Quality of Emba Program

riddle definition Background to the problem capital of Bangladesh Universitys train MBA computer classme started in 2002 as an safari to bring the Faculty of craft Studies up to the bill with opposite(a) private, public and worldwide academic institutions. The platformme is currently on its 18th batch. Although the University authority started the program closely-nigh 8 years ago, in that respect be lock up doubts among battalion ab forth the prize of the change surface MBA program offered by the Dhaka University Faculty of Business Studies and m some(prenominal) be conf customd near where this program stands against the MBA program offered by Dhaka University Institute of Business Administration (IBA).So a battleground was referable in this field to nail down the flavour of the DU heretoforetide MBA program. And no wiz knows round the program better than those who atomic number 18 studying in the program al throw. line Statement The problem narra tive for our look is The caliber of the Evening MBA program in Dhaka University is non in truth lofty. We ordain be victimisation several(prenominal) statistical theories and tools to try our problem tilt and create a laying from it and similarly test the signifi rear endce of the taste. Approach to the ProblemAs our problem instruction suggests, our documental of the study is to fasten the smell of the Evening MBA program in Dhaka University. instantly a products type hatful be easily checkerd through the enforce of antithetic type checks plainly what determines the tonicity of an academic program? later on expression into some secondary selective in contour lineation and fetching initial opinion from a dainty group we turn out come to coating that grapheme of an academic program is well related to quality of the learners, the environment the education outgrowth takes place, the teacher, content of the learning program, the trial run and the ou tcome of it.establish on these analyses we view come to the conclusion that we get out neediness to develop a check out questionnaire which will be utilise as our prime selective information and will eachow questions relating the preceding(prenominal) menti superstard criteria with the topic in question- quality of the evening MBA program. establish on the information equanimous we will run reversal outline to develop the model and so use Discriminant psychoanalysis to severaliseify the results. Research finaleA interrogation design should include each(prenominal) the of the essence(predicate) information about the look into process much(prenominal) as type of query done, information needs, data collection, scaling proficiencys, questionnaire development, consume proficiencys and fieldwork. Type of research To determine what type of research is essential to be done to achieve our objectives, we bind explored different possibilities. First let us revisit our objective- we would handle to determine the criteria that form quality of an educational program and then create a model out of it to identify signifi back toothce of each of the criteria in the model.In otherwise words, we will be determining the effect of some free lance vari confidents and determine how they affect the subordinate inconstant. Based on this initial assumption we could vocalise that we argon looking into a reach and effect relationship and hence we will be doing a Causal research. However, yet probing into the matter make us realize that the near valuable criteria of a causal relationship is to be able to manipulate the changeables and observe their effect on the model, which in our effort is not at entirely possible, neither we will be doing much experimentation.So ours is anything but a causal research. What our research is rather capable of is determine some characteristics relating to the problem in hand and based on unproblematic data and wa tching develop a model to show an overall relationship. Based on such(prenominal) analysis we contrive come to the conclusion that a descriptive research is to a greater extent appropriate in our parapraxis. Information Needs For our analysis, at offset printing we compulsory to know the cognition of schoolchilds about the quality of the evening MBA program.After that we inevi turn off their opinion on quality of the students enrolled in the program, students perception of the injury value of the program, quality of the teachers, grading system and access code test. So we father determined to create a questionnaire that will include questions about some(prenominal) the dependant variable (quality of the program) and item-by-item variables (quality of students, brand value perception, quality of teachers, grading system, admission tests etc. ) and use it to run a survey to gather all the essential information we will need in developing the model. Scaling Techniques this instant all of our required data atomic number 18 about opinions or perceptions of students. For such situations a Likert master is the most appropriate scaling technique to use because the main feature of Likert case is a list of opinions ranging from extreme cocksure to extreme ostracize about a statement. distributively opinion is assigned a score and based on the responders response the score is considered