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### 5. jilid 4 bil1 jun 2012

1. 1. ESTIMATION AND COMPUTATION ABILITIES OF MRSM STUDENTS ON ITEMS INVOLVING WHOLE NUMBERS Noraini Noordin Universiti Teknologi Mara, Perlis noraininoordin@perlis.uitm.edu.my Fadzilah Abdol Razak Universiti Teknologi Mara, Perlis fadzilah.ar@perak.uitm.edu.my Nooraini Ali Universiti Teknologi Mara, Perlis noorainiali@perlis.uitm.edu.my Abstract It is a well established fact that estimation and number sense are closely related. Number sense is the ability to understand and use the meaning of numbers in communicating, processing and interpreting information. However, students are found to demonstrate little understanding of numerical situations in which they solve number problems and this has been the concern of mathematics educators. This lack of number sense is usually brought about by not understanding the mathematical exercises they learned in schools. Students can normally manipulate and follow symbolic rules better than at making sense of these numerical practices. Estimation allows the students to find an approximate answer before carrying out the calculation, thus helps to determine whether an answer is reasonable or not. This paper discusses the relationship between the estimation and computation skills of Form One students at Maktab Rendah Sains Mara (MRSM) in Malaysia on items involving Whole Numbers. Keywords: Estimation; Number Sense; Numerical Situations
2. 2. INTRODUCTIONIn any estimation exercise, a student selects simple numbers to operate on mentallyto produce an approximate answer (Reys, 1984; Segovia & Castro, 2009). Thisdefinitive characteristic of an estimation exercise indicates the close relationshipbetween estimation and mental computation. However, mental computation is not aninborn or inherent trait. Experiences and practice are required before one is able todevelop strategies that are more sophisticated than traditional written methods(McIntosh, 2002; Asplin, Frid & Sparrow, 2006). This development can beexperienced in many ways and need not necessarily be in the form of a test(Heirdsfield, 2002).Ahmad Zanzali and Ghazali (2002) stated that out of five strands of number sense(understanding the meaning of size and numbers, understanding the use ofequivalent forms and representations of numbers, understanding the meaning andeffect of operations, understanding the use of equivalent expressions and computingand counting strategies), students demonstrated having difficulties in all strandsexcept the last two strands of number sense. It was found that although Year One toYear Three students were not formerly exposed to addition and subtraction mentalcalculation strategies, they were able to demonstrate intuitive mental computationstrategies to solve addition and subtraction problems varying from an inefficientstrategy of counting on their fingers to more sophisticated and simpler strategiessuch as using doubles, bridging through ten, separation and wholistic strategy(Ghazali, Alias, Anuar Ariffin & Ayub, 2010).These studies indicated the need to understand how students think when they solvemathematics problem. In another study, Ghazali, Abdul Rahman, Ismail, Idros andSalleh (2003) identified three types of strategies employed by Year One to YearThree students:i) Emergent strategies to refer to immature strategies of doing mathematics problemsii) Beginning strategies to refer to methods that exhibit some indication of thinking and employment of strategies of solving the problem that gives the correct answer but with no expert-like strategiesiii) Competent strategies to refer to expert-like method of doing the mathematics.These categories were used to formulate a matrix that will be able to define differentlevels of strategies employed by students as well as classify the levels of thestudents.
3. 3. Students undergo six years of learning Mathematics at primary schools in Malaysia.The section “points to note” in the Integrated Curriculum for Primary School specifiesthat mental computation is to be emphasized in Primary School Mathematics at YearOne and Year Two (Mathematics Year 1, 2002; Mathematics Year 2, 2003).However, mental computation is not formally taught in schools.As for estimation and approximation, students are exposed to these vocabularies atYear Three under the topic Numbers. By the end of Year Three, students will be ableto estimate quantities of objects up to 1000 beginning with smaller values to highervalues and round whole numbers less than 10000 to the nearest ten. Students arealso taught to ensure the reasonableness of any estimates made. They are alsoexposed to estimates in topics like Mass and Volume of Liquid (Mathematics Year 3,2003). At Year Five, estimation can be found in some other learning areas such asWhole Numbers, Decimals, Integers, Basic Measurements, Perimeter and Area, andSolid Geometry (Mathematics Year 5, 2006).Estimation is a significant topic in school mathematics. It is a process rather thancontent knowledge in the mathematics curriculum, and it applies to several strands(Bana & Dolma, 2006). According to the National Research Council (2001), beingable to do quick and accurate mental computations and estimations has twoadvantages:i) Students are able to check the reasonableness of an answerii) Students are able to better appreciate the knowledge of place value, mathematical operations and general number sense.However, studies found that a great number of students have encountered difficultiesdoing simple mental calculation or estimating answers to a problem, causing them toform a significant barrier to using mathematics in their everyday life (Case & Sowder,1990; Star, Lee, Chang & Glasser, 2007).This paper will discuss the results of a study carried out to assess the estimation andcomputation abilities of Form One students. The study focused on four major areas inthe curriculum, namely, Whole Numbers, Decimals, Money and Fractions. Thispaper will only focus on the results obtained with respect to only one area, namely,Whole Numbers.METHODOLOGYSelected samples were from MRSMs in the North Zone of Malaysia, namely MRSMBeseri in Perlis, MRSM Kubang Pasu in Kedah and MRSM Gerik and MRSMPengkalan Hulu in Perak. Data were collected in the first semester of 2010 at therespective colleges. A three-phase procedure was carried out on these samples of
