Data Analysis and Interpretation Practicum Order Instructions: This paper is critical and it must contain all the component listed below in the order. it is also important that the paper include the SPSS output file of the descriptive statistics analysis in a Word document.

Data Interpretation Practicum

This week, you will run descriptive statistics and a t test on your chosen dataset. This Assignment requires you to engage in data interpretation and to select the appropriate analyses for your hypotheses and for the data that you have at your disposal. Toward that end, you should consider which descriptive statistics will inform the reader and allow you to pursue your questions.

Your submission to your Instructor should include your SPSS output file of your descriptive statistics analysis in a Word document, along with each of the following elements: your SPSS output, including graphical representations; your narrative interpretation; the governing assumptions of the analyses you ran; the viable and nonviable hypotheses (null and alternative); and the relevant values (such as a P value indicating statistical significance or a lack thereof).

I will send the dataset via email same as the one use in previous papers in the past weeks.

Data Analysis and Interpretation Practicum Sample Answer

Introduction

The core purpose of this research study is to analyze the provided data so that insight can be obtained about the safety of people at different working sites. In particular, the research will seek to find out whether there exist any causation relationship between the rates of injuries in a working site, the gender of a supervisor at the site, the number of employees at the three different sites and the hours the employees are working. The research problem thus, will try to find the existence (if any) of the relationship between these variables. The significance of the study is that the findings can be applied in the practice of human management to assess the risk factor of employees at different fields. Nevertheless, this result can also be used by the insurance cover in calculation or determining the premium to be paid based on the risk factor of the clients.

On the same token, these variables will be analyzed to establish whether there exists any correlation between the individual supervisor’s genders contributes to the high injury rate in a site, increase the number of employees increases the injury rate and also if the increased number of working hours is positively correlated to the injury rate. The dataset provided will be analyzed using SPSS for Windows and the tables and graphs edited in accordance with APA writing style. The fundamental of this study is based on the hypothesis that are:

H₀: There is no significance difference in injury rate at a working site and supervisor’s gender, number of employees and the number of hours at work.

H­₁: There is a significance difference in injury rate at a working site and supervisor’s gender, number of employees and the number of hours at work.

This hypothesis will act as blueprints in the analysis part (section). Furthermore, the hypothesis was also the key to the formulation of the research question, which is the backbone any successful research (Ho, & Carol, 2015). On the same, the research will seek to infer about the population characteristics based on the sample data provided at the 95% level of significance. Thus, at the end of this research a conclusion will be made about the relationship between these variables.

Analysis

To find out the general nature of data, that is the spread and distribution of the data set, the descriptive statistical analysis was performed ad the results were as tabulated in Table 1.

This result indicates that all the data except the site were positively skewed, in other words, they are asymmetric and have a long tail to the right (Ho, & Carol, 2015). Furthermore, the number of employees, the number of hours at work, and injury rates have a positive kurtosis that indicates that these variables have a more picked plot relative to the normal curve (Blanca, Arnau, López-Montiel, Bono, Bendayan, 2015).

The decision rule, in this case, is to reject the null hypothesis when the t calculated is greater than the t _tabulated. In this case, t _{0.05, 50} = 2.021, which leads to the rejection of the null hypothesis. Furthermore, based on the p-value that are obtained from the analysis, the null hypothesis will similarly be rejected since the p-value < the set level of significance (O’Leary, 2013). Therefore, the inference made will be that there is a significance difference in injury rate at a working site and supervisor’s gender, a number of employees and the number of hours at work.

To test the nature of the relationship, Paired Samples Correlations generated after t-test analysis will be analyzed. The results were as illustrated in Table 3.

This result indicates that there exist a strong negative correlation between injury rate & a number of hours at work, and injury rate & number of employees (O’Leary, 2013). This means that when the injury rates increase the number of hours worked and the number of workers is expected to reduce, and the opposite holds. Furthermore, a weak negative correlation exists between the Injury rate & supervisor’s gender and also Injury rate & a site (Lowry, 2014).

From the analysis, it is clear that the research objectives have been achieved and also the hypothesis has been taken care.  In this study, there was adequate evidence to reject the null hypothesis thus the inference that will hold is there is a significant difference in injury rate at a working site and supervisor’s gender, a number of employees and the number of hours at work.

Data Analysis and Interpretation Practicum References

Blanca, M. J., Arnau, J., López-Montiel, D., Bono, R., & Bendayan, R. (2015). Skewness and kurtosis in real data sample. Methodology.

Ho, A. D., & Carol, C. Y. (2015). Descriptive Statistics for Modern Test Score Distributions Skewness, Kurtosis, Discreteness, and Ceiling Effects. Educational and Psychological Measurement, 75(3), 365-388.

Lowry, R. (2014). Concepts and applications of inferential statistics.

O’Leary, Z. (2013). The essential guide to doing your research project. Sage.

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