Data mining and predictive analysis on the employment of fresh graduate students in public universities in Malaysia

Data mining and predictive analysis on the employment of fresh graduate students in public universities in Malaysia

This study was aimed to use data mining to predict the employment of fresh graduate students in public universities in Malaysia. The research design of the study was model development using Rapid Miner Studio. The model used both supervised and unsupervised machine learning algorithms including k-Ne...

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Main Author: Nor Azziaty Abdul Rahman
Format: thesis
Language:eng
Published: 2020
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Nor Azziaty Abdul Rahman
Data mining and predictive analysis on the employment of fresh graduate students in public universities in Malaysia
description This study was aimed to use data mining to predict the employment of fresh graduate students in public universities in Malaysia. The research design of the study was model development using Rapid Miner Studio. The model used both supervised and unsupervised machine learning algorithms including k-Nearest Neighbor (kNN), Nave Bayes, Decision Tree, Logistic Regression, Support Vector Machine (SVM) and Neural Network. The sample consisted of 16,729 fresh graduate students were collected from the Tracer Study Unit of Ministry of Higher Education (MOHE). In order to build the classification model, Cross Industry Standard Process for Data Mining (CRISP-DM) methodology was applied. For the evaluation, 70% of the dataset were used as the training set and the remaining 30% were used as a testing set. To determine the error rate and to justify the accuracy of the proposed model objectively, classification error was used as the evaluation metric. The key finding of the predictive analysis revealed that employability among fresh graduate students can be predicted with 59.90% accuracy with a Neural Network as the most accurate predictive model. The significant factors contributing to graduates employment were problem-solving and decision making skills. The unemployment, on the other hand,vwas mainly attributed to these factors poor English competency, majoring in Malay, Education, and Science fields. In conclusion, the empirical data supported Neural Network model for predicting the employability among fresh graduate students in which the graduates should possess critical skills such as problem-solving and decision making skills. In implication, the predictive model was useful for graduate students, management of public institutions, Ministry of Higher Education, human resource personnel and academic staff in predicting the graduates employability.
format thesis
qualification_name
qualification_level Master's degree
author Nor Azziaty Abdul Rahman
author_facet Nor Azziaty Abdul Rahman
author_sort Nor Azziaty Abdul Rahman
title Data mining and predictive analysis on the employment of fresh graduate students in public universities in Malaysia
title_short Data mining and predictive analysis on the employment of fresh graduate students in public universities in Malaysia
title_full Data mining and predictive analysis on the employment of fresh graduate students in public universities in Malaysia
title_fullStr Data mining and predictive analysis on the employment of fresh graduate students in public universities in Malaysia
title_full_unstemmed Data mining and predictive analysis on the employment of fresh graduate students in public universities in Malaysia
title_sort data mining and predictive analysis on the employment of fresh graduate students in public universities in malaysia
granting_institution Universiti Pendidikan Sultan Idris
granting_department Fakulti Seni, Komputeran dan Industri Kreatif
publishDate 2020
url https://ir.upsi.edu.my/detailsg.php?det=6915
