MODELLING THE FACTORS THAT INFLUENCE CAREER CHOICE OF TECHNICAL AND VOCATIONAL STUDENTS (A CASE STUDY OF TAKORADI AND HO POLYTECHNICS)

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1 Vol., No.5,.6-80, December 04 MODELLING THE FACTORS THAT INFLUENCE CAREER CHOICE OF TECHNICAL AND VOCATIONAL STUDENTS (A CASE STUDY OF TAKORADI AND HO POLYTECHNICS) Francs Ayah-Mensah, Dr. Felx O. Mettle, John K. Coker Aymah Deartment of Mathematcs and Statstcs, Takorad Polytechnc, Ghana. Deartment of Statstcs, Unversty of Ghana, Ghana. Deartment of Mathematcs and Statstcs, Ho Polytechnc, Ghana. ABSTRACT: The study focused on modellng the factors that nfluence Polytechnc students career choce of techncal and vocatonal courses. A stratfed samlng technque was used to select a total of 430 students. The researcher develoed factor analytcally derved questonnare. Items one to three dealt wth the bo data of the resondents. Items four and fve sought after the arental occuaton of the resondents. Item sx examned how the resondent s career choce was nfluenced by close relatons on a fve ont Lkert scale and the last tem on the questonnare examned the level of mortance attached to varous factors nfluencng the career choce of techncal and vocatonal students n the olytechncs on a seven ont Lkert scale. Multvarate factor analyss method was used n the analyss. The results showed that there are three salent factors that nfluence career choce of techncal and vocatonal students n the Polytechncs. These were Job securty factor, Gender and close famly relatons factor as well as Fnancal and socetal nfluence factor. Ths results confrmed earler researches that sought to nvestgate the factors that nfluence students choce n Techncal and Vocatonal courses. It s therefore recommended that entrereneursh mndset and ndeendent decson should be emhaszed n career counsellng rograms for Techncal and Vocatonal students n ther course selecton. KEYWORDS: Techncal and Vocatonal Educaton, Influence, Decson, Communaltes, Salent factors. INTRODUCTION The techncal and vocatonal educaton reares an ndvdual for self relance. Ths tye of educatons s among the key essental tools an ndvdual can use to develo hm or herself as well as the communty. It s therefore rovdes educatonal tranng for useful emloyment n trade, agrculture, ndustres, homemakng and busness etc. Bascally, techncal and vocatonal tranng or courses amed at strengthenng the sklls base of an ndvdual. Also, techncal and vocatonal choces are a develomental rocess and length of tme almost through an ndvdual s lfetme. The choces focus secfcally on related ssues to the work. Exerences got n varety of work stuaton wll enhance one to reare for transton to a work envronment or tranng. A research conducted by Azubuke (0) revealed that nterest, gender, socoeconomc status, the qualfcaton of teachers/ nstructors and gudance counsellors were the fve major factors that nfluence students n the Techncal and vocatonal school. ISSN (Prnt), ISSN (Onlne) 6

