Significance Difference Of University Motivation Among University Students

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Universities being autonomous bodies as degree awarding institutions and given the present scenario of Pakistan, the level of students' engagement and motivation towards educational institutions have been doubtful. Numerous instances have been reported in media Pakistani media , print or otherwise, condemning educational institutions of not producing well motivated and equipped students which in turn add up to a big pool of jobless human resource of Pakistan. All the universities are required to teach a similar curriculum to students as the courses are being assigned from The Higher Education Commission of Pakistan per degree. The universities follow a tight set of requirements directed from the HEC and are rated on standards defined by HEC. On the basis of these ratings and the market stature and reputation of universities, students prefer one university over other before taking admission into any university. Fortunately, in Peshawar the City University Of Science And Information Technology and the Institute Of Management Sciences are the two most preferred institutions as the number of students enrolling every session is increasing continually since their foundation in the recent years. Both institutions have outgrown themselves and achieved marvels in recent years.

"City university of Science & Information Technology, Peshawar with its slogan "carving intelligence", is among pioneering private universities, in Khyber Pakhtunkhwa, and offers diverse range of bachelors and masters courses in undergraduate and post graduate programs"

"The Institute of Management Sciences, a revolutionary business school in the Peshawar, This government body of higher education grew out of the University of Peshawar; Established in May 1995, the Institute of Management Sciences was the first of its kind in Khyber Pakhtunkha to encourage education based on research and training in the fields of business administration, management sciences, and related disciplines."

The proposed topic is a very first attempt of its kind in vicinity of Peshawar. The study is founded on notion of school motivation. Moreover as school may be regarded as any education driven entity, so the word school or university would be used interchangeably here. Moreover, the version of the inventory of school motivation is used here as it was adopted by Japanese studies of university motivation (that would be discussed further on in here) and regarded as the Inventory of University Motivation. This study would be a great help in predicting whether if there is a difference in university motivation of students of both institutions? Moreover, the results would help to determine the efficacy of either of the mentioned institutions in keeping students motivated towards learning at their respective institutions. Further more some additional measures would also be taken into account and the flow of university motivation would be overlooked on different factors from the literature.

Literature

Micheal Rost, senior editor of world view quotes

"Motivation has been called the "neglected heart" of teaching. As teachers, we often forget that all of our learning activities are filtered through our students' motivation. In this sense, students control the flow of the classroom. Without student motivation, there is no pulse; there is no life in the class".

Plausibly the foremost job of all indigenous learning authorities is assisting students to get familiarized with an ambiance somewhere they know how to learn for themselves a delight of gaining possible worthy education. The objective of serving students to gain the "self-motivation" which than onwards, directs into a long-lasting aspiration to gain knowledge, ought to be of the prime importance in an educator's mind (Ron Renchler 1992).

Nearly all educationalists deem motivation an essential for learning effectively.. It is understood that at hand are numerous foundations for motivating a student, furthermore mostly everybody desires the students more "motivated" and "engaged".

single universal perception in studies going on student's motivation hold identifying learner traits which are contributive to commitment with education. studies hub on what learners carry toward their education via means of objectives, standards or rationale. from time to time these variables may be out looked upon as trait-like characters which are to be valid cross conditions. every now and then they may be used as variables being explicit to sigular context. A succeeding common loom begins by means of proposition so as to the education settings are vital. Numerous sorts of education occurrences encourage motivation and commitment. Accordingly what considered necessary is additional cautious concentration towards scheming and putting into practice settings that make the most of the prospect meant for vigorous, exigent learning occurrences. Simultaneously at hand are the characteristics of peer groups, class-settings, day to day responsibilities, and instructors which are well-known to generate downbeat atmosphere and discomfort, or personal standards mismatched to education and consequence is dullness, no commitment, troublemaking approaches and dropping out eventually.

Successful educational institutions with their high standards of academic achievement are known to have a culture famed to have a goal set that is well defined, valued and promoted by all members of management, that of the faculty and also on part of the students.