_version_ 1747833329603837952
spelling oai:ir.upsi.edu.my:69152022-04-04 Data mining and predictive analysis on the employment of fresh graduate students in public universities in Malaysia 2020 Nor Azziaty Abdul Rahman This study was aimed to use data mining to predict the employment of fresh graduate students in public universities in Malaysia. The research design of the study was model development using Rapid Miner Studio. The model used both supervised and unsupervised machine learning algorithms including k-Nearest Neighbor (kNN), Nave Bayes, Decision Tree, Logistic Regression, Support Vector Machine (SVM) and Neural Network. The sample consisted of 16,729 fresh graduate students were collected from the Tracer Study Unit of Ministry of Higher Education (MOHE). In order to build the classification model, Cross Industry Standard Process for Data Mining (CRISP-DM) methodology was applied. For the evaluation, 70% of the dataset were used as the training set and the remaining 30% were used as a testing set. To determine the error rate and to justify the accuracy of the proposed model objectively, classification error was used as the evaluation metric. The key finding of the predictive analysis revealed that employability among fresh graduate students can be predicted with 59.90% accuracy with a Neural Network as the most accurate predictive model. The significant factors contributing to graduates employment were problem-solving and decision making skills. The unemployment, on the other hand,vwas mainly attributed to these factors poor English competency, majoring in Malay, Education, and Science fields. In conclusion, the empirical data supported Neural Network model for predicting the employability among fresh graduate students in which the graduates should possess critical skills such as problem-solving and decision making skills. In implication, the predictive model was useful for graduate students, management of public institutions, Ministry of Higher Education, human resource personnel and academic staff in predicting the graduates employability. 2020 thesis https://ir.upsi.edu.my/detailsg.php?det=6915 https://ir.upsi.edu.my/detailsg.php?det=6915 text eng closedAccess Masters Universiti Pendidikan Sultan Idris Fakulti Seni, Komputeran dan Industri Kreatif Abdullah, F., Ward, R., & Ahmed, E. (2016). Computers in Human Behavior Investigating the in fl uence of the most commonly used external variables of TAM on students Perceived Ease of Use ( PEOU ) and Perceived Usefulness ( PU ) of e-portfolios. Computers in Human Behavior, 63, 7590. https://doi.org/10.1016/j.chb.2016.05.014Adam, J., Bore, M., Mckendree, J., Munro, D., & Powis, D. (2012). Can personal qualities of medical students predict in-course examination success and professional behaviour ? An exploratory prospective cohort study. 18.Aharony, N. (2013). Librarians attitudes towards mobile services. 2011. https://doi.org/10.1108/AP-07-2012-0059Aharony, N. (2014). Journal of Librarianship and Information Science. https://doi.org/10.1177/0961000614532120Ain, N., Kaur, K., & Waheed, M. (2015). The influence of learning value on learning management system use : An extension of UTAUT2. https://doi.org/10.1177/0266666915597546Alija, S., Snopce, H., & Aliu, A. (2016). Logistic Regression for Determining Factors Influencing Students Perception of Course. The Eurasia Proceedings of Educational and Social Sciences, 5, 99106. https://dergipark.org.tr/en/pub/epess/issue/30752/332916Arsad, P. M., Buniyamin, N., & Manan, J. A. (2014). Neural Network and Linear Regression Methods for Prediction of Students Academic Achievement. April, 916921.Authors, F. (2017). The International Journal of Information and Learning Technology Article information : https://doi.org/10.1108/IJILT-11-2016-0051Babi?, I. D. (2017). Machine learning