2 Vol., No.5,.6-80, December 04 Korr (0), used a samle sze of 0 students and descrtve statstcs for the analyss. Fndngs showed that majorty of students are nfluenced by oortunty, envronmental and ersonal factors. It was noted that students referred hostalty careers among other alternatve rograms. Techncal and vocatonal educaton name vares from one country to another. However, these names mean the same. Some of the names are; vocatonal educaton and tranng (VET) techncal and vocatonal educaton tranng (TVET), vocatonal techncal educaton (VTE), or vocatonal and techncal educaton and tranng (VOTECT). Techncal and vocatonal nsttutons requre workshos, tools, equment, and materals. The subjects also requre more nstructon and ractcal tme than the arts and scence educaton. The subjects need to be allotted enough tme to satsfy ther ractcal requrements. A large art of the educaton n techncal and vocatonal schools s hands-on tranng. The methods of assessng the subjects s n the form of assessment that requre the tranng of assessors who can assess students cometence n the classroom and n the worklace. These make techncal and vocatonal educaton more exensve than other tyes of educaton. Boateng (0) cted that Lewn (997) revealed that, there are fve justfcatons for governments worldwde to nvest n techncal vocatonal educaton and they are:. To ncrease relevance of schoolng by martng ndvduals wth sklls and knowledge necessary for makng the ndvdual a roductve member of the country.. To reduce unemloyment as a result of rovson of emloyable sklls esecally to the youth and those who cannot succeed academcally. 3. To ncrease economc develoment due to the fact that t mroves the qualty and skll level of the workng oulaton. 4. To reduces overty by gvng the ndvduals who artcate access to hgher ncome occuatons. 5. To transform the atttude of eole to favour occuatons where there are occuatonal rosects. The selecton of a rogram of study at techncal/vocatonal nsttuton s robably one of the most mortant decsons students make. It s beleved that some students choose rograms because of ersonal nterest, famly honours, career choce, just to menton a few. The reasons behnd these vary from one ndvdual to another. These resuose that there are a lot of factors whch nfluence students to choose rograms from the alternatves. The reasons for ths robably are due to student s erceton that t does not requre secalzed knd of tranng. For examle, an ndvdual may have the feelng that even f one s at home, there s the need to learn how to reare food, and ths can be acqured wthout any formal tranng. Students who are gnorant of the sgnfcance of ther choce n techncal and vocatonal subjects could fnd themselves dsaonted n ther future lfe. There s therefore the need to dentfy the salent factors that nfluence student s course selecton to hel them to make nformed decson. Also, ths nformaton wll enable the students to acqure sklls and abltes essental for job lacement more esecally n ths economc dffculty n Ghana where we have unemloyed graduate assocaton. Ths nvestgaton therefore s geared at some of the nfluental factors whch comel students to choose a rogram n the techncal/vocatonal nsttuton. Galott (999), found that n general students made relatvely nformed decsons about ther major selecton. It's also notable that ths same study found that ther nfluence or advce of other eole had very lttle mact on the decson. Taken together, these fndngs suggest that students see the choce of a major as one that both reflects mortant core characterstcs of them ISSN (Prnt), ISSN (Onlne) 63

3 Vol., No.5,.6-80, December 04 (ncludng ther gender role dentfcaton, nterest and values, and ther abltes) and has consequental mlcatons for ther futures. A number of studes have exlored ssues relatng to ndecson about future careers and the mact ths can have on choces relatng to course selecton. Vondracek, et al (990) stated that career ndecson should be recognzed as a normal stage n the career develoment rocess. Indecson may result from an nablty to regard any careers vable, dffcultes choosng between too many occuatons or roblems decdng on alternatves when the most referred oton s not a realstc ossblty. Babad and Tayeb (003) found that "Lecturer style" was among the to two consderatons when selectng a course. All these factors dscussed n ths study can contrbute to the success or falure of the students at the techncal and vocatonal nsttutons. On ths same ssue of course selecton (Whteley and Porter, 998) conducted a study on student ercetons on subject selecton. They found out that ersonal, socal, soco-oltcal factors nfluence students when t comes to decson regardng subject selecton. The man themes related to these factors were; ersonal factors conssted of the students self assessment of ther academc ablty, level of nterest and need for subjects for ost-school courses and career athways. Secondly, socal themes ncluded factors relatng to ther famly and socal networks as well as ther educatonal exerences. Lastly, the soco-oltcal envronment of the communty n whch the students and the schools are located also aeared to mact on the students' decson makng rocesses. Some students and teachers seem not to understand what t s all about and consequently, develo some contemt and not havng the feelng for subjects n the techncal and vocatonal educaton. As such, vocatonal and techncal subjects reman unhealthy. Majorty of the trades and occuatons are regarded as not good and unbecomng. Some Ghanaan arents do not want ther chldren to earn a lvng as a full tme carenter, farmer, a watch-rearer, a lumber or a house anter. Igbnedon (0), used a samle sze of 9 students and descrtve statstcs for the analyss. The hyotheses tested revealed that there were varatons n the erceved factors that nfluence students vocatonal choce of secretaral studes between male and female students from the unverstes and colleges of educaton dffered sgnfcantly wth regards to some of the factors that nfluence ther choce. The nfluence of arents n the develoment of students nterest n vocatonal/techncal subjects cannot be over emhaszed ths s because arents seem to have much nfluence on chldren s choce of educatonal career. How students see themselves n a role n whch ersonalty s a determnng factor may nfluence a chosen career. Some careers demand that you have the ersonalty to match the qualtes of the occuaton. Interest s also an mortant factor n students vocatonal choce. A study by Whteley and Porter (998), amed at dentfyng the mact of school olces and ractces on students as well as other nfluences whch affects ndvdual subject choces and career decsons. It was revealed that ntervews conducted wth students durng ther fnal year at school wll rovde further nsght nto ercetons of subject selecton and ther effect on decsons regardng ost-school otons and career decsons Objectves of the study.. Assess the factors that nfluence the career choce of techncal and vocatonal students n the Polytechncs 64 ISSN (Prnt), ISSN (Onlne)

