A Multi Criteria Approach For Statistical Software Selection in Education. Eğitimde İstatistiksel Yazılım Seçimine Çok Kriterli Bir Yaklaşım

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1 Hacettepe Ünverstes Eğtm Fakültes Dergs H. U. Journal of Educaton 292, [Nsan 2014] A Mult Crtera Approach For Statstcal Software Selecton n Educaton Eğtmde İstatstksel Yazılım Seçmne Çok Krterl Br Yaklaşım Zelha KAYGISIZ ERTUĞ *, Nuray GİRGİNER ** ABSTRACT: Statstcal software s commonly used n the statstcal lessons at unverstes. The developments and enhancement n statstcal software n recent years has consderably eased statstcs educaton n these nsttutons. The purpose of ths study s to develop an evaluaton model consderng the quanttatve and qualtatve crtera for statstcal software selecton n an outsourcng user of these programs varety felds, especally n educaton. An ntegrated model s proposed by combnng Analytc Herarchy Process AHP and Grey Relaton Analyss GRA nto a sngle evaluaton model. The model s llustrated wth a case study of a team of fve people ncludng academcs and software developers well versed n the use and development of such software to demonstrate the effectveness of ths ntegrated method. AHP has been appled to determnng weght of crtera and GRA has been performed for determnng the most approprate statstcal software. The results ndcate that when analyss characterstcs are the man crtera wth the hghest prorty, fnancal and vendor frm characterstcs are the man crtera wth the lowest prortes. Also accordng to GRA results, the most approprate statstcal software s SPSS and Statgraph s n last rank wth a low level of sgnfcance. Keywords: Statstcs educaton, statstcal software, analytc herarchy process, grey relaton analyss. ÖZ İstatstksel yazılımlar, ünverstelerdek statstk derslernde sıklıkla kullanılmaktadır. Son yıllarda statstk yazılımlarının yleştrlerek gelştrlmes, bu kurumlarda verlen statstk eğtmn de büyük ölçüde kolaylaştırmıştır. Bu çalışmanın amacı, eğtm başta olmak üzere çok çeştl alanlarda kullanılan statstksel yazılımların seçm çn ncel ve ntel krterlern br arada değerlendrldğ br model gelştrmektr. Çalışmada Analtk Hyerarş Sürec AHS ve Gr İlşksel Analzn GİA brlkte kullanımıyla bütünleşk br model önersnde bulunulmuştur. Söz konusu modeln etkllğn ortaya koyablmek çn, gerek kullanıcı gerekse programcı olarak uzman olan akademsyenler ve programcılardan oluşan beş kşlk br ekple br örnek olay uygulaması yapılmıştır. Krterlern ağırlıklarını belrlemek çn AHS, en uygun statstksel yazılım seçmn gerçekleştrmek çn se GİA kullanılmıştır. Elde edlen sonuçlar, en yüksek öncelğe sahp olan ana krtern analz özellkler, en düşük öncelklere sahp olan ana krterlern se fnansal özellkler le satıcı frma özellkler olduğunu ortaya koymuştur. Aynı zamanda GİA sonuçlarına göre SPSS en uygun statstksel yazılım olarak belrlenrken, Statgraph en düşük önem derecesne sahp yazılım olmuştur. Anahtar sözcükler: İstatstk eğtm, statstksel yazılım, analtk hyerarş sürec, gr lşksel analz. 1. INTRODUCTION Varous statstcal methods are commonly used n the analyss of obtaned data and the nterpretaton of the fndngs n today s scentfc studes due to the wde spreadng of the statstcal methods and technques n all areas startng especally from the last quarter of the 20 th century along wth the changes n the prortes of researchers n ths feld. However, snce the manual soluton of the requred statstcal analyses of comple and mult-varable problems contanng mass data takes too long and has a hgh probablty of error, the use of software developed for ths purpose has become oblgatory. The determnaton of software most suted to the data durng the analyss and evaluaton stage s mportant for the relablty of the obtaned results. Thus, the selecton of the proper statstcal software s more mportant than the carryng out of the statstcal operatons. * Assst. Proff. Dr, Esksehr Osmangaz Unversty Faculty of Economc and Admnstratve Scences, Eskşehr, zelhak@ogu.edu.tr ** Proffessor Dr, Esksehr Osmangaz Unversty Faculty of Economc and Admnstratve Scences, Eskşehr, ngrgner@gmal.com

2 130 Zelha KAYGISIZ ERTUĞ, Nuray GIRGINER Statstcal software dffers from each other n many dfferent areas the techncal support of the vendor, user frendlness of the software, ease of database access, ablty to generate outputreports, ncluded methods etc.. Thus, the satsfacton of the demands of the user depends on whch software s chosen. For eample, one of the places where such software s commonly used s the statstcal lessons at unverstes. The developments and enhancement n statstcal software n recent years has consderably eased statstcs educaton n these nsttutons. Such software enables the students to gan nformaton and develop sklls regardng the statstcal analyss and nterpretaton of results obtaned durng the study. Ths n turn brngs forth the necessty to ncorporate