Student’s Ways of Spending Leisure Time. A Case Study on the Bachelor Degree Students of Uasvm

Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development Vol. 17, Issue 2, 2017 PRINT ISSN 2284-7995, E-ISSN 2285-3952 STUDENT’S WAYS OF SPENDING LEISURE TIME. A CASE STUDY ON THE BACHELOR DEGREE STUDENTS OF UASVM Ionela Mituko VLAD, Elena STOIAN University of Agronomic Sciences and Veterinary Medicine Bucharest, 59 Marasti Boulevard, District 1, 011464, Bucharest, Romania, Phone: +40213182564, Fax:+40213182888, Emails: bmitsouko@yahoo.fr, stoian_ie@yahoo.com Corresponding author: bmitsouko@yahoo.fr Abstract In this paper we have analysed the determinants of the students’ leisure time, based on 198 respondents of an questionnaire. The students are at the bachelor level in the Faculty MIEADR, USAMV Bucharest. The questions have reffered to the conditions in which students used to spend their leisure time. The performed analysis consisted in several steps. First, we made a descriptive analysis of the variables, by doing the frequences tables; then, there were performed correlation tables and the crosstabulation between pairs of variables. In order to verify the level of the statistical significance among the variables, it was also performed a chi-square statistical test. As general requirement, we have chosen to emphasise the results by pointing out the influence of the gender and place of residence variables for the respondents. From the findings of this paper, we have concluded that students of our faculty have different approaches, divided by place of residence regarding the holidays spend abroad. Meanwhile, by taking into consideration the gender, we got significant results regarding the responses on level of disponible revenus (a), the average spending amount by stay (b) and on the persons that accompanies the respondent student during the holidays (c). There were other results reflected on the level of Pearson coefficient on correlations between variable regarding the average lenght of the stays, the averge amount spend on accommodation and food etc. Key words: leisure time, questionnaire, students, gender, residence INTRODUCTION Focusing in the leisure time, we have tried to design a frame of the young people – student at our faculty, by conducting on this purpose an questionnaire. This tool was composed 16 questions on personal data (age, gender, residence area), on spending amount for accommodation and food during the holidays, type of booking, touristic services, transportation and preferences for travel destinations abroad and the average spending amount that they are ready to pay for holidays. For this study, we relied on a methodology that has been targeted a number of analyzes conducted, particularly in light of coordinates gender and place of residence of the respondents. Through this approach we wanted to find out if perception and approach problems for the students spending holidays, reflected in the responses received, are closely related to their gender or their place of residence, in other words, if there is a significant difference between the coordinates listed on the possibilities to spend their free time. Reffering to the litterature on the same topics, we have mentioned the appreciation at a general level on tourism that was made by Ghazal [3], stated that “Tourism should be given the status of industry in order that the facilities and benefits available to the industry are also available to tourism projects”. Another approach given by Yoon, Heo and Lee [10] made from a different point of view, maybe strictly from a specific education profile, has shared experiences on “An adapted version of the Course Experience Questionnaire (CEQ) administered to tourism management students…with a questionnaire previously utilised with students taking tourism-related degree courses…and accepting that student loyalty is an important concern and When an educational institution enhances student loyalty, it is expected to improve its reputation and education quality…”. In order to validate the uses of our 401 Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development Vol. 17, Issue 2, 2017 PRINT ISSN 2284-7995, E-ISSN 2285-3952 tool, this time with a different purpose, we have cited another type of analyze based on structured questionnaires made by Wong Shun and Wong Kin [11] who clearly appreciated that ” Structural equation modeling approach was used to evaluate the explanatory power and casual links of the model. The results indicate that relationship commitment is a strong driver of student loyalty. Second, relationship benefits, relationship termination costs, and shared values are found to have positive influence on relationship commitment...”