4. 4. students, beginning with a short test on estimation that had a time limit of 30 seconds per item to minimize any precise calculations. Students were also not permitted to write anything on the test paper, other than their responses. This was followed by a short test on computation with a slightly longer time limit, namely three minutes for each item. Students were allowed to use any method/s of their choice, but calculators were not permitted in the computation test. After the test sessions, a selection of three male and three female students were interviewed using the Probing Interview and the interview sessions were audio-taped. The instruments used had 15 similar stemmed items which were constructed based on the topics in the curriculum for Mathematics Year Three to Year Six covering four areas: Whole Numbers (5 items – 33.33%), Fractions (4 items – 26.67%), Decimals (4 items – 26.67%), and Money (2 items – 13.33%). The multiple-choice format was chosen for the Estimation Test to safeguard against students doing precise calculations (Bana and Dolma, 2006). Rasch Measurement Model was used to analyze the responses to both tests. Table 1 summarizes statistics of measures of reliability and correlation for both tests: TABLE 1: Summary Statistics of Measures of Reliability and Correlation TYPE OF TEST ITEMS Computation Test Estimation TestItem Reliability 0.98 0.98Person Reliability 0.44 0.64Item Raw Score-To-Measure Correlation -0.96 -0.94Person Raw Score-To-Measure Correlation 0.97 0.98 The person and item reliability indices were rated using the rating scale instrument quality criteria. It reported the same index of 0.98 (> 0.94) for item reliability of both tests, thus the instrument measured an excellent item reliability rating. Since item reliability was not dependent on the length of a test, the recorded indices implied that both tests had a wide difficulty range, both tests were administered on a large sample and inferences were expected to be consistent. In addition, item ordering had a very high probability of being replicated if these same items were given to a different group of students (Bond & Fox, 2007). High reliability values also implied that the number of ranges in the scale that can be distinguished with confidence across samples also increased. Studies indicated that measures with reliabilities of 0.67 will tend to vary within two groups that can be separated with 95% confidence, measures of reliabilities of 0.80 will vary within three groups; of 0.90, four groups; of 0.94, five groups; of 0.96, six groups; of 0.97, seven groups, and so on (Fisher Jr., Elbaum & Coulter, 2010). However, the person reliability indices reported low values of 0.44 and 0.64 on the Computation Test and
5. 5. the Estimation Test, respectively. Since person reliability does not depend onsample ability variance, this paper can confidently say that this low value may implythat there was not much difference between the students’ abilities. It was also notpossible to discriminate the samples into different levels, thus, this sample did notdemonstrate a hierarchy of ability (Bond & Fox, 2007). In addition, a very highpositive person raw score-to-measure correlation and very low negative item rawscore-to-item correlation implied a low proportion of very high and very low scores(Winsteps, 2011). These values also confirmed the earlier finding that there was nodistinct ability difference between the selected students from these colleges.RESULTSHow students think cannot be observed directly, but can be evaluated from the workthat they produced. These evaluations can help identify their level of conceptualunderstanding or highlight misconceptions (Wong, Evans & Anderson, 2006). Thispaper will focus its discussion on only five items on Whole Numbers, namely items 4,5, 7, 8 and 11. Table 2 summarizes the objectives of these items: TABLE 2: Distribution of Items according to Topics and ObjectivesTOPIC ITEM OBJECTIVES 4 Develop number sense up to 1000000 5 Develop number sense up to 1000000WHOLE 7 Develop number sense up to 1000000NUMBERS Understand and use the vocabulary of estimation and 8 approximation 11 Multiply any two numbers with the highest product of 100000Item maps of the responses to both tests were built to determine the hierarchy ofdifficulty among these items according to the type of test. The item maps found item7 to be the easiest Whole Number item on both tests. The items ordered fromeasiest to most difficult were items 7, 5, 4, 8, and 11 for the Estimation Test, anditems 7, 11, 5, 8 and 4 for the Computation Test. The maps also found items 5 and 8on the Computation Test to lie on the same ability level.
6. 6. DISCUSSIONTable 3 displays items and percentage measures for items on Whole Numbers: TABLE 3: Item and Percentage Measures for Items on Whole Numbers Estimation Test Computation TestItem No. Item Percentage Item Measure Percentage Measure Measure Measure 4 294 76 156 41 5 338 88 304 79 7 381 99 352 91 8 260 68 299 78 11 238 62 333 86As can be seen, students were more able to handle items 4, 5 and 7 in theEstimation Test than on the Computation Test. From among these three items,students found item 7 to be the easiest and item 4 to be the most difficult. Inparticular, item 4 measured the largest range in percentage measure between bothtests. The small range in percentage measure for item 7 suggested that studentshad no problem finding the place value for a digit in a number up to 1000000 in bothtests. The level of performance shown on item 5 indicated that they have achievedthe first strand in number sense; they were able to understand and use the meaningand size of numbers. However, they were not able to utilize their mastery of theability achieved in item 5, on item 4; item 4 presented a situation that required ahigher thinking capability from the students to enable them to manipulate theknowledge of the size of numbers.These three items involved the recognition of place values under differentcircumstances. Students first recognized the place of a digit of a number at YearOne for numbers up to 20, at Year Two for numbers up to 1000, and the number ofdigits increased as they progressed through primary school, thus they should be ableto determine what value a digit represented in any number (Mathematics Year 2,2003). More than 90% of the students were found able to associate the digit “6” inthe given number for item 7 to 6000. This paper wishes to emphasize that betterstudent performances in the Estimation Test can be accounted for by the presence offour solution alternatives A, B, C and D provided in these items, as shown in Table 4.