4 Vol., No.5,.6-80, December 04. Examne the salent factors that could best descrbe the nfluence on career choce n the Polytechncs MATERIALS AND METHODS Data Collecton technque: The oulaton was regstered students studyng Techncal and Vocatonal courses n the olytechncs at the tme of data collecton. The courses were used as strata. A roortonal allocaton method was used to obtan the requred samle sze from each of the course reresentng a strata as shown on the table. The stratfed samlng method reduced the samlng error. Students from all vocatonal and techncal deartments were also well reresented, excet that those from the Industral Art and Desgn deartment were slghtly more reresented; ths s as a result of the roortonate allocaton method emloyed n the data collecton. Self admnstered questonnare was used to obtan data from the resondents. A total of 430 questonnares were admnstered and they were all retreved as shown n table but there were no resonses on some tems. It was a researcher develoed factor analytcally derved questonnare. It was a fve & seven ont Lkert scale tye. It s a close-ended questonnare. Items one to three deals wth the bo data of the resondents. Items four and fve deals wth the arental occuaton of the resondents. Item sx examnes how the resondent s career choce s nfluenced by close relatons on a fve ont Lkert scale and the last tem on the questonnare examnes the level of mortance attached to varous factors nfluencng the career choce of techncal and vocatonal student n the Polytechncs on a seven ont Lkert scale. Table : Samle selecton Program of Study Frequency Percent Art Buldng & Cvl Engneerng 9.4 Fashon Hotel Caterng & Insttuton Management Electrcal & Electronc Engneerng Mechancal Engneerng Non-Resonse 0. Total Varables n the Research The man varables n the research are the seven-ont tems of the questonnare. The frst fve tems are classfcatons varables; gender, age, rogram of study, mother s occuaton and father s occuaton. The sxth tem (seven sub-tems) sought to measure the extent to whch the occuaton of famly and teacher nfluence the student s choce of rogram. The seventh and the last tem of the questonnare consst of 7 sub-tems whch sought to measure level of mortance attached to each of the 7 ndcators as to how they nfluence student s choce of a rogram. ISSN (Prnt), ISSN (Onlne) 65

5 Vol., No.5,.6-80, December 04 Sharma (996), sad that, factor analyss was orgnally develoed to exlan student erformance n the varous courses and to understand the lnk between grades and ntellgence. Searman (904) hyothessed that student s erformance n the varous courses are ntercorrelated and ther ntercorrelaton could be exlaned by student s general ntellgence levels as cted by (Sharma,996). However, the technque s generally used n recent tmes n busness stuatons whch requre a scale or an nstrument to measure the varous constructs such as atttudes, mage, atrotsm, sales attude and resstance to nnovaton. If data s collected on a large number n of varables, most of whch are correlated, t may be desrable to reduce the number of varables nvolved. Ths requres an examnaton of the nterrelatonsh between the varables and then reresented by a few m new underlyng factors. The new fewer varables also referred to as latent factors are then used to aroxmate the correlatons between the orgnal varables. Mathematcally, factor analyss s somewhat smlar to multle regresson analyss, n that each varable s exressed as a lnear combnaton of underlyng factors. The amount of varance the varable shares wth all other varables s called communalty. The covaraton among the varables s descrbed n terms of a small number of common factors lus a unque factor for each varable. These factors are not overtly observed. If the varables are standardzed, the factor model may be reresented by X A F A F A F,, A F V U 3 3 m m Where X s th standardzed varable A j s standardzed multle regresson coeffcent of varable on common factor j F s common factor V s standardzed regresson coeffcent of varable on unque factor U s the unque factor for varable m s number of common factors The unque factors are uncorrelated wth each other and wth the common factors. The common factors themselves can be exressed as lnear combnatons of the observed varables. F W X W X W X,, W X 3 3 k k Where F s estmate of th factor W s weght or factor score coeffcent k s number of varables ISSN (Prnt), ISSN (Onlne) 66