computer applcatons as well as theory durng statstcs educaton. However, the selecton s not correct f the selected software does not concde wth the soluton of the problems that the students wll face durng ther professonal lves followng graduaton. On the other hand, selecton of the proper software s mportant to ensure the qualty of the scentfc studes carred out by unverstes apart from educaton. Sgnfcant problems arse when the user does not clearly know the algorthms and soluton formulatons ncluded n the software. The relablty of the analyses may be problematc when the users are people wthout any educaton n statstcs. Snce the proper and effectve use of statstcal software wll provde benefts; t s mportant that researchers show the attenton and senstvty they employ durng the plannng and eecuton of ther studes n the selecton of the proper software. However, snce the requred statstcal software necessary for both statstcs educaton and scentfc studes cannot be obtaned as part of ther budgets, t s an oblgaton that such software s purchased va the faculty budget. The lmted budgets of facultes along wth the fact that t s not possble to purchase all the software demanded by academcs pose a problem of selectng software. In ths study, the necessty of statstcal software both for statstcs educaton and scentfc studes along wth the problem of statstcal software selecton due to lmted budgets has been eamned. Techncal features, after sales servces provded by the sellng company, ncluded analyss propertes, ease of use, speed, graphcs support, and nteractvty between databases are consdered n addton to the cost when makng the software decson. In other words, there are many qualtatve and/or quanttatve crtera that affect the decson of the researcher. Therefore, a selecton method should be used where prortes of the factors wth respect to each other can be evaluated for all crtera concurrently. In practce, there s no standard method for ths selecton process. In addton to methods such as cost-beneft analyses, gradaton, rsk analyses and scorng; mult-crtera methods such as Analytc Herarchy Process AHP, Analytc Network Process ANP are used. On the other hand, Grey Relatonal Analyss GRA s another method that can be used for software selecton enablng the selecton of the best alternatve for all crtera based on the closeness wth the best alternatve among all alternatves for problems where more than one alternatve are consdered n the estence of more than one crtera. GRA successfully selects the best alternatve n total by calculatng the closeness of an alternatve n all crtera wth the best and worst values for each respectve crteron. In ths study, the weghts of the crtera and the sub-crtera that are thought to be effectve n the selecton of statstcal software obtaned va AHP have been used as data n GRA. Therefore, a degree of sgnfcance has been put forth for each alternatve software va GRA amng to select the most sutable statstcal software wth the hghest degree of sgnfcance Lterature Revew The large number of crtera that should be evaluated together has resulted n the frequent use of AHP n software selecton lterature. Ths comple and mult-crtera decson problem has attracted the attenton of many researchers thereby ncreasng the number of relevant studes n lterature. Başlıgl 2005 has studed the selecton of software for hghest level of customer satsfacton va fuzzy AHP; Koçak 2003 and Çörekçoğlu and Güngör 2005 have eamned the selecton of the most sutable enterprse resource plannng ERP software va AHP. Varous

3 A Mult Crtera Approach For Statstcal Software Selecton n Educaton 131 nternatonal study eamples can be gven n whch AHP has been used for software selecton. Santhanam and Kyparss 1996 have developed a non-lnear model for software selecton and have compared the relevant software based on the relatonshp between crtera whereas Jalal and Ray 1999 have eamned the evaluaton methods that can be used to select software for producton smulaton. La et.al 1999, 2002 have used AHP to carry out comparsons for software alternatves n mult-envronment control systems based on group decsons accordng to two man crtera whch they have named as techncal features and eecutve features. We, Chen and Wang 2004 have set out from AHP method to develop a model enablng ERP selecton accordng to the needs of an enterprse and durng ther studes have observed that ths method s qute accessble for eecutves and yelds productve results. Lee, Shen and Chh 2004 have developed a mult-crtera method for software selecton. In a smlar study, Mulabeke and Zheng 2006 have eamned the selecton of software used n product development va analytc network process. Ayağ and Özdemr 2007 have eamned ERP software selecton usng analytc network process whereas Perera and Costa 2008 have used AHP and Yazgan et.al have used artfcal neural networks and analytc