Statistics in Analyzing SPSS PASW Software Order Instructions: Application: Analyzing SPSS (PASW) Software: Part 2 For this assignment you use SPSS (PASW) software and learn to properly manipulate the data according the APA requirements.

This is an important skill and will be a major factor in future assignments in this course, your doctoral studies and dissertation. It is strongly encouraged that you review Chapter 5 of the APA Publication Manual to understand table and figure requirements before starting.
Follow the direction for using SPSS (PASW) in this assignment.
You will then write a 5 page paper in which you present your table and an analysis of your findings. Keep in mind that you cannot draw conclusions without further testing. Instead identify notable trends, patterns, relationships, associations, etc. Your paper must meet the following requirements.

• Include an opening including thesis statement, body and conclusion

• Include a properly stated research question

• Include an examination on whether your hypothesis was accepted or rejected through statistical testing

• Include a background, history and objective

• Include a properly formatted null and alternative hypothesis was validated or rejected through statistical testing

• Include a presentation, interpretation and discussion of results from the tables and figures created

• Follow APA (American Psychological Association) style and include in-text citations and a separate references page

Statistics in Analyzing SPSS PASW Software Sample Answer

Abstract

The core purpose of this research paper is to establish whether there exists a relationship between the injury rate at different working sites when the number of employees increases, when different genders are managing or when the numbers of working hours are increased. Thus, the analysis will be conducted to come up with a clear inference. Furthermore, detailed explanation will be given on the results of different descriptive statistics. This report will be subdivided into sections; background section will give fine details of the research like objectives, hypothesis, and research question. Also, in this section the goals of the study will be laid down. Results section will contain the SPSS output formatted in accordance with APA style. A full explanation for the results will be given in discussion section, where inference will be made about the hypothesis.

Background Statistics in Analyzing SPSS PASW Software

The objectives/goals of this study are to determine whether:

1. Working on a certain site has any relation with injury rate.
2. The gender of the supervisor increases the injury rate.
3. There exist a relationship between injury rate and working sites, and gender of the supervisor.

Based on these objectives, the research will try to answer the following research questions;

1. Does working at a particular site increase the injury rate?
2. Are supervisors gender a key factor in the increase in injury rate in a site?
3. Is there a significant difference and working sites, gender of the supervisor?

On the same note, the hypothesis of the research that will be tested is:

H₀: There is no significance difference in injury rate at different working sites when different genders are managing. Versus, H₁: There is a significance difference in injury rate at different working sites when different genders are managing. Thus, the whole analysis will revolve around making inference about this hypothesis (Lowry, 2014).

Analysis Statistics in Analyzing SPSS PASW Software

The analysis in this research paper is based on the research question, where the means of the injury rate will be compared against different factors that may contribute to the increased injury rate on a working site. This analysis is aimed at finding whether there is any significant difference in injury rate in relation to the categories (Marshall, 2014). This will also lead to the analysis of variance (ANOVA), which will examine whether there exist a significant difference in the injury rate between these different categories. This will help in drawing an inference from the test results (Fienup & Critchfield, 2010).

Results for Statistics in Analyzing SPSS PASW Software

The descriptive statistics of the injury rate by site are as summarized in Table 1:

Table 1: Descriptive of injury rate by the site.
	Site				Statistic		Std. Error
injury rate	Boston	Mean			15.6293		3.58322
		95% Confidence Interval for Mean		Lower Bound		7.9441
				Upper Bound		23.3146
		5% Trimmed Mean			14.3131
		Median			14.4200
		Variance			192.592
		Std. Deviation			13.87774
		Minimum			.00
		Maximum			54.95
		Range			54.95
		Interquartile Range			13.83
		Skewness			1.554		.580
		Kurtosis			3.920		1.121
	Phoenix	Mean			17.1774		4.86479
		95% Confidence Interval for Mean	Lower Bound		6.9568
			Upper Bound		27.3979
		5% Trimmed Mean			14.8126
		Median			8.7400
		Variance			449.657
		Std. Deviation			21.20512
		Minimum			.00
		Maximum			76.92
		Range			76.92
		Interquartile Range			18.88
		Skewness			2.074		.524
		Kurtosis			3.878		1.014
	Seattle	Mean			12.5376		3.96688
		95% Confidence Interval for Mean	Lower Bound		4.1282
			Upper Bound		20.9470
		5% Trimmed Mean			10.3696
		Median			9.0100
		Variance			267.514
		Std. Deviation			16.35585
		Minimum			.00
		Maximum			64.10
		Range			64.10
		Interquartile Range			17.03
		Skewness			2.222		.550
		Kurtosis			5.788		1.063

Table 1 illustrates that the injury rate in Boston was 15.6293 with a variance of 192.592, Seattle had a mean injury rate of 20.9470 with a variance of 267.514, and Phoenix had a mean of 17.1774 with a variance of 449.657. These results show that Seattle had the highest injury rate follows by Phoenix and Boston had the least injury rate. Nevertheless, the injury rate in the three sites has a positive skewness indicating that the distribution of the injury rate on these sites had a long tail to the right (higher values) (Lowry, 2014). Of the three, Seattle had the highest Skewness; this can be illustrated by Figure 1 below. In addition to this, the distribution of injury rate on the three sites had a Leptokurtic distribution, which is a sharper plot than a normal distribution plot. This is simply because their kurtosis was greater than 3.

