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Data Interpretation Practicum

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).

Data Interpretation Practicum


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:
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 1 : 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.
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.

Table 1:
Descriptive Statistics

Number of

Site Number of
hours at

Injury rate Supervisors


Valid 51 51 51 51 51
Missing 0 0 0 0 0

Mean 24.0196 2.04 49960.7843 15.1755 .47
Std. Deviation 7.49531 .799 15590.23590 17.47447 .504

Variance 56.180 .638

305.357 .254

Skewness .056 -.072 .056 2.046 .121
Std. Error of

.333 .333 .333 .333 .333

Kurtosis .506 -1.419 .506 4.309 -2.068
Std. Error of Kurtosis .656 .656 .656 .656 .656
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).

Table 2:
Paired Samples Test

Paired Differences t df Sig. (2-
tailed) Mean Std.


Std. Error

95% Confidence Interval
of the Difference
Lower Upper

Pair 1
injury rate –
number of

-8.84412 22.98329 3.21830 -15.30827 -2.37996 -2.748 50 .008

Pair 2
injury rate –
number of
hours at work

-49945.60882 15601.3616

8 2184.62760 -54333.56251 -45557.655

14 -22.862 50 .000


Pair 3
injury rate –

14.70490 17.52681 2.45424 9.77541 19.63440 5.992 50 .000

Pair 4 injury rate – site 13.13627 17.55168 2.45773 8.19978 18.07277 5.345 50 .000

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
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.
Table 3:
Paired Samples Correlations

N Correlation Sig.
Pair 1 Injury rate & number of employees 51 -.636 .000
Pair 2 Injury rate & number of hours at work 51 -.636 .000
Pair 3 Injury rate & supervisors gender 51 -.090 .532
Pair 4 Injury rate & site 51 -.074 .606
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 significance difference in injury rate
at a working site and supervisor’s gender, number of employees and the number of hours at


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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