# Multivariate Regression Proposal to the Gap between University Education and Labor Market: The Kingdom’s Vision 2030

Authors: Alanazi Talal Abdulrahman; Ishfaq Ahmad; Dalia Kamal Alnagar; R. A. Ramadan; T. A. Alraqad; Y. M. Jawarneh
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Background. University education in all countries of the world occupies an important placebecause universities are the stronghold of enlightened thought, the center of enlightenment, and the home of constructive scientific research. Through our survey of the published papers, there was no statistical study examining the reasons for this gap. If the reasons are studied, the compatibility between the outputs of education and the labor market will be achieved. Objective. To identify factors that cause the gap between university education and the labor market, then expand the compatibility between learning outcomes in Saudi universities and market requirements in accordance with the Kingdom’s Vision 2030. Methods and Materials. Questionnaires were given to the general population of Saudi Arabia, using Google forms for data collection. The target group was 384 people answered. Results. The findings showed, Resolution IV with regression analysis gave the factors that caused the gap between university education outcomes and the labor market: no training institutions and graduate centers, lack of awareness of children about the appropriate specialties and strengthening their inclinations towards itand the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market. In addition, the saturated design with regression analysis gave the factors that caused the gap between university education outcomes and the labor market: lack of awareness of children about the appropriate specialties, and strengthening their inclinations towards it and existence of non-national competencies, universities’ lack of materials, and support for the academic content required by the labor market and the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market, no training institutes and centers for graduates and the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market. Conclusion. No training institutions and graduate centers, lack of awareness of children about the appropriate specialties and strengthening their inclinations towards it and the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market are caused the gap between university education outcomes and the labor market.


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## Body

## 1. Introduction

‏University education in all countries of the world occupies an important place because universities are the stronghold of enlightened thought, the center of enlightenment, and the home of constructive scientific research. Universities are the institutions that perform a major societal function, which is to build the capabilities of the citizens of any country in the world, to refine their personalities, and to provide them with skills, knowledge, ways of thinking and analyzing problems [1]. If we consider the huge numbers of unemployed graduates as a strong indicator of the gap between the outputs of higher education and the needs of the labor market, and the inability of universities to provide the labor market with the appropriate and appropriate competencies and skills, this is considered related to the weak sensitivity of the higher education system to the needs of the local labor market. It is a crisis that lies in the nature of the development of higher education institutions in the Arab world, which calls for the need to pay attention to raising awareness and changing the culture of work among young people, in order to reduce unemployment and increase competitiveness [2].The authors [3] stated that many of the wage gaps between men and women have been found due to significantly lower levels of female work experience and seniority. The authors [4] reported that the growing desire to pursue higher education in Russia is creating a growing mismatch between educational qualifications and the demands of the labor market. Blue employee jobs at various levels are difficult to fill, and there is not enough demand for skills from people with advanced degrees to avoid high unemployment. We need to find a way to provide education according to the needs of the economy.The authors [5] applied a qualitative method that used semistructured interviews with a small sample of 38 senior year students at a UK university who had also participated in an earlier two-wave quantitative survey conducted on 387 penultimate and recent freshmen same university. University based in Great Britain. The results showed that students realize their investments in higher education for net financial gain. However, this is shrinking due to increased tuition fees and the associated student debts and interest payments, which are eroding income premiums. As their education progresses, students feel more employable from a personal point of view, but from a market point of view, they feel less employable due to the competition for jobs and costs for university graduates. The authors [6] stated that workers and educators face the challenges of major changes in the workplace. How to prepare undergraduate students for the world of employment is becoming more important to higher education providers. A gap-by-gap in the labor market in Vietnam started from the perspective of those who are dissatisfied with the current situation (Employee Competency Model). Then, the satisfaction criteria for each criterion were finalized by the quality criteria. This article [7] contributed to the literature using a variety of methodologies (ordinary least squares, Hickman’s correction, two-stage least squares, and fixed household effects) in order to estimate the economic returns to education. The most recent nationally representative data—the Pakistan Integrated Household Survey (2002) was used. Earnings function estimates consistently reveal significant gender asymmetry in the economic returns to education, with returns to women’s education being significant and statistically much higher than men’s. Returning to an additional school year ranges between 7% and 11% for men and between 13% and 18% for women. There are also large and direct returns to women’s education at lower levels of education, and the education earnings profile is more convex for women than for men. However, the disintegration of the gender pay gap (in the component “explained” by different male-female endowments and the remaining component) indicates that there is very differential treatment by employers. The authors [8] reported that entrepreneurship gender gaps negatively affect both income and overall productivity, as they reduce the average talent of entrepreneurs. Specifically, the expected income loss from excluding 5 percent of women is 2.5 percent, while the loss is 10 percent if they were all employers. We found that gender gaps lead to an average income loss of 15 percent in the OECD, 40 percent of which are due to entrepreneurship gaps.Through our survey of the published papers, there was no statistical study examining the reasons for this gap. If the reasons are studied, the compatibility between the outputs of education and the labor market will be achieved. One of the goals of the Kingdom’s Vision 2030 is the gap between the gaps between higher education degrees and the requirements of the labor market. This research seeks to obtain the Kingdom’s vision regarding the vision of the harmonization between the quality of registration in Saudi universities and the requirements of the labor market according to the vision during the time: determining the most important Saudi labor market. Students in Saudi universities, in addition to identifying the viewpoint of the university (the practitioners) and the community (the external beneficiary) and their satisfaction with the quality of learning outcomes in Saudi universities. In addition, it envisioned submitting a proposal to expand the compatibility between learning outcomes in Saudi universities and market requirements in accordance with the Kingdom’s Vision 2030. The novelty in the paper is to study the reasons that led to the gap between university education and the labor market using ready-made statistical designs and models that can be searched for in all the answers to the questions of the questionnaire. The main contribution of the paper is to extract the statistical results from the first and second designs, and in case the results of the factors are similar, we are taken into consideration for the research.The research paper is arranged as follows: In the second section, the method and materials used are present. In the third section the results are present with the applications of the two methods used. Finally, the conclusion and discussion are in the last section.


## 2. Materials and Methods

### 2.1. The Questionnaire

Questionnaires were used in this assessment to help individuals identifythe causes of the gap between university education outcomes and the labor market. The survey consisted of two sections. Table1 provided questions about personal information such as region, age, gender, university specialization, employment status, and exact field of work. The other section contained 14 questions about the causes of the gap between university education outcomes and the labor market. Participants were asked to identify the main causes of the gap between university education outcomes and the labor market for the following reasons:S1: there is no qualification by universities for the labor market properly.S2: lack of compatibility in university strategies with labor market strategies.S3: universities’ lack of materials and support for the academic content required by the labor market.S4: rapid job growth.S5: some specializations do not have areas in the labor market.S6: the lack of quality of education outputs in order to meet the desire and need of the public and private sectors to obtain a distinguished quality of graduates.S7: there are no training institutes and centers for graduates.S8: the private sector’s lack of contribution to the training and qualification of university graduates to attract the competencies that meet its needs.S9: lack of awareness of children about the appropriate specialties and strengthening their inclinations towards it.S10: the lack of new and modern specializations required by the labor marketS11: existence of nonnational competencies.S12: the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market.S13: the lack of a clear database and information that helps students and directs them to study applied and professional disciplines.S14: the requirements of the labor market are not compatible with the knowledge and skills that the new entrants hold in terms of the type of specializations and the level of mastery of graduates’ skills in their fields of specialization.Q: the response variable is how much time it took from graduating to getting a job, or not getting it.Table 1
The demographic information.
VariableFrequencyPercentRegionThe northern region20954.42Western region10026.04Central region4912.76Eastern region143.65Southern region123.13GenderMale23861.97Female14638.03Age22–3222658.8532–4211329.4242–523910.1652 and more61.57University specializationScientific18247.40Humanitarian14337.24Engineering359.11Medicine246.25Employment statusGovernment employee13936.20Private sector5815.10Unemployment18748.70Exact field of workIn the field of specialization13033.85Other than your field of expertise8622.40I do not work16843.75The target group was 238 male responders and 146 female responders.


### 2.2. Data Analysis and Study Test

Table1 shows demographic characteristics such asregion, age, gender, university specialization, employment status, and exact field of work. All participants were fluent in Arabic. We analyzed the data using SPSS version 25.0. Quantitative analysis calculated the frequency and percentage of demographic data and tested it using inference statistics.


### 2.3. Saturated Design (SD)

A two-level factorial testcould be aplan with the least number of runs that guarantees the fair estimation of the impacts and intuitive of intrigued given the remaining parameters are insignificant. The number of runsn retained in an SD is broken even with to the full number of parameters of intrigued. Hence a soakedplannetworkmay be a square non-singular lattice of the arrange n with sections from {−1, 1} that’s chosen so as to fulfill the conditions of the parameters of intrigued ([8]).