network process methods. The fact that most errors n statstcal calculatons arse due to user or statstcal software errors has led researchers to carry out studes regardng the relablty of statstcal software. Studes n lterature regardng statstcal software focus mostly on the comparson of the relabltes of statstcal software wth the ecepton of the study carred out by Grgner and Kaygısız For nstance; Altman and McDonald 2001 suggest a gude regardng the selecton of relable statstcal software. In addton, there are also studes whch compare wdely used statstcal software. For nstance; Delman 2002 has compared four statstcal software wdely used n busness admnstraton departments whereas Ktchen et.al have compared s of statstcal softwares two of whch are web based. The development of statstcal software among estmaton software has been ncluded n the study carred out by Küsters et.al Last study about ths topc has been carred out by Keelng and Pavur 2007 n whch they have compared the relablty of nne statstcal software. Dfferent from these studes, Grgner and Kaygısız 2009 have evaluated the selecton of the best statstcal software that wll be used for academc studes as well as durng educaton n unverstes from among three statstcal software usng AHP and 0-1 Goal Programmng GP methods accordng to fve basc crtera each of whch nclude sub-crtera. As seen n the lterature revew, no studes were found on the evaluaton of statstcal softwares usng GRA; meanwhle, no study n whch the statstcal softwares were ranked by ther prortes by obtanng the weghts of crtera and sub-crtera by applyng AHP was encountered ecludng the study carred out by Grgner and Kaygısız It has been stated by eamples above that apart from the study carred out by Grgner and Kaygısız 2009, a sngle method s used for the comparson of software used n felds outsde of statstcs nstead of combned methods. Also AHP determne the most approprate alternatve nstead of sortng them. However usng GRA, alternatves can be sorted. All these reasons n ths study, GRA had been used together wth AHP whch s another mult-crtera decson makng technque. The obectve of the study s to determne the crtera consdered durng the selecton of statstcal software whch should be evaluated together along wth ther prortes and to make the most sutable statstcal software decson usng GRA. It s thought that ths study and ts results wll be helpful to all partes faced wth such a decson. 2. METHOD The selecton of the most approprate statstcal program alternatve nvolves multple obectves or/and crtera and herarchy process. In ths study used analytcal herarchy process AHP proposed by Saaty, 1980 and grey relaton analyss GRA to select the statstcal program alternatves. AHP provdes an optmal soluton consderng both qualtatve and

4 132 Zelha KAYGISIZ ERTUĞ, Nuray GIRGINER quanttatve aspects of a decson. Another advantage of AHP s that t reduces the level of comparson from large number of factors to few. GRA provde the optmal decson makng usng relatonal coeffcent matrces whch s used to compute the weghts to the crtera. The obectve of ths study s to apply the AHP and GRA for optmal selecton of statstcal software alternatve Determnng the Weghts of Crtera by Usng AHP As a decson method that decomposes a comple mult-crtera decson problem nto a herarchy Saaty 1980, 1994; Tung and Tang, 1998; Lee et al., 1999; Machars et al., 2004, AHP s also a measurement theory that prortzes by group of decson makers. Assumng there s no nterdependence among sub-crtera should be more emphaszed n determnng the perspectve upper level crtera. AHP ncorporates the evaluatons of all decson makers nto a fnal decson Javalg et al., 1989; Forman and Penwat, 1998; Chou et al., 2004; Chang et al., 2007a; Wu et al., 2008, wthout havng to elct ther utlty functons on subectve and obectve crtera, by par-wse comparsons of the alternatves Saaty, 1990; Lpovetsky, 1996; Saaty, 2000; Altuzarra et al., Pror to the AHP applcaton of the statstcal software selecton problem, a team of fve people ncludng academcs and software developers well versed n the use and development of such software was assembled. These eperts conssted of a computer software developer, a computer teacher, an academc gvng statstcs lessons and two other academcans who use statstcal software n other numerc felds marketng, medcne, etc.. In lne wth the ntervews carred out wth the team and lterature survey studes regardng software selecton, crtera and sub-crtera that should be consdered n the selecton of statstcal software the herarchcal structure were determned and the herarchcal structure was prepared as shown n Fgure 1 for four wdely used software SPSS, Statstca, Mntab and Statgraph. Fve basc crtera fnancal, techncal, usage, analyss and vendor frm propertes all of whch nclude other sub-crtera were consdered durng the formaton of the herarchcal structure.