6 Vol., No.5,.6-80, December 04 It s ossble to select weghts or factor score coeffcents so that the frst factor exlans the largest roorton of the total varance. Then a second set of weghts can be selected, so that the second factor accounts for the resdual varance, subject to beng uncorrelated wth the frst factor. Ths same rncle could be aled to selectng addtonal weghts for the addtonal factors. Thus, the factors can be estmated so that ther factor scores, unlke the values of the orgnal varables are not correlated. Furthermore the frst factor accounts for the largest varance n the data, the second factor, the second largest and so on. Prncal comonent factor analyss Prncal Comonent s one of the rocedures for carryng out Factor analyss. To dentfy the latent factors underlyng the correlatons between ndcator varables, X, X,... X P, the correlaton matrx of the varables are examned by means of Prncal Comonent Analyss. Ths s done by formng new varables, ( y,,,... ), where y y y w x w w w x w x w x,, w x,, w x,, w x x x 3 That s, the new varables are lnear combnatons of the orgnal varables. The new varables are referred to as the Prncal Comonents. The coeffcent w, s the weght of the jth varable on the th rncal comonent. These coeffcents are determned such that, w w,..., w,,,... 4 w w w w,..., w w 0, j j j 5 These condtons ensure that the comonents are uncorrelated and consttute orthogonal axes wth each other. j j Suosng s the varance of the th comonent, also called ts egenvalue, and wj varance of the jth varable, the corresondng coeffcent defned by lj S j S j the s the loadng of the jth varable on the th comonent. Ths value then s a measure of the correlaton between the jth varable on the th comonent. In ths case, ( y,,,... ) may then be generally wrtten as y l j j x j 6 Equaton 6 may be wrtten n matrx form as ISSN (Prnt), ISSN (Onlne) 67

7 Vol., No.5,.6-80, December 04 Y X 7 where Y s a P vector of standardzed comonents; s a P P orthonormal matrx of loadngs; X s a P vector of ndcator varables. Thus I s P P dentty matrx. From Equaton 7, X s obtaned as X Y 8 That s, the orgnal varables x j ( j,,... ), now exressed n terms of the comonents as x x l l y l y l y,, l y,, l y y Or x j l y l y,, ly, x s generally exressed as 9 j x l j y 0 Snce orthogonalty condtons are met, the y accounts for the th largest varaton n the data and y here s referred to as the th factor. Usng the rules of factor extracton roosed by (Zwck and Velcer, 986), the factor s nterreted by consderng those hgh loadngs l ) ndcates the factor s mortance n exlanng the varablty n that varable. Some condtons for conductng factor analyss In determnng whether a artcular data set s sutable for factor analyss, the samle sze and the strength of the relatonsh among the varables are some of the man ssues to consder. There s lttle agreement among authors concernng how large a samle should be. The recommendaton generally s that, the larger, the better. In small samles the correlaton coeffcents among the varables are less relable, tendng to vary from samle to samle. (Tabachnk and Fdell, 00) revew ths ssue and suggested that t s comfortng to have at least 300 cases for factor analyss. The second ssue to be addressed concerns the strength of the ntercorrelatons among the tems. Tabachnck and Fdell recommend an nsecton of the correlaton matrx for evdence of coeffcents greater than 0.3. Two statstcal measures oerated by SPSS to hel assess the sutablty of the data are Bartlett s test of shercty and the Kaser-Meyer- Olkn (KMO) measure of samlng adequacy. ( j ISSN (Prnt), ISSN (Onlne) 68

8 Vol., No.5,.6-80, December 04 It has been seculated by some Factor analysts (Zwck and Velcer, 986) that the recson of the recommendaton of the KMO measure s deendent on the number of ndcators underlyng a artcular factor. If the number of ndcators er factor s large, recson ncreases. By the dervaton of the KMO measure, the value s hgh f each varable has an ndvdual KMO. In other words, the value can be ncreased by deletng those varables under study whose ndvdual KMO are small. Sometmes analyss of the data may not be ossble as a result of few nformaton on some varables that does not allow for the comutaton of arwse correlatons between the varables. Snce the technque utlzes the correlaton matrx, the varable nvolved n such a case mght be droed and the correlaton matrx obtaned for the remanng varables for the study. Another condton on the number of varables that can be used n the study s known as the Ledermann bound. Ledermann (937), has derved a bound for the number (m) of factors that can be extracted from orgnal varables. The bound s gven by m 8 that s, the number of common factors cannot exceed the largest nteger satsfyng the Equaton (). Now by defnton, m. It can be deduced from the range n the equaton that f the number, of varables s less than 4, the condton on m s volated. On the other hand, the source of ths bound, gven by the quadratc nequalty m m 0, naturally rules out the ossblty of the value of m beng equal to. Therefore, factor analyss s meanngful as a dmensonalty reducton technque f the number of varables under study s qute large and greater than 3. Ths usefulness s also true f the number of common factors extracted s strctly less than the ntal number of varables under study. Determnaton of the number of factors In order to summarse the nformaton contaned n the orgnal varable, a smaller number of factors should be extracted. Several rocedures have been suggested for determnng the number of factors. These nclude a ror determnaton, aroaches based on egenvalues, scree lot, ercentage of varance accounted for, slt-half relablty and sgnfcance test. Sometmes because of ror knowledge the researcher knows how many factors to exect and thus can secfy the number of factors to be extracted beforehand. The extracton of factors ceases when the desred number of factors have been extracted. Most comuter rograms allow the user to secfy the number of factors, allowng for an easy mlementaton of ths aroach. We can also determne the number of factors based on egenvalues of extracted factors. In ths aroach only factors wth egenvalues greater than.0 are retaned and the other factors excluded n the model. An egenvalue reresents the amount of varance assocated wth the factor. Hence only factors wth a varance greater than.0 are ncluded. Factors wth varance less than.0 are not better than a sngle varable, because due to standardzaton, each varable has a varance of.0. If the number of varables s less than 0, ths aroach wll end n a conservatve number of factors. The number of extracted factors can also be determned based on ercentage of varance. In ths aroach the number of factors extracted s determned so that the cumulatve ercentage of ISSN (Prnt), ISSN (Onlne) 69

