Sequence of the domestic duck (Anas platyrhynchos) growth hormone-encoding gene and genetic...

IMPACT OF ADDICTIONAND OTHER FACTORS ON ACADEMIC PERFORMANCE

OF STUDENTS IN ARAB MINCH UNIVERSITY: ACASE STUDY IN ABAYA

CAMPUS.

\

ASINIOR RESEARCH

PROJECT

PREPARED

BY

EYO EYO

EL LOMBOREEL LOMBORE

ADVISOR: ADVISOR:

GEZAHEGN M. (M.Sc.)GEZAHEGN M. (M.Sc.)

A SENIOR RESEARCH SUMITED TO:A SENIOR RESEARCH SUMITED TO:

DEPARTMENT OF STASISTICS, COLLEGE OF NATURAL SCIENCEARBADEPARTMENT OF STASISTICS, COLLEGE OF NATURAL SCIENCEARBA

MINCH UNIVERSITYMINCH UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENT FORIN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR

THE B.SC. DEGREE IN STATISTICS THE B.SC. DEGREE IN STATISTICS

JUNE, 2013JUNE, 2013

ARBAMINCH, ETHIOPIAARBAMINCH, ETHIOPIA

Approval Sheet of B.Sc. Research Proposal

Submitted by:

_EYOEL LOMBORE____ _07/06/2005

E.C_________________ Name of Student

Signature Date

Approved by:

___ GEZAHEGN M GEZAHEGN M . . _____________

___________________ Name of Advisor

Signature Date

________________ ___________________

__________________

Name of Dep’t Head Signature

Date

Approval sheet 1

This is to certify that the proposal entitled “Impact of

Addiction on Academic Performance of Students in Arab Minch

University: A case Study in Abaya Campus.

”submitted in partial fulfillment of bachelor degree in

Statistics of undergraduate program at Department of

Statistics Collage of Natural Science, Arba Minch

University, and is a record of the original proposal carried

out by__EYOEL LOMBORE_Id. No__678/02 under my supervision

and no part of this proposal has been submitted for any

degree or diploma away

The assistance and the help received during thecourse of this investigation have been duly acknowledged.

Therefore, I recommend that it would be accepted as

fulfilling the B.Sc. research project requirement.

___________________ ___________________

__________________

Name of Advisor Signature

Date

___________________ __________________

_________________

Name of Dep’t Head Signature

Date

APPROVAL SHEET-2

We, the undersigned, members of the board of examiners of

the final open defense by __EYOEL LOMBORE_Id. No__678/02

have read and evaluated his senior research entitled “IMPACT

OF ADDICTIONAND OTHER FACTORS ON ACADEMIC PERFORMANCE OF

STUDENTS IN ARAB MINCH UNIVERSITY: ACASE STUDY IN ABAYA

CAMPUS.” and examined the candidate. This is therefore to

certify that the senior research has been accepted in

partial fulfillment of the requirements for the B.Sc. degree

in Statistics.

_____________________ ____________

________________

Name of Main Advisor Signature

Date

_____________________ ____________

________________

Name of Examiner (1) Signature

Date

______________________ ____________

_________________

Name of Examiner (2) Signature

Date

ACKNOWLEDGEMENT

First of all, I would like to give my heart full thank to my

God who helped me to come up with this research.

Next I would like to thank my Advisor GEZAHEGN MOKONENGEZAHEGN MOKONEN

(M.sc) whose professional advice was not separated from me

until the completion of this research paper, and who helped

me all his best through his knowledge by sharing different

ideas and follows me by devoting his valuable time.

Thirdly I am interested to thank all staff members of

statistics department who directly or indirectly

participated for the accomplishment of the paper.

Finally, I would like to thank my parent and friend for

their valuable suggestions and financial support.

.

ABSTRACT

This study was focused on the depth study about the impact of addiction

on students academic. The main source of data for this study is primary as

a sample by using stratified-sampling techniques, and descriptive statistics

and multiple linear recreations model were used to analyze the data. From

the sample of 56.45%, 57.25% and37.6% of chat, alcohol and cigarette

respectively respondents are addictive, while the rest 43.5%, 42.5% and69%

are not addictive. Regression analysis result shows that those students who

are addicted are lower in academic performance.tha means the students

who denote use stimulants (addicted to the addiction than women) and

addicted students where associated with poorer not addicted) get a good

academic result then those who are addicted .