5 A Mult Crtera Approach For Statstcal Software Selecton n Educaton 133 Selecton of the most approprate Software Fnancal Propertes Acquston Cost SC1 Operaton Cost SC2 Update Cost SC3 InfrastructureSC4 Techncal Propertes Interface SC5 Graphc Support SC6 Multenvronment support SC7 Data fles support SC8 Usage Propertes Content sutablty SC9 Ease of accesssc10 Ease of use SC11 Speed SC12 Analyss Propertes Descrptve Statstcs SC13 Comparson analyses SC14 Classfyng /Dscrmnatory analysessc15 Relatonal analyses SC16 Cause-consequence analyses SC17 Vendor Frm Propertes Techncal support SC18 Tranng support SC19 User requrements SC20 Contnuous development SC21 SPSS STATISTICA STATGRAPH MINITAB Fgure 1: Herarchcal Structure of Problem The net mportant stage of the AHP decson process followng the formaton of the herarchcal structure of the problem s obtanng pared comparson values. Survey form has been prepared based on the pared comparson matrces formed for each level of the herarchy gven n Fgure 1. Usng the scale suggested by Saaty, partcpants have udged the pared comparsons between the alternatves lsted n the survey form, man crtera and sub-crtera accordng to ther levels of sgnfcance. Thnkng that the udgments of people who have knowledge about statstcal software through ther work as academc or through tranngs wll dffer, the geometrc mean was calculated and AHP group decson makng was appled. Followng the pared comparsons for all man and sub-crtera, the values obtaned for each man and sub-crtera based on respectve sgnfcance levels and alternatves were obtaned va group decson makng and lsted n Table 1.

6 134 Zelha KAYGISIZ ERTUĞ, Nuray GIRGINER Table 1: AHP Result Matr for the Statstcal Software Selecton Problem Man-Crtera Sub-Crtera Alternatves SPSS Statstca Mntab Statgraph SC Fnancal Propertes Techncal Propertes Usage Propertes Analyss Propertes Vendor Frm Propertes SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC When Table 1 s eamned, t s observed that the crtera wth the hghest mportance n the selecton of statstcal software s analyss propertes %49.8 followed by the man crtera of usage propertes %23.7 and techncal propertes %16.6. Fnancal propertes %5.1 and vendor frm propertes %4.9 were ranked last based on ther levels of sgnfcance. When fnancal propertes are consdered as a man crtera, the sub-crtera wth the hghest relatve sgnfcance level among all sub-crtera came out to be update cost %1.8 followed by acquston cost and operaton cost %1.1 whereas nfrastructure %1.0 was ranked last. When techncal propertes are consdered, t s observed that the sub-crteron wth the hghest level of relatve sgnfcance s data fles support %6.6. Ths sub-crteron was followed by multenvronment support wth a sgnfcance level of %4.6 and graphc support of the software wth a sgnfcance level of %2.8. The sub-crteron wth the lowest level of sgnfcance was the software nterface wth a value of %2.5. The ease of use for the software was the crtera wth the hghest sgnfcance level for the usage propertes man crtera %11.2 whereas the sub-crtera wth the lowest sgnfcance level was ease of access wth a value of %3.8. The sub-crtera wth the hghest sgnfcance level among analyss features man crtera were cause-effect analyses %14. Ths was followed respectvely by comparson analyses, relatonal analyses and classfyng/dscrmnatory analyses whereas sub-crteron named descrptve statstcs was ranked last wth a sgnfcance level value of %3.2. For vendor frm features whch s the last man

7 A Mult Crtera Approach For Statstcal Software Selecton n Educaton 135 crtera, tranng support %1.4 was determned to be the most mportant sub-crteron and user requrements %1 was determned to be the least mportant sub-crtera Determnng the Most Approprate Statstcal Software by Usng GRA Deng 1982 ntroduced The Grey System Theory to supplement the lmtatons of usng tradtonal statstcal methods. Grey System Analyss GRA s useful for capturng the correlatons between the reference factor and other factors whch can be compared wthn a system Deng, One of the features of GRA s that both qualtatve and quanttatve relatonshp can be dentfed among comple factors wth nsuffcent nformaton relatve to conventonal statstcal methods. Under such condtons, the results generated by conventonal statstcal technques may not be acceptable wthout suffcent data to acheve desred confdence levels. In contrast, grey system theory can be used to dentfy maor correlatons among factors of a system wth a relatvely small amount of data. Because of these features GRA has been etensvely appled n many felds, such as fnancal nsttutons, hosptals, banks, arlnes frms, etc. The procedure for calculatng the GRA s as follows Wu et. al., 2010: 1. Calculate the Grey Relaton Grade Let X 0 be the referental seres wth k enttes or crtera of X 1, X 2,, X,.X N or N measurement crtera. Then X 0={ 0 1, 0 2,, 0 k}, X 1={ 1 1, 1 2,, 1k}, X ={ 1, 2,, k},.. X N={ N 1, N 2,, Nk}.. The grey relaton coeffcent between the compared seres X and the referental seres of X 0 at the -th entty s defned as: 1 mn ma 0 ma 0 Where s the absolute value of dfference between X 0 and X at the -th entty, that s and ma Ma ma 0 The grey relatonal grade GRG for seres of X s gven as:, mn Mn mn 0 2 K 0 w 0 1

8 Zelha KAYGISIZ ERTUĞ, Nuray GIRGINER 136 Where, w s the weght of -th entty. If t s not necessary to apply the weght, take K 1 as an average. 2. Data Normalzaton or Data Dmensonless Before calculatng the grey relaton coeffcents, the data seres can be treated, based on the followng three knds of stuaton and the lnearty of data normalzaton, to avod dstortng the normalzed data Hsa and Wu, These are: Beneft target: Upper-bound effectveness measurng.e. larger-the-better mn ma mn * 3 Cost Target: Lower bound effectveness measurng.e. smaller-the-better mn ma ma * 4 c Medum Target: Moderate effectveness measurng.e. nomnal-the- best If ma mn ob, then mn ma * ob 5 If ma ob, then ob mn mn * 6 If mn ob, then ma ma * ob 7 Where ob s the obectve value of entty.