9 Vol., No.5,.6-80, December 04 varance extracted by the factors reaches a satsfactory level. What level of varance s satsfactory deends uon the roblem. It s ossble to determne the statstcal sgnfcance of the searate egenvalue and retan only those factors that are statstcally sgnfcant. A drawback s that wth a large samle (sze greater than 00) many factors are lkely to be statstcally sgnfcant, although from a ractcal vew ont, many of these accounts for only a small roorton of the total varance. The orthogonal factor model Accordng to Johnson and Wchern (99), the observable random vector X wth comonents has mean u and covarance matrx. The factor model ostulates that X s lnearly deendent uon a few unobservable random varables F, F,... Fm, called common factors and addtonal sources of varaton,,... called errors or sometmes secfc factors. In artcular, the factor analyss model s: X X X P L F L L L P F L F L F,, L m F,, L m F,, L F m F m e m F e m e the equvalent matrx notaton s where X L F e ( ) ( m) ( m ) ( ) Lj s the loadng of the th varable on the L s the matrx of factor loadngs e s assocated only wth the th resonse The devatons th j factor. X X, X, X are exressed n terms of m, F Fm, e, e, e random varables F..., whch are unobservable. Ths dstngushes the matrx notaton factor model from the multvarate regresson model n whch the ndeendent varables whose ostons are occued by F n the matrx notaton can be observed. Rotaton of comonent Sharma (996), stated that the objectve of rotaton s to acheve a smler factor structure that can be meanngfully nterreted by the researcher. He mentoned an orthogonal rotaton whch s most oular, the rotated factors are orthogonal to each other, whereas n oblque rotaton the rotated factors are not orthogonal to each other. The nterretaton of the factor structure resultng from an oblque rotaton s more comlex than that resultng from orthogonal rotaton. Varmax and Quartmax are the most oular tyes of orthogonal rotatons. In the varmax rotaton the major objectve s to have a factor structure n whch each varable loads hghly on one and only one factor. That s a gven varable should have a hgh loadng on 70 ISSN (Prnt), ISSN (Onlne)

10 Vol., No.5,.6-80, December 04 one factor and near zero loadngs on the other factors. Such a factor structure wll result n each factor reresentng a dstnct construct. The major objectve of ths rotaton technque s to obtan a attern of loadngs such that all the varables have a farly hgh loadng on one factor and near zero loadngs on the remanng factors. Obvously, such a factor structure wll reresent one factor that mght be consdered as an overall factor and the other factors that mght be secfc constructs. Thus, quartmax rotaton wll be most arorate when the researchers susect the resence of general factor. Varmax rotaton destroys or suresses the general factor and s not arorate to be used when the resence of the general factor s susected. RESULTS AND DISCUSSION The results are summarzed n table form and dscussons beneath the tables. Conscuous values are bolded and also form the bass of the dscussons. The analyss of data was organzed under two man headngs relmnary and further analyses. The relmnary analyss contans mostly descrtve statstcs about the oulaton of study whle the further analyss used advance statstcal tool of factor analyss to extract salent factors resonsble for nfluencng the resondents choce of rogram at the olytechncs. Prelmnary Analyss Ths art of the analyss resents the data on the varous classfcaton varables n the research. It s exected that the general descrton of the oulaton under study would be catured for further analyss to be carred out. Table : Demograhc Characterstcs of Resondents ( ) Varable Gender Frequency Percent (%) Male Female Non Resonse.6 Age Grou Non Resonse 8.9 Table resents the demograhc characterstc of the resondents; t shows that there are more male than female reresentaton n ths research. Ths means that the conclusons made here are ISSN (Prnt), ISSN (Onlne) 7

