HSC RESULT AND ECONOMIC CONDITION’S EFFECTS ON CGPA OF DU-BBA STUDENTS
A study by
Md. Azim- Ul- Islam
ID: 325
Strategic and International Management
Department of Management
University of Dhaka
Date of Completion: 25th August, 2016
The study has been conducted to fulfill the partial requirement of Advanced Business Research course under MBA program
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Prepared for,
Mr. Ali Ahsan
Professor
Strategic and International Management
Department of Management
University of Dhaka
Prepared by,
Md. Azim- Ul- Islam
ID: 325
Strategic and International Management
Department of Management
University of Dhaka
Date of Submission: 28th August, 2016
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28th August, 2016
To
Mr. Ali Ahsan
Professor
Strategic and International Management
Department of Management
University of Dhaka
Subject: Submission of Research on HSC Result and Economic Condition’s Effects on CGPA of DU-BBA Students
Dear Sir,
With due respect, It is my pleasure to submit a research paper on “HSC Result and Economic Condition’s Effects on CGPA of DU-BBA Students” which is prepared as a partial requirement of the course “ Advanced Business Research” of MBA program under Department of Management of faculty of Business studies, University of Dhaka.
The study has given me the opportunity to learn new knowledge and increased my experience about how to prepare a research paper. I would like to convey my special thanks and gratitude to you for patronizing my effort and for giving me proper guidance and valuable advice. The study addresses the key question: Does HSC result and economic condition have an effect on the CGPA holding by DU-BBA students. I would be very pleased to answer any sort of query if you think necessary when required.
Sincerely yours,
Md. Azim- Ul- Islam
Roll: 325
Strategic and International Management
Department of Management
University of Dhaka
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Executive Summary
The study addresses the question regarding the HSC result and economic condition’s effect on the CGPA about 1000 students who are currently enrolled in MBA in the Faculty of Business Studies, University of Dhaka. An amount of 30 observation was drawn to work with which were chosen on the basis of judgment because of time constraint and cost mainly. This was done because other methods of sample size selection result more than 200 sample unit.
Data mainly collected from the primary source by structured questionnaire also secondary sources of data have used to enrich the study and knowledge. Analyzing was done using popular research software named STATA which is easy, time consuming and very efficient.
Data analysis resulted out that there is no significant relationship between HSC result and CGPA of DU-BBA students which is the first null hypothesis. So the hypothesis is accepted. Analysis also has found out that there is no significant relationship between economic conditions of the student and his/her CGPA which is the second null hypothesis. So this hypothesis has also accepted. HSC result and economic conditions may have a little impact on the CGPA of the students or may work as modifier with other factors but it cannot be said that these relationships are significant and the effects are massive.
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Table of Contents
Letter of Transmittal.........................................................................……......(iii)
Executive Summary…………...............…………………………………………………...(iv)
1.3 Objectives of the Study
Sample Questionnaire
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Chapter 1.0: Introduction
1.1 Background
The number of people who graduated from a university has increased in Bangladesh. There is a high competition among the graduates in white collar job market. One of the indicators that highlight the university students’ qualification is the academic performance mostly measured with the Cumulative Grade Point Average (CGPA). Many employers use the CGPA to screen out the job candidates and they mostly prefer a candidate with a higher CGPA.
Many factors are considered as determiners of the students’ CGPA such as gender, previous academic performance, living place and income level of family, social environment, the type and quality of the high school graduated, the high school grade point average, the score obtained from SSC and HSC exam, time spend for studying, learning ability and living place during the university life. This study has conducted to test two major factors from these, to find out whether the effect of these factors is massive or not.
1.2 Literature review
A number of studies have been carried out to identify and analyze the numerous factors that affect academic performance. Their findings identify students’ effort, previous schooling (Siegfried & Fels, 1979; Anderson & Benjamin, 1994), parents’ education, family income (Devadoss & Foltz, 1996), self motivation, age of student, learning preferences (Aripin, Mahmood, Rohaizad, Yeop, & Anuar, 2008), class attendance (Romer, 1993), residence and entry qualifications as factors that have a significant effect on the students’ academic performance in various settings.
Urien (2003) found that personal characteristics, family background and study discipline have impact on the academic performance of students. Fertig and Schmidt (2002) reported that there is a positive relationship between students’ verbal performances and having a working mother and father who have high education level and unbroken family structure.