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School motivation's importance is also highlighted by Eccles, Midgeley, and Adler (1984), they advocate

"As individuals mature they turn out to be additionally dexterous, well-informed, and capable; they happen to be better able to take responsibility, make decisions, and control their lives. They also feel more able to take responsibility and to make academic decisions. One would hope that with increasing grade level, students would assume greater autonomy and control over their lives and learning. In addition, one would hope that institutions would provide an environment that would facilitate task involvement."

Motivation is the main reason behind every behavior and its dynamics. Proper energy channeled in a suitable direction is what motivation is all about. in recent years, couple of studies (Hidi & Harackiewicz, 2000), (Eccles & Wigfield, 2002) aggregated towards youngsters distinguishable on part of their inability of bonding with education and institutional settings of so They are chiefly known to be bored, uninterested, cut off, not engaged to schooling activities and display minimal motivation towards learning. Further more these were described of being of the disengaged kind, who do not value their relationships with their teachers, do not deem school to be important in their life and they do not consider any value for their peers. These students display feelings of not fitting in, often frequently, and they eventually tend to sack their schooling off.

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Associations with instructors, relations with peers, atmosphere of the classroom, as well as methods that are accounted ingeniously for teaching and learning, and almost everything that happens in the class were deemed vital aspects underneath students' engagement. Student's participation in extracurricular was adopted as an index of affiliation with schooling and identifying ones self with it. Later when gender differences were considered, male students' engagement was related by key factors as classroom climate and perception of schooling (Finn 1989). Students are "more likely to be engaged if they attend schools with high average socio-economic status, strong disciplinary climate, good student-teacher relations and high expectations for student success" (Willms 2003). Almost similar outcomes were reported via diverse facet of "school participation" for determining engagement (Fullarton, 2002)

Engagement and Commitment in comprehension activities is also a key forecaster of academic accomplishment upon considerations of explicit reading practices like time depleted for reading and variety of the reading content. Upon taking engagement behavior an outcome, residential environment and availability of reading material deemed important. Teacher- student relationship and classroom environment were also contributive (Kirsch et al. 2002). Taking in account from reading, its obvious that the contextual environmental support from teachers and parents or home are important for stronger achievement.

Most small scale researches have identified and investigated definite motivational components and their relationship to behavior of students and their achievement. They comprise of performance and mastery goals, self efficacy, situational and individual importance towards interest, affiliation, task averting, intrinsic and extrinsic motivation, self and perceived competence, social loafing and learned helplessness.

Family factors such as parents' level of education and parental support and expectations for their children seem to exert some influence on students' motivation (Beyer, 1995). Hossler and Stage (1992) suggest a positive relationship between the level of parental education and students' tendency to register in postsecondary institutions. The link between parents' level of education and student's motivation might exist because the more educated parents might be more concerned in their children's education than the less educated parents. Paulson showed that parental involvement has a positive effect in student's achievement (1996).

Hardly any studies have investigated the importance of the educational institution's environment on motivation (Quaglia & Perry, 1996; Wilson & Wilson, 1992). The institute environment may obstruct or maintain children's development and motivation (Esposito, 1999; Goodenow, 1993; Mouton & Hawkins, 1996). Factors within the institution environment that may influence students' motivation include sense of safety, belonging, and support in the school and classroom.

Goodenow (1993) recognized that a sense of belonging and support was strongly related with motivation and academic achievement. As Mouton and Hawkins specified, lack of attachment may lead to a sense of loneliness at study setting and may perhaps ultimately result in school failure (1996). Wilson and Wilson pointed out that students' perceived teachers' aspiration had a significant outcome on students' aspirations (1992). Esposito (1999) recognized that the most major factor related with children's adjustment at a study setting is the teacher/student association, and that safety of the school, and the parent and school relationship, contribute to the child's academic achievement.

Given below figure 1 embosses the theoretical framework of this study:

Hypothesis:

In light of the literature, following hypothesis are developed:

Hypothesis

H1: There are no statistically significant mean differences between mean university motivation of students at IMS and mean university motivation of students at CUSIT

H01: There are no statistically significant mean differences between mean university motivation of students at IMS and mean university motivation of students at CUSIT

H2: There are no statistically significant mean differences between mean university motivation of female students and mean university motivation of male students

H02: There are no statistically significant mean differences between mean university motivation of female students and mean university motivation of male students

H3: There are no statistically significant mean differences between mean university motivation of bachelors students and mean university motivation of masters students.