in further data analysis. While using Likert outgo we had to metrical to maintain a consistency in the statements so that the lucrativeness of an opinion always gets the gameest score and the negative one the wretchedest.Based on this sway we needed to reverse the scoring on questions that leaned to negativity. Questionnaire Development and Pre- interrogatory We have already delineate our dependent and autarkical variables for the research and what we needed was to collect the consume populations opinion about each of the statements made using a Likert scale. So context up the questionnaire was quite truthful for us. only we had to do is form a statement relating to each of the variables and attach a Likert scale table to let the responders choose to what direct they concur or disagreed to the statements.Based on our initial analysis, we have determined 1 dependent and 12 fencesitter variable and so we created a questionnaire with 13 statements and some apparently-relevant open-ended questions. To avoid confusion, the statements were worded as honest as possible. However, after pre-testing the questionnaire among a very small group, we observed that people still got confused about a question (question 12) related to grading. While our objective was to jibe fairness in grading with the quality of education, students related it to the grading system.So this statement didnt serve its intended purport and hence in our final analysis we have heady to distribute it out. cod to this change, from now on every exercise of Q12 will r efer to Q13 in real. A sample copy of the questionnaire used has been include in the appendix section of this report. sample technique Sampling is another significant part of a research design. The premier(prenominal) and foremost job in try out is to define the objective population. Now we have already discussed that as the problem is to determine whether the quality of the EMBA program is up to the standard or not, no one but the students would know the most about it.So our target population is defined as all the students in the EMBA program that includes not only the students from marketing department but all other departments too. After the target population was defined, the next job was to determine a sampling technique. Now there are a lot of sampling technique available but due to several limitations not all of them were appropriate. Considering the item that it would be a lot baffling to contact and convince students from other departments to record in the survey, we have decided to leave them out.And considering the fact that we started working on the research project right at the end of the semester made it difficult to communicate with all the students in marketing department too. So to run our survey we had to rely on the places of convenience where we would be present on with more students from different batches. In other words, the sampling technique we used was more of a convenience sampling. however we were careful not to select one answerer more than once. After the sampling technique was determined, our next job was to determine the sample surface.Now determining the sample size is a very complicated process even on situations where relevant data like total population size etc. are available. When no such data exists, the sample size role becomes just that much harder. However, we were lucky not to have gone through any hardship at all because we were instructed by our respectable course teacher to sustentation the sample size to somewhere some 30 and we followed his instructions to the book. Fieldwork As this is a very small-scale academic research, there was no need for additional fieldworker to run the survey.Instead we, the researchers took matters into our own hands and did the fieldwork ourselves. Now the positive side of it was that we didnt have to train anyone to run the survey most effectively. As we were the ones setting up the questionnaire, we had a clear idea about what to do and how to do it. We used our available classes as place of convenience and used the class breaks to collect our data. info Preparation After the fieldwork was done, we were left with 36 responses, out of which 4 were name to be uncompleted.On such situations it is suggested to assign missing values to the incomplete survey papers. However, as we still had a margin from the required minimum of 30, we decided to leave the incomplete ones out. Once we agreed on that, the 32 valid survey papers were coded and transcribed into the computer to be used with SPSS, the statistical tool we ought to use. Data abridgment Once the data were transferred into SPSS, we were ready to start data analysis. Methodology The data were analyzed by conducting quaternate regression analysis and Discriminant analysis. relapsing was conducted to fool whether a relationship exists between quality of EMBA program (dependant variable) and factors we have determined to indicate the quality (independent variables). Discrminant analysis was used to give us further insights. Plan for Data Analysis For regression we have considered Q1 as dependant variable and Q2-Q12 as independent variables. accordingly we have used SPSS to get the turnout. For Discriminant analysis, independent variables were converted from nine-point Likert scale into two-group categorical variable. For transformation we followed the followers rule -4= 1 (Low) that is EMBA program is discriminated to be of low quality. 