### 2.4. Resolution IV

These are plans in which no fundamentalimpact is aliased with any other primaryimpact or with any two-factor interaction, but two-factor intelligent are aliased with others. The example of Resolution IV is shown down In Table2 [9].Table 2
Resolution IV.
−1−1−1−1−1−1−1−1−1111−1−11−111−1−111−1−1−11−1−11−1−11−11−11−111−1−11−11111−11−1−1−1−111−1−111−11−11−11−11−111−1111−1−11111−11−1−1111−1−1−1111111


### 2.5. Regression Analysis

Consider modeling between dependent and independent variables. The dependent is how much time it took from graduating to getting a job, or not getting it and independent variables are cause the gap between university education and the labour market. An analysis of the data using linear regression with the software package SPSS with equation model, mean and standard deviation.The taking after segment, presents the outcomes about of this consider. The outcomes about are displayed concurring to the objective of this study. The designs are taken from all the data using the two methods mentioned above in the form of applications and taking into account the different elements selected.


## 2.1. The Questionnaire

Questionnaires were used in this assessment to help individuals identifythe causes of the gap between university education outcomes and the labor market. The survey consisted of two sections. Table1 provided questions about personal information such as region, age, gender, university specialization, employment status, and exact field of work. The other section contained 14 questions about the causes of the gap between university education outcomes and the labor market. Participants were asked to identify the main causes of the gap between university education outcomes and the labor market for the following reasons:S1: there is no qualification by universities for the labor market properly.S2: lack of compatibility in university strategies with labor market strategies.S3: universities’ lack of materials and support for the academic content required by the labor market.S4: rapid job growth.S5: some specializations do not have areas in the labor market.S6: the lack of quality of education outputs in order to meet the desire and need of the public and private sectors to obtain a distinguished quality of graduates.S7: there are no training institutes and centers for graduates.S8: the private sector’s lack of contribution to the training and qualification of university graduates to attract the competencies that meet its needs.S9: lack of awareness of children about the appropriate specialties and strengthening their inclinations towards it.S10: the lack of new and modern specializations required by the labor marketS11: existence of nonnational competencies.S12: the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market.S13: the lack of a clear database and information that helps students and directs them to study applied and professional disciplines.S14: the requirements of the labor market are not compatible with the knowledge and skills that the new entrants hold in terms of the type of specializations and the level of mastery of graduates’ skills in their fields of specialization.Q: the response variable is how much time it took from graduating to getting a job, or not getting it.Table 1
The demographic information.
VariableFrequencyPercentRegionThe northern region20954.42Western region10026.04Central region4912.76Eastern region143.65Southern region123.13GenderMale23861.97Female14638.03Age22–3222658.8532–4211329.4242–523910.1652 and more61.57University specializationScientific18247.40Humanitarian14337.24Engineering359.11Medicine246.25Employment statusGovernment employee13936.20Private sector5815.10Unemployment18748.70Exact field of workIn the field of specialization13033.85Other than your field of expertise8622.40I do not work16843.75The target group was 238 male responders and 146 female responders.


## 2.2. Data Analysis and Study Test

Table1 shows demographic characteristics such asregion, age, gender, university specialization, employment status, and exact field of work. All participants were fluent in Arabic. We analyzed the data using SPSS version 25.0. Quantitative analysis calculated the frequency and percentage of demographic data and tested it using inference statistics.


## 2.3. Saturated Design (SD)

A two-level factorial testcould be aplan with the least number of runs that guarantees the fair estimation of the impacts and intuitive of intrigued given the remaining parameters are insignificant. The number of runsn retained in an SD is broken even with to the full number of parameters of intrigued. Hence a soakedplannetworkmay be a square non-singular lattice of the arrange n with sections from {−1, 1} that’s chosen so as to fulfill the conditions of the parameters of intrigued ([8]).


## 2.4. Resolution IV

These are plans in which no fundamentalimpact is aliased with any other primaryimpact or with any two-factor interaction, but two-factor intelligent are aliased with others. The example of Resolution IV is shown down In Table2 [9].Table 2
Resolution IV.
−1−1−1−1−1−1−1−1−1111−1−11−111−1−111−1−1−11−1−11−1−11−11−11−111−1−11−11111−11−1−1−1−111−1−111−11−11−11−11−111−1111−1−11111−11−1−1111−1−1−1111111


## 2.5. Regression Analysis

Consider modeling between dependent and independent variables. The dependent is how much time it took from graduating to getting a job, or not getting it and independent variables are cause the gap between university education and the labour market. An analysis of the data using linear regression with the software package SPSS with equation model, mean and standard deviation.The taking after segment, presents the outcomes about of this consider. The outcomes about are displayed concurring to the objective of this study. The designs are taken from all the data using the two methods mentioned above in the form of applications and taking into account the different elements selected.


## 3. Results

### 3.1. Applications of Resolution IV

An application was taken from the data starting from the second factor to the seventh, which appears in the following table.Table3 is analyzed by obtaining the regression analysis and the p values for these factors. The outcomes about the regression investigation are shown in Table 4. In the table, estimated mean, standard deviation, and p values for all factors are given. The mean was 1.3813, and the standard deviation was 1.66302. Factors S2, S3, S4, S5, S6, and S7 were not the significant predictor of the causes of the gap between university education outcomes and the labor market because the p values (0.800, 0.800, 0.780, 0.424, 0.439, 0.199) are greater than 0.05. The equation generated in the analysis is Q = 1.381 + ɛ. In conclusion, there is no significant cause for the gap between university education outcomes and the labor market.Table 3
Application of resolution IV from S2 to S7.
N/factorS2S3S4S5S6S7Q1−1−1−1−1−1−152−1−1−111113−1−11−11104−1−111−1−125−11−1−11−116−11−11−1107−111−1−1108−11111−1391−1−1−1−111101−1−111−10111−11−11−10.1121−111−1111311−1−11121411−11−1−1015111−1−1−15161111111Table 4
The regression of application 3.1.1.
VariableMeanStd. deviationNModelBValueQ1.38131.6630216(Constant)1.3810.014S20.00001.0328016S2−0.1190.800S30.00001.0328016S30.1190.800S40.00001.0328016S40.1310.780S50.00001.0328016S5−0.3810.424S60.00001.0328016S6−0.3690.439S70.00001.0328016S7−0.6310.199An application was taken from the data starting from the fourth factor to the ninth, which appears in the following table.Table5 was analyzed by regression analysis of these factors and obtaining p values. The results of the regression analysis are shown in Table 6. This table shows the estimated mean, standard deviation, and p value for all factors. The average was 2.6375 and the standard deviation was 3.93885. The factor S7 was an important predictor of creating a gap between higher education outcomes and the labor market because its p value (0.028) was greater than 0.05. Generated equation in the analysis, Q = 2,638 + 2,250 S7 + ɛ. In summary, no training institutions and graduate centers are the main cause of the gap between higher education outcomes and the labor market.Table 5
Application of resolution IV from S4 to S9.
N/factorS4S5S6S7S8S9Q1−1−1−1−1−1−10.12−1−1−111113−1−11−11114−1−111−1−1105−11−1−11−106−11−11−1117−111−1−1108−11111−1391−1−1−1−110101−1−111−110111−11−11−11121−111−1131311−1−11101411−11−1−10.115111−1−1−111611111111Table 6
The regression of application 3.1.2.
VariableMeanStd. dNModelBValueQ2.63753.9388516(Constant)2.6380.013S40.00001.0328016S40.6250.486S50.00001.0328016S5−0.6250.486S60.00001.0328016S61.1130.228S70.00001.0328016S72.2500.028S80.00001.0328016S80.7380.413S90.00001.0328016An application was taken from the data from the ninth to the fourteenth factor, which appears in the following table.In Table7, analyzes were performed by obtaining regression analysis and p values for these factors. The results of the regression study are presented in Table 8. Table showing the calculated mean, standard deviation, and p value for all factors. The mean was 2.6188 and the standard deviation was 4.07941. Factors S9 and S12 were important predictors of the disparity between college performance and the labor market as the p values (0.048, 0.043) exceeded 0.05. The equation obtained during the analysis is Q = 2.619 + 2.006 S9 + 2.069 S12 + ɛ. In conclusion, the lack of awareness of children about relevant specialties and increasing children’s tendency the continued increase in the number of graduates in light of the weak absorption capacity of the labor market gap in the outcomes of university education and the labor market.Table 7
Application of resolution IV from S9 to S14.
N/factorS9S10S11S12S13S14Q1−1−1−1−1−1−102−1−1−111183−1−11−11104−1−111−1−185−11−1−11−106−11−11−11117−111−1−1108−11111−11091−1−1−1−110.3101−1−111−10111−11−11−10.1121−111−110.51311−1−11131411−11−1−1015111−1−1−11161111110Table 8
The regression of application 3.1.3.
VariableMeanStd. deviationNModelBValueQ2.61884.07941161(Constant)2.6190.015S90.00001.0328016S9−2.0060.048S100.00001.03280162S100.5060.579S110.00001.0328016S11−0.1690.852S120.00001.0328016S122.0690.043S130.00001.0328016S130.0190.983S140.00001.0328016S140.2310.798


### 3.2. Applications of Saturated Design

In this section, we will search for the factors collectively in the data in the form of a saturated design, and it is taken randomly, taking into account the non-repetition of the design for applications.