H03: There are no statistically significant mean differences between mean university motivation of bachelors students and mean university motivation of masters students.

H4: There are no statistically significant mean differences between mean university motivation of morning students and mean university motivation of evening students

H04: There are no statistically significant mean differences between mean university motivation of morning students and mean university motivation of evening students

H5: There are no statistically significant mean differences between mean university motivation of employed students and mean university motivation of unemployed students

H05: There are no statistically significant mean differences between mean university motivation of employed students and mean university motivation of unemployed students

Methodology:

Data collection would be done by questionnaires. Inventory of university motivation in general (IUM-Gen) would be used as a likert scale based questionnaire and would be distributed among student sample. The feed back on the questionnaires would b collected and scored.

Participants

The study would be conducted upon the City University Of Science And Information Technology, Pakha Ghulam Peshawar and the Institute Of Management Sciences, Hayatabad Phase 7, Peshawar. The students of morning and evening shift would constitute the sample, which is targeted to be a mix from various departments of both universities. Simple random sampling would be done off the population of both universities combined. The aim is to get as many as possible participants from all shifts and all departments.

Instrument

The Inventory of School Motivation (McInerney & Sinclair, 1991, 1992; McInerney et al., 1997; McInerney, Yeung, & McInerney, 2001) was designed as an exploratory instrument through which a range of motivation salient constructs drawn from Maehr's Personal Investment (Maehr, 1984; Maehr & Braskamp, 1986) model could be identified in educational settings across a diversity of groups. There is considerable empirical evidence drawn from both exploratory and confirmatory factor analytic studies for the validity and reliability of scales drawn from the ISM (McInerney & Ali, 2005). From the ISM, Da Silva and McInerney in 2005 devised two forms of this questionnaire, one to assess student motivation towards learning English specifically, the Inventory of University Motivation towards English (IUM-Eng), and the other to measure student motivation towards university study in general, the Inventory of University Motivation in General (IUM-Gen).

In this study I am going to follow the Inventory of University Motivation In General (IUM-Gen).

Inventory questions relate to the perceived goals of behaviour, each of which has two elements:

Task (Mastery): Task involvement (e.g., "I like to see that I am improving in my schoolwork") and Effort (e.g., "When I am improving in my schoolwork I try even harder").

Ego (Performance): Competition (e.g., "I like to compete with others at school") and Social Power (e.g., "I work hard at school to be put in charge of a group").

Social solidarity: Affiliation (e.g., "I prefer to work with other people at school rather than work alone") and Social concern (e.g., "I like to help other students do well at school").

Extrinsic: Praise (e.g., "I want to be praised for my good schoolwork") and Token rewards (e.g., "I work best in class when I get some kind of rewards").

The students would respond to each item on a 5-point scale (1 = strongly disagree to 5 = strongly agree). The responses to the items would be coded such that higher scores reflected higher levels of motivation.

Along with the IUM-Gen, behavioral responses based on likert scale referring to parental support and expectations, parental involvement, university climate and teacher support and expectation would be used as reported by Maya in her study in 2001.

Bio- demographic index for students would also be included, referring to students' age, gender, employment, class shifts and grade/ semester level attained.

Sample and Analysis:

Simple random sampling would be done off the population of both universities combined. The aim is to get as many as possible participants from all shifts and all departments. The students of both morning and evening shift would constitute the sample.

The Course of analysis was done first by taking means of the variables from factor analysis and an Independent Samples t-test was used for analyzing the differential relationship.

Data Collection:

The questionnaires were circulated online on a Google Spreadsheet link. Online mode of data collection was selected because of lesser availability of time and ease of instant computation of the questionnaire form values. Second reason being the fact of higher attention and ease on part of respondents as they had filled up the questionnaire form in their free time. It was an assumption that, this way, respondents would be volunteering willingly.