methods in predicting the student academic motivation. Croatian Operational Research Review, 8(2), 443461. https://doi.org/10.17535/crorr.2017.0028Beccaria, L., Kek, M., Huijser, H., Rose, J., & Kimmins, L. (2014). Nurse Education Today The interrelationships between student approaches to learning and group work. YNEDT. https://doi.org/10.1016/j.nedt.2014.02.006Beck, H. P., & Milligan, M. (2014). Internet and Higher Education Factors in fl uencing the institutional commitment of online students. The Internet and Higher Education, 20, 5156. https://doi.org/10.1016/j.iheduc.2013.09.002Bekiari, A. (2012). Perceptions of instructors verbal aggressiveness and physical education students affective learning 1, 2. 325335. https://doi.org/10.2466/06.11.16.PMS.115.4.325-335Benbow, C. P. (2012). Identifying and Nurturing Future Innovators in Science , Technology , Engineering , and Mathematics : A Review of Findings From the Study of Mathematically Precocious Youth Identifying and Nurturing Future Innovators in Science , Technology , Engineering . October 2014, 3741. https://doi.org/10.1080/0161956X.2012.642236Bertholet, N., Gaume, J., Faouzi, M., Gmel, G., & Daeppen, J. (2012). Predictive value of readiness , importance , and confidence in ability to change drinking and smoking.Bickerton, G. R., Miner, M. H., Dowson, M., & Griffin, B. (2015). Incremental Validity of Spiritual Resources in the Job Demands-Resources Model. 7(2), 162172.Bosompem, M., Dadzie, S. K. N., Tandoh, E., & Tandoh, E. (2017). UNDERGRADUATE STUDENTS WILLINGNESS TO START OWN AGRIBUSINESS VENTURE AFTER GRADUATION : A GHANAIAN.https://doi.org/10.1108/S2040-724620170000007009Bote-Lorenzo, M. L., & Gmez-Snchez, E. (2017). Predicting the decrease of engagement indicators in a MOOC. ACM International Conference Proceeding Series. https://doi.org/10.1145/3027385.3027387Bozeman, B., Fay, D., & Gaughan, M. (2013). Power to Do What ? Department Heads Decision Autonomy and Strategic Priorities. 303328. https://doi.org/10.1007/s11162-012-9270-7Buchanan, K., & Bardi, A. (2015). The Roles of Values , Behavior , and Value-Behavior Fit in the Relation of Agency and Communion to Well-Being. June. https://doi.org/10.1111/jopy.12106Buckless, F., & Krawczyk, K. (2016). The relation of student engagement and other admission metrics to Master of Accounting student performance. Accounting Education, 25(6). https://doi.org/10.1080/09639284.2016.1218778Buckless, Frank, & Krawczyk, K. (2016). The relation of student engagement and other admission metrics to Master of Accounting student performance. 9284(September). https://doi.org/10.1080/09639284.2016.1218778Cai, Y. (2013). Graduate employability: A conceptual framework for understanding employers perceptions. Higher Education, 65(4), 457469. https://doi.org/10.1007/s10734-012-9556-xCantwell, B., & Taylor, B. J. (2013). Internationalization of the postdoctorate in the United States : analyzing the demand for international postdoc labor. 551567. https://doi.org/10.1007/s10734-013-9621-0Chen, C. C. C. H. I. (2013). The relationship between the playfulness climate in the classroom and student creativity. 14931510. https://doi.org/10.1007/s11135- 011-9603-1Chen, I. (2016). Computers in Human Behavior Work engagement and its antecedents and consequences : A case of lecturers teaching synchronous distance education courses. Computers in Human Behavior, 19. https://doi.org/10.1016/j.chb.2016.10.002Chin, E. C. H., Williams, M. W., Taylor, J. E., & Harvey, S. T. (2017). The in fl uence of negative affect on test anxiety and academic performance : An examination of the tripartite model of emotions. Learning and Individual Differences, 54, 18. https://doi.org/10.1016/j.lindif.2017.01.002Chraif, M., & Dumitru, D. (2017). The HEXACO Model of Personality and Risky Driving Behavior. 