#### 3.2.1. Application One

In Table9, the data items extracted from the dataset are validatedwith saturated design and performing stepwise regression analysis. The results of the regression study are shown in Table 10. The table shows the calculated average, standard deviation, and p value of all factors. The average is 2.0231 and the standard deviation is 2.36014. Since the p value (0.01, 0.005) exceeds 0.05, factors S9 and S11 are important predictors of the difference between university performance and the labor market. The equation obtained during the analysis is Q = 3.221–1.724 S9 − 1.391 S11 + ɛ. To sum up, the lack of awareness of children about the appropriate specialties, and strengthening their inclinations towards it and the existence of non-national competencies are the main cause of the gap between higher education outcomes and the labor market.Table 9
Application one of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q11111111111111102111−1111−1−1−1111113111111111111112411−11111−1−1−11−1−1−1351111−1−1−1−1−111−1−1−1561−11111111−1−111127111−111111111110.281111111−111111−10.19111−111−1111−111−1310−1−1−1−1−1−1−11−1−1−1111811−1−1−111111111111312−111−1−1−11111−111−1213−11−11−1−1−111−11−1−1−10Table 10
Stepwise regression analysis of application one.
VariableMeanStd. deviationNModelBValueQ2.02312.36014131(Constant)2.6420.001S10.38460.9607713S9−1.6080.015S20.53850.87706132(Constant)3.2210.000S30.38460.9607713S9−1.7240.001S40.23081.0127413S11−1.3910.005S50.38460.9607713S60.38460.9607713S70.38460.9607713S80.38460.9607713S90.38460.9607713S100.23081.0127413S110.38460.9607713S120.53850.8770613S130.53850.8770613S140.07691.0377513S110.00001.0328016S120.00001.0328016


#### 3.2.2. Application 2

In Table11, the information items extracted from the dataset are validated with saturated design and the performing stepwise multivariate analysis. The results of the regression study are shown in Table 12. The table shows the calculated average, Std deviation, and p value of all factors. The average is 2.2538 and therefore the Std deviation is 2.25485. Since the p value (0.000, 0.002) exceeds 0.05, factors S3 and S12 are important predictors of the difference between university performance and therefore the marketplace. The equation obtained during the analysis is Q = 1.700 + 2.000S3 − 2.800 S12 + ɛ. To sum up, universities’ lack of materials and support for the academic content required by the labor market and the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market are the most explanation for the gap between educational activity outcomes and also the marketplace.Table 11
Application two of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1−11−1−1−1−11−1−1−11−1−1−112111−1111111111113111−1−1−1−11−11−11110.14−1−1−11−1−111111−11135−1−1−11−111−1−1−1−1−1−1136111−1−1−111111−11−1871111111111111108111−1111−1−1−11111191111111111111121011−11111−1−1−11−1−1−13111111−1−1−1−1−111−1−1−15121−11111111−1−1111213111−111111111110.2Table 12
Stepwise regression analysis of application two.
VariableMeanStd. deviationNModelBValueQ2.25382.25485131(Constant)2.3670.000S10.53850.8770613S12−1.4670.011S20.53850.87706132(Constant)1.7000.001S30.38460.9607713S12−2.8000.000S40.07691.0377513S32.0000.002S50.07691.0377513S60.23081.0127413S70.69230.7510713S80.23081.0127413S90.07691.0377513S100.23081.0127413S110.53850.8770613S120.07691.0377513S130.38460.9607713S140.38460.9607713


#### 3.2.3. Application 3

In Table13, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 14. This table shows the calculated means, standard deviations, and p values for all factors. The average is 2.1846, so the deviation per hour is 2.30917. Coefficients S3, S7 and S12 are important predictors of university performance and market differences, as p values (0.001, 0.046, 0.000) exceed 0.05. The equation obtained from the analysis is Q = 1.200 + 2.150 S3 + 0.800S7 − 3.150 S12 + ɛ. In summary, universities’ lack of materials and support for the academic content required by the labor market, no training institutes and centers for graduates and the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market are most of the gaps between the results. This is an explanation of educational activities and markets.Table 13
Application three of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1111−1111111111112111−1−1−1−11−11−11110.13−1−1−11−1−111111−11134−1−1−11−111−1−1−1−1−1−1135111−1−1−111111−11−1861111111111111107111−1111−1−1−1111118111111111111112911−11111−1−1−11−1−1−13101111−1−1−1−1−111−1−1−15111−11111111−1−1111212111−111111111110.2131111111−111111−10.1Table 14
Stepwise regression analysis of application 3.
VariableMeanStd. deviationNModelBValueQ2.18462.30971131(Constant)2.6000.000S10.69230.7510713S12−1.8000.001S20.53850.87706132(Constant)1.9000.000S30.53850.8770613S12−2.8500.000S40.23081.0127413S31.7500.003S50.23081.01274133(Constant)1.2000.017S60.38460.9607713S12−3.1500.000S70.69230.7510713S32.1500.001S80.23081.0127413S70.8000.046S90.23081.0127413S100.38460.9607713S110.53850.8770613S120.23081.0127413S130.53850.8770613S140.38460.9607713


#### 3.2.4. Application 4

In Table15, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 16. This table shows the calculated means, standard deviations, and p values for all factors. The average is 2.7273, so the deviation per hour is 2.53341. Coefficient S9 is important predictors of university performance and market differences, as p values (0.011) exceed 0.05. The equation obtained from the analysis is Q = 2.567 + 1.767 S9 + ɛ. In summary, the private sector’s lack of contribution to the training and qualification of university graduates to attract the competencies that meet its needs is most of the gaps between the results.Table 15
Application 4 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q11111111111111112−1−11−1111111111103111−1111111111124−1−1−1−1−1−1−1−1−1−1−1−1−1−10٫55−1−1−1−1−1−111111−11116−1−1−1−1−1−1−1−1−1−11−1−1−127−1−1111−1−1−11−11−1−1−118111−11111111111691−1−1−1−1−1−1−11−1−1−1−1−1510111−111−1−111−1−1110٫1111−1−1−1−1−1−1−1−1−1−1−1−1−1712−1−1−1−1−1−1−11−1−1−1−1−1−10131−11−1−1−1−1111111−15Table 16
Stepwise regression analysis of application 4.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ2.72732.5334111S10.09091.0444711(Constant)2.5670.001S2−0.45450.9342011S91.7670.011S30.09091.0444711S4−0.63640.8090411S5−0.09091.0444711S6−0.27271.0090511S7−0.09091.0444711S80.27271.0090511S90.4545.9342011S100.09091.0444711S110.4545.9342011S12−0.09091.0444711S130.09091.0444711S14−0.09091.0444711


#### 3.2.5. Application 5

In Table17, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 18. This table shows the calculated means, standard deviations, and p values for all factors. The average is 3.0231, so the deviation per hour is 2.74231. Coefficients S3 and S5 are important predictors of university performance and market differences, as p values (0.27, 0.003) exceed 0.05. The equation obtained from the analysis is Q = 4.539–1.930 S5 − 1.437 S3 + ɛ. In summary, universities’ lack of materials and support for the academic content required by the labor market and some specializations do not have areas in the labor market are most of the gaps between the results.Table 17
Application 5 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1111−1−1−111111−11−1821111111111111103111−1111−1−1−1111114111111111111112511−11111−1−1−11−1−1−1361111−1−1−1−1−111−1−1−1571−11111111−1−111128111−111111111110.291111111−111111−10.110111−111−1111−111−1311−1−1−1−1−1−1−11−1−1−1111812−1−1−111111111111313−111−1−1−11111−111−12Table 18
Stepwise regression analysis of application 5.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ3.02312.7423113S10.53850.87706131(Constant)3.781<0.001S20.53850.8770613S5−1.9690.009S30.53850.87706132(Constant)4.539<0.001S40.07691.0377513S5−1.9300.003S50.38460.9607713S3−1.4370.27S60.38460.9607713S70.53850.8770613S80.38460.9607713S90.38460.9607713S100.38460.9607713S110.38460.9607713S120.53850.8770613S130.69230.7510713S140.07691.0377513


#### 3.2.6. Application 6

In Table19, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 20. This table shows the calculated means, standard deviations, and p values for all factors. The average is 1.538, so the deviation per hour is 3.78255. Coefficient S9 is important predictors of university performance and market differences, as p values (0.033) exceed 0.05. The equation obtained from the analysis is Q = 2.988 + 2.155 + ɛ. In summary, the private sector’s lack of contribution to the training and qualification of university graduates to attract the competencies that meet its needs is most of the gaps between the results.Table 19
Application 6 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1−1−11−1−111111−11115211−1−111111−1111183−1−1−1−1−1−1−1−1−1−1−1−1−1−114−1−11−1−1−1−1−1−1−11−1−1−10٫15−1111−1−11111111−1061111111111111112711−1−11111−1−1−11110811−1−11−111-1-1-1-1-1-11911111111-11111101011111111111111711−1−1−11−1−1−1−1−1−1−1−1−1−1312−1−11−1−1−1−1−1111−111213−111−111−111−1−11110٫1Table 20
Stepwise regression analysis of application 6.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ0.15383.7825513S1−0.07691.0377513(Constant)2.9880.006S20.23081.0127413S92.1550.033S30.23081.0127413S4−0.23081.0127413S50.07691.0377513S60.07691.0377513S70.23081.0127413S80.38460.9607713S90.07691.0377513S10−0.07691.0377513S110.07691.0377513S120.23081.0127413S130.38460.9607713S140.23081.0127413