. Because the perception is related also to the touristic sector and education, the authors Yong and Chih [9] stated that in order « To investigate and identify critical attributes for education, we focused on the professional competency held by the course designers and on the delivery creativity and entrepreneurship concepts for students participating in knowledge or skills training.” Another approach on gender was made by Iorga [5] who said that “The contemporary rural family is subjected to structural and major identity changes. In this respect the role of gender identity signifies the degree to which a person assumes his or hers behaviors specific to their cultural role”. We have had in this paper an approach based on area of residence, and if we think about this structured approach on gender, there were studies on that issue; among these, we have mentioned those where Iorga [6] has made statements regarding the rural area such as “... studying gender equality within the rural family setting we must take into account the social construction of both genders (male and female) manifested along the interaction between the sexes. Gender equality defines the principle goal of family and social development, in which the rights, responsibilities and personal growth of each individual is not influenced by being born male or female but by how they make the upmost use of their potential.” On the other hand, Kuan [7], examining gender differencies with a different approach and has found that with his purpose to examine the gender differences „in creative performance by using the Consensual Assessment Technique and taken as a whole, the CAT 402 shows some value in examining creative performance in adult learners.“ To underline, once again, that obtaining results on differences in gender, Baer and Kaufman [1] concluded in their study that “Lack of differences between girls and boys, and between men and women, is the most common outcome of the many studies reported above. In some cases, especially in the area of divergent-thinking testing, there are significant numbers of studies in which one group or the other scores higher, but these are generally counter-balanced by studies showing just the opposite.” Even if there is not a very recent study, the paper written by Dale and Robinson [2] expressed at that date, in theirs research area, habits and youngs’ choisis, that are actually present also in our country; thus, they stated that “Explores developments in tourism education to date is drawing on wider theoretical perspectives including the ``McDonaldization'' and the ``Disneyization'' of society…”. Another validation of the methods employed in this study, was made also by Stoian, Dinu et al. [8] where it was stated that “…questionnaires are a good tool that allows quantification and comparison of observation sets of information. The limits the use this tool for analysis are those that it requires time for implementation, significant resources and also important logistics and finally, we will provide a simplified picture of reality”. With a very specific investigation over a destination, Hassan and Shahnewaz [4], have made by explorations which pointed out a different point of view; thus, they “examines existing tourism services of the highly trafficked destination of Cox’s Bazar Sea beach in Bangladesh using a tourist satisfaction analysis. Using observation and selfdetermined questionnaire survey, the study classified tourist to their demographic and socioeconomic characteristics. MATERIALS AND METHODS In this paper we have conducted an analyse shaped on three types of computations. The first approach was based on analysis of frequency tables. Another approach was the Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development Vol. 17, Issue 2, 2017 PRINT ISSN 2284-7995, E-ISSN 2285-3952 correlation coefficients, in which we obtained statistically significant coefficients for a number of variables. These correlations were also conducted and presented in the light of the gender and the place of residence of the respondents. Thirdly, it was followed a crosstabulation tables analysis and by contingency tables with the results of chisquare tests coefficient; in turn, they were discussed in terms of gender and place of residence. Thus, we have divided the content of the questionnaire into three major groups, by the meaning of the questions. The first group refers to the general conditions of deploying stays, the second group refers to the practical aspects of the trips with two direct questions, and a third group of the questions, refers strictly to the financial aspects of the touristic services from which benefited students surveyed. The second approach of the study was made by using the correlation tables with the Pearson’ coefficient, by bivariate correlation. The pairs for which the correlation coefficient was with a high level of signification, were naturaly found later in the next step; this step consisted in crosssection tables, which enphasized the level and statistical signification on pairs of variables. In that sens, we have investigated on three dimensions crosstabs and focused, once again, on gender and place of residence. In order to decide on the level of statistical significance, this analyse was, like as usually, followed by the contingency table of the chisquare test. RESULTS AND DISCUSSIONS In this article we used the results of 198 questionnaires processed based on responses from students of the MEEARD Faculty. In order to analyze these responses, we used primarily frequency table, especially for your personal profile. Thus, we mention that the sample was made up of 128 women (64.6%), of which 71 were from urban area and 57 came from rural