9 A Mult Crtera Approach For Statstcal Software Selecton n Educaton 137 The GRA calculaton process eplaned above has been appled as shown below n steps n lne wth the purpose of the study. Step 1. Establshng decson makng matr: The weght s estmated for fve eperts wth each respondent usng Saaty s relatve mportance scale and averagng ther scale to assess canddates, then establshng a decson makng matr as shown Table 2. Table 2: The Decson-Makng Matr Sub- Statstcal Software Crtera Reference SPSS STATISTICA MINITAB STATGRAPH SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC The twenty one sub-crtera are the-larger-the-better because of each of them are compared accordng to whch one more mportant. Accordngly the referental seres can be X 0 = 0.388,, The statstcal softwares alternatves are X 1 SPSS, X 2 Statstca, X 3 Mntab and X 4 Statgraph. Step 2. Normalzng Data: After establshng a decson makng matr Table 2, t s establshed referental seres can be X0 = {1.00, 1.00, 1.00,, 1.00}. The statstcal softwares are X1, X2, X3 and X4. Data are normalzed for 21 sub-crtera by usng equatons 6. Table 3 summarzes normalzaton data.

10 138 Zelha KAYGISIZ ERTUĞ, Nuray GIRGINER Table 3: Summary of Normalzaton Data Statstcal Softwares Sub- Reference SPSS Statstca Mntab Statgraph Crtera X0 X1 X2 X3 X4 SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC Step 3. Computng absolute values [ 0 ]: Absolute value s the dfference X0 dfferental seres and X at the -th sub-crtera. Computed absolute values are dsplayed n Table 4. Step 4. Computng Grey Relaton Coeffcents [ 0 ]: The relatonal coeffcents, compared seres are computed usng equaton 4. Table 5 presents the results. Table 4: Absolute Values Statstcal Software Sub- SPSS Statstca Mntab Statgraph Crtera X1 X2 X3 X4 SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC of the

11 A Mult Crtera Approach For Statstcal Software Selecton n Educaton 139 Table 5: Grey Relaton Coeffcents mn P ma 0 P ma 0 Man Crtera Sub-Crtera Statstcal Softwares w SPSS X1 Statstca X2 Mntab X3 Statgraph X4 SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC SC Step 5: Computng Grey Relaton Grade: The sub-crtera weghts from AHP and GRA are derved by equaton 5. Sub-crtera weghts w obtaned from AHP are shown second column n parentheses n Table 5. Equaton 5 has been appled for each alternatve statstcal software as the rato of the sum of grey relaton coeffcents multply relatve weghts by the sum of the relatve weghts. Table 6 summarzes these results. Table 6: Grey Relaton Grades K w Statstcal Software 0 Rank SPSS % Statstca % Mntab % Statgraph % As shown n Table 6, SPSS %93.8 s the most approprate statstcal software. Other software follow such as Statstca %63.8, Mntab %46.8 and n last rank Statgraph wth % Accordng to AHP results Table 1, the man crteron wth the hghest mportance n the selecton of statstcal software s analyss propertes followed by the man crtera of usage propertes and techncal propertes Fnancal propertes and vendor frm propertes were ranked last based on ther levels of sgnfcance. On the other hand accordng to GRA results Table 6, frst software s SPSS as the most approprate statstcal software. SPSS s followed by Statstca and Mntab, respectvely. Statgraph s last n the rankng.