Mr Muhammad Nauman Habib is a lecturer at management science department at City University of Science and Information Technology (CUSIT). He is a friend and my class-fellow as well. I asked for his kind support and thanks to him I got to email quite a handful bunch of students at CUSIT. Besides providing me with the email addresses of his students, he also shared the spreadsheet link on his Facebook page where his friend list (over 250 friends) includes his students and fellows at CUSIT. This was a great help for which I am eternally thankful to him. I ended up 189 responses from CUSIT's both morning and evening shift (bachelors and masters) students which are currently in session.

For data from Institute of Management Sciences (IMSciences) I had a bit of luck with my friends and class-fellows ab-initio. Later on I turned to my class-fellow and a lecturer in morning shift Mrs. Sumera Khan. She too helped me in the same way Mr. Muhammad Nauman Habib did. With her exquisite Facbook friends list (about 500 friends) including most of her students, I ended up with 251 responses from IMSciences' both morning and evening shift (bachelors masters and MS) students which are currently in session.

Overall 440 responses came back included with 189 students from CUSIT and 251 students from IMsciences. Out of these 440 respondents, 187 are studying in Bachelors Program, 167 in Masters Program and the respondents from MS Program were 86. Unemployed respondents were 394 and 46 respondents are currently employed too. Female respondents accounted for 141 in count and there were 299 male respondents overall. 194 respondents study in evening shift and 246 respondents study in morning shift.

Given below are the tabulated details of the frequencies

Statistics

DEGREE

EMPLOYED

GENDER

SHIFT

University

N

Valid

440

440

440

440

440

Missing

0

0

0

0

0

DEGREE

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Bechelor

187

42.5

42.5

42.5

Masters

167

38.0

38.0

80.5

MS

86

19.5

19.5

100.0

Total

440

100.0

100.0

EMPLOYED

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

No

394

89.5

89.5

89.5

Yes

46

10.5

10.5

100.0

Total

440

100.0

100.0

GENDER

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Female

141

32.0

32.0

32.0

Male

299

68.0

68.0

100.0

Total

440

100.0

100.0

SHIFT

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Evening

194

44.1

44.1

44.1

Morning

246

55.9

55.9

100.0

Total

440

100.0

100.0

University

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

CUSIT

189

43.0

43.0

43.0

IMS

251

57.0

57.0

100.0

Total

440

100.0

100.0

From CUSIT, out of 189 respondents, 99 were from bachelors program, 90 from masters program, of which 12 were employed and 177 unemployed. 48 respondents were females, 141 male respondents were there. 120 respondents were from morning shift while .69 respondents were from the evening shift.

Given below are the tabulated frequency details from CUSIT.

Statistics

University

Degree

Employed

Gender

Shift

N

Valid

189

189

189

189

189

Missing

0

0

0

0

0

Degree

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Bechelors

99

52.4

52.4

52.4

Masters

90

47.6

47.6

100.0

Total

189

100.0

100.0

Employed

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

No

177

93.7

93.7

93.7

Ye

12

6.3

6.3

100.0

Total

189

100.0

100.0

Gender

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Female

48

25.4

25.4

25.4

Male

141

74.6

74.6

100.0

Total

189

100.0

100.0

Shift

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Evening

69

36.5

36.5

36.5

Morning

120

63.5

63.5

100.0

Total

189

100.0

100.0

From IMSciences, total of 251 respondents were gathered. Out of these 251 respondents, 77 were from masters program, 88 from bachelors program and 86 were from MS program. 34 respondents were employed and remaining 217 were unemployed. Female respondents accounted for 93 and male respondents accounted for 158 in count. 125 respondents study in evening shift and in morning shift there are 126 respondents. The responses from MS students would be removed in hypothesis testing as the respondants from MS are only from the IMsciences and no student of city university were from MS program

Given below are the tabulated frequency details from IMSciences..