90. https://doi.org/10.1177/0033294116688890Chuang, S., Lin, F., & Tsai, C. (2015). Computers in Human Behavior An exploration of the relationship between Internet self-efficacy and sources of Internet self- efficacy among Taiwanese university students. COMPUTERS IN HUMAN BEHAVIOR, 48, 147155. https://doi.org/10.1016/j.chb.2015.01.044Clements, A. D., Fletcher, T. R., Cyphers, N. A., Ermakova, A. V, & Bailey, B. (2013). RSAS-3 : Validation of a Very Brief Measure of Religious Commitment for Use in Health Research. https://doi.org/10.1007/s10943-013-9791-1Coifman, K. G., Flynn, J. J., & Pinto, L. A. (2016). When context matters : Negative emotions predict psychological health and adjustment. Motivation and Emotion. https://doi.org/10.1007/s11031-016-9553-yokluk, . (2010). Logistic regression: Concept and application. Kuram ve Uygulamada Egitim Bilimleri, 10(3), 13971407.Cooke, R., Dahdah, M., Norman, P., French, D. P., Cooke, R., Dahdah, M., Norman, P., & How, D. P. F. (2014). How well does the theory of planned behaviour predict alcohol consumption ? A systematic review and meta-analysis. 7199(November 2015). https://doi.org/10.1080/17437199.2014.947547CRD University of York. (2009). CRDS GUIDANCE FOR UNDERTAKING REVIEWS IN HEALTH CARE.Creed, P. A., & Hughes, T. (2013). Journal of. https://doi.org/10.1177/0894845312437207Cucina, J. M., Su, C., Busciglio, H. H., & Peyton, S. T. (2015). Intelligence Something more than g : Meaningful Memory uniquely predicts training performance ?. Intelligence, 49, 192206. https://doi.org/10.1016/j.intell.2015.01.007Deepak, E., Pooja, G. S., Jyothi, R. N. S., Kumar, S. V. P., & Kishore, K. V. (n.d.). SVM Kernel based Predictive Analytics on Faculty Performance Evaluation.Duckworth, A. L., Quinn, P. D., & Tsukayama, E. (2012). What No Child Left Behind Leaves Behind : The Roles of IQ and Self-Control in Predicting Standardized Achievement Test Scores and Report Card Grades. 104(2), 439451. https://doi.org/10.1037/a0026280Duffy, R. D., Douglass, R. P., Autin, K. L., & Allan, B. A. (2016). Examining Predictors of Work Volition Among Undergraduate Students. Journal of Career Assessment, 24(3), 441459. https://doi.org/10.1177/1069072715599377Educ, S. P. (2014). Teachers high maintenance behaviour as perceived by university students in Taiwan , and their coping strategies. 32. https://doi.org/10.1007/s11218-013-9230-xEuropean Environment Agency (EEA). (2019). A Comparison of Machine Learning Models Predicting Student Employment. 53(9), 16891699. https://doi.org/10.1017/CBO9781107415324.004Fazilat-pour, M. A. M. (2015). Simple and Multivariate Relationships Between Spiritual Intelligence with General Health and Happiness. https://doi.org/10.1007/s10943- 015-0004-yFischbach, A., Keller, U., Preckel, F., & Brunner, M. (2013). PISA pro fi ciency scores predict educational outcomes. Learning and Individual Differences, 24, 6372. https://doi.org/10.1016/j.lindif.2012.10.012Fischer, F. T., Schult, J., & Hell, B. (2013). Sex-Specific Differential Prediction of College Admission Tests : A Meta-Analysis. 105(2), 478489.Freyr, H., Halldorsson, F., & Kristinsson, K. (2016). Personality in Gneezy s cheap talk game : The interaction between Honesty-Humility and Extraversion in predicting deceptive behavior. PAID, 96, 222226. https://doi.org/10.1016/j.paid.2016.02.075Frisby, B. N., Slone, A. R., & Bengu, E. (2016). Rapport , motivation , participation , and perceptions of learning in U . S . and Turkish student classrooms : a replication and cultural comparison. Communication Education, 0(0), 113. https://doi.org/10.1080/03634523.2016.1208259Gao, L. (2015). Analysis of Employment Data Mining for University Student based on Weka Platform. 2(4), 130133.Garn, A., & Shen, B. (n.d.). International Journal of Sport and Physical self-concept and basic psychological needs in exercise : Are there reciprocal effects ? May 2015, 3741. https://doi.org/10.1080/1612197X.2014.940994Ghosh, A., & Fouad, N. A. (2016). Career Transitions of Student Veterans. 