#### 3.2.7. Application 7

In Table21, the information items extracted from the dataset are validate with saturated design and performing stepwise multivariate analysis. The results of the regression study are shown in Table 22. The table shows the calculated average, Std deviation, and p value of all factors. The average is 2.3308 and therefore the Std deviation is 2.47366. Since the p value (<0.001, <0.001) exceeds 0.05, factors S9 and S11 are important predictors of the difference between university performance and therefore the marketplace. The equation obtained during the analysis is Q = 3.771–2.049 S9 − 1.459 S11 + ɛ. To sum up, lack of awareness of children about the appropriate specialties and strengthening their inclinations towards it and existence of non-national competencies are the most explanation for the gap between educational activity outcomes and also the marketplace.Table 21
Application 7 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1111111111111112211−11111−1−1−11−1−1−1331111−1−1−1−1−111−1−1−1541−11111111−1−111125111−111111111110.261111111−111111−10.17111−111−1111−111−138−1−1−1−1−1−1−11−1−1−111189−1−1−1111111111113.010−111−1−1−11111−111−1211−11−11−1−1−111−11−1−1−1012−1−1−1−1−1−1−1−111−1−1−1−1513111−1111111111−10Table 22
Stepwise regression analysis of application 7.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ2.33082.4736613S10.23081.01274131(Constant)3.382<0.001S20.38460.9607713S9−1.9520.009S30.23081.01274132(Constant)3.771<0.001S40.07691.0377513S9−2.049<0.001S50.23081.0127413S11−1.459<0.001S60.23081.0127413S70.23081.0127413S80.38460.9607713S90.53850.8770613S100.38460.9607713S110.23081.0127413S120.38460.9607713S130.38460.9607713S14−0.23081.0127413


#### 3.2.8. Application 8

In Table23, the information items extracted from the dataset are validate with saturated design and performing stepwise multivariate analysis. The results of the regression study are shown in Table 24. The table shows the calculated average, Std deviation, and p value of all factors. The average is 1.0077 and therefore the Std deviation is 1.21824. Since the p value (0.028, 0.002) exceeds 0.05, factors S6 and S12 are important predictors of the difference between university performance and therefore the marketplace. The equation obtained during the analysis is Q = 0.693–0.931 S12 + 0.633 S6 + ɛ. To sum up, the lack of quality of education outputs in order to meet the desire and need of the public and private sectors to obtain a distinguished quality of graduates and the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market are the most explanation for the gap between educational activity outcomes and also the marketplace.Table 23
Application 8 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q111−11−111−1−11−1−11132111−1−11−1−111−111−103−1−1−1−1−1−1−1−1−1−1−1−1−1−11411−1−111−1−11−1−11−1105−1−1111111111−11116111−1−1−111−1111110٫171−111111111−111108111−1111111111109−1−1−11−11−1−11−11−1−1−13101−1−11−1−1−1−1−111−1−11011−1−11111−1111−1−111312−1−1−1−1−1−1−1−1−11−1−1−1−111311111111−1111111Table 24
Stepwise regression analysis of application 8.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ1.00771.2182413S10.23081.01274131(Constant)0.9490.005S2−0.07691.0377513S12−0.7650.016S30.07691.03775132(Constant)0.6930.016S40.07691.0377513S12−0.9310.002S5−0.07691.0377513S60.6330.028S60.38460.9607713S7−0.07691.0377513S8−0.07691.0377513S90.07691.0377513S100.53850.8770613S11−0.07691.0377513S12−0.07691.0377513S130.23081.0127413S140.38460.9607713


#### 3.2.9. Application 9

In Table25, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 26. This table shows the calculated means, standard deviations, and p values for all factors. The average is 3.6923, so the deviation per hour is 2.65784. Coefficients S4, S8 and S12 are important predictors of university performance and market differences, as p values (<0.001, 0.003, 0.022) exceed 0.05. The equation obtained from the analysis is Q = 1.936–3.548 S4 − 2.151 S8 + 1.385 S12 + ɛ. In summary, rapid job growth, the private sector’s lack of contribution to the training and qualification of university graduates to attract the competencies that meet its needs and the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market. are most of the gaps between the results.Table 25
Application 9 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q11−1111−1−1−11−11−1−1−112−111−1111111111163−1−1−1−1−1−1−1−11−1−1−1−1−154−111−111−1−111−1−1110٫15−1−1−1−1−1−1−1−1−1−1−1−1−1−176−1−1−1−1−1−1−11−1−1−1−1−1−107−1−11−1−1−1−1111111−158−11−111−1−1−11−1−11110٫39−11−1−11111−11−1−1−11110−1−1−1−1−11−1−1−1−1−1−1−110٫311−1−11−111−11−11−1−1−11312−11−1−11111111111513−1111111−11111111Table 26
Stepwise regression analysis of application 9.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ3.69232.6578413S1−0.8462.55470131(Constant)2.7500.004S2−0.07691.0377513S4−1.7500.039S3−0.07691.03775132(Constant)2.1670.009S4−0.53850.8770613S4−2.6250.005S50.23081.0127413S8−1.4580.039S60.07691.03775133(Constant)1.9360.005S7−0.38460.9607713S4−3.548<0.001S8−0.07691.0377513S8−2.1510.003S90.23081.0127413S121.3850.022S100.07691.0377513S11−0.23081.0127413S123.69232.6578413S13−0.84620.5547013S14−0.07691.0377513


#### 3.2.10. Application 10

In Table27, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 28. This table shows the calculated means, standard deviations, and p values for all factors. The average is 2.9462, so the deviation per hour is 2.66477. Coefficient S6 is important predictors of university performance and market differences, as p values (0.001) exceed 0.05. The equation obtained from the analysis is Q = 2.567–2.206 S6 + ɛ. In summary, the lack of quality of education outputs in order to meet the desire and need of the public and private sectors to obtain a distinguished quality of graduates is most of the gaps between the results. This is an explanation of educational activities and markets.Table 27
Application 10 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1−1−1−11−1−111111−11132−1−1−11−111−1−1−1−1−1−1133111−1−1−111111−11−1841111111111111105111−1111−1−1−1111116111111111111112711−11111−1−1−11−1−1−1381111−1−1−1−1−111−1−1−1591−11111111−1−1111210111−111111111110.2111111111−111111−10.112111−111−1111−111−1313−1−1−1−1−1−1−11−1−1−11118Table 28
Stepwise regression analysis of application 10.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ2.94622.6647713S10.53850.8770613(Constant)3.794<0.001S20.38460.9607713S6−2.2060.001S30.38460.9607713S40.23081.0127413S50.23081.0127413S60.38460.9607713S70.53850.8770613S80.23081.0127413S90.23081.0127413S100.23081.0127413S110.38460.9607713S120.23081.0127413S130.53850.8770613S140.23081.0127413


## 3.1. Applications of Resolution IV

An application was taken from the data starting from the second factor to the seventh, which appears in the following table.Table3 is analyzed by obtaining the regression analysis and the p values for these factors. The outcomes about the regression investigation are shown in Table 4. In the table, estimated mean, standard deviation, and p values for all factors are given. The mean was 1.3813, and the standard deviation was 1.66302. Factors S2, S3, S4, S5, S6, and S7 were not the significant predictor of the causes of the gap between university education outcomes and the labor market because the p values (0.800, 0.800, 0.780, 0.424, 0.439, 0.199) are greater than 0.05. The equation generated in the analysis is Q = 1.381 + ɛ. In conclusion, there is no significant cause for the gap between university education outcomes and the labor market.Table 3
Application of resolution IV from S2 to S7.
N/factorS2S3S4S5S6S7Q1−1−1−1−1−1−152−1−1−111113−1−11−11104−1−111−1−125−11−1−11−116−11−11−1107−111−1−1108−11111−1391−1−1−1−111101−1−111−10111−11−11−10.1121−111−1111311−1−11121411−11−1−1015111−1−1−15161111111Table 4
The regression of application 3.1.1.
VariableMeanStd. deviationNModelBValueQ1.38131.6630216(Constant)1.3810.014S20.00001.0328016S2−0.1190.800S30.00001.0328016S30.1190.800S40.00001.0328016S40.1310.780S50.00001.0328016S5−0.3810.424S60.00001.0328016S6−0.3690.439S70.00001.0328016S7−0.6310.199An application was taken from the data starting from the fourth factor to the ninth, which appears in the following table.Table5 was analyzed by regression analysis of these factors and obtaining p values. The results of the regression analysis are shown in Table 6. This table shows the estimated mean, standard deviation, and p value for all factors. The average was 2.6375 and the standard deviation was 3.93885. The factor S7 was an important predictor of creating a gap between higher education outcomes and the labor market because its p value (0.028) was greater than 0.05. Generated equation in the analysis, Q = 2,638 + 2,250 S7 + ɛ. In summary, no training institutions and graduate centers are the main cause of the gap between higher education outcomes and the labor market.Table 5
Application of resolution IV from S4 to S9.
N/factorS4S5S6S7S8S9Q1−1−1−1−1−1−10.12−1−1−111113−1−11−11114−1−111−1−1105−11−1−11−106−11−11−1117−111−1−1108−11111−1391−1−1−1−110101−1−111−110111−11−11−11121−111−1131311−1−11101411−11−1−10.115111−1−1−111611111111Table 6
The regression of application 3.1.2.
VariableMeanStd. dNModelBValueQ2.63753.9388516(Constant)2.6380.013S40.00001.0328016S40.6250.486S50.00001.0328016S5−0.6250.486S60.00001.0328016S61.1130.228S70.00001.0328016S72.2500.028S80.00001.0328016S80.7380.413S90.00001.0328016An application was taken from the data from the ninth to the fourteenth factor, which appears in the following table.In Table7, analyzes were performed by obtaining regression analysis and p values for these factors. The results of the regression study are presented in Table 8. Table showing the calculated mean, standard deviation, and p value for all factors. The mean was 2.6188 and the standard deviation was 4.07941. Factors S9 and S12 were important predictors of the disparity between college performance and the labor market as the p values (0.048, 0.043) exceeded 0.05. The equation obtained during the analysis is Q = 2.619 + 2.006 S9 + 2.069 S12 + ɛ. In conclusion, the lack of awareness of children about relevant specialties and increasing children’s tendency the continued increase in the number of graduates in light of the weak absorption capacity of the labor market gap in the outcomes of university education and the labor market.Table 7
Application of resolution IV from S9 to S14.
N/factorS9S10S11S12S13S14Q1−1−1−1−1−1−102−1−1−111183−1−11−11104−1−111−1−185−11−1−11−106−11−11−11117−111−1−1108−11111−11091−1−1−1−110.3101−1−111−10111−11−11−10.1121−111−110.51311−1−11131411−11−1−1015111−1−1−11161111110Table 8
The regression of application 3.1.3.
VariableMeanStd. deviationNModelBValueQ2.61884.07941161(Constant)2.6190.015S90.00001.0328016S9−2.0060.048S100.00001.03280162S100.5060.579S110.00001.0328016S11−0.1690.852S120.00001.0328016S122.0690.043S130.00001.0328016S130.0190.983S140.00001.0328016S140.2310.798


