Data Interpretation Practicum using SPSS Software Order Instructions: 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.
Use the attached dataset and respond to the following. Explain how it relates to your interests (EFFECTS OF HRM PRACTICES ON EMPLOYEE PERFORMANCE), 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.

Data Interpretation Practicum using SPSS Software Sample Answer

Introduction

Thepurpose and objective of this paper are to analyse the ‘Data for week one assignment’. This research will establish whether there exists any relationship between the rate of injuries in a working site with the gender of supervisor managing the site, the number of employees at the site, and a number of hours the employees are working.This isa great research problem for research on since it is related to the practice of human resource management on the employee’s performance. In particular, it will establish whether an individual supervisor’s gender 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.

In light of this, the provided data will be analyzed using SPSS for windows and inference given about the population. The basis of the analysis will be to infer about the following hypothesis:

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 workingsite and supervisor’s gender, number of employees and the number of hours at work.

The stated hypothesis leads to the formulation of the following research question: Is there a significance difference in injury rate at a workingsite and supervisor’s gender, number of employees or the number of hours at work. Thus, the primary task carried out in data analysis will be to infer about the population using the sample data given at the 95 % level of significance.

Analysis

In search of answering the research question, a one-way ANOVA was performed. This was an appropriate measure since it measures the variability of data relative to the mean of the variables. In this case, the injury rate was considered as the factor (dependent variable) (Murphy, 2014). The results of the off the analysis are summarized in Table 1.

The decision here is to reject the null hypothesis when |F _calculated| > F _tabulated. The F _{0.05, (33, 17­)} = 2.15, at critical value (α = 0.05). Since the F _calculated values ofnumber of employees, hours worked, supervisors gender are less than 2.15 we fail to reject the null hypothesis that they have no significance difference. In fact, we state that they show no significant difference at the 95% level of significance(Ashton, 2013). In other words, there is no significant difference in injury rate at a working site and supervisor’s gender, number of employees and the number of hours at work.

To test the nature of the association between this variable. A correlation analysis was performed in agreement with (Wilcox, 2012). The results are as tabulated in Table 2.

The Pearson’s correlation coefficient indicate that the association between the injury rate with the number of employees, and number of hours at work is negative(Lowry, 2014). In particular, the number injury rates reduceas the number of workers and number of working hours increases. Nevertheless, supervisor gender has a weak negative association with injury rate.

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 no adequate evidence to reject the null hypothesis thus the inference that will hold is there is no significance difference in injury rate at a workingsite and supervisor’s gender, number of employees and the number of hours at work.

Data Interpretation Practicum using SPSS Software References

Wilcox, R. R. (2012). Introduction to robust estimation and hypothesis testing. Academic Press.

Ashton, J. C. (2013). Experimental power comes from powerful theories [mdash] the real problem in null hypothesis testing. Nature Reviews Neuroscience,14(8), 585-585.

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

Murphy, K. R., Myors, B., &Wolach, A. (2014). Statistical power analysis: A simple and general model for traditional and modern hypothesis tests.Routledge.