24(1), 99 111. https://doi.org/10.1177/1069072714568752Groeneveld, T. A. S. (2010). ?? (Article) ? ??? (Information) [. The Eletronic Library, 34(1), 15.Hamaideh, S. H., & Hamdan-mansour, A. M. (2014). Nurse Education Today Psychological , cognitive , and personal variables that predict college academic achievement among health sciences students ?. YNEDT, 34(5), 703708. https://doi.org/10.1016/j.nedt.2013.09.010Health, N. (1999). NMHCR Guidebook on Systematic Reviews.Heller, M. L., & Cassady, J. C. (2015). Predicting Community College and University Student Success : A Test of the Triadic Reciprocal Model for Two Populations. https://doi.org/10.1177/1521025115611130Hottenrott, H., & Lawson, C. (2015). Studies in Higher Education Flying the nest : how the home department shapes researchers career paths. 5079(October). https://doi.org/10.1080/03075079.2015.1076782Hsia, J. (2015). mobile learning adoption. Journal of Computing in Higher Education, 707. https://doi.org/10.1007/s12528-015-9103-8Huang, J. (2014). Hardiness , Perceived Employability , and Career Decision Self- Efficacy Among Taiwanese College Students. 415, 114. https://doi.org/10.1177/0894845314562960Ibrahim, M. (2013). The Art of Data Analysis. The Art of Data Analysis, October. https://doi.org/10.1002/9781118413357Iglesias-pradas, S., Ruiz-de-azcrate, C., & Agudo-peregrina, . F. (2015). Computers in Human Behavior Assessing the suitability of student interactions from Moodle data logs as predictors of cross-curricular competencies. Computers in Human Behavior, 47, 8189. https://doi.org/10.1016/j.chb.2014.09.065Iglesias-Pradas, S., Ruiz-De-Azcrate, C., & Agudo-Peregrina, . F. (2015). Assessing the suitability of student interactions from Moodle data logs as predictors of cross-curricular competencies. Computers in Human Behavior, 47. https://doi.org/10.1016/j.chb.2014.09.065International, D. D. (2015). Specificity Matters : Criterion-Related Validity of Contextualized and Facet Measures of Conscientiousness in Predicting College Student Performance. 97(3), 301309. https://doi.org/10.1080/00223891.2014.1002134Jantawan, B., & Tsai, C. (2013). The Application of Data Mining to Build Classification Model for Predicting Graduate Employment. International Journal of Computer Science and Information Security, 11(10), 18. https://doi.org/10.1016/j.bdr.2015.01.001Journal, I., & Technology, R. (2020). Employability Prediction of Engineering Graduates using Machine Learning Algorithms. International Journal of Recent Technology and Engineering, 8(5), 45214524. https://doi.org/10.35940/ijrte.e6823.018520Kementerian Pendidikan Tinggi. (n.d.). Kajian Pengesanan Graduan. Retrieved November 21, 2016, from https://www.mohe.gov.my/ms/pelajar/pelajar- tempatan/kajian-pengesanan-graduanKementerian Pengajian Tinggi (KPT). (2015). Laporan kajian Pengesanan Siswazah 2015. Kpt, 2015, bab 6.Kementerian Pengajian Tinggi Malaysia. (2015). Status Pekerjaan Graduan (Warganegara) 2015 (p. 46).Kerssen-griep, J., & Witt, P. L. (2015). Instructional Feedback III : How Do Instructor Facework Tactics and Immediacy Cues Interact to Predict Student Perceptions of Being Mentored ? May, 3741. https://doi.org/10.1080/03634523.2014.978797Kim, C., Park, S. W., & Cozart, J. (2014). mathematics courses. 