12 140 Zelha KAYGISIZ ERTUĞ, Nuray GIRGINER 4. DISCUSSION and CONCLUSIONS Ths study proposes an ntegrated approach for evaluatng and selectng statstcal software of AHP and GRA n a preference measurement model for users of these programs. The man advantage of ths research s that t can be used for both qualtatve and quanttatve crtera. Ths study ndcates that the AHP and GRA are powerful tools whch can be mult crtera decson makng problem as selectng statstcal software problem. The proposed method comprses two parts. The frst part employs the AHP to determne the weghts of crtera. The second part apples the GRA to rank alternatves and select the best statstcal software. In ths model f new crtera added, they can be ncluded n the proposed model to select the best software. Also any new potental software can be ncluded n the evaluaton process. Hence n comparson wth other models proposed model s more fleble, applcable and effectve. Accordng to the results of the AHP carred out to determne the man and sub-crtera affectng the most sutable software for researchers usng statstcal software; the man crtera were ranked accordng to ther levels of sgnfcance as follows: analyss propertes, usage propertes, fnancal propertes and vendor frm propertes. Whereas accordng to the results of the GRA carred out to determne the most sutable statstcal software; SPSS package software was determned as the most sutable software followed respectvely by Statstca, Mntab and Statgraph. On the other hand, when the man crtera weghts obtaned accordng to the GRA results are eamned, t s strkng to see that smlar to AHP results, the two man crtera wth the hghest level of mportance were analyss propertes and usage propertes. Snce academcans purchase statstcal software usng the budget allocated by ther unverstes, fnancal propertes man crtera were not determned to be sgnfcant n ths study accordng to both the AHP and the GRA results. In addton, when vendor frm propertes crtera are consdered, snce users generally meet ther own techncal and tranng support requrements followng the purchase and nstallaton of the software, ths crtera was also not specfed as sgnfcant n the selecton of statstcal software. On the other hand, descrptve statstcs property was determned to be a more sgnfcant analyss feature n comparson wth the others whereas relatonal analyss was determned to be the least sgnfcant. Wth respect to the usage propertes, the sub-crtera wth the hghest level of sgnfcance was determned to be the speed of the software whereas ease of use was determned to be the property wth the lowest sgnfcance level See Table 5. Dependng upon the mportance of the statstcal software mentoned n ths study, the epectaton among unverstes regardng software that researchers or academcans cannot purchase personally makes t necessary to ncrease relevant costs. Ths necessty has ncreased n recent years and has comprsed the basc competton ponts among unverstes. It s thought that another factor provdng the motvaton to ncrease these costs s the shapng of the competton between unverstes around nternatonal relatons and the nternatonal publcatons of the academcans. Even though the most sutable statstcal software was determned wthn the scope of data obtaned by appealng to the opnons of fve eperts wthn the scope of the herarchcal model, the same study can be repeated wth a dfferent group of eperts and updated data n addton to consderng dfferent crtera and sub-crtera. Weghts of crtera weghts can also be obtaned by usng the Analytc Network Process ANP allowng the analyss of the statstcal software selecton problem n a network structure, GRA applcaton can be repeated and results can be compared.