In a study, Betts and Morell (1998) reported that; the factors such as sex, ethnic origin, family income and social economic environment are the sources of the differences in the graduate point average. Smith and Naylor (2004) examined the effect of the students’ school characteristics on the university performance and found out that the probability of the graduating from the university with a higher CGPA for a student who completes his high school in private school is, on the average, 5.9 percent higher than a student who graduates from a public school. Horowitz and Spector (2004) reported that the students who graduate from religious high school show higher performance than the students who graduate from private and public high schools.
Cohn et al. (2004) examined the effect of socio-economic and demographic factors on student performance in South Carolina University and found out that white students gets higher scores than non-white students. They also found that males are less likely than females to achieve the 3.0 CGPA. Durden (1981) and Romer (1993) reported that absenteeism reduces the CGPA.
1.3 Objectives of the Study
(i) To identify the factors those affects the CGPA of DU-BBA students.
(ii) To determine which factors have bigger impact on the probability of getting a higher CGPA for the Students of Business Faculty of Dhaka University.
(iii) To find out the relationship between different factors and student performance measured in terms of the CGPA of the students.
Chapter 2.0: Methodology
Data analysis have been done using STATA 12.0 data editor and analyzed against two specific hypotheses. Statistical tools like, mean, standard deviation, correlation are used. To test the statistical association between the considered variables, null and alternative hypothesis are designed as the following format:
2.1 Research Hypotheses
Hypothesis 1:
H0: There is no significant relationship between CGPA and HSC Results of the students.
H1: There is significant relationship between CGPA and HSC Results of the students.
Hypothesis 2:
H0: There is no significant relationship between CGPA and family income of the students.
2.2 Research Design
This is a descriptive type study where two hypotheses have been developed and relevant data have been analyzed against these specific hypotheses to prove them. Hypotheses have been developed based on prior exploratory observation from where several factors which may affect the CGPA have generated by the respondents’ opinion and the best two factors are being tested to find out the degree at which it can affect.
2.3 Sample Design
The target population for this study is the students of the Business Faculty of Dhaka University who have completed their BBA last year and are currently enrolled in MBA program. An amount 30 observation have been made to collect the data. Sample size have selected based on judgment method because of cost saving and time constraint where other methods of sample size selection predicted more than 200 unit as sample to generate result at 95% confidence level. Respondents have been chosen according to convenience sampling technique.
2.4 Data Collection
Data which are used in this study are mainly qualitative in natures which are converted into quantitative type by assigning values to the degree of responses. Responses have been collected using questionnaire involving a combination of like-art scale of 5 categories and some selective box questions.
2.5 Data Analysis
Stata 12 has been used to analyze the collected data using 95% level of confidence where coefficient alpha is 0.5. As there are one independent variable such as CGPA and two dependent variables such as HSC result and Economic condition are present in this study regression analysis of two tailed test have been conducted.
The study tries to find out the relationship between these factors and students’ graduation CGPA. Our research used such factors in students’ background, which can have an impact on their CGPA in their BBA programs.
Chapter 3.0: Data Analysis and Findings
These data were collected on 30 students belonging to Business Faculty of University of Dhaka including CGPA, HSC Result (HSCResult) and Monthly family income (MFincome). Data testing using regression model are presented below followed by the Stata output.
3.1 Analysis of STATA Table
Source - Looking at the breakdown of variance in the outcome variable, these are the categories we will examine: Model, Residual, and Total. The Total variance is partitioned into the variance which can be explained by the independent variables (Model) and the variance which is not explained by the independent variables (Residual, sometimes called Error).
SS - These are the Sum of Squares associated with the three sources of variance, Total, Model and Residual.
df - These are the degrees of freedom associated with the sources of variance. The total variance has N-1 degrees of freedom. The model degrees of freedom corresponds to the number of coefficients estimated minus 1. Including the intercept, there are 3 coefficients, so the model has 3-1=2 degrees of freedom. The Residual degrees of freedom is the DF total minus the DF model, 29 - 2 =27.
MS - These are the Mean Squares, the Sum of Squares divided by their respective DF.
3.2 Tests for the Significance of the Model
v Number of obs - This is the number of observations used in the regression analysis that is 30.
v F( 2, 27) - This is the F-statistic is the Mean Square Model (.292067736) divided by the Mean Square Residual (.319106094), yielding F=0.92. The numbers in parentheses are the Model and Residual degrees of freedom are from the STATA table above.
v Prob > F - This is the p-value associated with the above F-statistic. It is used in testing the null hypothesis that all of the model coefficients are 0.4125
v R-squared - R-Squared is the proportion of variance in the dependent variable (CGPA) which can be explained by the independent variables (HSCResult, and Family income). This is an overall measure of the strength of association and does not reflect the extent to which any particular independent variable is associated with the dependent variable. The value of R-squared in the model is 0.0635.
v Adj R-squared - This is an adjustment of the R-squared that penalizes the addition of extraneous predictors to the model. Adjusted R-squared is computed using the formula 1 - ((1 - Rsq)((N - 1) /( N - k - 1)) and yielding = -.0059. Here k is the number of predictors.
v Root MSE - Root MSE (.56489) is the standard deviation of the error term, and is the square root of the Mean Square Residual (or Error).