The result of these study allow campus to recognizes students who are

addicted or on the average of becoming addicted and stress risk factors

indicating need for intervention in order to prevent the appearance of

addiction.

Generally addiction seriously affects the academic performance of the

students. Data which is directly collected from students through

questionnaires that are selected.

Table of Contents

Page

1.INTRODUCTION............................................1

1.1 Back ground of the Study..............................1

1.2 Statement of the Problem...................................2

1.3 Objectives of The Study...............................2

1.4. Significance Of the Study.............................3

2. Literature review......................................4

3. Methodology............................................6

3.1 Description of the Study Area.........................6

3.2 Study Population......................................6

3.3 Sample Design.........................................6

3.4 Sample Size Determination.............................7

3.5Allocation of Sample Survey............................8

3.6 Method of Data Collection.............................9

3.7 Variable of the Study.................................9

3.7.1 Method of data Analysis.............................9

3.7.2 Descrptive Statistics...............................9

3.7.2.2 .................................................10

3.7.2.3 Multiple Linear Regression.......................11

4. RESULT AND DISCUSSION………………………………………………………..204.1 Descriptive Analysis

Results………………………………………………………………………………………………….20

4.2 Inferential Statistics

Analysis………………………………………………………………………………………………..24

4.2.2 Multiple Linear Regration

Results…………………………………………………………………………………….24

5 Conclusions and Recommendation

5.1 Conclusion………………………………………………………………………………………………………………………….34

5.2 Recommendations………………………………………………………………………………………………………………34

Reference……………………………………………………………………………………………………………………………….37

Appendix………………………………………………………………………………………………………………………………….37

1. Introduction

1.1. Background on Addiction

An addiction is an overpowering urge to abuse a particular

substance or engage in a particular activity. This physical

or emotional dependence causes addicts to seek fuel for

their addictions in compulsive ways, often sacrificing some

combination of health, safety, relationships, employment and

financial stability. Information on addiction, including

issues such as causes, diagnosis and treatment. Addiction is

a disease that affects both brain and behavior. Biological

and environmental factors have been identified and

scientists are beginning to search for the genetic variant

Addiction Background

not conducive to our health or general mental well being.

With an addiction there can be a psychological,

physiological, or even physical dependence of one particular

thing. When it comes to the possibility of an addiction

forming Addiction is dependence or a need to do something or

use something that in general is anyone is susceptible to

this. The addiction is usually something that interferes

with everyday life. The addicted person will start off

believing that they can control the scenario and the affects

that it has over their life, but as the addiction progresses

it begins to affect their work, their families, and all

other aspects of life.

There are many types of addiction that can form; addiction

to gambling is one of the most prominent addictions in the

United States. The addicted person can spend thousands of

dollars over a period of time, in hopes of trying to win

more money. This can be seen in people who buy scratch off

tickets and those who spend too much time at a casino.

One addiction that is becoming a more prominent issue in

the United States is Sex Addiction. The addicted person

is not able to control their sexual behavior. Often times

this causes impulsive and dangerous lengths for sexual

activity. There is much dispute in the between scientists

and sexologists when it comes to Sex

Addiction.http://www.google.com.et the impact of addiction on students on

their academic performance.The most well know addictions are

those of drug or alcohol dependency. In cases of alcohol

dependency, the addicted person will spend a great portion

of their earnings on alcohol. This addiction can lead to

various other issues within the family structure or the

work place. And drug addiction is based on the excessive

use of illegal narcotics or the abuse of over the counter

products.

When it comes to any addiction there are several symptoms or

warning signs that are indicative of this nature. The

addicted person will have a strong compulsion to engage in

their addictive activity often. This is typically a desire

that is hidden for quite a while, but eventually surfaces

noticeable level. The addicted person will display feelings

of regret or guilt about their participation, but the desire

to continue will often overpower them. If an addiction

becomes too powerful it can interfere with their everyday

lives including work and family time; this disruption can

often further the addiction even more.

If someone is addicted to a dangerous behavior like

promiscuous sexual activity, gambling, drugs, or alcohol, it

is important to get them the proper medical treatment.

Addictions are very dangerous because they can destroy

lives. In some cases there are therapies and programs that

can help to breakdown the rationale and need behind the

addiction. These programs offer support groups and gradual

movements towards healthier, non-addicted living. There are

also programs dedicated to helping the family as a whole

rebuild once an addiction is identified and treated.