45(1), 171185. https://doi.org/10.1111/j.1467-8535.2012.01382.xKitchenham, B. (2004). Procedures for Performing Systematic Literature Reviews. Joint Technical Report, Keele University TR/SE-0401 and NICTA TR-0400011T.1, 33.LabourforceSurvey. (2015). Department of Statistics Malaysia Press Release. Department of Statistics Malaysia, 2018(June), 59. https://doi.org/10.1017/CBO9781107415324.004Li, Z. (2016). A Novel Multidimensional Professionalism Evaluation Model.Liaw, S., & Huang, H. (2013). Computers & Education Perceived satisfaction , perceived usefulness and interactive learning environments as predictors to self- regulation in e-learning environments. Computers & Education, 60(1), 1424. https://doi.org/10.1016/j.compedu.2012.07.015Lievens, F., & Sackett, P. R. (2012). The validity of interpersonal skills assessment via situational judgment tests for predicting academic success and job performance. Journal of Applied Psychology, 97(2), 460468. https://doi.org/10.1037/a0025741Liu, W., & Cross, J. A. (2016). ScienceDirect A comprehensive model of project team technical performance. JPMA, 34(7). https://doi.org/10.1016/j.ijproman.2016.05.011Marks, A. B., & Moss, S. A. (2016). What Predicts Law Student Success ? A Longitudinal Study Correlating Law Student Applicant Data and Law School Outcomes. 13(2), 205265.Masethe, M. A., & Masethe, H. D. (2014). Prediction of Work Integrated Learning Placement Using Data Mining Algorithms. I, 2224.Masserini, L., Bini, M., & Pratesi, M. (2016). for predicting first-year performance in university career : a zero-inflated beta regression approach. Quality & Quantity. https://doi.org/10.1007/s11135-016-0433-zMatherly, L. L. (2012). A causal model predicting student intention to enrol moderated by university image : using strategic management to create competitive advantage in higher education. 6, 3855.Mazlan, A. S., Manaf, Z. A., Ahmad, N., & Zawawi, D. (2017). Employability in Malaysia : Selected Works. In Ministry of Higher Education Malaysia.Michelle, L. (2016). Fresh Graduate Unemployment in Malaysia | EduAdvisor. EduAdvisor. https://eduadvisor.my/articles/what-didnt-know-fresh-graduate- unemployment-malaysia-infographic/Mishra, T. (2016). Students Employability Prediction Model through Data Mining.11(4), 22752282.Mohamed, A., Husain, W., & Rashid, A. (2015). The Third Information Systems International Conference A Review on Predicting Student s Performance using Data Mining Techniques. Procedia - Procedia Computer Science, 72, 414422. https://doi.org/10.1016/j.procs.2015.12.157Nhmrc, N. H. and M. R. C., & Australian National Health and Medical Research Council. (1999). How to review the evidence: systematic identification and review of the scientific literature Handbook series on preparing clinical practice guidelines. How to Use the Evidence: Assessment and Application of Scientific Evidence, 84.Nubailah, S., Salwa, U., Mohd, W., & Azdi, F. (2015). Predictors of Graduate Employability : Mediating Roles of Leadership , Ethics , and Religiosity. International Academic Research Journal of Business and Technology, 1(2), 126136.Pienaar, J., & Zhao, X. (2017). Factors Influencing Student Progression in Built Environment and Engineering Programs : Case of Central Queensland University. 143(4), 19. https://doi.org/10.1061/(ASCE)EI.1943-5541.0000341.Pradeu, T., Laplane, L., Prvot, K., Hoquet, T., Reynaud, V., Fusco, G., Minelli, A., Orgogozo, V., & Vervoort, M. (2016). Defining Development. Current Topics in Developmental Biology, 117(February), 171183. https://doi.org/10.1016/bs.ctdb.2015.10.012Prouty, A. M., Helmeke, K. B., & Fischer, J. (2015). Development of the Mentorship in Clinical Training Scale ( MiCTS ). Contemporary Family Therapy. https://doi.org/10.1007/s10591-015-9351-9Rahmat, N., Ayub, A. R., & Buntat, Y. (2016). Employability skills constructs as job performance predictors for Malaysian polytechnic graduates: A qualitative study. Geografia - Malaysian Journal of Society and Space, 12(3), 154167.Ramaswamy, S., & Hill, M. (2000). Efficient Algorithms for Mining Outliers from Large Data Sets.Rasipuram, S., B, P. R. S., & Jayagopi, D. B. (n.d.). Automatic Prediction of Fluency in Interface-based Interviews.Sapaat, M. A., Mustapha, A., Ahmad, J., & Chamili, K. (2011). A Data Mining Approach to Construct Graduates Employability Model in Malaysia. 