. The hybrid model was able to capture the non-linear relationships and complex patterns in the rice yield data more effectively, resulting in more accurate predictions. Similarly, a study by Wang and Ding (2019) compared the performance of various time series forecasting models, including ARIMAX, ANN, and the....
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