6-9= 2 (High), that is EMBA prog ram is perceived to be of high quality. Note that we have disregarded the immaterial value 5. From our analysis we have found that none of the respondents have chosen this. So we have taken only two variables and planned for Two-Group Discriminant analysis. So in this case the newly converted group variable is the dependent and Q2Q12 are considered as soothsayers. Results infantile fixation Strength of Association In the SPSS getup Table at a lower place we spate plan the value of R2 is . 878. The R-square value is an indicator of how well the model fits the data e. . , an R2 close to 1. 0 indicates that we have accounted for almost all of the divergence with the variables specified in the model. Our R2 is close to 1. ModelRR2Adjusted R SquareStd. Error of the EstimateChange Statistics R2 ChangeF Changedf1df2Sig. F Change 1. 937(a). 878. 811. 989. 87813. 0651120. 000 (a) Predictors (Constant), Q12, Q7, Q3, Q2, Q4, Q9, Q6, Q11, Q10, Q8, Q5 moment Testing The following formul a is used to test whether an R2 calculated is satisfyingly different than Zero. The Null Hypothesis is that the population R2 is Zero. where N is the number of subjects, k is the number of predictor variables and R? s the squared multiple correlation coefficient. The F is based on k and N k 1 classs of freedom. In our case, N = 32, k = 12, and R? = . 878. In the SPSS output Table below we can see that F = 13. 065 which is significant at ? =0. 05. We can also see that significance is . 000 as it is smaller than . 05 we can say that it is highly significant. ANOVA (b) ModelSum of SquaresdfMean SquareFSig. 1Regression140. 6461112. 78613. 065. 000(a) Residual19. 57320. 979 Total160. 21931 (a) Predictors (Constant), Q12, Q7, Q3, Q2, Q4, Q9, Q6, Q11, Q10, Q8, Q5 (b) mutually beneficial Variable Q1 In addition to testing R? or significance, it is possible to test the individual regression coefficients (Beta) for significance and it is shown in the SPSS output in the following table. C oefficients (a) ModelUnstandardized Coefficients like CoefficientstSig. 95% Confidence Interval for Bcorrelation coefficients BStd. ErrorBetaLower BoundUpper BoundZero-orderPartialPart 1(Constant). 4491. 171. 383. 705-1. 9942. 892 Q2-. 046. 188-. 035-. 246. 808-. 437. 345. 395-. 055-. 019 Q3-. 091. 131-. 074-. 694. 496-. 364. 182. 236-. 153-. 054 Q4. 102. 190. 084. 539. 596-. 294. 499. 407. 120. 042 Q5-. 188. 232-. 129-. 809. 428-. 672. 296. 464-. 78-. 063 Q6. 507. 196. 4372. 582. 018. 097. 916. 856. 500. 202 Q7. 015. 141. 011. 103. 919-. 279. 308. 273. 023. 008 Q8. 508. 170. 4652. 982. 007. 153. 864. 878. 555. 233 Q9. 035. 151. 029. 231. 819-. 280. 350. 464. 052. 018 Q10-. 132. 167-. 111-. 791. 438-. 482. 217. 524-. 174-. 062 Q11. 188. 145. 1711. 292. 211-. 115. 491. 600. 277. 101 Q12. 165. 119. 1461. 386. 181-. 083. 414. 621. 296. 108 (a) parasitical Variable Q1 In the above table, we can see that all of significant levels agree to individual Beta are greater than . 05 except tw o. The significant for coefficient for Q6 and Q8 is less than . 5. So these are found to be significant. indeed teachers delivery and students unassumingness are important in explaining quality of education program. Regression Model From the whole regression analysis, we can finally generate a model that shows the total relationship between the independent variables selected and the dependent variable. Assigning each of the independent variables with Xn starting with Q2 as X1, Q3 as X2, Q4 as X3 and so on and assigning the dependent variable Q1 as Y, we form a generic regression model- Y= C + B1X1+ B2X2+ B3X3+ B4X4+ B5X5+ B6X6+ B7X7+ B8X8+ B9X9+ B10X10+ B11X11Now putting the relevant Bs in the equation, we get- Y=0. 449 0. 046X1 0. 091X2 + 0. 102X3 0. 188X4 + 0. 507X5 + 0. 015X6 + 0. 508X7 + 0. 035X8 0. 132X9 + 0. 188X10 + 0. 165X11 This is our regression model to determine the quality of education in the EMBA program. Discriminant Analysis The significance of univariate F rat ios shown in table below indicates that when the predictors are considered distributively Q8, Q6 and Q12 are highly significant (significant level . 000) in differentiating between those who perceive EMBA program to be of high quality and those who perceive it to be low quality.That is teachers delivery (Q8), students seriousness (Q6) and seriousness of administration in enforcing quality (Q12) are important differentiating factors toward high or low quality perception of EMBA program. Tests of equating of Group Means Wilks LambdaFdf1df2Sig. Q2. 8007. 501130. 010 Q3. 9531. 480130. 233 Q4. 8067. 240130. 012 Q5. 72311. 518130. 002 Q6. 29073. 336130. 000 Q7. 9551. 410130. 244 Q8. 26881. 874130. 000 Q9. 8584. 949130. 034 Q10. 8037. 350130. 011 Q11. 8087. 123130. 012 Q12. 62517. 996130. 000 Because there are only two groups, only one discriminant incline is estimated. The Eigenvalue associated with the cash in ones chips is 5. 37 as shown in table below and it accounts for nose cand y percent of the explained variance. The canonical correlation associated with this social function is 0. 