7. 7. TABLE 4: Items 4, 5 and 7 in the Computation and Estimation TestsItem Computation Test Estimation Test 4 Find the missing numbers in the Find the missing numbers in the sequence given sequence given below: below: 917 158, _____, _____, 887 158 917 158, ____, ____, 887 158 A 927 158, 937 158 B 927 158, 907 158 C 907 158, 897 158 D 897 158, 892 158 5 Round up the following numbers to Which of the following numbers when rounded the nearest ten thousand. off to the nearest thousand becomes 580 000? 579 324 A 579 324 C 580 516 580 516 B 579 562 D 581 105 579 562 581 105 7 The value of the digit 6 in the The value of the digit 6 in the number number 856 943 is 856 943 is A 60 C 6 000 B 600 D 60 000The students demonstrated having more problems rounding up numbers to thenearest ten thousand in item 5 of the Computation Test than rounding up to thenearest thousand in item 5 of the Estimation Test. It was perhaps much easier inthe Estimation Test because the solution alternatives given in the test may haveprovided the students with better chances of determining if the thousands in thenumber was equal or greater than 5, or smaller than 5 to enable rounding off to thenearest 10000 (Mathematics Year 5, 2006). Since the third digit in the number579562 was “5”, an increment of one was added to the fourth digit “9”, thus thesolution alternative 579562 qualified to be rounded off to the nearest thousand as580000. In terms of frequency of students failing item 5 of the Estimation Test,581805 ranked the highest followed by 579324 and 580516. However, theseanswers did not suggest any particular rule used by the students in the estimationprocess. Similarly, item 5 of the Computation Test showed that the fourth digitexceeded 5 in the first and the third numbers, thus all the given numbers shouldround off to 580000. In order to better understand the mistakes that may have beenperformed by students in recognizing and applying the place value of digits in anumber, this paper will take a look at the wrong answers given by students for item 5of the Computation Test, as displayed in Table 5.
9. 9. values would be the best estimate of the total number of sweets that she bought?”while the Estimation Test wanted students to “Estimate the total number of sweetsthat she bought.” Contrary to the paper’s earlier perception that performances inboth tests should be almost the same for this item, analysis of answers to this itemnoted a 10% difference in percentage measure between both tests as displayed inTable 3. Thus, students were better at finding the correct counting and computingstrategies; hence demonstrating they were less competent and had lower numbersense at estimation than computation. The paper wishes to point out that the natureof the construct in the Computation Test may have had some influence on howstudents should think. Unlike item 8, the construct of item 11 was the same for bothtests. Surprisingly, Table 3 indicates that there was a 24% difference in percentagemeasure between performances on both tests.In tackling item 11 in the Computation Test, students had to find all the products for750×45, 833×25, 961×40 and 1025×27, then round them up before deciding whichamong these products lie between 30000 and 35000. These computations took timeto finish. However, analysis of answers indicates that this was easier for the studentsto do than to eliminate the products 833×25 and 1025×27 because they were smallerthan 30000, and 961×40 because it was larger than 35000 in the Estimation Testbefore deciding that 750×45 would be the most appropriate answer. This paperwishes to emphasize that the performance on this item may be taken as a cue thatstudents were not equipped with the knowledge on how to estimate product ofnumbers, thus causing 38% of the students to fail this item in the Estimation Test.Students may not understand the mathematical exercises they learned in schools,and may have approached mathematics as a set of rules to memorize. Thus thisexplains the lack of number sense shown by the students for this item.IMPLICATIONS OF THE STUDYIn this study, Rasch Measurement Model was used to build item maps that generateda preference item schedule for items on estimation and computation which wouldhelp suggest significant differences in the estimation and computation abilities ofstudents. Item measures helped to identify problematic items in both tests. Inparticular, this study was also able to list some issues on estimation and computationabilities of students with respect to numbers.LIMITATIONS OF THE STUDYFirstly, samples were selected Form One students of only four MRSMs in the NorthZone of Malaysia. Data were collected in the first semester of schooling in 2010.Two instruments, a Computation Test and an Estimation Test were developed by theresearchers. Both tests had 15 similar stemmed items based on four topics in the
10. 10. curriculum for Mathematics Year Three to Year Six, namely, Whole Numbers,Fractions, Decimals, and Money.Secondly, the tests were conducted in classrooms or halls provided by the college,thus there was no control on the space between the students when they sat for thetests. Thirdly, the Estimation Test was given first, with a time limit of 30 seconds peritem to minimize any precise calculations. To further enhance the estimation aspectof the test, students were not permitted to write anything on the test paper, other thantheir responses and the items was structured using the multiple-choice format tosafeguard against students doing precise calculations (Bana & Dolma, 2006).Fourthly, the Computation Test was given after a short break, with a three-minutetime limit for each item to enable analysis of computation abilities. In addition,students were instructed that the computations could be done using any method/s oftheir choice, but calculators were not permitted. Lastly, three male and three femalestudents were selected for an interview right after completing the test sessions inrooms allocated by the colleges. There was no possibility of barring off sound fromthe interview sessions. The interview sessions were audio-taped.CONCLUSIONSTable 6 displays the distribution of scores on both tests. As can be seen, studentsperformed better on the Computation Test than on the Estimation Test. However,the percentage difference in the performance distribution was not large. TABLE 6: Distribution of Scores on Both Tests Computation Test Estimation Test SCORES Number of Number of % students % students Students Students Above Mean 208 54.03 202 52.47 Score Below Mean 177 45.97 183 47.53 Score 385 100 385 100Rasch Measurement Model was used to analyze the responses to both tests.Summary statistics of the responses concluded that the students performed better onthe Computation Test with a mean of 11.4 as compared to a mean of 10.3 on theEstimation Test. Both tests indicated the same maximum score (15) and minimumscore (3). Interestingly, there were however four more students with a maximumscore on the Estimation Test than there were on the Computation Test.
