Results
The provision of adequate parking space is becoming an integral part of modern live. Organizations and institutions have been forced to think of the means of ensuring enough parking space is available to improve efficiency and productivity (Farris & Neil, 2010; Anthony & Johnson, 2008). This is especially for events that are organized and hosted by institutions.
Lack of parking space is a counterproductive situation. A lot of time and energy is spent in looking for parking space that would otherwise be used gainfully in other areas (Kiechel, 2010) . As such, this makes availability and utilization of parking space an issue of concern for stakeholders in organizations and administrations in institutions.
The parking problem becomes a point of interest especially when it gets in the way of the normal operations in an organization or institution. This problem may have adverse effects on the productivity of the staff and other individuals that use the facilities and services offered (Kotler, 2009).
University of Tasmania is faced with a problem in parking spaces. This research aims at investigating the parking space situation in the university. Focus will be on observing the different entry points and how they are used during different periods of the learning cycle. The data will assist in gaining a better understanding of the trend in the parking practices by the staff, students and other individuals that frequent the university.
The data used for the analysis in this paper was collected from the University of Tasmania for the year 2018. The collection focused on four entry points to the university. These are; Alexander Street, Earl Street, Churchill Avenue and Grace Street. The number of cars observed at these entry points was recorded on a daily basis between January 1st 2018 and June 30th 2018.
Table 1: Car Parking Usage Data Description
The analysis in Table 1: Car Parking Usage Data Description show the average number of cars observed at each of the four entrances during the research period. The Churchill Avenue had on average the highest number of car observations daily at 376 cars This was followed by the Alexander Street at 374 cars daily, Earl Street at 365 cars daily and Grace Street at 272 cars daily in that order. (The numbers of cars have been rounded off to the nearest whole number)
Figure 4: Semester Days Comparison for Cars Observed on Grace Street (Source SPSS)
The results plotted in the graphs show that in the in-semester days there are more cars entering the university through all the four entries as compared to non-semester days.
The margins are also almost similar for the four entry points. The average number of cars entering the university through the four entries during the in-semester days ranged between 300 and 500 cars. During the non-semester days, the average number of cars entering the university through the four entries ranged between 100 and 250 cars.
Table 2: T-Test of Semester Days for Alexander Street (Source SPSS)
Description and Illustration of Dataset
In Table 2: T-Test of Semester Days for Alexander Street (Source SPSS) we observe that the sig. (2 tailed) = 0.000. Considering a significance level = 0.05, then p = 0.000 < 0.05. Thus implying a difference in the car park usage in Alexander Street during the in-semester and non-semester days.
Table 3: T-Test of Semester Days for Churchill Avenue (Source SPSS)
In Table 3: T-Test of Semester Days for Churchill Avenue (Source SPSS) we observe that the sig. (2 tailed) = 0.000. Considering a significance level = 0.05, then p = 0.000 < 0.05. Thus implying a difference in the car park usage in Churchill Avenue during the in-semester and non-semester days.
Table 4: T-Test of Semester Days for Earl Street (Source SPSS)
In Table 4: T-Test of Semester Days for Earl Street (Source SPSS) we observe that the sig. (2 tailed) = 0.000. Considering a significance level = 0.05, then p = 0.000 < 0.05. Thus implying a difference in the car park usage in Earl Street during the in-semester and non-semester days.
Table 5: T-Test of Semester Days for Grace Street (Source SPSS)
In Table 5: T-Test of Semester Days for Grace Street (Source SPSS) we observe that the sig. (2 tailed) = 0.000. Considering a significance level = 0.05, then p = 0.000 < 0.05. Thus implying a difference in the car park usage in Grace Street during the in-semester and non-semester days.
The two independent samples test is a t-test technique applied when interest is in comparing the averages of the dependent variable in two independent categories (Barbara & Susan, 2014; F & S, 2008). The alternative, Wilcoxon-Mann Whitney test is applicable for instances when the dependent variables are measured in the ordinal scale (Freedman, 2009; Babbie, 2010; Oscar Marban, 2009). This therefore makes it inapplicable since the dependent variables in this research are measured on the ratio scale.
In our case, we had four dependent variables and one independent variable. The dependent variables were; Grace Street Entrance, Earl Street Entrance, Churchill Avenue Entrance and Alexander Street Entrance. The independent variable, Semester days had two different categories; in-semester days and non-semester days.
The t-test was done separately for the four dependent variables to establish whether there was any difference in car park usage for the four entrances with regards to semester cycles.
The plots for all the four entrances are displayed in the graphs above. The plots indicate that the data on the observed number of cars have a similar trend for the four entrances.
The highest frequency of number of cars ranges between 0 to 50 cars, while the lowest frequency of the number of cars ranges between 200 and 600 cars.
Table 6: ANOVA (Kruskal Wallis) Test for Car Park Entrances
The results in Table 6: ANOVA (Kruskal Wallis) Test for Car Park Entrances show that for all the four car park entrances the Asymp. Sig = 0.000. Therefore, considering 0.05 as the significance level, then p = 0.000 < 0.05. Hence we conclude that there exist significant difference in the observations made in the car park entrances to the university.
Table 7: T-Test Analysis for Car Park Entrances
Reading the results from the Sig. (2-tailed) column in Table 7: T-Test Analysis for Car Park Entrances above, the values for all the four entrances = 0.000.
Considering the significance level = 0.05, then p = 0.000 < 0.05. This implies that the observations from the four car park entrances are significantly different.
The results in Table 7: T-Test Analysis for Car Park Entrances conforms to the results in Table 6: ANOVA (Kruskal Wallis) Test for Car Park Entrances. This thus confirms that outcome on the differences in the car park entrances.
The Kruskal Wallis Test can be described as the non-parametric statistics version of the ANOVA Test (Corder & Foreman, 2009; Han Kamber & Jaiwei, 2011; O’Neil & Schutt, 2013). The Kruskal Wallis Test applies for a situation where we have a single nominal independent variable and several dependent variables (Howitt & Cramer, 2010; Everitt & Skrondal, 2010; Gareth, et al., 2013).
The t-test on the other hand, is only sufficient for when the difference in the categories of the independent variable(s) is being investigated with respect to the dependent variables (Christian & Griffiths, 2017).
This therefore leaves the Kruskal Wallis as the most appropriate for our case.
The results from the analysis in this research paper show that;
- The car parking usage in the four entrances have similar characteristics. This is both in terms of the frequencies and with respect to the semester days (semester periods).
- Although the car parking usage in the four entrances have similar characteristics, there are significant differences between the observations made in the four entrances.
- There are significant differences between the observations made during the in-semester and non-semester days in the university.
References
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