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14 142 Zelha KAYGISIZ ERTUĞ, Nuray GIRGINER Mulabeke, J.A.W. & Zheng, L Analytc network process for software selecton n product development: A case study, Journal of Engneerng Technology Management, 23, Perera, H.S.C. & Costa, W.K.R Analytc Herarchy Process for selecton of ERP software for manufacturng companes, The Journal of Busness Perspectve, 12 4, Saaty, T.L The Analytc Herarchy Process, McGraw Hll, New York. Saaty, T.L Egenvector and logarthmc least squares, European Journal of Operatonal Research, 48, Saaty, T.L Fundamentals of decson makng and prorty theory wth the AHP. RWS Publcatons, Pttsburgh, PA, U.S.A. Saaty, T.L Fundamentals of the Analytc Herarchy Process, RWS Publcatons, Pttsburgh, PA. Santhanam, R. & Kyparss, G.J A decson model for nterdependent nformaton system proect selecton, European Journal of Operatonal Research, 89 2, Tung, S.L. & Tang, S.L A comparson of the Saaty's AHP and modfed AHP for Rght and left egenvector nconsstency, European Journal of Operatonal Research, 106 1, Wu, W.Y., Shh, H.A. & Chan, H.C The Analytc Network Process for partner selecton crtera n strategc allances, Epert Systems wth Applcatons, 36 3, Wu, C.R., Ln, C.T. & Tsa, P.H Evaluatng busness performance of wealth management banks, European Journal of Operatonal Research, 207 2, We, C.C., Chen, C.F. & Wang, M.J An AHP-based approach to ERP system selecton, Internatonal Journal of Producton Economcs, 96, Yazgan, H.R., Boran, S. ve Göztepe, K An ERP software selecton process wth usng artfcal neural network based on Analytc Network Process approach, Epert Systems wth Applcatons, 36 5, Uzun Özet Özellkle yrmnc yüzyılın son çeyreğnden tbaren br yandan statstk yöntem ve teknklernn her alanda yaygınlaşması, dğer yandan bu alanda çalışma yapanların öncelklernn değşmes gb nedenlerle günümüzde blmsel araştırmalarda, elde edlen verlern çözümlenmesnde ve bulguların yorumlanmasında çeştl statstksel yöntemler yoğun olarak kullanılmaktadır. Ancak yığın ver çeren, karmaşık ve çok değşkenl problemlern çözümlenmesnde gereken statstksel analzlern elle çözümünün çok zaman alması ve hesaplama şlemlernde hata yapma olasılığının artması, bu yönde gelştrlen yazılımların kullanımını da zorunlu hale getrmştr. Verlern analz ve değerlendrlmes aşamasında hang yazılımların çalışma verlerne en uygun olduğunun saptanması, sonuçların güvenrllğ açısından önem taşımaktadır. Dolayısıyla statstksel şlemlern yapılmasından zyade uygun statstksel teknğn ve bu teknk çn kullanılacak olan statstksel yazılımın seçm ön plana çıkmaktadır. Dğer taraftan ünverstelern eğtmöğretm dışında gerçekleştrdkler blmsel araştırmaların kaltesnde, amaca uygun, doğru yazılımın seçlmes etkldr. Her fakültenn sınırlı br bütçeye sahp olması ve akademsyenler tarafından talep edlen yazılımların tamamının karşılanamaması da araştırmacıları çoğu zaman br yazılım seçm problem le karşı karşıya bırakmaktadır. Bu çalışmada, statstksel yazılımların hem statstk eğtmndek hem de blmsel araştırmalardak gerekllğ ve fakültelern sınırlı bütçelernden dolayı statstksel yazılım seçm problem ele alınmıştır. Çalışmanın amacı, statstksel yazılımların seçmnde dkkate alınan ve brlkte değerlendrlmes gereken krterler ve öncelklern Analtk Hyerarş Sürec AHS le belrleyerek, Gr İlşksel Analz GİA le en uygun statstksel yazılım seçmn gerçekleştrmektr. İstatstksel yazılım seçmnde etkl olduğu düşünülen krter ve alt krterlern AHP le elde edlen ağırlıkları, GİA de ver olarak kullanılmıştır. Böylelkle GİA le her br alternatf yazılım çn br önem dereces ortaya konularak, en yüksek önem derecesne sahp statstksel yazılımın en uygun statstksel yazılım olarak belrlenmes amaçlanmıştır. İstatstksel yazılımlar konusunda gerek kullanıcı gerekse programcı olarak uzman olan akademsyenler ve programcılardan oluşan beş kşlk br ekp oluşturulmuştur. Söz konusu uzmanlar br blgsayar programcısı, br blgsayar öğretmen, statstk eğtm veren br akademsyen ve dğer sayısal alanlarda pazarlama, tıp, vb. statstksel yazılımları kullanan k akademsyenden oluşmaktadır. Ekp le gerçekleştrlen görüşmeler ve lteratürde yazılım seçmyle lgl yapılan çalışmalar doğrultusunda,

15 A Mult Crtera Approach For Statstcal Software Selecton n Educaton 143 statstksel yazılım seçm problemnde göz önüne alınması gereken krterler ve alt krterler belrlenmş ve problemn yaygın kullanımı olan dört yazılım seçeneğ SPSS, Statstca, Mntab ve Statgraph çn hyerarşk yapısı oluşturulmuştur. Hyerarşk yapı oluşturulurken, her brs alt krterler çeren beş temel krter fnansal, teknk, kullanım, analz ve satıcı özellkler dkkate alınmıştır. Problemn hyerarşk yapısının oluşturulmasından sonra statstksel yazılımlar hakkında blgs olan ve gerek akademk gerek öğretm amaçlı kullanıcı durumundak kşlern, kl karşılaştırmalardak yargılarının farklılaşacağı düşüncesyle