Employee Behavior and Safety
Percentage of Employee Behavior Deemed to Represent Safe Acts Based on 12-Month Observation
Variable Range Min. Max Stat. Sd. Error SD Var.
Site 2 1 3 2.04 .112 .799 .638
Supervisor 18 1 19 9.08 .712 5.083 25.834
Emp. Numbers 40 5 45 24.02 1.050 7.495 56.180
Employee Hrs. Worked 83200 10400 93600 49960.78 2183.070 15590.236 243055455.373
Supervisor Gender 1 0 1 .47 .071 .504 .254
Percent Safe Behavior .577 .423 1.000 .86582 .019 .138945 .019
Injury Rate 76.923 .000 76.923 15.17570 2.446 17.47 305.364
Safety Climate 4.300 2.500 6.800 4.69706 .144 1.034 1.071
Risk 6 1 7 4.59 .282 2.012 4.047
Note. Data based on 51 sites across three different location areas.
Employee Behavior and Safety
Through descriptive statistical analysis, informative information provides managers with a tool for the analyzation and the evaluation of progress within a company. This information can provide value for managers, researchers, and other readers. Data, that is output is used to help demonstrate to readers an understanding of detailed discussions when necessary and beneficial for research. Data sets that reveal statistics allows researchers to form output analysis that can be useful in determining corporate strategy. Descriptive statistical analysis in Table 1 allows a researcher to evaluate the percentage of employee behavior deemed to represent safe acts within companies based on 12-month observations.
Question and Hypotheses
What is the impact of the number of employees and the number of employee hours worked on corporate risk? This is the central research question based on statistical analysis of employee behavior and safety. This question involves reviewing observations of employee behavior and safety over a 12-month period. The number of employees and the number of employee hours worked has no impact on corporate risk. This is a null hypothesis based on the initial inquiry. Furthermore, the number of employees and employee hours worked has a statistical significant impact on corporate risk, is an alternative hypothesis to the null hypothesis. The central research question and null and alternative hypotheses are research components needed to begin supportive research and analyzing of collections of data.
Table 1 is an example of a data collection set based on active research. The researcher conducted observations at 51 sites in three different area locations. These locations were Boston, Phoenix, and Seattle. The observations included observing employees and their behaviors while performing company duties. The duties included various forms of operations including, manual labor, and office labor. Safety was the main consideration in viewing employees at work. In addition, calculations of employee behaviors towards company safety measured in terms of corporate risk.
Quantitative researchers use an approach whereby they identify specific, narrow questions or hypotheses based on a few variables (Creswell, 2009). As demonstrated in Table 1, there are a limited amount of variables, however quantitative research provides the best method for researching the central question and formulated hypotheses. Therefore, until an additional amount of research is constructed, I will provide a notable trend, patterns, or relationship. Based on the information provided, and given the central research question, and null and alternative hypothesis, there is a notable tendency in the data that as the numbers of employees increase corporate risk goes up and respectively goes down as it decreases. This is also a pattern considering the number of hours worked. These values are constructed based on perceptive analysis of the maximum, minimum values along with the range across the view of the variables number of hours worked, number of employees, and risk.
In conclusion, through descriptive statistical analysis an output of data was constructed in table form to for researched to gain detailed understanding of trends based on observations of employees performing safety habits at work. A central question and hypotheses were drawn to further extend the knowledge of the data. An overview of Table 1 allowed for speculation of possible trends and patterns prior to final research. Finally, through data analysis, a trend and pattern was discussed.
Creswell, J.W. (2013). Qualitative inquiry & research design: Choosing among five approaches. (3rd ed.). Thousand Oaks, CA: Sage
Your Data Interpretation Practicum
Throughout the course, you will learn to run various statistical analyses using SPSS (PASW) software. Part of your work will include analysis and interpretation as well. In addition, doctoral-level thinking and practice involve the formation of hypotheses, their testing, working with data, and the selection and execution of appropriate analyses. Toward that end, your Instructor will expect you to demonstrate your ability to engage in these doctoral-level competencies as you progress through the course.
A series of incremental Applications will allow you to demonstrate your accrued competency. This week, you will select the dataset from which you will work in subsequent weeks. Having learned to perform various analyses in SPSS (PASW), you will then perform them on your chosen data in subsequent weeks, interpreting the results and presenting them in accordance with APA conventions.
This week, select and propose a dataset to your Instructor (The SPSS (PASW) maximum variable data length is 1,500 variables) or you may use the dataset in Week 1: Resources. (See attached)
Explain how it relates to your interests. What hypotheses might you test? What would you expect to find? At this point, an educated guess and a general idea are sufficient. However, your submission should demonstrate that you have examined the data and that you understand what it comprises. Be sure to include the name of the database as well as 1–2 paragraphs that address these questions.