11. 11. i) Relationship between Computation and Estimation AbilitiesEstimation helps students to not only approximate answers before carrying out thecalculations but it also helps them to determine whether an answer is reasonable ornot, hence developing number sense in the students (Segovia & Castro, 2009). Itrequires mental computation, thinking and making sense of the computation anddoes not rely on rules or mechanical procedures. However, students are usuallymore successful on written computations than on number sense (Bana & Dolma,2006). Therefore, if the scores on the Estimation Test can be taken as thebenchmark for the development of number sense in students, then this paper positsthat only 52.47% of the students have developed number sense in thinking.Analysis of the responses found that students were better at computation thanestimation in ten out of 15 items, including items 8 and 11 under the topic of WholeNumbers. Table 6 indicates that there was a small range of difference between thestudents’ abilities. Thus, further investigation of these scores has to be done in orderto assess and understand the strong relationship between computation andestimation abilities of these students. There is also a need to compare the items onboth tests that involved learning areas other than Whole Numbers in order to identifyproblematic items. There is also a need to look into those items that have studentsperforming better on the Estimation Test than the Computation Test.ii) Problematic Items on the Computation TestIf a percentage measure of 60% can be taken as benchmark for identifying itemsthat students had little ability to handle, then there were more items on the EstimationTest that need to be given extra focus by the educators than the Computation Test.Items 2, 6, and 14 under the topic Fractions and 15 under the topic Money wereidentified as problematic on the Estimation Test. On the other hand, only items 4 and6 were found to be problematic on the Computation Test. In particular, there were noitems under Whole Numbers identified as problematic to the students in theEstimation Test. On the contrary, item 4 on Whole Numbers with a percentagemeasure of 41% fell into this category for the Computation Test. This paper tends tobelieve that students were not exposed to methods of estimation that can enablethem to develop the number sense needed in the finding of possible strategies totackle this item. Analysis of answers to this item indicated that they were not onlyunable to see the pattern in the sequence of numbers given in this item; they werealso not able to make sense of the end numbers 917158 and 887158.
13. 13. The findings of this study have shown that it is important for mathematics educatorsto pay attention to the development of number sense in the teaching of mathematicsin schools. Item 4 was identified as one of the items students performed better atestimation than computation, but it recorded percentage passes of only 76%. Thatmeans that 24% of the students had problems with higher order thinking in problemsolving, thus it was impossible for them to come up with the correct strategies to findmissing numbers in the sequence 917158, _________, ___________, 887158. Lackof number sense can be felt more in item 15 with only 52% passes in the Estimationtest.This paper wishes to point out that failure to succeed at item 15 may have takenplace because analysis of answers given suggests the possibility that students mayhave misread the question. However, if it were not due to misreading, then attentionshould be given to help students with mixed operations on numbers in solvingmathematics problem in real life. What might be the best approach to teach mixedoperations? To conclude, this study has opened our eyes to some details in thelearning of Whole Numbers that should be given more focus in order to increasenumber sense in thinking and to develop estimation capabilities. It is hoped that thisstudy has contributed ideas to mathematics educators on improving the approachesin teaching these items.
14. 14. REFERENCESAhmad Zanzali, N. A. and Ghazali, M. (2002). Assessment of School Childrens’ Number Sense. Retrieved from math.unipa.it/~grim/ENoor8.PDFAsplin, P., Frid, S., Sparrow, L. (2006). Game Playing to Develop Mental Computation: a case study. Merga Conference Proceedings.Bana, J. and Dolma, P. (2006). The Relationship between Estimation and Computation Abilities of Year 7 Students. Merga27 Conference Proceedings.Bond, T. and Fox, C. M. (2007). Applying the Rasch Model: Fundamental Measurement in the Human Sciences. 2nd Edition. Lawrence Erlbaum Associates. Inc.Case, R., & Sowder, J. T. (1990). The development of computational estimation: A Neo- Piagetian analysis. In Star et al. (2007). Investigating Student Thinking about Estimation: What Makes a Good Estimate? Retrieved from sitemason.vanderbilt.edu/files/e7pCrS/BRJ Vitae8_08.pdfFisher Jr., W.P., Elbaum, B. & Coulter, A. (2010). Reliability, Precision, and Measurement in the context of Data from Ability Tests, Surveys, and Assessments. Journal of Physics: Conference Series 238 (2010) 012036. doi:10.1088/1742- 6596/238/1/012036.Ghazali, M., Abdul Rahman, S., Ismail, Z., Idros, S. N., and Salleh, F. (2003). Development of a framework to assess primary students’ number sense in Malaysia. The Mathematics Education into the 21st Century Project Proceedings of the International Conference: The Decidable and the Undecidable in Mathematics Education, Brno, Czech Republic.Ghazali, M., Alias, R., Anuar Ariffin, N. A. and Ayob, A. (2010). Identification of Students’ Intuitive Mental Computational Strategies for 1, 2 and 3 Digits Addition and Subtraction: Pedagogical and Curricular Implications. Journal of Science and Mathematics Education in Southeast Asia 2010, 33(1), 17-38.Heirdsfield, A. (2002). Mental methods moving along. In Asplin, P., Frid, S., Sparrow, L. Merga26 Conference Proceedings.Mathematics Year 1. (2002). Integrated Curriculum for Primary School. Curriculum Development Center. Ministry of Education Malaysia.Mathematics Year 2. (2003). Integrated Curriculum for Primary School. Curriculum Development Center. Ministry of Education Malaysia.Mathematics Year 3. (2003). Integrated Curriculum for Primary School. Curriculum Development Center. Ministry of Education Malaysia.