3.3 Parameter Estimates
v CGPA - This column shows the dependent variable at the top (CGPA) with the predictor variables below it (HSCResult, MFincome and _cons). The last variable (_cons) represents the constant or intercept.
v Coef. - These are the values for the regression equation for predicting the dependent variable from the independent variable.
HSCResult - The coefficient is .3092692. So for every unit increase in HSCResult, a .3893102 unit increase in CGPA is predicted, holding all other variables constant.
MFincome- For every unit increase in MFincome, we expect a .0196078 unit increase in CGPA , holding all other variables constant.
v Std. Err. - These are the standard errors associated with the coefficients.
v t - These are the t-statistics used in testing whether a given coefficient is significantly different from zero.
v P>|t| - This column shows the 2-tailed p-values used in testing the null hypothesis that the coefficient (parameter) is 0.4125. Using an alpha of 0.05:
The coefficient for HSCResult is not statistically significant at the 0.05 level since the p-value (0.196) is greater than .05.
The coefficient for MFincome (0.0196078) is not statistically significant at the 0.05 level since the p-value (0.754) is greater than .05.
The constant (_cons) is not statistically significant at the 0.05 alpha level.
v p. [95% Conf. Interval] - These are the 95% confidence intervals for the coefficients. The confidence intervals are related to the p-values such that the coefficient will not be statistically significant at alpha = .05 if the 95% confidence interval includes zero. These confidence intervals can help you to put the estimate from the coefficient into perspective by seeing how much the value could vary.
3.4 Hypotheses Testing
Hypothesis 1: There is no significant relationship between CGPA and HSC Results of the students.
We fail to reject null hypothesis as there is no significant relationship between HSC Results and the CGPA of the students. The coefficient for HSCResult is not statistically significant at the 0.05 level since the p-value (0.196) is greater than .05. That means, HSC Results of students seem to be an insignificant factor for CGPA.
Hypothesis 2: There is no significant relationship between CGPA and Family income of the students.
We fail to reject null hypothesis as there is no significant relationship between Family income and the CGPA of the students. The coefficient for Family income (MFincome) is (0.0196078) which is not statistically significant at the 0.05 level since the p-value (0.754) is greater than .05. That means, Family income of students seem to be an insignificant factor for CGPA.
3.5 Problems and Limitations
Despite the merits of this study, it has certain limitations that should be recognized. Such as-
1. The study is based on only the students of BBA in the University of Dhaka. Students of other faculties may be different in many aspects.
2. Lack of time to run the survey in a large scale.
3. More appropriate models could be searched through various tests and trials. Therefore, there are numerous avenues for future research and extensions of this study.
4. Cost was also a big problem to restrict the scope of this study
1. The study is based on only the students of BBA in the University of Dhaka. Students of other faculties may be different in many aspects.
2. Lack of time to run the survey in a large scale.
3. More appropriate models could be searched through various tests and trials. Therefore, there are numerous avenues for future research and extensions of this study.
4. Cost was also a big problem to restrict the scope of this study
Among the various factors that affect the CGPA of DU-BBA students HSC Results and Family income seem to have an insignificant impact over the CGPA of the students. Which conclude this study predicting students having high family income range and good HSC result may incur having bad CGPA in BBA while alternative can happen too. On the other hand students having poor family income and bad HSC result have a good chance of increasing CGPA in the BBA program in Dhaka university figuring other things remain the same.
Appendices
Appendix- A
Table 1:
Table 2:
Sample Questionnaire
Following are the related personal information of the respondents. Please tick (√) on the appropriate box which matches with you most.