## 3.2. Applications of Saturated Design

In this section, we will search for the factors collectively in the data in the form of a saturated design, and it is taken randomly, taking into account the non-repetition of the design for applications.


### 3.2.1. Application One

In Table9, the data items extracted from the dataset are validatedwith saturated design and performing stepwise regression analysis. The results of the regression study are shown in Table 10. The table shows the calculated average, standard deviation, and p value of all factors. The average is 2.0231 and the standard deviation is 2.36014. Since the p value (0.01, 0.005) exceeds 0.05, factors S9 and S11 are important predictors of the difference between university performance and the labor market. The equation obtained during the analysis is Q = 3.221–1.724 S9 − 1.391 S11 + ɛ. To sum up, the lack of awareness of children about the appropriate specialties, and strengthening their inclinations towards it and the existence of non-national competencies are the main cause of the gap between higher education outcomes and the labor market.Table 9
Application one of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q11111111111111102111−1111−1−1−1111113111111111111112411−11111−1−1−11−1−1−1351111−1−1−1−1−111−1−1−1561−11111111−1−111127111−111111111110.281111111−111111−10.19111−111−1111−111−1310−1−1−1−1−1−1−11−1−1−1111811−1−1−111111111111312−111−1−1−11111−111−1213−11−11−1−1−111−11−1−1−10Table 10
Stepwise regression analysis of application one.
VariableMeanStd. deviationNModelBValueQ2.02312.36014131(Constant)2.6420.001S10.38460.9607713S9−1.6080.015S20.53850.87706132(Constant)3.2210.000S30.38460.9607713S9−1.7240.001S40.23081.0127413S11−1.3910.005S50.38460.9607713S60.38460.9607713S70.38460.9607713S80.38460.9607713S90.38460.9607713S100.23081.0127413S110.38460.9607713S120.53850.8770613S130.53850.8770613S140.07691.0377513S110.00001.0328016S120.00001.0328016


### 3.2.2. Application 2

In Table11, the information items extracted from the dataset are validated with saturated design and the performing stepwise multivariate analysis. The results of the regression study are shown in Table 12. The table shows the calculated average, Std deviation, and p value of all factors. The average is 2.2538 and therefore the Std deviation is 2.25485. Since the p value (0.000, 0.002) exceeds 0.05, factors S3 and S12 are important predictors of the difference between university performance and therefore the marketplace. The equation obtained during the analysis is Q = 1.700 + 2.000S3 − 2.800 S12 + ɛ. To sum up, universities’ lack of materials and support for the academic content required by the labor market and the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market are the most explanation for the gap between educational activity outcomes and also the marketplace.Table 11
Application two of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1−11−1−1−1−11−1−1−11−1−1−112111−1111111111113111−1−1−1−11−11−11110.14−1−1−11−1−111111−11135−1−1−11−111−1−1−1−1−1−1136111−1−1−111111−11−1871111111111111108111−1111−1−1−11111191111111111111121011−11111−1−1−11−1−1−13111111−1−1−1−1−111−1−1−15121−11111111−1−1111213111−111111111110.2Table 12
Stepwise regression analysis of application two.
VariableMeanStd. deviationNModelBValueQ2.25382.25485131(Constant)2.3670.000S10.53850.8770613S12−1.4670.011S20.53850.87706132(Constant)1.7000.001S30.38460.9607713S12−2.8000.000S40.07691.0377513S32.0000.002S50.07691.0377513S60.23081.0127413S70.69230.7510713S80.23081.0127413S90.07691.0377513S100.23081.0127413S110.53850.8770613S120.07691.0377513S130.38460.9607713S140.38460.9607713


### 3.2.3. Application 3

In Table13, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 14. This table shows the calculated means, standard deviations, and p values for all factors. The average is 2.1846, so the deviation per hour is 2.30917. Coefficients S3, S7 and S12 are important predictors of university performance and market differences, as p values (0.001, 0.046, 0.000) exceed 0.05. The equation obtained from the analysis is Q = 1.200 + 2.150 S3 + 0.800S7 − 3.150 S12 + ɛ. In summary, universities’ lack of materials and support for the academic content required by the labor market, no training institutes and centers for graduates and the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market are most of the gaps between the results. This is an explanation of educational activities and markets.Table 13
Application three of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1111−1111111111112111−1−1−1−11−11−11110.13−1−1−11−1−111111−11134−1−1−11−111−1−1−1−1−1−1135111−1−1−111111−11−1861111111111111107111−1111−1−1−1111118111111111111112911−11111−1−1−11−1−1−13101111−1−1−1−1−111−1−1−15111−11111111−1−1111212111−111111111110.2131111111−111111−10.1Table 14
Stepwise regression analysis of application 3.
VariableMeanStd. deviationNModelBValueQ2.18462.30971131(Constant)2.6000.000S10.69230.7510713S12−1.8000.001S20.53850.87706132(Constant)1.9000.000S30.53850.8770613S12−2.8500.000S40.23081.0127413S31.7500.003S50.23081.01274133(Constant)1.2000.017S60.38460.9607713S12−3.1500.000S70.69230.7510713S32.1500.001S80.23081.0127413S70.8000.046S90.23081.0127413S100.38460.9607713S110.53850.8770613S120.23081.0127413S130.53850.8770613S140.38460.9607713


### 3.2.4. Application 4

In Table15, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 16. This table shows the calculated means, standard deviations, and p values for all factors. The average is 2.7273, so the deviation per hour is 2.53341. Coefficient S9 is important predictors of university performance and market differences, as p values (0.011) exceed 0.05. The equation obtained from the analysis is Q = 2.567 + 1.767 S9 + ɛ. In summary, the private sector’s lack of contribution to the training and qualification of university graduates to attract the competencies that meet its needs is most of the gaps between the results.Table 15
Application 4 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q11111111111111112−1−11−1111111111103111−1111111111124−1−1−1−1−1−1−1−1−1−1−1−1−1−10٫55−1−1−1−1−1−111111−11116−1−1−1−1−1−1−1−1−1−11−1−1−127−1−1111−1−1−11−11−1−1−118111−11111111111691−1−1−1−1−1−1−11−1−1−1−1−1510111−111−1−111−1−1110٫1111−1−1−1−1−1−1−1−1−1−1−1−1−1712−1−1−1−1−1−1−11−1−1−1−1−1−10131−11−1−1−1−1111111−15Table 16
Stepwise regression analysis of application 4.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ2.72732.5334111S10.09091.0444711(Constant)2.5670.001S2−0.45450.9342011S91.7670.011S30.09091.0444711S4−0.63640.8090411S5−0.09091.0444711S6−0.27271.0090511S7−0.09091.0444711S80.27271.0090511S90.4545.9342011S100.09091.0444711S110.4545.9342011S12−0.09091.0444711S130.09091.0444711S14−0.09091.0444711