15. 15. Mathematics Year 5. (2006). Integrated Curriculum for Primary School. Curriculum Development Center. Ministry of Education Malaysia.McIntosh, A. (2002). Common errors in mental computation of students in grades 3 - 10. In Asplin, Frid, and Sparrow, Merga 26 conference Proceedings..National Research Council. (2001). Adding it up: Helping children learn mathematics. In J. Watson & K. Beswick (Eds), Students’ Conceptual Understanding of Equivalent Fractions. (Proceedings of the 30th annual conference of the Mathematics Education Research Group of Australasia © MERGA Inc.)Reys, R. (1984). Mental computation and estimation: Past, present and future. In Segovia, I. & Castro, E. Electronic Journal of Research in Educational Psychology.Segovia, I. & Castro, E. (2009). Computational and Measurement Estimation: Curriculum Foundations and Research carried out at the University of Granada, Mathematics Didactics Department. Electronic Journal of Research in Educational Psychology. 17, 7 (1) 2009, pp. 499-536.Star, J. R., Lee, K., Chang, K. and Glasser, H. (2007). Investigating Student Thinking about Estimation: What Makes a Good Estimate? Paper presented at the 2007 annual meeting of the American Educational Research Association, Chicago, IL, at the Mathematics Cognition, Design, and Learning paper session. Retrieved from sitemason.vanderbilt.edu/files/e7pCrS/BRJ Vitae8_08.pdfWinsteps Help for Rasch analysis. (2011). Retrieved from 99.236.93.8/winman/index.htm?disfile.htmWong, M., Evans, D. and Anderson, J. (December 2006). Developing a Diagnostic Assessment Instrument for Identifying Students’ Understanding of Fraction Equivalence. The University of Sydney. ACSPRI Conference. Sydney, Australia.
16. 16. FACTORS AFFECTING ORGANIZATIONAL COMMITMENT AMONG LECTURERS IN HIGHER EDUCATIONAL INSTITUTION IN MALAYSIA Munirah Salim Kolej Profesional MARA Bandar Melaka munirahsalim@yahoo.com Halimahton Kamarudin Kolej Profesional MARA Bandar Melaka halimahton@yahoo.com.my Mumtaz Begam Abdul Kadir Kolej Profesional MARA Bandar Melaka mumtazabdulkadir@yahoo.com Abstract A study was conducted to determine MARA Professional Colleges lecturers’ perception on organizational commitment. The study builds on social exchange theory and organizational model to identify the factors influencing the organizational commitment of these lecturers. The study analyzes whether or not there is a significant relationship between job satisfaction, job involvement, perceived organizational support and organizational commitment among lecturers in MARA Professional Colleges. Data were collected via questionnaires from 132 lecturers of three different MARA Professional Colleges. The study utilizes correlation and regression statistics to analyze the data. The findings of the survey show there is a significant relationship between job satisfaction (r=0.307), job involvement (r=0.536) and perceived organizational support (r=0.489). Job involvement contributed the most which is 28.8%, followed by perceived organizational support 23.9% and job satisfaction contributed 9.4% towards organizational commitment among MARA Professional College lectures. The study focuses on MARA Professional Colleges and concentrates only on the organizational commitment among academicians. The results suggest an improvement of social change by increasing job involvement, perceived organizational support and job satisfaction is an efficient way of obtaining highly committed human resource. The results of the study have valuable implications for policy makers in MARA Higher Education Division, college administrators and educators. Keywords: Organizational Commitment, Job Satisfaction, Job Involvement, Perceived Organizational Support
17. 17. INTRODUCTIONCommitted human resources are organization’s greatest assets. In order to ensure excellentand experienced academic staff always attached with the educational institutions.Committed employee should receive superior attention. Moreover, when committed lecturerquits, the college will be burden with high cost and implications for the education system.Committed and quality lecturer will take with them their teaching skills and experience.Meyer and Allen (1993) have recognized that organizational commitment as a leading factorimpacting the level of achievement in many organizations. A lot of studies have beenconducted on the relationship of organizational commitment either towards job satisfaction,job involvement or perceived organizational support only. However, only few have beencarried out on the collaboration of these three factors towards the organizationalcommitment. Besides, there is very little research done to identify factors that impactorganizational commitment among academics (Chang & Choi, 2007; Chen et al., 2007;Freund, 2005; Obeng & Ugboro, 2003).LITERATURE REVIEWOrganizational commitment is as “a strong belief in and acceptance of the organization’sgoals and values; a willingness to exert considerable effort on behalf of the organization; anda strong desire to maintain membership in the organization” (Mowday, R.T., Steers, R.M., &Porter, L.W. (1979). The concept of organizational commitment has been conceptualizedfrom various perspectives. In this current study, the concept of organizational commitmentwill be discussed from the behavioral approach and psychological approach. From thebehavioral approach, organizational commitment has been studied from the output ofrewards/ contribution exchange processes between employers and employees (Morris &Sherman, 1981). On the other hand, the psychological approach looks at organizationalcommitment from the view of the attachment or identification of employees with theorganization at which they work.The model of Meyer and Allen (1997) used in this current study proposed a three-componentmodel of organizational commitment according to the nature of the bond that exists betweenan employee and employer as below:1. Affective commitment is employee’s emotional attachment to, identification with and involvement in the organization (Meyer et.al., 1993; Shore and Tetrick, 1991; Romzek, 1990)2. Continuance commitment that is based on the costs that the employee links with leaving the organization or on a perceived lack of alternative employment opportunities.(Buitendach and De Witte, 2005;Reichers, 1985; Murray, Gregoire, & Downey,1991)
18. 18. 3. Normative commitment that involves the employee’s feelings of obligation to stay with the organization.(Meyer & Allen, 1991; Wiener and Gechman, 1977; Roussenau, 1995)Job satisfaction is one of the most regularly measured organizational variables andfrequently referred to as an employee’s global attitudinal or affective response to their job.Makanjee et al. (2006) explained that job satisfaction was basically the way individualsthought and felt about their multifaceted work experience. Loui (1995) examined therelationship between job satisfaction and organizational commitment among 109 workersand reported that there are positive relationship between organizational commitment and jobsatisfaction. Another study by Coleman & Cooper (1997) explained that job satisfaction waspositively related to both affective and normative commitment. A study by Rajendran andRaduan (2005) also showed the same result that is job satisfaction has a positive influenceon affective and normative commitmentMathieu and Zajac (1990) define job involvement as a belief descriptive of an employee’srelationship with the present job. Joiner and Bakalis (2006) suggested that job involvementdescribes how interested, enmeshed, and engrossed the worker is in the goals, culture, andtasks of a given organization. A study by Uygur and Kilic (2009) involving employeesworking in the central Organization of the Ministry of Health revealed that there is a positivecorrelation between organizational commitment and job involvements.In organization researchers, the social exchange theory (Blau, 1964), and the concept ofperceived organizational support (POS) have been applied to explain the psychologicalprocess underlying the employee attitudes and behaviors (Settoon, Bennet & Liden , 1996;Wayne et al.,2002). Exchanges between an employee and employing organization arecalled POS. Review of POS literature uses social exchange theory interpretation oforganizational commitment to explain how an employee’s commitment to an organization isinfluenced by the organization’s commitment to employee (Jackson et al, 2004). Manyresearchers have investigated the effects of POS on important work outcomes such asaffective commitment and turnover intention (Eisenberger et al., 1986; Eisenberger et al.,1990; Setton et al., 1996; Wayne et.al., 1997).PROBLEM STATEMENTEducational institution is considered as a service industry playing key role in developingsmart, well educated with first class mentality human capital required in vision 2020.Therefore, the main player is academicians who are responsible to produce future humancapital needed by the nation. As per Atan (2007), academic staffs that are committed toimprove teaching and learning methods, strengthening research and innovation are the mainfactor in order to turn Malaysia into leading education hub.