katılımcıların yargılarının geometrk ortalamaları alınarak grup karar vermel AHP uygulanmıştır. AHP le elde edlen krter ve alt krter ağırlıkları, GİA de ver olarak kullanılarak her br alternatf yazılım çn br önem dereces ortaya konulmuş ve en yüksek önem derecesne sahp statstksel yazılım en uygun statstksel yazılım olarak belrlenmştr. Bulgulara göre statstksel yazılım seçmnde en fazla önem verlen ana krtern analz özellkler %49,8 olduğu; bu krter kullanım özellkler %23,7 ve teknk özellkler %16,6 ana krterlernn zledğ görülmektedr. İstatstksel yazılım seçmnde fnansal özellkler %5,1 ve satıcı frmaya at özellkler %4,9 önem dereces bakımından son sıralarda yer almaktadır. Fnansal özellkler ana krter göz önüne alındığında, alt krterler arasında görel önem değer en yüksek olan alt krter güncelleme fyatı 0,018 olurken, bunu ednme ve şletm malyet 0,011 zlemektedr. Alt yapı 0,010 se son sırada yer almaktadır. Teknk özellkler ana krter dkkate alındığında, görel önem değer en yüksek olan alt krtern ver dosyası desteğ 0,066 olduğu görülmektedr. Bu alt krter 0,046 önem düzey le çoklu ortam desteğ zlerken, yazılımın grafk desteğnn önem dereces 0,028 dr. En az önem derecesne sahp olan alt krter se 0,025 önem derecesyle yazılımın ara yüzüdür. Kullanım özellkler ana krter çn; en yüksek önem derecesne sahp olan yazılımın kullanımının kolaylığıdır 0,112. En düşük önem düzeyne sahp olan alt krter se 0,038 önem düzey le erşm kolaylığıdır. Analz özellkler ana krternde se en yüksek öneme sahp olan alt krter neden-sonuç analzler 0,140 olarak ortaya çıkmıştır. Bunu sırasıyla karşılaştırma analzler, lşksel analzler ve sınıflayıcı/ayırma analzler zlemektedr. Tanımlayıcı statstkler se 0,032 lk önem derecesyle en son sırada ortaya çıkmıştır. Son ana krter olan satıcı özellklernde se eğtm desteğ 0,014 en öneml alt krter, kullanıcı gereksnmler 0,010 se en az öneml alt krter olarak belrlenmştr. Dğer taraftan, GRA sonucunda elde edlen ana krter ağırlıklarına bakıldığında da AHP sonuçlarına benzer şeklde en fazla önem verlen lk k ana krtern sırasıyla analz özellkler ve kullanım özellkler olduğu dkkat çekmektedr. Ayrıca GRA bulgularına göre, en uygun statstksel yazılım %93.8 le SPSS olmuştur. SPSS %63.8 le Statstca, %46.8 le Mntab ve %34.46 le Statgraph zlemştr. Akademsyenler statstksel yazılımlara bağlı bulundukları ünverste bütçes çersnden yapılan alımlarla sahp olduklarından, fnansal özellkler krter bu çalışmada hem AHP hem de GRA sonuçlarına göre öneml br krter olarak ortaya çıkmamıştır. Ayrıca satıcı özellkler krter açısından düşünüldüğünde se, yazılımlar satın alınıp kullanılmaya başlandıktan sonra gerekl olan teknk ve eğtm desteğ gb br takım gereksnmler, kullanıcılar genellkle kendler karşıladıklarından yne bu krter de statstksel yazılım seçmnde öneml br krter olarak belrlenmemştr. İstatstksel yazılımların bu çalışmada da sözü edlen önemne bağlı olarak araştırmacıların ve akademsyenlern kşsel olarak ednemeyecekler ya da zorlukla edneblecekler yazılımları sağlama konusunda ünversteler çndek beklent, bu yöndek harcamaların artırılmasını zorunlu kılmaktadır. Bu zorunluluk son yıllarda daha yüksek düzeyde kendn göstermektedr ve br ölçüde ünversteler arasındak rekabetn temel noktalarından brn oluşturmaktadır. Ünversteler arasındak rekabetn ve ntelk ölçütünün uluslararası lşkler ve akademsyenlern uluslararası yayınları etrafında şekllenmesnn de bu harcamaların artışında motve edc br unsur olacağı düşünülmektedr. Her ne kadar kurulan hyerarşk model kapsamında beş uzman görüşüne başvurularak elde edlen verler kapsamında en uygun statstksel yazılım belrlenmş olsa da, ele alınablecek farklı krter ve alt krterlern yanı sıra sadece akademsyenlerden oluşan br uzman grup le de güncellenen verlerle çalışma tekrarlanablr. Aynı zamanda krterler, alt krterler ve alternatfler arasındak bağımlılığın da dkkate alınacağı ve böylelkle statstksel yazılım seçm problemnn br ağ yapısında ncelenmesn sağlayacak Analtk Network Sürec kullanılarak krter ağırlıkları tekrar elde edlp, GRA uygulaması yneleneblr ve sonuçlar karşılaştırılablr. Ctaton Informaton Kaygısız-Ertuğ, Z., & Grgner, N A mult crtera approach for statstcal software selecton n educaton. Hacettepe Ünverstes Eğtm Fakültes Dergs [Hacettepe Unversty Journal of Educaton], 292,