### 3.2.5. Application 5

In Table17, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 18. This table shows the calculated means, standard deviations, and p values for all factors. The average is 3.0231, so the deviation per hour is 2.74231. Coefficients S3 and S5 are important predictors of university performance and market differences, as p values (0.27, 0.003) exceed 0.05. The equation obtained from the analysis is Q = 4.539–1.930 S5 − 1.437 S3 + ɛ. In summary, universities’ lack of materials and support for the academic content required by the labor market and some specializations do not have areas in the labor market are most of the gaps between the results.Table 17
Application 5 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1111−1−1−111111−11−1821111111111111103111−1111−1−1−1111114111111111111112511−11111−1−1−11−1−1−1361111−1−1−1−1−111−1−1−1571−11111111−1−111128111−111111111110.291111111−111111−10.110111−111−1111−111−1311−1−1−1−1−1−1−11−1−1−1111812−1−1−111111111111313−111−1−1−11111−111−12Table 18
Stepwise regression analysis of application 5.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ3.02312.7423113S10.53850.87706131(Constant)3.781<0.001S20.53850.8770613S5−1.9690.009S30.53850.87706132(Constant)4.539<0.001S40.07691.0377513S5−1.9300.003S50.38460.9607713S3−1.4370.27S60.38460.9607713S70.53850.8770613S80.38460.9607713S90.38460.9607713S100.38460.9607713S110.38460.9607713S120.53850.8770613S130.69230.7510713S140.07691.0377513


### 3.2.6. Application 6

In Table19, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 20. This table shows the calculated means, standard deviations, and p values for all factors. The average is 1.538, so the deviation per hour is 3.78255. Coefficient S9 is important predictors of university performance and market differences, as p values (0.033) exceed 0.05. The equation obtained from the analysis is Q = 2.988 + 2.155 + ɛ. In summary, the private sector’s lack of contribution to the training and qualification of university graduates to attract the competencies that meet its needs is most of the gaps between the results.Table 19
Application 6 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1−1−11−1−111111−11115211−1−111111−1111183−1−1−1−1−1−1−1−1−1−1−1−1−1−114−1−11−1−1−1−1−1−1−11−1−1−10٫15−1111−1−11111111−1061111111111111112711−1−11111−1−1−11110811−1−11−111-1-1-1-1-1-11911111111-11111101011111111111111711−1−1−11−1−1−1−1−1−1−1−1−1−1312−1−11−1−1−1−1−1111−111213−111−111−111−1−11110٫1Table 20
Stepwise regression analysis of application 6.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ0.15383.7825513S1−0.07691.0377513(Constant)2.9880.006S20.23081.0127413S92.1550.033S30.23081.0127413S4−0.23081.0127413S50.07691.0377513S60.07691.0377513S70.23081.0127413S80.38460.9607713S90.07691.0377513S10−0.07691.0377513S110.07691.0377513S120.23081.0127413S130.38460.9607713S140.23081.0127413


### 3.2.7. Application 7

In Table21, the information items extracted from the dataset are validate with saturated design and performing stepwise multivariate analysis. The results of the regression study are shown in Table 22. The table shows the calculated average, Std deviation, and p value of all factors. The average is 2.3308 and therefore the Std deviation is 2.47366. Since the p value (<0.001, <0.001) exceeds 0.05, factors S9 and S11 are important predictors of the difference between university performance and therefore the marketplace. The equation obtained during the analysis is Q = 3.771–2.049 S9 − 1.459 S11 + ɛ. To sum up, lack of awareness of children about the appropriate specialties and strengthening their inclinations towards it and existence of non-national competencies are the most explanation for the gap between educational activity outcomes and also the marketplace.Table 21
Application 7 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1111111111111112211−11111−1−1−11−1−1−1331111−1−1−1−1−111−1−1−1541−11111111−1−111125111−111111111110.261111111−111111−10.17111−111−1111−111−138−1−1−1−1−1−1−11−1−1−111189−1−1−1111111111113.010−111−1−1−11111−111−1211−11−11−1−1−111−11−1−1−1012−1−1−1−1−1−1−1−111−1−1−1−1513111−1111111111−10Table 22
Stepwise regression analysis of application 7.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ2.33082.4736613S10.23081.01274131(Constant)3.382<0.001S20.38460.9607713S9−1.9520.009S30.23081.01274132(Constant)3.771<0.001S40.07691.0377513S9−2.049<0.001S50.23081.0127413S11−1.459<0.001S60.23081.0127413S70.23081.0127413S80.38460.9607713S90.53850.8770613S100.38460.9607713S110.23081.0127413S120.38460.9607713S130.38460.9607713S14−0.23081.0127413


### 3.2.8. Application 8

In Table23, the information items extracted from the dataset are validate with saturated design and performing stepwise multivariate analysis. The results of the regression study are shown in Table 24. The table shows the calculated average, Std deviation, and p value of all factors. The average is 1.0077 and therefore the Std deviation is 1.21824. Since the p value (0.028, 0.002) exceeds 0.05, factors S6 and S12 are important predictors of the difference between university performance and therefore the marketplace. The equation obtained during the analysis is Q = 0.693–0.931 S12 + 0.633 S6 + ɛ. To sum up, the lack of quality of education outputs in order to meet the desire and need of the public and private sectors to obtain a distinguished quality of graduates and the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market are the most explanation for the gap between educational activity outcomes and also the marketplace.Table 23
Application 8 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q111−11−111−1−11−1−11132111−1−11−1−111−111−103−1−1−1−1−1−1−1−1−1−1−1−1−1−11411−1−111−1−11−1−11−1105−1−1111111111−11116111−1−1−111−1111110٫171−111111111−111108111−1111111111109−1−1−11−11−1−11−11−1−1−13101−1−11−1−1−1−1−111−1−11011−1−11111−1111−1−111312−1−1−1−1−1−1−1−1−11−1−1−1−111311111111−1111111Table 24
Stepwise regression analysis of application 8.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ1.00771.2182413S10.23081.01274131(Constant)0.9490.005S2−0.07691.0377513S12−0.7650.016S30.07691.03775132(Constant)0.6930.016S40.07691.0377513S12−0.9310.002S5−0.07691.0377513S60.6330.028S60.38460.9607713S7−0.07691.0377513S8−0.07691.0377513S90.07691.0377513S100.53850.8770613S11−0.07691.0377513S12−0.07691.0377513S130.23081.0127413S140.38460.9607713


### 3.2.9. Application 9

In Table25, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 26. This table shows the calculated means, standard deviations, and p values for all factors. The average is 3.6923, so the deviation per hour is 2.65784. Coefficients S4, S8 and S12 are important predictors of university performance and market differences, as p values (<0.001, 0.003, 0.022) exceed 0.05. The equation obtained from the analysis is Q = 1.936–3.548 S4 − 2.151 S8 + 1.385 S12 + ɛ. In summary, rapid job growth, the private sector’s lack of contribution to the training and qualification of university graduates to attract the competencies that meet its needs and the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market. are most of the gaps between the results.Table 25
Application 9 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q11−1111−1−1−11−11−1−1−112−111−1111111111163−1−1−1−1−1−1−1−11−1−1−1−1−154−111−111−1−111−1−1110٫15−1−1−1−1−1−1−1−1−1−1−1−1−1−176−1−1−1−1−1−1−11−1−1−1−1−1−107−1−11−1−1−1−1111111−158−11−111−1−1−11−1−11110٫39−11−1−11111−11−1−1−11110−1−1−1−1−11−1−1−1−1−1−1−110٫311−1−11−111−11−11−1−1−11312−11−1−11111111111513−1111111−11111111Table 26
Stepwise regression analysis of application 9.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ3.69232.6578413S1−0.8462.55470131(Constant)2.7500.004S2−0.07691.0377513S4−1.7500.039S3−0.07691.03775132(Constant)2.1670.009S4−0.53850.8770613S4−2.6250.005S50.23081.0127413S8−1.4580.039S60.07691.03775133(Constant)1.9360.005S7−0.38460.9607713S4−3.548<0.001S8−0.07691.0377513S8−2.1510.003S90.23081.0127413S121.3850.022S100.07691.0377513S11−0.23081.0127413S123.69232.6578413S13−0.84620.5547013S14−0.07691.0377513


### 3.2.10. Application 10

In Table27, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 28. This table shows the calculated means, standard deviations, and p values for all factors. The average is 2.9462, so the deviation per hour is 2.66477. Coefficient S6 is important predictors of university performance and market differences, as p values (0.001) exceed 0.05. The equation obtained from the analysis is Q = 2.567–2.206 S6 + ɛ. In summary, the lack of quality of education outputs in order to meet the desire and need of the public and private sectors to obtain a distinguished quality of graduates is most of the gaps between the results. This is an explanation of educational activities and markets.Table 27
Application 10 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1−1−1−11−1−111111−11132−1−1−11−111−1−1−1−1−1−1133111−1−1−111111−11−1841111111111111105111−1111−1−1−1111116111111111111112711−11111−1−1−11−1−1−1381111−1−1−1−1−111−1−1−1591−11111111−1−1111210111−111111111110.2111111111−111111−10.112111−111−1111−111−1313−1−1−1−1−1−1−11−1−1−11118Table 28
Stepwise regression analysis of application 10.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ2.94622.6647713S10.53850.8770613(Constant)3.794<0.001S20.38460.9607713S6−2.2060.001S30.38460.9607713S40.23081.0127413S50.23081.0127413S60.38460.9607713S70.53850.8770613S80.23081.0127413S90.23081.0127413S100.23081.0127413S110.38460.9607713S120.23081.0127413S130.53850.8770613S140.23081.0127413