19. 19. Majlis Amanah Rakyat (MARA) through Bahagian Pendidikan Tinggi (BPT) has taken manysteps to strengthen its education sector in order to support Malaysia into a leading educationhub. Since, committed human resources are organization’s greatest assets, thereforeidentifying factors that help to foster organizational commitment among MARA lecturers isimportant. Moreover, when committed lecturer quits, MARA will be burden with high cost andimplications for the education system. Committed and quality lecturer will take with themtheir teaching skills and experiences.Due to this, there is a desire to conduct a study focussing on factors that will influenceorganizational commitment among lecturers in MARA Professional Colleges. This study willinvestigate whether or not job satisfaction, job involvement and perceived organizationalinfluence organizational commitment among MARA lecturers.PURPOSE OF THE STUDYThe purpose of this study is to examine the relationship between job satisfaction, jobinvolvement, and perceived organizational support towards organizational commitmentamong academicians. It is hoped that the findings of the study will provide empiricalevidences in the aspects of factor impacting organizational commitment among academicsand fulfil the research gap due to lack of studies conducted among academicians onorganizational commitment. At the same time, the findings from this research will be usefulto policy makers in MARA Higher Education Division and college administrator in order tomaximize the capacity and capability of its lecturers by increasing their level of commitment.RESEARCH QUESTIONSThe current study is thus conducted to address the following research questions:1. Does job satisfaction contribute towards organizational commitment (Affective, continuance and normative)2. Does job involvement contribute towards organizational commitment (Affective, continuance and normative)3. Does perceived organizational support contribute towards organizational commitment (Affective, continuance and normative)METHODOLOGYThis study was carried out through a survey method using questionnaires as the maininstrument. The sample consists of respondents among lecturers from three MARAProfessional Colleges.
20. 20. The conceptual framework for this current study is suggested in Figure 1. This frameworkwas imitative from earlier theories on antecedents and consequences of organizationalcommitment such as social exchange theory (Blau, 1964) and model of organisationalcommitment by Meyer & Allen (1997). The concept of exchange says that individualbecomes attached to the organization in return for gains provided by the organization.This conceptual framework explains that organizational commitment among academics isinfluenced by job satisfaction, job involvement and perceived organizational support .Thedependent variable in this research is organizational commitment. Organizationalcommitment can be defined through the strength of employee’s identification with, andinvolvement, in a particular organization. The independent variables are job satisfaction, jobinvolvement, and perceived organizational support. JOB SATISFACTION ORGANIZATIONAL COMMITMENT AFFECTIVE JOB INVOLVEMENT CONTINUANCE NORMATIVE PERCEIVED ORGANIZATIONAL SUPPORT Figure 1: Research Conceptual FrameworkThe questionnaires consist of five parts to measure the studied elements, where theindependent variables are job satisfaction (Spector, 1997), job involvement (Kanugo, 1982)and perceived organizational support (Eisenberger et al., 1986). The dependent variablewas organizational commitment with three subscales that are affective, continuance andnormative commitment.The method used to measure job satisfaction in this current study is Job Satisfaction Survey(JSS) (Spector, 1997). No modification was made on the current questionnaires. The surveyuses a faceted approach to the measurement of satisfaction in terms of specific identifiablecharacteristics related to the job (Luthans, 1998). It measures nine aspects of an employee’ssatisfaction: Pay, Promotion, Supervision, Fringe Benefits, Contingent Rewards(performance based rewards), Operating Procedures (required rules and procedures),Coworkers, Nature of Work, and Communication (Spector, 1997). The JSS consist of 36items, and there are 4 items for each facet.