924. The square of this correlation, (0. 924)2 = 0. 85, indicates that 85% of the variance in the dependent variable (High/low quality perception) is explained or accounted for by the model. Eigenvalues FunctionEigenvalue% of VarianceCumulative %Canonical Correlation 15. 837(a) one C. 0100. 0. 924 (a) First 1 canonical discriminant function was used in the analysis. It can be noted from table below Wilks Lambda associated with the function is 0. 146 which transforms to a chi-square of 47. 98 with 11 degree of freedom. This is significant beyond the . 05 level. Wilks Lambda Test of Function(s)Wilks LambdaChi-squaredfSig. 1. 14647. 09811. 000 The table below shows the inter-correlation between the predictors and we can chance on a low correlation. Q2Q3Q4Q5Q6Q7Q8Q9Q10Q11Q12 CorrelationQ21. 000. 324. 512. 340. 215-. 209. 064. 441. 317. 131. 125 Q3. 3241. 000. 088. 243. 218. 094. 0 72. 066. 044. 398. 261 Q4. 512. 0881. 000. 667. 096-. 236. 170. 197. 497. 266. 055 Q5. 340. 243. 6671. 000. 290-. 438. 053. 080. 336. 364. 149 Q6. 215. 218. 096. 2901. 000. 032. 186. 095. 323. 529. 015 Q7-. 209. 094-. 236-. 438. 0321. 000. 29. 100. 220. 163. 113 Q8. 064. 072. 170. 053. 186. 1291. 000. 450. 390. 260. 186 Q9. 441. 066. 197. 080. 095. 100. 4501. 000. 531. 296. 206 Q10. 317. 044. 497. 336. 323. 220. 390. 5311. 000. 517. 153 Q11. 131. 398. 266. 364. 529. 163. 260. 296. 5171. 000. one hundred thirty-five Q12. 125. 261. 055. 149. 015. 113. 186. 206. 153. 1351. 000 An examination of standardized discriminant function coefficient shown in the following table stipulation the low inter-correlation between predictors, it is revealed that Q8 (teachers nice slash delivery) and Q6 (seriousness of the students to learn) is the most important predictors (having highest value of . 13 and . 704 respectively) in discriminating between groups, followed by Q12 ( administrations serio usness in enforcing quality) and Q5 (competitive value of achieving degree in the industry). Standardized Canonical Discriminant Function Coefficients Function 1 Q20. 164 Q3-0. 199 Q40. 122 Q50. 245 Q60. 704 Q70. 276 Q80. 713 Q9-0. 118 Q10-0. 387 Q11-0. 264 Q120. 254 It is interesting to note that the same observation is obtained from examination of the structure correlations (structure matrix shown in table below). In this table these simple correlation between predictors and discrminant function are listed in order of magnitude.Structure intercellular substance Function 1 Q80. 684 Q60. 647 Q120. 321 Q50. 256 Q20. 207 Q100. 205 Q40. 203 Q110. 202 Q90. 168 Q30. 092 Q70. 090 SPSS offer a leave-one-out cross check option. In this option, the discriminant model is re-estimated as many times as there are respondents in the sample. Each re-estimated model leaves out one respondent and the model is used to predict for that respondent. The output for this is shown in the table on the foll owing page. From the table hit ratio or the percentage of cases in good order classified can be estimated as (18+13)/32*100 =96. % considering correct number of predictions of 18 and 13 for two groups Classification Results (b,c) GroupPredicted Group MembershipTotal 1. 002. 00 OriginalCount%1. 0018018 2. 0011314 1. 00100. 0. 0100. 0 2. 007. 192. 9100. 0 Cross-validated(a)Count%1. 0017118 2. 0011314 1. 0094. 45. 6100. 0 2. 007. 192. 9100. 0 (a)Cross validation is done only for those cases in the analysis. In cross validation each case is classified by the functions derived from all cases other than that case. (b) 96. 9% of original grouped cases correctly classified. (c) 93. 8% of cross-validated grouped cases correctly classified. therefrom we can say most important factors are Q8 (teachers adequate bawl out delivery) and Q6 (seriousness of the students to learn). This result is consistently found both by regression and correlation. Limitations No research project is free from limi tations of some form- be it time or resources. Same is adjust for our research project. Although time given was adequate for a small-scale project like this, but considering the topic of our discussion such small-scale research hardly marrow anything. Considering the number of students currently enrolled in the EMBA program as a whole, a sample size of 30 is hardly vox of the population.Added to that is our inability to communicate and select respondents from other departments. So considering all these, this project, although best efforts were given to complete, doesnt completely satisfies its main excogitation of determining the quality level of the EMBA program. last In conclusion, we can say that even though the research project didnt serve its purpose completely, it at the least gives some idea about students perception of quality education and overall quality of the EMBA program.From the research, based on multiple data analysis, we have found out that people put great emp hasis on students eagerness to learn and teachers delivery of intimacy to determine the quality of education program. So it is imperative that students are encouraged in different ways so that they encounter inspired to learn new things on their own. And teachers also should keep in headspring the duty they have sworn to reach and give their best efforts in teach the students properly while staying above all influences. X