Data Interpretation Practicum
Data Interpretation Practicum
Order Instructions:
Your Data Interpretation Practicum
It is important for the writer to note that this paper is a combination of order #113631, 113672, 113741, 113768. The writer will have to combine the solutions that where giving in the above orders and will also include what is added here below in a well-structured APA paper including all what is mention here below. The previous analysis mentioned here below is contain in the solutions of the above mention order.
In order to widely disseminate or publish your research findings, they must be presented in a manner that facilitates comprehension by the educated reader. This week, you will present the findings from the analyses you conducted on your chosen data from Week 3.
Your submission should include your previous analyses and interpretation, along with appropriate sections to address the limitations of your work and opportunities for further inquiry. Follow APA format to present your findings as a paper, including a title page, abstract, introduction, sections in the main body as needed, conclusions, references, and appendices as needed
I will email the dataset that has to be use for this paper as it has also been use for the past 7 weeks for previous assignment.
SAMPLE ANSWER
This paper seeks to compile all the analysis that was performed to determine the safety of people at different working stations. In particular, the paper will bring together all the analytical techniques employed on the data and make the inference about the population parameters. The fundamental of this paper thus is to answer the question whether there exists a difference in injury rate in a different working site, when different genders supervise employees, when workers have worked on a different number of hours, and when a site has a different number of workers (O’Leary, 2013). The analysis will give an insight of the safety of workers, which is vital for firms like the insurance company, and also for policy planning of the company.
The research formatting will utilize the APA writing style, and the analysis will be performed using SPSS for Windows. Nevertheless, the research will seek to infer about the following hypothesis:
H_{0}: 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.
H_{1a}: 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.
It is important to notice that the research hypothesis and research question are intertwined (Creswell, 2013).
Results
Data distribution was evaluated using descriptive measures, which are tabulated in Table 1.
Table 1:
Descriptive Statistics |
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number of employees | number of hours at work | supervisors gender | injury rate | safety climate | ||
N | Valid | 51 | 51 | 51 | 51 | 51 |
Missing | 0 | 0 | 0 | 0 | 0 | |
Mean | 24.02 | 49960.78 | .47 | 15.1755 | 4.6971 | |
Std. Error of Mean | 1.050 | 2183.070 | .071 | 2.44692 | .14493 | |
Median | 23.00 | 47840.00 | .00 | 9.1600 | 4.7600 | |
Std. Deviation | 7.495 | 15590.236 | .504 | 17.47447 | 1.03497 | |
Variance | 56.180 | 243055455.373 | .254 | 305.357 | 1.071 | |
Skewness | .056 | .056 | .121 | 2.046 | .101 | |
Std. Error of Skewness | .333 | .333 | .333 | .333 | .333 | |
Kurtosis | .506 | .506 | -2.068 | 4.309 | -.697 | |
Std. Error of Kurtosis | .656 | .656 | .656 | .656 | .656 | |
Minimum | 5 | 10400 | 0 | .00 | 2.50 | |
Maximum | 45 | 93600 | 1 | 76.92 | 6.80 |
The descriptive statistics, results show that on average there are 24 workers at each site with a minimum of five workers and a maximum of 45. Notably, the skewedness of the number of workers is close to zero, thus, the normal plot of this parameter will be almost asymmetrical. On average the workers work for 49960.78 hours, with a minimum of 10400 hours and a maximum of 93600 hours. Similarly, the distribution of the number of working hours in almost asymmetric, which is deduced from a low skewness coefficient (Ho, & Carol, 2015).
On average, the injury rate of all the working site is 15.1755, which the minimum of zero injury rate and a maximum of 76.92. Further, the injury rate has a positive skewedness, which means that its standard normal curve will have a long tail to the left (on the higher values of the injury rate) (Ho, & Carol, 2015). Furthermore, these four working sites have on average 4.6971 safety climate, with the safest site having 2.50 safety climate and not safest site have a 6.8 safety climate.
To compare the sample mean, an ANOVA technique was applied, and the results were as follows:
Table 2:
ANOVA |
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Sum of Squares | df | Mean Square | F | Sig. | ||
safety climate | Between Groups | 34.210 | 33 | 1.037 | .911 | .605 |
Within Groups | 19.348 | 17 | 1.138 | |||
Total | 53.558 | 50 | ||||
supervisors gender | Between Groups | 7.973 | 33 | .242 | .868 | .648 |
Within Groups | 4.733 | 17 | .278 | |||
Total | 12.706 | 50 | ||||
number of hours at work | Between Groups | 9791279435.294 | 33 | 296705437.433 | 2.136 | .050 |
Within Groups | 2361493333.333 | 17 | 138911372.549 | |||
Total | 12152772768.627 | 50 | ||||
number of employees | Between Groups | 2263.147 | 33 | 68.580 | 2.136 | .050 |
Within Groups | 545.833 | 17 | 32.108 | |||
Total | 2808.980 | 50 | ||||
site | Between Groups | 18.655 | 33 | .565 | .724 | .792 |
Within Groups | 13.267 | 17 | .780 | |||
Total | 31.922 | 50 |
In this case, the injury rate was used as a factor. The decision rule is to reject the null hypothesis when the P-value < level of significance. The p-values show that we will fail to reject the null hypothesis, following the critical rule. Thus, we infer that there is no significance difference in injury rate at a working site and supervisor’s gender, a number of employees and the number of hours at work (Murphy, et al. 2014).