1(4), 1086 1098.Schmitt, N. (2012). Development of Rationale and Measures of Noncognitive College Student Potential. 47(1), 1829. https://doi.org/10.1080/00461520.2011.610680Schultz, N. M., Wong, W. B., Coleman, A. L., & Malone, D. C. (2016). AC. American Journal of Ophthalmology. https://doi.org/10.1016/j.ajo.2016.05.001Sheldon, K. M., Turban, D. B., Brown, K. G., Barrick, M. R., & Judge, T. A. (2012). Motivation to learn and learning strategies IT courses in a library and information. https://doi.org/10.1108/00242531211207415Shukla, S. (2018). Variables, Hypotheses and Stages of Research. Icssr, 10(1), 5567.Stoll, G., Rieger, S., Ldtke, O., Nagengast, B., Trautwein, U., Brent, W., Ldtke, O., Nagengast, B., Trautwein, U., & Roberts, B. W. (2016). Journal of Personality and Social Psychology Vocational Interests Assessed at the End of High School Predict Life Outcomes Assessed 10 Years Later Over and Above IQ and Big Five Personality Traits Vocational Interests Assessed at the End of High School Pr.Tajul, M., Ab, R., & Yusof, Y. (2016). Graduates Employment Classification using Data Mining Approach. 020002. https://doi.org/10.1063/1.4960842Tang, C., & Ding, X. (2014). Computers in Human Behavior Graduate students creative professional virtual community behaviors and their creativity. COMPUTERS IN HUMAN BEHAVIOR.https://doi.org/10.1016/j.chb.2014.09.055Taylor, P., Ye, T., & Pan, X. (2015). A convenient prediction model for complete recovery time after exhaustion in high-intensity work. March, 3741. https://doi.org/10.1080/00140139.2015.1008587Taylor, T. Z., Heijden, B. I. J. M. V. A. N. D. E. R., & Genuchi, M. C. (2017). The Police Of fi cer Tacit Knowledge Inventory ( POTKI ): Towards Determining Underlying Structure and Applicability as a Recruit Screening Tool. 246, 236 246. https://doi.org/10.1002/acp.3321The Cochrane Collaboration. (2001). the Cochrane Reviewers Handbook. Database, March, 132.The Relationship between Perceived Organizational Support and Organizational Cynicism of Research. (2014). 14(1), 125134. https://doi.org/10.12738/estp.2014.1.1765The, U., Model, C. F., Compute, T. O., Probability, T. H. E., Detecting, O. F., Bias, P., Ordinary, W., Squares, L., The, I., States, U., & Court, S. (2012). Using the criterion-predictor factor model to compute the probability of detecting prediction bias with ordinary least squares regression. 561580.Thompson, M. N., Nitzarim, R. S., & Her, P. (2015). Financial Stress and Work Hope Beliefs Among Adolescents. 114. https://doi.org/10.1177/1069072715621517Tsao, J., & Wang, C. (2017). The Effects of Writing Anxiety and Motivation on EFL College Students Self-Evaluative Judgments of Corrective Feedback. https://doi.org/10.1177/0033294116687123Viola, M., Feldt, R., & Angelis, L. (2014). Personality , emotional intelligence and work preferences in software engineering : An empirical study. INFORMATION AND SOFTWARE TECHNOLOGY. https://doi.org/10.1016/j.infsof.2014.03.004Williamson, R. C. N. (2001). How to write a review article. Hospital Medicine, 62(12), 780782. https://doi.org/10.12968/hosp.2001.62.12.2389Xu, W., Li, Z., Cheng, C., & Zheng, T. (2012). Data mining for unemployment rate prediction using search engine query data. Service Oriented Computing and Applications, 7(1), 3342. https://doi.org/10.1007/s11761-012-0122-2Yang, J., Development, E., Human, S., Management, R., & Development, E. (2013). THE THEORY OF PLANNED BEHAVIOR AND PREDICTION OF ENTREPRENEURIAL INTENTION AMONG CHINESE UNDERGRADUATES. 41(71002112), 367376.Zacher, H. (2016). Within-person relationships between daily individual and job characteristics and daily manifestations of career adaptability. Journal of Vocational Behavior, 92, 105115. https://doi.org/10.1016/j.jvb.2015.11.013

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