1
|
Gender
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1
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Male
|
2
|
Female
|
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2
|
Religion
|
1
|
Muslim
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2
|
Hindu
|
3
|
Christian
|
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4
|
Buddhist
|
5
|
Others ( please specify)
|
|||||||||||||||||||||||||||||||
3
|
Marital Status
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1
|
Single
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2
|
Married
|
3
|
Divorced
|
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4
|
Current CGPA
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1
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2.00-2.99
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2
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3.00-3.49
|
3
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3.50-3.75
|
4
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3.75-4.00
|
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5
|
Residential area (living area)
|
1
|
Hall
|
2
|
With family
|
3
|
Others
|
|||||||||||||||||||||||||||
6
|
Nature of College
|
1
|
Government
|
2
|
Private
|
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7
|
Board of HSC
|
1
|
Dhaka
|
2
|
Rajshahi
|
3
|
Commilla
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4
|
Jessor
|
9
|
Dinajpur
|
|||||||||||||||||||||||
5
|
Barishal
|
6
|
Chittagong
|
7
|
khulna
|
8
|
sylhet
|
|||||||||||||||||||||||||||
8
|
What is your father’s occupation
|
1
|
Farmer
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2
|
Businessman
|
3
|
Service holder
|
4
|
others
|
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9
|
Marks obtained in Intermediate or equivalent studies exam
|
1
|
2.00-2.99
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2
|
3.00-3.49
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3
|
3.5-3.99
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|||||||||||||||||||||||||||
4
|
4.00-4.49
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5
|
4.5-4.99
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6
|
5.00
|
|||||||||||||||||||||||||||||
10
|
Monthly Family income (tk.)
|
1
|
Less than 10,000
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2
|
10,000-20,000
|
3
|
20,000-30,000
|
4
|
30,000-40,000
|
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5
|
40,000-50,000
|
6
|
50,000-60,000
|
7
|
60,000-1,00,000
|
8
|
More than 1,00,000
|
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The following statements represent your feelings regarding various factors that may affect your CGPA. Please indicate the degree of your agreement or disagreement with each statement [please tick (√) the appropriate box
Strongly Disagree
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1
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Disagree
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2
|
Neutral
|
3
|
Agree
|
4
|
Strongly Agree
|
5
|
Study Habits
|
S.D
1
|
D.
2
|
Neu.
3
|
Agree
4
|
S.Agree
5
|
|
1
|
I do my study regularly
|
|||||
2
|
I regularly go to library or seminar for study
|
|||||
3
|
I always follow the course material given in the opposite section
|
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4
|
I study the lessons I missed if I was absent from the class
|
|||||
5
|
I study and prepared for midterms and test
|
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6
|
I exert more effort when I face difficult subjects
|
|||||
7
|
I regularly take part in extracurricular activities
|
|||||
8
|
I study harder to improve my grade when I get low grade
|
|||||
9
|
I participate in group discussion with my friends whenever I can’t understand a topic.
|
|||||
10
|
I attentively listen to the lecture of my teachers
|
|||||
11
|
I actively participate in the class, answering questions and clarifying things I didn’t understand
|
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Anderson, G., & Benjamin, D. (1994). The determinants of success in university introductory economics courses. Journal of Economic Education, 25(2), 99 – 119.
Aripin, R., Mahmood, Z., Rohaizad, R., Yeop, U., & Anuar, M. (2003). Students’ learning styles and academic performance. 22nd Annual SAS Malaysia Forum, 15th July 2008, Kuala Lumpur Convention Center, Kuala Lumpur, Malaysia.
Betts, J. R. and D. Morell, 1998, The Determin ants of Undergraduate Grade Point Average: The Relative Importance of Family Background, High School Resources, and Peer Group Effects, The Journal of Human Resources, 34, 268–293.
Cohn, E., S. Cohn, D. C. Balch and J. Bradley, 2004, Determinants of Undergraduate GPAs: SAT Scores, High-School GPA and High-School Rank, Economics of Education Review, 23, 577-586.
Devadoss, S., & Foltz, J. (1996). Evaluation of factors influencing students attendance and performance. American Journal of Agricultural Economics, 78(3), 499 – 507.
Durden, G. C. and L. V. Ellis, 1995, The Effects of Attendance on Student Learning in Principles of Economics, The American Economic Review , 5, 343–346.
Fertig, M. and C. M. Schmidt, 2002, The Role of Background Factors for Reading Literacy: Straight National Scores in the PISA 2000 Study, IZA Discussion Paper No. 545.
Romer, D. 1993, Do Students Go to Class? Should They?, The Journal of Economic Perspectives, (Summer), pp.167–174.
Siegfried, J., & Fels, R. (1979). Research on teaching college economics: A Survey. Journal of Economic Literature, 17(3), 923 – 939.
Smith, J. and R. Naylor, 2005, Schooling Effects on Subsequent University Performance: Evidence for the UK University Population, Economics of Education Review, 24, 549–562.
Urien S. A., Determinants of Academic Performance of Hec-Lausanne Graduates”, (http://www.hec.unil.ch/modmacro/recueil/Sakho.pdf). (accessed on 20.08.2016).
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