11 Vol., No.5,.6-80, December 04 more lkely to be reresentng that of males than females. The age groung of the resondents also revealed that the conclusons made for ths research would many tmes be attrbuted to students wthn the age of 5 to 4 than those of other age grou. Table 3: Occuaton of Resondents Parents ( ) Varable Frequency Percent (%) Mother s Occuaton Trader Formal Sector Engneerng 0. Professonal/Vocatonal Others Non Resonse 0.3 Father s Occuaton Trader Formal Sector Engneerng 54.6 Professonal/Vocatonal 6.0 Others Informal Occuatons Non Resonse 5. Source: Feld Survey, 04 The dstrbuton of the occuaton of resondents mother aears to be much towards tradng than to other occuatons, as shown n Table 3 above. On the other hand, the dstrbuton of the occuaton of resondents father s rather towards rofessonal vocatonal sectors than to others, yet about 4% of fathers engaged n other nformal occuatons lke farmng, carentry, masons, etc. The decson as to what course to offer at the Polytechnc level could be nfluenced by a number of factors; what s of nterest n ths research s to fnd whether the occuaton of arent s sgnfcant n dong so. Table 4: Extent to Whch Occuaton of others Influence Choce of Program ( ) Very Low Low Somehow Hgh Very Hgh Non Resonse Father Mother Brother Sster Other Relatves Frends Teacher Source: Feld Survey, 04 ISSN (Prnt), ISSN (Onlne) 7

12 Vol., No.5,.6-80, December 04 Table 4 Contd.: Extent to Whch Occuaton of others Influence Choce of Program ( ) Very Low Low Somehow Hgh Very Hgh Non Resonse Father Mother Brother Sster Other Relatves Frends Teacher Source: Feld Survey, 04 In almost all, excet for other relatves, as shown n the table 4, the resondents have ndcated that the extent to whch the occuaton of others nfluences ther choce of rogram s hgh or very hgh. Ths suggests that, the average techncal/vocatonal student s qute lkely to be nfluenced by the occuaton of father, mother, sblngs and teacher. Further Analyss The man objectve of ths research s to dentfy underlyng construct, f any, that nfluence the choce of rogram of techncal/vocatonal students at the tertary level. Factor analyss s the tool that s mostly credted wth the ablty to acheve ths objectve. The analyss nvolves followng a number of rocedures n turns. For the uroses of clarty, the varables to be used n the factor analyss are redefned as follows. X=Personal Interest X=Gender X3=Career Oortunty X4=Ethncty X5=Sblngs nfluence X6=Exected Earnngs X7=Parent Preference X8=Oortunty for further studes X9=Peer Influence X0=Job avalablty X=Role Model Influence X=Fnancal Constrant X3=Ablty/ talent X4=Prestge Attached to the rogramme X5=Teacher Influence X6=Dffculty of the Programme X7=Self emloyment The resondents were to ndcate the level of mortance attached to each of the ndcators as to how they nfluence ther choce of rogram usng the Lkert scale below: = Least Imortant = Less Imortant ISSN (Prnt), ISSN (Onlne) 73

13 Vol., No.5,.6-80, December 04 3= Lttle Imortant 4= Imortant 5=Much Imortant 6=More Imortant 7=Most Imortant Table 5: Relablty Statstcs Cronbach's Alha No. of Items Source: SPSS Outut of Feld Data, 04 The cronbach s alha suggests strongly that there s nternal consstency n scalng the varables by the resondents, and that, about 8.7% of the tme, the resonses for the 7 varables were consstent. The hgh cronbach s alha value here further ndcates that the varables are correlated amongst themselves and that the factorng would be lausble. Table 6: Summary Ratng Statstcs Mean Std. Devaton X X X X X X X X X X X X X X X X X Source: SPSS Outut of Feld Data, 04 Table 6 clearly hghlghts the average ratng assgned to each varable. The varable bolded are averagely of much mortance n nfluencng the choce of rogram for the students. On average, all varables are deemed to be at least, of lttle mortance n nfluencng the choce of rogram. ISSN (Prnt), ISSN (Onlne) 74