areas. For males, there were in total 70 respondents (35.4%), of which 46 were from urban areas and 24 from rural areas. Overall, the 117 respondents in urban areas (59.1%) and 81 coming from rural areas (40.9%) were registered an average age of 22.48 years. Another details to be mentioned here is the existence of other occupations, apart from those of being student. In this respect, the answers showed 85 students (42.9%) which recorded another paid occupation, while the remaining 113 (57.1%) have the only current profession, that of student (see table 1 below). Table 1.Gender and residence frequency F Gender M Total Count % Count % Count % Residence 1 2 71 57 55.5% 44.5% 46 24 65.7% 34.3% 117 81 59.1% 40.9% Total 128 100.0% 70 100.0% 198 100.0% Source : own calculations, 2017 For the correlation coefficients, we only mentioned that for the bivariate correlation with the Pearson’ coefficient, and the test of significance with two tailed, there was one variable (responses for the questions regarding level of the average revenu) which is correlated with 10 other variable and among these with gender (but not with the residence) and for instance, not with the one related to the type of payement for the touristic services. There were another four variables correlated with 8 other variables. A closer look was made for the variables gender and residence. The first one registered significant correlations only with three other variables (level of disponible revenus (a), the average spending amount by stay (b) and the persons that accompanies the respondent during the holidays (c)); meanwhile, the second one is correlated only with one variable (the 12th) and is the one regarding the student’ habits to spend their holidays abroad. Getting these results, the follow up computation was the cross-tabulation in order to establish the statistical significance of the correlation coefficient. The analyse based on the crosstabs was made for summarize cathegorical variables, in our case with three types of variables; in order to get more than two dimensions in the responses variables, we have introduced a third control variable. Thus, from the three way cross-tabulation; one first result is returned in a summary casses with 403 Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development Vol. 17, Issue 2, 2017 PRINT ISSN 2284-7995, E-ISSN 2285-3952 valid, missing and total casses (values). Other results were given by counting and percentage within respondents, divided, in our case by gender, than by residence. These will tell us, what was happend, in average among the female and males, respectivly, among the urban and the rural areas. The next output is the continuously contingency table which shows two options for the independent variables (or predictor variable). We can thus see, the response’ categories of the dependent variable; we can also identify the actual observed values associated with the outcome and the expected values (values if there is no association between the two variables). Next step was to determine if we have statistical significance in this relationship. Because of the third control variable, in our case gender and residence, we have got two partial tables and one for the total. Thus, in order to generate and interpret a three-way crosssection tables with a chi-square test for independence, we displayed in the tables below the results of the variable 3 („the level of the average revenue”), controlled by the third variable, residence. So, we observed that the lowest percentage of disponible revenu in urban places is for the higher amount and the highest percentage is for the interval 5001000lei. We have the same results for the rural areas in what concern the percentage of the highest level of the revenu, while the bigest percentage for the this revenu was registered for the level 1,000-1,500 lei. The results splited by gender and on total level are displayed below (table 2). The chi-square test indicated us that, on total level, the variable gender and the revenu can be consider, with the Person’s corelation coefficient, statistical significant (p-value < 0.05). Table 2.Crosstab on gender * variable 3 Level of the average revenue * Residence Crosstab on gender * variable 3 Level of the average revenue * Residence Residence 1 11 10.9 2 27 21.8 % within Gender Count Expected Count % within Gender 15.5% 7 7.1 15.2% 38.0% 9 14.2 19.6% female Count Expected Count % within Gender 15 12 26.3% male Count Expected Count % within Gender Count Expected Count % within Gender female Urban Gender male Rural Gender Total Total Count Expected Count Residence Pearson Chi-Square Likelihood Ratio In similar conditions, the second computation was made for the variable 4 (the average amount usually designated to be spend on holiday) together with gender, also controlled by the third variable residence. With one of three posibles responses (500 lei/person; 500404 Total 4 12 15.2 5 4 6.1 71 71 23.9% 11 11 23.9% 16.9% 13 9.8 28.3% 5.6% 6 3.9 13.0% 100.0% 46 46 100.0% 15 12.7 26.3% 13 14.8 22.8% 10 10.6 17.5% 4 7 7.0% 57 57 100.0% 2 5 8.3% 3 5.3 12.5% 8 6.2 33.3% 5 4.4 20.8% 6 3 25.0% 24 24 100.0% 35 35 17.7% 54 54 27.3% 49 49 24.7% 40 40 20.2% 20 20 10.1% 198 198 100.0% Chi-Square Tests Value 12.692 12.724 Linear-by-Linear Association N of Valid Cases Source: own computation, 2017 Variable 3 3 17 17 10.866 198 df 4 4 Asymp. Sig. (2-sided) 0.013 0.013 1 0.001 1,000 lei/person, >1,000lei/person), the results were that students from both urban and rural zones spending in average 500 lei/person during a stay. The results divided by gender are slightly different (Table 3). Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development Vol. 17, Issue 2, 2017 PRINT ISSN 2284-7995, E-ISSN 2285-3952 Table 3. Crosstab on gender* variable 4 Average amount usually designated to be spend on holiday * residence Crosstab on gender* variable 4 Average amount usually designated to be spend on holiday * residence Residence Variable 4 1 female Urban Gender male female Rural Gender male Total Total 2 3 Count 43 21 7 71 Expected Count 36.4 25.5 9.1 71 % within Gender 60.6% 29.6% Count 17 21 8 46 Expected Count 23.6 16.5 5.9 46 % within Gender 37.00% 45.70% 17.40% Count 33 21 3 57 Expected Count 30.3 21.8 4.9 57 % within Gender 57.9% 36.8% Count 10 10 4 24 Expected Count 12.7 9.2 2.1 24 % within Gender 41.7% 41.7% 16.7% Count 103 73 22 198 Expected Count 103 73 22 198 % within Gender 52.0% 36.9% 11.1% 9.9% 100.0% 100.00% 5.3% 100.0% 100.0% 100.0% Chi-Square Tests Residence Total Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases Value df Asymp. Sig. (2-sided) 8.926 8.885 8.812 198 2 2 1 0.012 0.012 0.003 Source: own computation, 2017 From the chi-square tests with the value of the Pearson’ coefficient and the p-value (Asymp.Sig. 2-sided), we have got statistical significance for the urban area and for the total. The 7th variable tabulated with the gender and controlled by the residence variable, reffered to the persons that accompanies the respondents during their holidays. There were three type of responses; these people could be only family members (a), family and friends (b) or only the student’ colleagues (c). The cross-tabulation is listed below and indicated us that definetly, our students spend their holidays mainly with the family and friends; meanwhile, the lowest score is registered for the first response (spending holidays with family members). Still, there is a difference between gender; the female students, spend more time with the family, then the male students. So, for this variable 7, there were registered differences between gender. Thus, we pointed out the fact that in urban area, both females and males spend the lowest nomber of holidays with family; while in the rural area, there is a difference between gender: females spend more time with family members, while the males did that in a very small proportion. All in all, the students spend their holidays first with family and friends, secondly only with colleagues and third only with family members. The significance of these results comes from the chi-square tests, which indicated us that the results are significants for the rural area and for the Total (p-value, associated to the chi-square value is < 0.05). From a statistical significant chi-square tests, we are going to reject the null hypothesis and say that there is a significant relationship between the two variables, so the variable contribute to the realisation of the analysed variable (in our case variable 3, 4 and 7). 405 Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development Vol. 17, Issue 2, 2017 PRINT ISSN 2284-7995, E-ISSN 2285-3952 Table 4. Crosstab on gender* var 7 Persons that accompanies the respondent during holidays * residence Crosstab on gender* var 7 Persons that accompanies the respondent during holidays * residence Residence 1 female Urban Gender male female Rural Gender male Total Count Expected Count % within Gender Count Expected Count % within Gender Count Expected Count % within Gender Count Expected Count % within Gender Count Expected Count % within Gender 7 6.1 9.9% 3 3.9 6.5% 12 9.1 21.1% 1 3.9 4.2% 23 23 11.6% Variable 7 2 3 52 12 48.5 16.4 73.2% 16.9% 28 15 31.5 10.6 60.9% 32.6% 39 6 38.7 9.1 68.4% 10.5% 16 7 16.3 3.9 66.7% 29.2% 135 40 135 40 68.2% 20.2% Total 71 71 100.0% 46 46 100.0% 57 57 100.0% 24 24 100.0% 198 198 100.0% Chi-Square Tests Total Residence Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases Value 10.453 10.501 10.169 198 df 2 2 1 Asymp. Sig. (2-sided) 0.005 0.005 0.001 Source: own computation, 2017 Thus, from the table of chi-square test, we can say that there is a very strong evidence (agains the null hypothesis) that among respondents residence there are two variables that are independent or not associated in the population, so we could confidently reject (with a risck smaller then 5%) the same hypothesis; thus, there is a strong evidence that among the analised respondents, there is a relationship, at least of 5% significance level. In the opposite case, so when the pvalue associated to the chi-square Pearson value is greater then 0.05, we can interpret the statistics that among respondents are insufficient evidence against our null hypothesis that the two variables are independent or not associated. So, in other words, because the p-value >0.05 (5% significance level), we will fail to reject our null hypothesis, so there is no relationship between the two