21. 21. To measure the job involvement, 10 items from the Job Involvement Questionnaire (JIQ)developed by Kanugo (1982) is used. However, modification was made by the currentresearcher due to a reliability test. Therefore, in the current study only 9 items were used.Perceived organizational support is measured using Survey of Perceived OrganizationalSupport adapted from Eisenberger et al., (1986). Modifications were made by theresearchers in terms of rewording the construct in order to fit with a particular sample. Theshorter version which consists of 8 items was used in the current study. Organizationalcommitment survey developed by Meyer and Allen (1997) was used. Modifications were alsodone by the researchers in terms of the construct in order to fit with a particular sample. Itidentifies 24 items that can be broken into 3 subscales based on the definition oforganizational commitment that is affective commitment, continuance commitment andnormative commitment. A likert scale format with 7 choices per item is used ranging from"strongly disagree" to "strongly agree”.A pilot study was carried out to revise the questionnaires and for item analysis. The validityand reliability of the questionnaires were measured. The internal consistencies of scale wereassessed through computing Cronbach’s Alpha. The components of factor affectingorganizational commitment show the reliability value ranging from 0.6 to 0.9. Implication fromthese values indicates that all of the items used for each component in the questionnairehave a high and consistent reliability values.FINDINGSi) The relationship between job satisfaction, job involvement, perceived organizational support and organizational commitment (Affective, continuance and normative).Correlations were calculated to determine to what extent job satisfaction, job involvementand perceived organizational support correlated with organizational commitment. As canbe seen in Table 1, significant positive correlations (p < .05) were formed for all threevariables. Correlations ranged from 0.307 for job satisfaction, 0.489 for perceivedorganizational support to 0.536 for job involvement. Table 1: Analysis of Pearson Correlation-Zero Order Job Job Perceived Satisfaction Involvement Organizational SupportOrganizational 0.307 0.536 0.489Commitment (132) (132) (132) P=0.00 P=0.00 P=0.00Job Satisfaction 1.000 0.150 0.512 (0) (132) (132) P=0.00 P=0.087 P=0.00
22. 22. Job Involvement 0.150 1.000 0.422 (132) (0) (132) P=0.087 P=0.00 P=0.00Perceived 0.512 0.422 1.000Organizational Support (132) (132) (0) P=0.00 P=0.00 P= *p<0.05The correlation coefficient value gained from this analysis shows a solid relationshipbetween the variables (Davies in Baharom, 2004). This results show that there is arelationship between job satisfaction, job involvement and perceived organizational supporttowards organizational commitment among MARA Professional Colleges lecturers.ii) Contribution of job satisfaction, job involvement, perceived organizational support towards organizational commitment (Affective, continuance and normative)The result from the correlation as shown in Table 1 fulfils the required conditions forregression analysis. The correlation analysis shows that the studied dependent variabledoes not have a high correlation. Tabachnik and Fidell (1996) in Pallant (2001) stated thatregression analysis can only be done if the correlation value between studied enabler is <0.7. Thus, the regression analysis can be carried out. Linear regression analysis was used todetermine the contribution of the independent variable which is job satisfaction, jobinvolvement and perceived organizational support towards organizational commitmentamong lecturers in MARA Professional College as stated in hypothesis below:H1: There is significant contribution from job satisfaction towards organizational commitment (affective, continuance and normative commitment).H2: There is significant contribution from job involvement towards organizational commitment (affective, continuance and normative commitment).H3: There is significant contribution from perceived organizational support towards organizational commitment (affective, continuance and normative commitment).Table 2 and 3 show the results of linear regression analysis for the influence of jobsatisfaction towards organizational commitment. The linear regression analysis shows thatthe independent enabler which is job satisfaction is the indicator with correlation (β=0.346,t=3.679 and p=0.000) (p<0.05) and the value of R² (R²=0.094) contributes 9.4% towardsorganizational commitment among MARA Professional College lecturers. Thus H1 will beaccepted.
23. 23. Table 2: Analysis of Linear Regression between Job Satisfactions towards Organizational CommitmentIndependent β Beta t Sig.-t R² Contribution Variable (β) (%)Job 0.346 0.307 3.679 0.000 0.094 9.4SatisfactionConstant 2.926 7.035 0.000R 0.307aR squared 0.094Adjusted R squared 0.087Standard Error 0.671 Table 3: Analysis of Variance Sum of Source Squares df Mean Square F Sig. Regression 6.098 1 6.098 13.533 .000a Residual 58.573 130 .451 Total 64.670 131The contribution of job satisfaction towards organizational commitment among MARAProfessional College lecturers forms the linear regression as below:Y= 2.926 + 0.346X1 + 0.671Y= Organizational CommitmentX1= Job SatisfactionConstant 2.926Standard Error 0.416The result of linear regression analysis for the influence of job involvement towardsorganizational commitment is shown in Tables 4 and 5. The linear regression analysis showsthat the independent enabler which is job involvement is the indicator with correlation(β=0.419, t=7.246 and p=0.000) (p<0.05) and the value of R² (R²=0.288) contributes 28.8%towards organizational commitment among MARA Professional College lecturers. Thus H2will be accepted. Table 4: Analysis of Linear Regression between Job Involvements towards Organizational CommitmentIndependent β Beta t Sig.-t R² Contribution Variable (β) (%)Job 0.419 0.536 7.246 0.000 0.288 28.8InvolvementConstant 2.604 10.066 0.000R 0.536aR squared 0.288
24. 24. Adjusted R squared 0.282Standard Error 0.595 Table 5: Analysis of Variance Sum of Source Squares df Mean Square F Sig. Regression 18.607 1 18.607 52.512 .000a Residual 46.064 130 .354 Total 64.670 131The contribution of job involvement towards organizational commitment among MARAProfessional College lecturers forms the linear regression as below:Y= 2.604 + 0.419X1 + 0.595Y= Organizational CommitmentX1= Job InvolvementConstant 2.604Standard Error 0.259The regression linear analysis in Tables 6 and 7 show that the independent enabler which isperceived organizational support is the indicator which has the correlation of (β=0.332,t=6.386 and p=0.000) (p<0.05) and the value of R² (R²=0.239) contributes 23.9% towardsorganizational commitment among MARA Professional College lecturers. Thus H3 will beaccepted.Table 6: Analysis of Linear Regression between Perceived Organizational Supports towards Organizational Commitment Independent β Beta t Sig.-t R² Contribution Variable (β) (%)Perceived 0.332 0.489 6.386 0.000 0.239 23.9OrganizationalSupportConstant 3.080 14.012 0.000R 0.489aR squared 0.239Adjusted R squared 0.233Standard Error 0.615