14 Vol., No.5,.6-80, December 04 Table 7: Communaltes amongst the Varables Extracton X.448 X.490 X3.46 X4.64 X5.557 X6.463 X7.4 X8.47 X9.57 X0.479 X.39 X.48 X3.39 X4.9 X5.445 X6.385 X7.38 Source: SPSS Outut of Feld Data, 04 In factorng, emhass s laced on dentfyng groungs wthn the data set that share smlar characterstcs, ths s called communaltes. From the communalty table above, the amount of varance the varables shared on each other aears to be hgh for three varables Ethncty, Sblngs nfluence and Peer Influence. Moreover, these varables, X4, X5 and X9, recorded a mean ratng value of around 3 n Table 6 ndcatng that they are of lttle mortance when t comes to nfluence on choce of rogram. Another set of varables share qute hgh varance wth others (n Table 7) and have mean ratng of 4 or 5 showng a hgher mortance attached to them are; Gender, Career Oortunty, Exected Earnngs, Job avalablty and Fnancal Constrant. Yet another set of varables have low varance shared wth others but have hgh mean ratng of at least 5; they ndcate hgher mortance attached. They are; Personal Interest, Oortunty for further studes, Ablty/ talent, Prestge Attached to the rogramme and Self emloyment. The nterretatons above suggest three salent comonents that seek to exlan the nfluence on career choce of techncal and vocatonal students. Table 8: KMO and Bartlett s Test TEST VALUE Kaser-Meyer-Olkn Measure of samlng Adequacy Bartlett s Test of Shercty Arox. Ch Square Degree of Freedom 36.0 Sgnfcance ISSN (Prnt), ISSN (Onlne) 75

15 Vol., No.5,.6-80, December 04 The Kaser-Meyer-Olkn Measure of samlng Adequacy (KMO) value s as shown on the table 8. Moreover, the Bartlett s Test of Shercty s sgnfcant ( = 0.000). These fgures also satsfy the assumton of the sutablty of the data for factor analyss. Here the Kaser-Meyer- Olkn Measure of samlng Adequacy was exected to be 0.6 or above whle the Bartlett s Test of Shercty should be sgnfcant wth < Thus, the data meets the requrement for the use of Factor analyss. Table 9: Total Varance Exlaned Intal Egenvalues Comonent Total % of Varance Cumulatve % Source: SPSS Outut of Feld Data, 04 The total varance exlaned by the three comonents s 44.5%. Ths suggests that 44.5% of the 8.7% nternal consstency n the ratng assgned by the resondents can be exlaned by three comonents. The next table would hel rovde a label for the new comonents. ISSN (Prnt), ISSN (Onlne) 76

16 Vol., No.5,.6-80, December 04 Fgure : Scree lot of addng Egenvalue aganst Comonent Number It s mortant to also look at the scree lot as shown on the fgure. What s needed on the scree lot s to look for a change or elbow n the shae of the lot. It reveals a qute break between the second and the fourth comonents. Hence the frst three comonents are to be retaned because they cature a reasonable roorton of the total varance. Ths s n suort of the use of the ntal egenvalues. ISSN (Prnt), ISSN (Onlne) 77

17 Vol., No.5,.6-80, December 04 Table 0: Rotated Comonent Matrx Comonent 3 X X X X X X X X X X X X X X X X X Source: SPSS Outut of Feld Data, 04 From Table 0, the 7 orgnal ndcators can be ut nto three grous base on ther loadng on the new comonents. At a cut off loadng of at least 0.5, we have the followng sets The ndcators are X=Personal Interest X3=Career Oortunty X6=Exected Earnngs X8=Oortunty for further studes X0=Job avalablty X3=Ablty/ talent X4=Prestge Attached to the rogramme X7=Self emloyment The ndcators above are descrbng the ndvdual s quest towards havng job securty n the future. The frst comonent could therefore be labelled as the job securty factor. Ths suort Korr (0) fndngs whch showed that majorty of students are nfluenced by oortunty and envronmental factors and nfluenced by ersonal factors. The ndcators are X=Gender X4=Ethncty X5=Sblngs nfluence ISSN (Prnt), ISSN (Onlne) 78