## 3.2.1. Application One

In Table9, the data items extracted from the dataset are validatedwith saturated design and performing stepwise regression analysis. The results of the regression study are shown in Table 10. The table shows the calculated average, standard deviation, and p value of all factors. The average is 2.0231 and the standard deviation is 2.36014. Since the p value (0.01, 0.005) exceeds 0.05, factors S9 and S11 are important predictors of the difference between university performance and the labor market. The equation obtained during the analysis is Q = 3.221–1.724 S9 − 1.391 S11 + ɛ. To sum up, the lack of awareness of children about the appropriate specialties, and strengthening their inclinations towards it and the existence of non-national competencies are the main cause of the gap between higher education outcomes and the labor market.Table 9
Application one of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q11111111111111102111−1111−1−1−1111113111111111111112411−11111−1−1−11−1−1−1351111−1−1−1−1−111−1−1−1561−11111111−1−111127111−111111111110.281111111−111111−10.19111−111−1111−111−1310−1−1−1−1−1−1−11−1−1−1111811−1−1−111111111111312−111−1−1−11111−111−1213−11−11−1−1−111−11−1−1−10Table 10
Stepwise regression analysis of application one.
VariableMeanStd. deviationNModelBValueQ2.02312.36014131(Constant)2.6420.001S10.38460.9607713S9−1.6080.015S20.53850.87706132(Constant)3.2210.000S30.38460.9607713S9−1.7240.001S40.23081.0127413S11−1.3910.005S50.38460.9607713S60.38460.9607713S70.38460.9607713S80.38460.9607713S90.38460.9607713S100.23081.0127413S110.38460.9607713S120.53850.8770613S130.53850.8770613S140.07691.0377513S110.00001.0328016S120.00001.0328016


## 3.2.2. Application 2

In Table11, the information items extracted from the dataset are validated with saturated design and the performing stepwise multivariate analysis. The results of the regression study are shown in Table 12. The table shows the calculated average, Std deviation, and p value of all factors. The average is 2.2538 and therefore the Std deviation is 2.25485. Since the p value (0.000, 0.002) exceeds 0.05, factors S3 and S12 are important predictors of the difference between university performance and therefore the marketplace. The equation obtained during the analysis is Q = 1.700 + 2.000S3 − 2.800 S12 + ɛ. To sum up, universities’ lack of materials and support for the academic content required by the labor market and the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market are the most explanation for the gap between educational activity outcomes and also the marketplace.Table 11
Application two of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1−11−1−1−1−11−1−1−11−1−1−112111−1111111111113111−1−1−1−11−11−11110.14−1−1−11−1−111111−11135−1−1−11−111−1−1−1−1−1−1136111−1−1−111111−11−1871111111111111108111−1111−1−1−11111191111111111111121011−11111−1−1−11−1−1−13111111−1−1−1−1−111−1−1−15121−11111111−1−1111213111−111111111110.2Table 12
Stepwise regression analysis of application two.
VariableMeanStd. deviationNModelBValueQ2.25382.25485131(Constant)2.3670.000S10.53850.8770613S12−1.4670.011S20.53850.87706132(Constant)1.7000.001S30.38460.9607713S12−2.8000.000S40.07691.0377513S32.0000.002S50.07691.0377513S60.23081.0127413S70.69230.7510713S80.23081.0127413S90.07691.0377513S100.23081.0127413S110.53850.8770613S120.07691.0377513S130.38460.9607713S140.38460.9607713


## 3.2.3. Application 3

In Table13, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 14. This table shows the calculated means, standard deviations, and p values for all factors. The average is 2.1846, so the deviation per hour is 2.30917. Coefficients S3, S7 and S12 are important predictors of university performance and market differences, as p values (0.001, 0.046, 0.000) exceed 0.05. The equation obtained from the analysis is Q = 1.200 + 2.150 S3 + 0.800S7 − 3.150 S12 + ɛ. In summary, universities’ lack of materials and support for the academic content required by the labor market, no training institutes and centers for graduates and the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market are most of the gaps between the results. This is an explanation of educational activities and markets.Table 13
Application three of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1111−1111111111112111−1−1−1−11−11−11110.13−1−1−11−1−111111−11134−1−1−11−111−1−1−1−1−1−1135111−1−1−111111−11−1861111111111111107111−1111−1−1−1111118111111111111112911−11111−1−1−11−1−1−13101111−1−1−1−1−111−1−1−15111−11111111−1−1111212111−111111111110.2131111111−111111−10.1Table 14
Stepwise regression analysis of application 3.
VariableMeanStd. deviationNModelBValueQ2.18462.30971131(Constant)2.6000.000S10.69230.7510713S12−1.8000.001S20.53850.87706132(Constant)1.9000.000S30.53850.8770613S12−2.8500.000S40.23081.0127413S31.7500.003S50.23081.01274133(Constant)1.2000.017S60.38460.9607713S12−3.1500.000S70.69230.7510713S32.1500.001S80.23081.0127413S70.8000.046S90.23081.0127413S100.38460.9607713S110.53850.8770613S120.23081.0127413S130.53850.8770613S140.38460.9607713


## 3.2.4. Application 4

In Table15, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 16. This table shows the calculated means, standard deviations, and p values for all factors. The average is 2.7273, so the deviation per hour is 2.53341. Coefficient S9 is important predictors of university performance and market differences, as p values (0.011) exceed 0.05. The equation obtained from the analysis is Q = 2.567 + 1.767 S9 + ɛ. In summary, the private sector’s lack of contribution to the training and qualification of university graduates to attract the competencies that meet its needs is most of the gaps between the results.Table 15
Application 4 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q11111111111111112−1−11−1111111111103111−1111111111124−1−1−1−1−1−1−1−1−1−1−1−1−1−10٫55−1−1−1−1−1−111111−11116−1−1−1−1−1−1−1−1−1−11−1−1−127−1−1111−1−1−11−11−1−1−118111−11111111111691−1−1−1−1−1−1−11−1−1−1−1−1510111−111−1−111−1−1110٫1111−1−1−1−1−1−1−1−1−1−1−1−1−1712−1−1−1−1−1−1−11−1−1−1−1−1−10131−11−1−1−1−1111111−15Table 16
Stepwise regression analysis of application 4.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ2.72732.5334111S10.09091.0444711(Constant)2.5670.001S2−0.45450.9342011S91.7670.011S30.09091.0444711S4−0.63640.8090411S5−0.09091.0444711S6−0.27271.0090511S7−0.09091.0444711S80.27271.0090511S90.4545.9342011S100.09091.0444711S110.4545.9342011S12−0.09091.0444711S130.09091.0444711S14−0.09091.0444711


## 3.2.5. Application 5

In Table17, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 18. This table shows the calculated means, standard deviations, and p values for all factors. The average is 3.0231, so the deviation per hour is 2.74231. Coefficients S3 and S5 are important predictors of university performance and market differences, as p values (0.27, 0.003) exceed 0.05. The equation obtained from the analysis is Q = 4.539–1.930 S5 − 1.437 S3 + ɛ. In summary, universities’ lack of materials and support for the academic content required by the labor market and some specializations do not have areas in the labor market are most of the gaps between the results.Table 17
Application 5 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1111−1−1−111111−11−1821111111111111103111−1111−1−1−1111114111111111111112511−11111−1−1−11−1−1−1361111−1−1−1−1−111−1−1−1571−11111111−1−111128111−111111111110.291111111−111111−10.110111−111−1111−111−1311−1−1−1−1−1−1−11−1−1−1111812−1−1−111111111111313−111−1−1−11111−111−12Table 18
Stepwise regression analysis of application 5.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ3.02312.7423113S10.53850.87706131(Constant)3.781<0.001S20.53850.8770613S5−1.9690.009S30.53850.87706132(Constant)4.539<0.001S40.07691.0377513S5−1.9300.003S50.38460.9607713S3−1.4370.27S60.38460.9607713S70.53850.8770613S80.38460.9607713S90.38460.9607713S100.38460.9607713S110.38460.9607713S120.53850.8770613S130.69230.7510713S140.07691.0377513


## 3.2.6. Application 6

In Table19, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 20. This table shows the calculated means, standard deviations, and p values for all factors. The average is 1.538, so the deviation per hour is 3.78255. Coefficient S9 is important predictors of university performance and market differences, as p values (0.033) exceed 0.05. The equation obtained from the analysis is Q = 2.988 + 2.155 + ɛ. In summary, the private sector’s lack of contribution to the training and qualification of university graduates to attract the competencies that meet its needs is most of the gaps between the results.Table 19
Application 6 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1−1−11−1−111111−11115211−1−111111−1111183−1−1−1−1−1−1−1−1−1−1−1−1−1−114−1−11−1−1−1−1−1−1−11−1−1−10٫15−1111−1−11111111−1061111111111111112711−1−11111−1−1−11110811−1−11−111-1-1-1-1-1-11911111111-11111101011111111111111711−1−1−11−1−1−1−1−1−1−1−1−1−1312−1−11−1−1−1−1−1111−111213−111−111−111−1−11110٫1Table 20
Stepwise regression analysis of application 6.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ0.15383.7825513S1−0.07691.0377513(Constant)2.9880.006S20.23081.0127413S92.1550.033S30.23081.0127413S4−0.23081.0127413S50.07691.0377513S60.07691.0377513S70.23081.0127413S80.38460.9607713S90.07691.0377513S10−0.07691.0377513S110.07691.0377513S120.23081.0127413S130.38460.9607713S140.23081.0127413