25. 25. Table 7: Analysis of Variance Sum of Source Squares df Mean Square F Sig. Regression 15.444 1 15.444 40.786 .000a Residual 49.226 130 .379 Total 64.670 131The contribution of perceived organizational support towards organizational commitmentamong MARA Professional College lecturers forms the linear regression as below:Y= 3.080 + 0.332X1 + 0.615Y= Organizational CommitmentX1= Perceived Organizational SupportConstant 3.080Standard Error 0.220From the linear regression analysis can be concluded that job involvement contributed themost which is 28.8%, followed by perceived organizational support 23.9% and jobsatisfaction contributed 9.4% towards organizational commitment among lecturers in MARAProfessional Colleges.DISCUSSION & PRACTICAL IMPLICATIONSSocial exchange theory is the driving force that primarily influences employee organizationalcommitment specifically; job satisfaction, job involvement and perceived organizationalsupport were identified as key drivers of organizational commitment.In the current study, job involvement was found to have a strong positively linked withorganizational commitment. Job involvement also was identified as a major contributor toorganizational commitment among lecturers in MARA Professional Colleges. Research byJanis (1982) and Loui (1995) also support these findings. Literature review regarding jobinvolvement provided evidence of job involvement as significant predictor of organizationalcommitment (Kanugo, 1982; Hafer &Martin, 2006; Wegge et al., 2007; Uygur & Kilic, 2009).Since job involvement is a strong predictor of organizational commitment, HigherEducational Division and college administrator must take action to increase job involvementof the lecturers. The multidimensional model of job involvement by Yoshimura (1996),suggests that the individual variable which affect the job involvement can be divided intoindividual personality and organizational variables. Individual personality such as locus ofcontrol, growth needs, working values, way of being socialized, career stage and successfuljob experience whereas for organizational variables are like participation in decision making,job type and human resource management (Yoshimura,1996). Therefore, people who arevery involved in their job will not feel the need to leave the organization. Thus, by increasing
26. 26. the degree of employees’ self-esteem will enrich job involvement and may lead to highercommitment.Job satisfaction is said to have direct impact on organizational commitment even though it isnot a strong predictor. It reflects that when the level of job satisfaction increases, the level oforganizational commitment also increases slightly. Therefore, this factor should be increasedto improve an employee’s commitment to an organization. Findings from the current study inrelation to facets of job satisfaction revealed that most of the lecturers are satisfied with thenature of work and least satisfied with operating condition and promotion. Results from thecurrent study is consistent with the study conducted by Clay-Warner et al.,(2005) onorganizational justice and job satisfaction. Procedural justice and the level of fairness in themethods by which rewards were distributed among employees by the organization directlyimpacted an employee’s level of satisfaction. Therefore, it is recommended that theinstitution’s rules, policies and procedures should be fair and equitable According to McFarlinand Sweeney (1992), the fairness of an institution’s procedures defines the institution’scapacity to treat its employees fairly. Thus, if employees see the procedures as fair, they arelikely to view the organization positively, which in turn would motivate them to remaincommitted to their respective institutions. Therefore, higher authorities in MARA HigherEducation Division should make an intensive effort to improve procedures and rewarddistributions at MARA Professional Colleges.Since most of the lecturers are satisfied with the nature of work, the current jobs should beenriched so as to make them more interesting, challenging, and motivating. Furthermore,most research indicated that the presence of certain core job dimensions such as autonomy(Dunham et al., 1994), job challenge (Meyer et al., 1997), variety (Steers, 1977) and positivefeedback (Hutchison & Garstka, 1996) direct to greater commitment. As a result, it isrecommended that MARA Higher Education Division should give more autonomy to teacherssuch as giving them more freedom to choose text books, determine the teachingmethodology, set grading and evaluation criteria for their courses and also be given somediscretion in scheduling their classes. Besides autonomy, current jobs can be enriched byadding variety to their work like giving a right balance between teaching and research.Presently, conducting research in their respective areas of specialization is not arequirement for lecturers in MARA Professional Colleges. If there is a right blend of teachingand research, the lecturers will not only have a greater variety of work to do but will also geta chance to upgrade their skills and abilities.The present study shows a moderately significant relationship between perceivedorganizational support and organizational commitment. In line with the current studies, Tek(2009) found evidence that perceived organizational support has a direct influence onorganizational commitment based on research amongst 134 academicians in four privateuniversities in Malaysia. Several studies have provided evidence that perceivedorganizational support plays a critical role in enhancing organizational commitment(Eisenberger et al., 1986; Mottaz, 1988; Vancouver et al., 1994). As perceived organizationalsupport is related to organizational commitment, organizations should find ways to promotehigher perceived organizational support employees. Hence, the organizations should alwaysrecognize the academician’s contributions and care for their well being in order to achieve
27. 27. the organization’s mission so that the academicians can deliver high quality teaching andsupport Malaysia into a leading education hub.Job satisfaction, job involvement and perceived organizational support have been identifiedas significant factors that influence organizational commitment among academicians.Director of each MARA Professional Colleges may use this useful information as anopportunity to create committed team of lecturers. This is because lecturers are part of aninfluential force that plays a key role in the success of students which at the end shows thesuccess of the institution.CONCLUSIONFrom the above discussion, it is clear that fostering commitment among faculty membershas important implications for educational institutions. Therefore, highly committed lecturerswould make a positive contribution to their respective institutions and may lead to increasethe effectiveness of the educational institutions. Thus, institutions which seek to retain theirlecturers by building strong organizational commitment are in a better position to reap thebenefits of a more dedicated, motivated, and reliable teaching staff.In total, this study contributes to the limited body of knowledge underlying the formation oforganizational commitment among academicians through the perspectives of socialexchange theory. Besides, it justifies the importance of creating organizational commitmentamong academicians in order to turn Malaysia into a leading educational hub.
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