18 Vol., No.5,.6-80, December 04 X7=Parent Preference The ndcators n ths category seek to relate the ndvdual s sex wth close famly relatons. The second comonent could also be labelled as the gender and close famly relatons factor. A research conducted by Azubuke (0) revealed that the nterest, gender, soco-economc status, the qualfcaton of teachers and nstructors and gudance counsellors were the fve major factors that nfluence students n the Techncal and vocatonal school. Wth ndcators X9=Peer Influence X=Role Model Influence X=Fnancal Constrant X5=Teacher Influence X6=Dffculty of the Programme Here too, fnancal constrant aears to be lnked wth nfluence from vtal socetal ersectves. The thrd comonent could hence be labelled as the fnancal and socetal nfluence factor. Galott (999), found that n general students made relatvely nformed decsons about ther major selecton. It's also notable that ths same study found that ther nfluence or advce of other eole had very lttle mact on the decson. The new revelaton here s the fnancal constrants. These fndngs suggest that students see the choce of major as one that both reflects mortant core characterstcs of them. CONCLUSIONS The study has revealed that there are three salent factors that nfluence career choce of techncal and vocatonal students n the Takorad and Ho Polytechncs. These are; Job securty factor, Gender and close famly relatons factor and Fnancal and socetal nfluence factor. Interestngly the results suggest that job securty s a very mortant consderaton n course selecton n the techncal and vocatonal rogram, there s therefore the need to ether establsh more ndustres or equ the graduate to set u ther own busnesses after ther tranng. The Gender and close famly relatons factor shows that there s the need to gve adequate counsellng to students to have ther ndeendent onon to study the techncal and vocatonal rograms that s of nterest to them. Fnancal and socetal nfluence factor ndcate and suort the onon that some Ghanaan arents do not want ther chldren to earn a lvng as a full tme farmer, a watch-rearer, carenter, a lumber or a house anter. For many Ghanaans, these jobs are for the oor and those who have less money and fewer oortuntes. Students also takes fnancal consderaton very mortant n makng a choce n the techncal and vocatonal rogram. RECOMMENDATION Stake holders n educaton should show more nterest n the area of Techncal and Vocatonal Educaton wth emhass on student s course selecton. It s also recommended that 79 ISSN (Prnt), ISSN (Onlne)

19 Vol., No.5,.6-80, December 04 entrereneursh mndset should be emhaszes n career counsellng rogramme for Techncal and Vocatonal students n ther course selecton. Ths wll enable the students to have nformed decson on ther course selecton. REFERENCES Azubuke, O. C.(0). Influental Factors affectng the atttude of students Towards Vocatonal/Techncal subject n secondary schools n Southeastern Ngera. Journal of Educatonal and socal research. Vol () Setember 0. Babad, E. and Tayeb, A. (003). Exermental analyss of students, course selecton. Brtsh Journal of Educatonal Psychology, 73, Boateng C. (0), Restructurng Vocatonal and Techncal Educaton n Ghana: The Role of Leadersh Develoment. Internatonal Journal of humantes and Socal scence Vol. No 4 Galott, K. M., & Kozberg, S. F. (999). Older adolescent s thnkng about academc/vocatonal and Adolescence, 6, Igbnedon V. I. (0). Perceton of factors that nfluence students vocatonal choce of secretaral studes n tertary nsttutons n edo state of Ngera. Euroean Journal of Educatonal studes 3(), 0. Johnson, R. A. and Wchern, D. W.(99). Aled Multvarate Statstcal Analyss. Prentce- Hall Internatonal. Inc. Unted States of Amerca. Korr J. (0). Factors that nfluence career choce of hostalty students n Mo Unversty, Kenya. Journal of Educatonal and Practce. Vol 3, No 4, 0. Lederman, W. ( 937 ). On the Rank of the Reduced Correlaton Matrx n Multle Factor Analyss. Psychometrca,, Sharma S. (996). Aled Multvarate Technques. John Wley and sons. Inc. Unted States of Amerca. Tabachnck B.G., & Fdell, L.S.( 00 ). Usng Multvarate statstcs. 4 th edton. New York : Harer Collns. Chater 3. Vondracek, F.W., Hosteler, M., Schulenberg, J.E., & Shmzu, k.(990). Dmensons of career ndecson. Journal of Counsellng Psychology,37(), Whteley S. and Porter J. (998) Student ercetons of subject selecton: Longtudnal ersectves from Queensland schools. Tertary Entrance Procedures Authorty (TEPA). Zwck, W. R., and Velcer, W.F.(986). Comarson of fve rules for determnng the number of comonents to retan. Psychologcal Bulletn, 99, ISSN (Prnt), ISSN (Onlne) 80