Addictions are very serious issue that needs to be handled

with care.

ions that contribute to the development and progression of

the disease.

1.2 Statement of the Problem

Today large students are coming to use different kind of

stimulants as their means of satisfaction.Farther more,

economical, social, cultural and behavioral change and also

impacts on physical and mental change. It also shows the way

assure the quality of addicted with non addicted base on

academic performance by specifying the reason why addicted

students share large population of academic dismissal or

readmission

The study would end crucial to have good participant and

practicable young generation in order to insure that all

development efforts under taken for the future will apply by

a new generation with stable mental, physical, social and

cultural well being. Nevertheless, now a day adolescents

faced several treats and their future is hampered by various

negative impacts, especially for males, one of these

problems being many intelligent students caused by addictive

and consequences low CGPA, especially in developing

countries.

1.3 Objectives

General Objective

The general objective of this study was to assess the impact

of addiction on academic performance of students.

Specific Objectives

To determine the factors responsible for students academic

performance

To see weather addicted students have low performance or

not.

To investigate whether addiction and study time are

independent or not.

To assess the consequences of addiction on academic

performances of students by comparing CGPA of addictive and

students.

1.4. Significance of the Study

The study would end crucial to have good participant and

practicable young generation in order to insure that all

development efforts under taken for the future will apply by

a new generation with stable mental, physical, social and

cultural well being specifying the reason why addicted

students share large population of academic dismissal or

readmission. This study would be used to increase a good

attitude and decrement of the addiction would recognize

growing importance of development of the country.

1.6 Limitation of the study.

In any research there are different types of constraint that

depends on the availability of the resources and the area

where the research was taken place. In this investigation, I

encounter some limitation which is listed below:

Some respondents may not write the correct information

to all questions.

Shortage of time, money and computer service

It does not know the actual number of use stimulants.

2.

Literature Review

One of the recurrent proposal topics to be found in

education literature involves the attempt to isolate the

complex determinants of academic performance. A wide variety

of proposal topics has been drawing attention to the

importance of high school performance matriculate on result,

social and environmental factors as well as personality

characteristics and behaviors such as self perception, self

confidence, attitude, motivational drive and strategies in

studying.

Chat has direct effects on the cardiovascular system causing

clear increases in heart rate and blood pressure in humans

(Brenneisen et al., 1990). As an illustration of the

cardiovascular affects the results of Brenneisen et al.,

(1990) are reproduced in more detail.

In 1992 Bowman reported that measure academic ability

between addicted and non addicted students show significant

difference due to the difference in back ground educational

activities.

Kennedy et al (1983) noted over 25 years ago that few of the

allegations of health effects made against chat were

substantiated in controlled studies in humans.

Gaskin and Mironychev (1994) reported that up to 80% of the

adult population of Yemen use chat. Upon first chewing chat,

the initial effects were unpleasant and included dizziness,

lassitude, tachycardia, and sometimes epigastria pain.

Gradually more pleasant feelings replaced these inaugural

symptoms. The subjects had feelings of bliss, clarity of

thought, and became euphoric and overly energetic. Sometimes

chat produced depression, sleepiness, and then deep sleep.

In 1994, the hypothesis of equal response CG PA between

addictive and non addictive variable a student would rejects

by Gashed. He suggested that there are different factors

that are responsible for their difference in academic

performance. To sum up in this chapter we revised some

relate proposal that would be done on university students’

academic performance.

In 1995, Entwist showed that study methods attitudes the

most consistent predictors of academic performance in higher

education.

In 1999, Goldman and Hudson found the college student’s

abilities have no significance effect on the difference

between high, middle and low grade point groups, but they

did find significant difference among addicted and none

addicted based on studying strategies.

It is assume that intellectual and scholastic aptitude is

prerequisite to success in college.