Figure 1: Boxplot illustrating injury rate by Supervisor’s Gender,

 

Figure 2: Histogram illustrating injury rate distribution based on site.

To test the validity of the hypothesis that there is no significance difference in injury rate between different working site, an ANOVA analysis was performed at the 95% level of significance and results are as follows.

 

Table 2: ANOVA Table
			Sum of Squares	df	Mean Square	F	Sig.
injury rate * site	Between Groups	(Combined)	197.522	2	98.761	.315	.732
		Linearity	83.574	1	83.574	.266	.608
		Deviation from Linearity	113.948	1	113.948	.363	.550
	Within Groups		15070.334	48	313.965
	Total		15267.856	50

These results indicate that there is a no significance difference between injury rates in the different working site. This is portrayed since sig. Value is greater than 0.05. In particular,  0.550, 0.608, and 0.732 p-values are greater than 0.05. Thus, in agreement with (Fienup, & Critchfield, 2010)we fail to reject the null hypothesis.

The descriptive results of the injury rate analysis based on the gender of the supervisor are as illustrated in Table 2.

 

Table 2: Descriptive based on supervisors gender.
	supervisors gender			Statistic	Std. Error
injury rate	Female	Mean		16.6363	4.00593
		95% Confidence Interval for Mean	Lower Bound	8.4020
			Upper Bound	24.8706
		5% Trimmed Mean		14.3499
		Median		8.7400
		Variance		433.282
		Std. Deviation		20.81543
		Minimum		.00
		Maximum		76.92
		Range		76.92
		Interquartile Range		18.42
		Skewness		1.934	.448
		Kurtosis		3.193	.872
	Male	Mean		13.5321	2.65125
		95% Confidence Interval for Mean	Lower Bound	8.0475
			Upper Bound	19.0166
		5% Trimmed Mean		12.1820
		Median		9.3900
		Variance		168.699
		Std. Deviation		12.98843
		Minimum		.00
		Maximum		54.95
		Range		54.95
		Interquartile Range		16.12
		Skewness		1.610	.472
		Kurtosis		3.243	.918

The Table 3 indicates that the mean injury rate, when there is a female supervisor in different sites, is 16.6363, while the average rate of injury is 13.5321 when male are supervisors. Both have a variance of 433.282 (female) 168.699 (male); these results indicate male gender has a smaller variation in injury rate.  Furthermore, both have positive skewness thus they have a long tail to the right. This spread can be illustrated by the boxplot below.

 

Figure 3: Boxplot illustrating injury rate distribution based on supervisors gender.

 

Figure 4: Histogram illustrating injury rate distribution based on supervisors gender.

To test whether the means of injury rate was statistically significant at 95% confidence level based on the gender of the supervisor, an ANOVA analysis was carried out (Lowry, 2014). The results are summarized in Table 4.

 

Table 4: ANOVA Table
			Sum of Squares	df	Mean Square	F	Sig.
injury rate * supervisors gender	Between Groups	(Combined)	122.436	1	122.436	.396	.532
	Within Groups		15145.421	49	309.090
	Total		15267.856	50
a. With less than three groups, linearity measures for injury rate * supervisors gender cannot be computed.

These results in accordance with (Samuels, 2012) indicate that there is no significant difference between the mean of injury rate since p-value obtained is greater than 0.05 (Fienup &Critchfield, 2010). Thus, in this case, we fail to reject the null hypothesis is and infer that there is no statistically significant difference in injury rate in different working sites (Lowry, 2014).

Discussion and conclusion for Statistics in Analyzing SPSS PASW Software

The results show that there is no discrepancy in injury rate in different sites and also when different genders are supervisors. This indicates that no site is better than the other, and thus we can generalize that the injury rate in all sites is equal. Furthermore, the inferences made show that the injury rate is not higher when one gender is supervising. Thus, we can state that the risk is equal when any gender is supervising.

From the analysis part, it is clear that the research question that was given at the beginning of the paper has been fully answered. It has been thoroughly exhausted, and the results show that there is no significant difference in injury rate at different working sites when different genders are managing. Thus, we can at the 95% level of significance conclude that injury rate is affected by site or gender of the supervisor.

Statistics in Analyzing SPSS PASW Software References

Fienup, D. M., & Critchfield, T. S. (2010)m. Efficiently Establishing Concepts Of Inferential Statistics And Hypothesis Decision Making Through Contextually Controlled Equivalence Classes. Journal of Applied Behavior Analysis, 43(3), 437-62. Retrieved from http://search.proquest.com/docview/755631101?accountid=45049

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

Marshall, C., & Rossman, G. B. (2014). Designing qualitative research. Management Learning, 8(20) 81-99.

Samuels, M. L., Witmer, J. A., & Schaffner, A. (2012). Statistics for the life sciences. Pearson Education.

Weiss, N. A., & Weiss, C. A. (2012). Introductory statistics. Pearson Education.