A paired t-test was also performed in an attempt to evaluate the difference in the variables mean. The results were as illustrated in Table 3. Notably, the assumption (null hypothesis) is that the mean of the paired variable is equal, versus, the alternative that the mean of paired variables is not equal (Murphy, et al. 2014).
Table 3:
Paired Samples Test |
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Paired Differences | t | df | Sig. (2-tailed) | ||||||
Mean | Std. Deviation | Std. Error Mean | 95% Confidence Interval of the Difference | ||||||
Lower | Upper | ||||||||
Pair 1 | injury rate – number of employees | -8.84412 | 22.98329 | 3.21830 | -15.30827 | -2.37996 | -2.748 | 50 | .008 |
Pair 2 | injury rate – supervisors gender | 14.70490 | 17.52681 | 2.45424 | 9.77541 | 19.63440 | 5.992 | 50 | .000 |
Pair 3 | injury rate – safety climate | 10.47843 | 17.51818 | 2.45304 | 5.55136 | 15.40550 | 4.272 | 50 | .000 |
Pair 4 | injury rate – number of hours at work | -49945.60882 | 15601.36168 | 2184.62760 | -54333.56251 | -45557.65514 | -22.862 | 50 | .000 |
In this case, the rejection rule is: reject null hypothesis if |t _{calculated}| > t_{ tabulated }= 1.684. In that light, all the t_{ calculated }values are greater than 1.684, and thus, conclusively we say that the paired variables means are not equal.
To find a linear model that can be used to predict injury rate using safety climate, number of hours at work, supervisors’ gender as the predictors in the model
Table 4:
Coefficients^{a} |
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Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
B | Std. Error | Beta | ||||
1 | (Constant) | 42.248 | 10.767 | 3.924 | .000 | |
number of hours at work | -.001 | .000 | -.678 | -5.774 | .000 | |
supervisors gender | 3.564 | 4.194 | .103 | .850 | .400 | |
safety climate | 1.958 | 2.007 | .116 | .976 | .334 | |
a. Dependent Variable: injury rate |
The model is:
Injury rate = 42.248 – 0.001* (number of hours at work) + 3.564*(supervisors gender) + 1.958*(safety climate)
The regression model summary is as given in 5.
Table 5:
Model Summary |
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Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
1 | .649^{a} | .421 | .384 | 13.71976 |
a. Predictors: (Constant), safety climate, number of hours at work, supervisors gender |
The coefficient of determination shows that the fitted model can explain 38.4% of the variation (Lowry, 2014).
Correlations | ||||||
site | number of employees | injury rate | safety climate | number of hours at work | ||
Site | Pearson Correlation | 1 | ||||
Sig. (2-tailed) | ||||||
N | 51 | |||||
number of employees | Pearson Correlation | .130 | 1 | |||
Sig. (2-tailed) | .363 | |||||
N | 51 | 51 | ||||
injury rate | Pearson Correlation | -.074 | -.636^{**} | 1 | ||
Sig. (2-tailed) | .606 | .000 | ||||
N | 51 | 51 | 51 | |||
safety climate | Pearson Correlation | .331^{*} | .147 | -.013 | 1 | |
Sig. (2-tailed) | .018 | .303 | .930 | |||
N | 51 | 51 | 51 | 51 | ||
number of hours at work | Pearson Correlation | .130 | 1.000^{**} | -.636^{**} | .147 | 1 |
Sig. (2-tailed) | .363 | 0.000 | .000 | .303 | ||
N | 51 | 51 | 51 | 51 | 51 | |
*. Correlation is significant at the 0.05 level (2-tailed). | ||||||
**. Correlation is significant at the 0.01 level (2-tailed). |
Notably, there exists a perfect correlation between the number of hours at work and the number of employees, this implies that is the number of workers in a site changes that will be the same proportional change in the number of working hours (Wilcox, 2012). Also, there is a significant negative correlation between injury rate and a number of working hours, and the number of workers in a site at the 95% level of significance. This means that when the number of working hours or the number of employees increases, there will be a decline in injury rate (Wilcox, 2012).
Conclusion
The paper has compiled all the analysis performed previously, discussion of the results were given and inference made. Thus, the primary objective was achieved, on the other hand, the results indicated that there was no adequate evidence to reject the null hypothesis. For this reason, a conclusion was made that 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.
References
Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approach. Sage publications.
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.
Murphy, K. R., Myors, B., & Wolach, A. (2014). Statistical power analysis: A simple and general model for traditional and modern hypothesis tests. Routledge.
O’Leary, Z. (2013). The essential guide to doing your research project. Sage.
Wilcox, R. R. (2012). Introduction to robust estimation and hypothesis testing. Academic Press.
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