The substance was often taken in larger amounts or over a

longer period than was intended 1. There is a persistent

desire or unsuccessful efforts to cut down or control

substance use 2. A great deal of time was spent in

activities to obtain the substance, use the substance, or

recover from its effects 3. Important social, occupational

or recreational activities ware given up or reduced because

of substance use 4. the substance use was continued despite

knowledge of having a persistent or recurrent physical or

psychological problem that was likely to have been caused or

exacerbated by the substance (e.g., continued chewing

despite recognition that an ulcer, depression, or loss of

appetite was made worse by chat consumption) The following

operational definitions are appropriate to this study Life

time prevalence of chat chewing: the proportion of youth who

had ever chewed chat life time prevalence of smoking: the

proportion of youth who had ever smoked cigarette Life time

prevalence of drinking: the proportion of youth who had ever

drunk alcohol Current prevalence of chat chewing: proportion

of youth who chew currently and have chewed

In a wide- Some authors estimate that 10 million people chew

chat daily, worldwide (Stefan & Mathew, 2005; Pinning’s et

al, 2008). Studies of chat consumption in the United Kingdom

suggest that the context of consumption (i.e. displacement

and social marginalization) may have significant effects in

shaping the outcomes from chat consumption (Griffiths et

al., 1997 Nabozoka & Badhadhe, 2000; UK Advisory Council on

the Misuse of Drugs, 2005; Patel, 2008; Pinning’s et al.,

2008).

3. Methodology

3.1 Description of the Study Area

The study was conducted in the Arab Minch University case

study in Ababa Campus. Arba Minch University was located in

SNNPR (Southern nation nationality people region in Gamo

Goff Zone at Arab Minch town in Addis Ababa road. From this

University Abaya has one of the Campuse which was located in

secha kifle kitema and contains eight

departments.

3.2 Study Population

The study on academic performance of students in Abaya

Campus” focuses in the total number of students in the

campus and the subclass of the total population can be

grouped as different departments and batches.

3.3 Sampling Design

There are many techniques of sampling designs in statistical

methods. Among these stratified random sampling techniques

was employed, because the nature of the population forced

them to used it and in order to increase the precision of

their findings. Thus, to assume that based on graduated

students at{ 7}departments in 2005 batch students as the

first stratum, Geology department, second stratum Mathis

department ,third stratum Metrology Department, fourth

stratum statistics Department and fifth stratum physics

Department, Sixth stratum Biology Department and Seventh

stratum Chemistry Department. Stratification itself has its

own advantages to keep homogeneity of the population to

achieve minimum variability among population for separate

estimation.

3.4 Sample size determination

Determining sample size has important issue in research

since samples that are too large may waste time, resource

and money whereas too small sample may lead under estimation

and an inaccurate result

Accordingly, determine the minimum required sample size

assuming the significance level at 95%, margin error to be

10% and study variable as categorical, using the following

formula as usual by using proportional allocation for

categorical data in stratification for any element in the

population in the sample. The total number of population in

each stratum is: N1, N2, N3, N4, N5, N6andN7 the proportion

of each stratum to the total population is computed by using

the formula, wh=NhN

As follows.

wh=NhN

W1= 49662

=0.074

W2=108662=0.1631

W3¿ 51662

=0.0770

W4=112662=0.1692

W5= 37662

=0.0559

W6=170662=0.2568

W7135662=0.2039

The Total Population In The Sven Stratums Is 0btained by

And the total sample size which represents the whole

population was calculated

The general formula of the sample size determination for

this case is follows

n=no/1+no/N

v=d2Z2=

Wh =NhN

no=1V∑ whSh2

n=∑Whsh2

V+1N∑ WhSh2

The total populations of each stratum are given below in the

table.

No Department Total1 Geology 492 State 1123 Metrology 514 Physics 375 Mathematics 1086 Chemistry 1357 Biology 170

Total population 662To determine the total sample size calculates the variance

of the strata by using preliminary survey method. But I take

the sample variance from previous research then the sample

variance is the following.

s12 =0.137s5

2 =0.038

s22 =0.113s6

2 =0.0179

s32 0.202 s7

2 =0.22

s42 =0.032

∑ whsh2 =W1s1

2+W2s22+…………………..+W7s7

2

∑ whsh2 = 0.074*0.137+0.169*0.113+………………∑

+0.20393*0.22=0.39586

V= 0.12

1.962=0.0026

no =whsh

2

v=0.395860.0026 =152

n=∑Whsh2

V+1N∑ WhSh2=

0.39586

0.0026+0.39586662

=124

n=no

1+noN=

152

1+152662

= 124

The total sample size is.

n=n1+n2+n3+n4+n5+n6+n7

Where N-is the total number of population, that is the sum

total of students in each batch

n=the required total sample size, no=the initial

sample size

sh2-is the sample variance of the population

wh - is the population proportion in each

batch

d-is the marginal error, i.e. d=10%=0.1

3.5Allocation of sample survey

An important consideration in stratified sampling has the

way in which the total sample size was allocated to each and

every stratum. A good allocation has one where there was a

reasonable balance between precision and resources. In this

stud I would tire to used proportional allocation since

there was different number of size in each stratum, from

other methods it has appropriate to this study in such a

way to achieve the least variance for the overall mean per

unit of cost, because of this researchers needed the

proportional allocation formula given by:

nh=n∗NhN , where nh be the sample drawn from hth stratum.