Statistics

University

Degree

Employed

Gender

Shift

N

Valid

251

251

251

251

251

Missing

0

0

0

0

0

Degree

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Bechelors

88

35.1

35.1

35.1

Masters

77

30.7

30.7

65.7

MS

86

34.3

34.3

100.0

Total

251

100.0

100.0

Employed

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

No

217

86.5

86.5

86.5

Yes

34

13.5

13.5

100.0

Total

251

100.0

100.0

Gender

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Female

93

37.1

37.1

37.1

Male

158

62.9

62.9

100.0

Total

251

100.0

100.0

Shift

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Evening

125

49.8

49.8

49.8

Morning

126

50.2

50.2

100.0

Total

251

100.0

100.0

Analysis:

Questionnaire was coded in order to run for the factor analysis. The coded factor items of the questionnaire are as shown in appendix 9.

A) FACTOR ANALYSIS

Following is the analysis for factors of Questionnaire used here for University Motivation:

Correlation Matrix:

For initial solution in factor analysis we need at least three correlations to show magnitude of at least 0.3 or greater. The results of correlation matrix suggested the initial solution test has been passed and the data will now be exposed to other diagnostic tests (see appendix 1)

Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO Test):

This is a second diagnostic test for the applicability of factor analysis. This test checks the adequacy of sample for factor analysis. If the value of KMO is greater than 0.5 the sample is adequate and the analysis can be started. See the results in appendix 2, the KMO is 0.85 and hence we have sufficient grounds to proceed with our analysis.

Bartlett's Test of Sphericity:

This also a diagnostic test, if the Bartlett's test of Sphericity is significant, we can proceed with our factor analysis. Results in appendix 2, suggested that test is significant and we can go for factor analysis.

Anti-Image:

This test is also used for the initial solution and check the sample adequacy. The diagonal values represent the anti-image and it's a partial correlation. Anti-image value of a variable which is greater than 0.5, represents its adequacy and a value lesser than 0.5 will require removing from analysis. The variables had adequacy through anti-image.

Communalities:

In factor analysis the amount of variance in each variable is showed by Communalities. The initial variances shared by a variable in factors are indicated as initial communalities. The principle components should always equal 1 in factor analysis. For extraction through correlation analyses, these values are the proportion. The small extracted communalities estimates indicate variables are not fit in factor solution and those will be removed from analysis, and the process will be iterated. In appendix 4, the extraction through correlation showed that all variables' communalities are above the specified range of 0.5 and they are sharing sufficient amount of variation with factors. Those with values under 0.5 were phased out and the process was iterated until all values were above 0.5.

Eigen Values and Total Variance Explained:

The table given in appendix 5 gives the Eigen-values, the variance explained by each factor and the cumulative variance explained by all factors. The Eigen value greater than 01 is selected as a factor, from given below analysis we have eight factors. The variance explained by each factor in percentage term is given in cumulative % column. All the eight factors individually contributed the variances of 23.517%, 9.774%, 8.372%, 6.882%, 5.656%, 5.064%, 4.192% and 3.527% cumulatively these four factors contributed 69.985% variances.

Component Matrix:

The rotated component matrix shows the factor loadings for each variable, and this is un-rotated method of factor loadings. Each value in un-rotated component matrix shows the correlation between the variable and un-rotated factor. The appendix 6 gives the values of component matrix. Factor loadings of variables in component matrix are achieved when a variable load a value of 0.4 or greater. But some time a variable show its loading in more than two factors in that case rotated component matrix are preferred than component matrix. The component matrix shows that most of the variables are loaded on factor 1. The values below 0.4 were asked to be suppressed so they would not be visible in the table. The variables with no values above 0.4 were removed and the whole process was repeated again until all variables had values above 0.4.

Rotated Component Matrix:

The rotated component matrix is also called pattern matrix for oblique rotations, this method reports the factor loadings of each variable on factors after rotation. Values in rotated component matrix show the partial correlation between each variable and loaded factor. The values greater 0.4 indicated the item load on that specific factor. However, it is possible that an item may load on two or more factors, we call it complex structure. In case of complex structure, the items should be removed and the process will be iterated. In appendix 7, many complex structures are identified, which will now be removed one by one.. After each removal the process was iterated and finally we got a reduced number of factors along with new eigen-values and % variations. In appendix 8, the variables of TS3 TS4 Ts5 TS6 TS7 load on factor 1, P1 P2 P3 P4 P5 load on factor 2, SC1 SC2 SC3 SC4 load on factor 3, Sp3 Sp4 Sp5 Sp6 load on factor 4, PS1 PS2 PS3 load on factor 5, E4 E5 E6 load on factor 6, T2, E2 E3 load on factor 7 and C1 C2 C5 load on factor 8,