## 3.2.7. Application 7

In Table21, the information items extracted from the dataset are validate with saturated design and performing stepwise multivariate analysis. The results of the regression study are shown in Table 22. The table shows the calculated average, Std deviation, and p value of all factors. The average is 2.3308 and therefore the Std deviation is 2.47366. Since the p value (<0.001, <0.001) exceeds 0.05, factors S9 and S11 are important predictors of the difference between university performance and therefore the marketplace. The equation obtained during the analysis is Q = 3.771–2.049 S9 − 1.459 S11 + ɛ. To sum up, lack of awareness of children about the appropriate specialties and strengthening their inclinations towards it and existence of non-national competencies are the most explanation for the gap between educational activity outcomes and also the marketplace.Table 21
Application 7 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1111111111111112211−11111−1−1−11−1−1−1331111−1−1−1−1−111−1−1−1541−11111111−1−111125111−111111111110.261111111−111111−10.17111−111−1111−111−138−1−1−1−1−1−1−11−1−1−111189−1−1−1111111111113.010−111−1−1−11111−111−1211−11−11−1−1−111−11−1−1−1012−1−1−1−1−1−1−1−111−1−1−1−1513111−1111111111−10Table 22
Stepwise regression analysis of application 7.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ2.33082.4736613S10.23081.01274131(Constant)3.382<0.001S20.38460.9607713S9−1.9520.009S30.23081.01274132(Constant)3.771<0.001S40.07691.0377513S9−2.049<0.001S50.23081.0127413S11−1.459<0.001S60.23081.0127413S70.23081.0127413S80.38460.9607713S90.53850.8770613S100.38460.9607713S110.23081.0127413S120.38460.9607713S130.38460.9607713S14−0.23081.0127413


## 3.2.8. Application 8

In Table23, the information items extracted from the dataset are validate with saturated design and performing stepwise multivariate analysis. The results of the regression study are shown in Table 24. The table shows the calculated average, Std deviation, and p value of all factors. The average is 1.0077 and therefore the Std deviation is 1.21824. Since the p value (0.028, 0.002) exceeds 0.05, factors S6 and S12 are important predictors of the difference between university performance and therefore the marketplace. The equation obtained during the analysis is Q = 0.693–0.931 S12 + 0.633 S6 + ɛ. To sum up, the lack of quality of education outputs in order to meet the desire and need of the public and private sectors to obtain a distinguished quality of graduates and the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market are the most explanation for the gap between educational activity outcomes and also the marketplace.Table 23
Application 8 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q111−11−111−1−11−1−11132111−1−11−1−111−111−103−1−1−1−1−1−1−1−1−1−1−1−1−1−11411−1−111−1−11−1−11−1105−1−1111111111−11116111−1−1−111−1111110٫171−111111111−111108111−1111111111109−1−1−11−11−1−11−11−1−1−13101−1−11−1−1−1−1−111−1−11011−1−11111−1111−1−111312−1−1−1−1−1−1−1−1−11−1−1−1−111311111111−1111111Table 24
Stepwise regression analysis of application 8.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ1.00771.2182413S10.23081.01274131(Constant)0.9490.005S2−0.07691.0377513S12−0.7650.016S30.07691.03775132(Constant)0.6930.016S40.07691.0377513S12−0.9310.002S5−0.07691.0377513S60.6330.028S60.38460.9607713S7−0.07691.0377513S8−0.07691.0377513S90.07691.0377513S100.53850.8770613S11−0.07691.0377513S12−0.07691.0377513S130.23081.0127413S140.38460.9607713


## 3.2.9. Application 9

In Table25, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 26. This table shows the calculated means, standard deviations, and p values for all factors. The average is 3.6923, so the deviation per hour is 2.65784. Coefficients S4, S8 and S12 are important predictors of university performance and market differences, as p values (<0.001, 0.003, 0.022) exceed 0.05. The equation obtained from the analysis is Q = 1.936–3.548 S4 − 2.151 S8 + 1.385 S12 + ɛ. In summary, rapid job growth, the private sector’s lack of contribution to the training and qualification of university graduates to attract the competencies that meet its needs and the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market. are most of the gaps between the results.Table 25
Application 9 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q11−1111−1−1−11−11−1−1−112−111−1111111111163−1−1−1−1−1−1−1−11−1−1−1−1−154−111−111−1−111−1−1110٫15−1−1−1−1−1−1−1−1−1−1−1−1−1−176−1−1−1−1−1−1−11−1−1−1−1−1−107−1−11−1−1−1−1111111−158−11−111−1−1−11−1−11110٫39−11−1−11111−11−1−1−11110−1−1−1−1−11−1−1−1−1−1−1−110٫311−1−11−111−11−11−1−1−11312−11−1−11111111111513−1111111−11111111Table 26
Stepwise regression analysis of application 9.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ3.69232.6578413S1−0.8462.55470131(Constant)2.7500.004S2−0.07691.0377513S4−1.7500.039S3−0.07691.03775132(Constant)2.1670.009S4−0.53850.8770613S4−2.6250.005S50.23081.0127413S8−1.4580.039S60.07691.03775133(Constant)1.9360.005S7−0.38460.9607713S4−3.548<0.001S8−0.07691.0377513S8−2.1510.003S90.23081.0127413S121.3850.022S100.07691.0377513S11−0.23081.0127413S123.69232.6578413S13−0.84620.5547013S14−0.07691.0377513


## 3.2.10. Application 10

In Table27, the information elements extracted from the dataset are validated by saturation design and stepwise multivariate analysis. The results of the regression analysis are shown in Table 28. This table shows the calculated means, standard deviations, and p values for all factors. The average is 2.9462, so the deviation per hour is 2.66477. Coefficient S6 is important predictors of university performance and market differences, as p values (0.001) exceed 0.05. The equation obtained from the analysis is Q = 2.567–2.206 S6 + ɛ. In summary, the lack of quality of education outputs in order to meet the desire and need of the public and private sectors to obtain a distinguished quality of graduates is most of the gaps between the results. This is an explanation of educational activities and markets.Table 27
Application 10 of saturated design.
N/factorS1S2S3S4S5S6S7S8S9S10S11S12S13S14Q1−1−1−11−1−111111−11132−1−1−11−111−1−1−1−1−1−1133111−1−1−111111−11−1841111111111111105111−1111−1−1−1111116111111111111112711−11111−1−1−11−1−1−1381111−1−1−1−1−111−1−1−1591−11111111−1−1111210111−111111111110.2111111111−111111−10.112111−111−1111−111−1313−1−1−1−1−1−1−11−1−1−11118Table 28
Stepwise regression analysis of application 10.
Descriptive statisticsCoefficientsMeanStd. deviationNModelBValueQ2.94622.6647713S10.53850.8770613(Constant)3.794<0.001S20.38460.9607713S6−2.2060.001S30.38460.9607713S40.23081.0127413S50.23081.0127413S60.38460.9607713S70.53850.8770613S80.23081.0127413S90.23081.0127413S100.23081.0127413S110.38460.9607713S120.23081.0127413S130.53850.8770613S140.23081.0127413


## 4. Discussion and Conclusion

University education in all countries of the world occupies an important placebecause universities are the stronghold of enlightened thought, the center of enlightenment, and the home of constructive scientific research. The aim of this research was to studythe actual reasons that led to the gap between university education and the labor market. Where the method used was distributing the questionnaires and then sorting the answers to the questionnaires in the form of selected designs, namely: saturated design and Resolution IV. Then, we used the regression analysis method to achieve the research objective. Where the analysis of Resolution IV gave the factors that caused that gap, namely: no training institutions and graduate centers, lack of awareness of children about the appropriate specialties and strengthening their inclinations towards itand the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market. In addition, the analysis of the saturated design with regression analysis gave the factors that caused that gap, namely: the lack of awareness of children about the appropriate specialties, and strengthening their inclinations towards it and existence of non-national competencies, universities’ lack of materials, and support for the academic content required by the labor market and the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market, no training institutes and centers for graduates, the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market, the private sector's lack of contribution to the training and qualification of university graduates to attract the competencies that meet its needs, some specializations do not have areas in the labor market, the lack of quality of education outputs in order to meet the desire and need of the public and rapid job growth. In summary, the recurring factors in the two methods are the actual reasons that led to the occurrence of the gap, that are: no training institutions and graduate centers, lack of awareness of children about the appropriate specialties and strengthening their inclinations towards it and the continuous increase in the number of graduates in light of the weak absorptive capacity of the labor market. Therefore, The Saudi government should take into consideration these reasons to achieve compatibility with the Kingdom’s Vision 2030.



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*Source: 7219523-2022-03-12.xml*