analysed variables. However, in some situations, when controlling by gender, it could be a partial association between two variables, the relationship between the two variables is no longer statistically significant, but a partial association between remains, for those respondents where p-value < 0.05. When 406 controlling respondent’ gender, we can resume that it does appear not to have an impact on whenever or not their level will affect (so the first variabe will affect the second one). The analysis of the variable referring to the terms in which „spending holidays abroad” was analysed by gender and place of residence; finally this has revealed the following: in urban areas for females who „go seldom abroad” for holidays has recorded the highest score, while the lowest score was recorded for the answer "always go abroad for holidays". Respondents coming from the rural areas said that they mostly „never go abroad”, and the smallest level was for the responses „always go abroad for holidays”. The male category, in both areas, urban and rural, have the highest options for the answers "rarely go abroad" and, as well as for girls and boys, answered that only the least go abroad to spend their holiday. We obtained here though overall, a positive association, so that we can say there is a very strong evidence (agains the null hypothesis) that among respondents gender has two independent variables that is associated in the population (Table 5). Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development Vol. 17, Issue 2, 2017 PRINT ISSN 2284-7995, E-ISSN 2285-3952 Table 5. Crosstab on residence* var 12 Spending holidays abroad * gender Crosstab on residence* var 12 Spending holidays abroad * gender Gender 1 Urban Male Residence Rural Urban Female Residence Rural Total Total Count Expected Count % within Residence Count Expected Count % within Residence Count Expected Count % within Residence Count Expected Count % within Residence Count Expected Count % within Residence Chi-Square Tests Gender Value Pearson Chi-Square 7.358 Likelihood Ratio 7.355 Linear-by-Linear Association 6.589 N of Valid Cases 198 24 30 33.8% 30 24 52.6% 13 15.8 28.3% 11 8.2 45.8% 78 78 39.4% df 2 2 1 Variable 12 2 3 37 10 33.3 7.8 52.1% 14.1% 23 4 26.7 6.2 40.4% 7.0% 31 2 28.3 2 67.4% 4.3% 12 1 14.7 1 50.0% 4.2% 103 17 103 17 52.0% 8.6% Total 71 71 100.0% 57 57 100.0% 46 46 100.0% 24 24 100.0% 198 198 100.0% Asymp. Sig. (2-sided) 0.025 0.025 0.01 Source: own computation, 2017 From the tables presented above, we can conclud that in terms of statistical signification there is one at the level at 5% between the variables associated, so there is a signification between the students’ answers to the question on „Spending holidays abroad” and place of residence. CONCLUSIONS The first conclusion of the study, regarding the gender was that the highest level of the revenu disponible for holidays, is registered for males, both on urban and rural areas.The analysis of bivariate correlations of variables where there was added place of residence, we obtained results which confirmed that gender differentiation was most relevant in this study. Upon the second analysed variable reffered to the the average amount usually destinated to be spend on holiday, here once again, the male gender seems to spend more money for holidays. Meanwhile, the highest level of the revenu allocated to the holidays, registered very low scores and there are very small differences between the genders and between the urban and the rural areas. From the analysis of bivariate correlations of variables that were added like the residence, we obtained results indicating that gender differentiation was the most relevant in this study. Answers to the question related to the level of the revenue were relevant for respondents in urban areas, and partialy for those from rural areas; then, for the question on the amount that students assign for each stay – the results were relevant for urban respondents. The answers for question related to the persons that accompanies during the holidays the respondent, was relevant for the respondents in rural areas. The other variable analyzed (the average amount usually destinated to be spend on holiday), once again, the male gender tend to allocate more money for holidays. Meanwhile, the highest level of the revenu designated to holidays registered very low scores and there are very small differences between the genders and between the urban and the rural areas. The last computation we have made, was on possibility to spend the holidays abroad; thus, we have got statistical significance (with the value of the coefficient Pearson Chi-Square and its p-value < 0.05) by taking into account the place of residence; so there is a significant difference among the people coming from the different place of residence. 407 Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development Vol. 17, Issue 2, 2017 PRINT ISSN 2284-7995, E-ISSN 2285-3952 REFERENCES [1]Baer, J., Kaufman, J.C., 2008, Gender differences in creativity. Journal of Creative Behavior, 42(2), 75–105. [2]Dale, C., Robinson, N., 2001, The theming of tourism education: a three-domain approach. 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