In all 32 variables were removed by factor analysis. Facets of School Climate (ScC4, ScC3, ScC2, and ScC1), Affiliation (A1, A2 and A3) and Parental Involvement (PI1 and PS5) were entirely removed due to failure of any of their variables to load on any of the factors. Other facets also had some of the variables removed and the remaining list of facets and variables is shown in appendix 10:

Hypothesis Testing By an Independent Samples T-Test

The variables from factor analysis were taken mean of respective of each of their nine facets. Summing up the means of all these nine facets the result was mean university motivation of all the respondents. Now we would look at the results of independent samples t-test respectively with each of the hypothesis.

H1: There are no statistically significant mean differences between mean university motivation of students at IMS and mean university motivation of students at CUSIT

H01: There are no statistically significant mean differences between mean university motivation of students at IMS and mean university motivation of students at CUSIT

Since the significance value is not less than 0.05, so the null hypothesis of no difference is not rejected and its concluded that there is a no significant difference between the mean university motivation of students of CUSIT and IMS.

H2: There are no statistically significant mean differences between mean university motivation of female students and mean university motivation of male students

H02: There are no statistically significant mean differences between mean university motivation of female students and mean university motivation of male students

Since the significance value is less than 0.05, so the null hypothesis of no mean differences is rejected and its concluded that there is are significant mean differences between the mean university motivation of male and female students overall of both universities.

H3: There are no statistically significant mean differences between mean university motivation of bachelors students and mean university motivation of masters students.

H03: There are no statistically significant mean differences between mean university motivation of bachelors students and mean university motivation of masters students.

Since the significance value is less than 0.05, so the null hypothesis of no mean differences is rejected and its concluded that there are significant mean differences difference between the mean university motivation of bachelors and masters students overall of both universities.

H4: There are no statistically significant mean differences between mean university motivation of morning students and mean university motivation of evening students

H04: There are no statistically significant mean differences between mean university motivation of morning students and mean university motivation of evening students

Since the significance value is less than 0.05, so the null hypothesis of no mean differences is rejected and its concluded that there are significant mean differences difference between the mean university motivation of morning and evening students overall of both universities.

H5: There are no statistically significant mean differences between mean university motivation of employed students and mean university motivation of unemployed students

H05: There are no statistically significant mean differences between mean university motivation of employed students and mean university motivation of unemployed students

Since the significance value is greater than 0.05, so the null hypothesis of no mean differences is not rejected and its concluded that there are a no significant mean differences between the mean university motivation of employed and unemployed students overall of both universities.

Conclusion:

The aim of the study was primarily to find whether if there was a significant difference in university motivation of students studying in the Institute Of Management Sciences (IMSciences) and City University Of Science And Information Technology (CUSIT). The inventory of university motivation is used in this study which is based on inventory of school motivation, variably used in distinct cross cultural and other studies. Later on more variables were included in the inventory which was mentioned in several researches mentioned in the literature review. Then factor analysis was done on all the variables' factors and hence the facets of university motivation were limited to nine. Then the means of these variables were corresponded to the respective facets and added up to give mean university motivation of the respondents. Independent samples t-test was then results on hypothesis were obtained. With significant level of 0.876 there is a no significant difference between the mean university motivation of students of CUSIT and IMS. Then onward with analyzing the relation ships overall on both universities over gender, shift timings (morning\evening), employed\unemployed and grade level (bachelors\masters). So, with significant level of 0.000 there are significant mean differences between the mean university motivation of male and female students overall of both universities. With significant level of 0.030 there are significant mean differences difference between the mean university motivation of bachelors and masters students overall of both universities. With significant level of 0.005 there are significant mean differences difference between the mean university motivation of morning and evening students overall of both universities. With significant level of 0.242 there are a no significant mean differences between the mean university motivation of employed and unemployed students overall of both universities.

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