Nh be the population size in the h

th stratum.

Applying similar procedure the size from each stratum is

found as n1 from the geology, n2 from statistics, n3, from

Math’s, n4, from Physics, n5, from Metrology,n6, from Biology

andn7 , from chemistry batch students respectively as

follow. To allocate each department sample size, I used

proportional allocation method of sampling techniques.

nh=Nh∗nN Where, N total population

Where N= N1+N2+N3+N4+N5+N6+N7

n=no

1+noN=

152

1+152662

= 124

n1=N1∗nN =49∗124662 =9

n2=N2∗nN =112∗124662 =21

n3=N3∗nN =108∗124662 =20

n4=N4∗nN =37∗124662 =7

n5=N5∗nN =51∗124662 =10

n6=N6∗nN =170∗124662 =32

n7=N7∗nN =135∗124662 =25

The total sample size is: n=n1+n2+n3+n4+n5+n6+n7

n=9+21+20+7+10+32+25=1

24

3.6 Method of data Collection.

The main sources of data for this study was primary data was

directly collected from students through questionnaires that

are selected as a sample. Some secondary sources of data

were used for this study. This secondary source of data has

used as a literature to get more relevant information

related to this study. This data was collected from

different books, written documents associated with this

study, internet and other references related to this study.

The study was conduct by using primary data that has

collected by using self-administered questionnaire in both

closed and open-ended way, that was distributed to the

students of the selected department in Arab Minch University

case study in Abaya campus students and secondary data that

has revised from recorded documents in register department

records to find target population of the students (N).

3.7 Variables of the Study

Dependant variable

Student’s academic performance (CGPA).

Independent variables

Average monthly income of student’s

Alcohol (yes and no)

Chat (yes and no)

Cigarette (yes and no)

Departments

Sex (male and female)

Time spent on the study (2:00-3:00, 3:00-5:00, 5:00-7:00

and>7:00)

3.5. Method of data collection

For this study the primary data collection method used from

ARBA MINCH UNIVERSITY IN ABAY CAMPUS in grauguated student

in 2005 by using questionnaire and interviews from the

sample households of that area. And I could collect the data

from the sample is that using self administer questioners

and face-to-face interviewing methods.

3.6 Method of data Analysis

To summarize and analyze the data the descriptive and

inferential statistics were used. Descriptive statistics is

deal with the method used to collection, organization,

analyzing, and interpreting of data; such as mean, variance,

percentages, etc. Inferential statistics is a set of methods

used to make generalize from sample to population by

performing estimation and hypothesis testing; such as tests

for comparing means, test of association and regression

model are particular methods employed for this study.

3.6.1. Independent Sample T-test

T-tests used to compare two group’s score on the same

variables; the observation from the first sample must not

have any bearing on the observation from the second sample.

Moreover the scores must be continuous and follow normality.

It tests the equality of mean of two groups in such a way

that: H0:µ1=µ2 versus H1:µ1≠µ2, where µ1 is the mean of the

first group of population, and µ2 is the mean of the second

group of the population. Based on the independent sample

from the population we reject the null hypothesis if the

absolute value of the test statistic is greater than the

critical value at α= 5% (/T/>tα/2(n1+n2-2)), or we reject H0 if

p-value is less than α level of significance otherwise

retain. Where, test statistic

T=x̄1−x̄2

Spooled√ 1n1

+1n2

~t(n1+n2−2)

3.6.2. One-way ANOVAIt involves the comparison of several means of a treatment

based on independent random sample drawn from population

associated with the treatment. It used to study the effects

of some variables on the mean of the level of treatment.

Model: yij=µ+ti+εijfori=1,2………….a,j=1,2………….n

Where:yijis the jthobservation in the ith treatment, is the

overall mean, tiis the ithtreatment effect and εijis the

random error of the fixed effect model.

The used of ANOVA has to test the overall significance of

the regression model and by listing the individual

regressions coefficients used F-test to check if there was a

relationship between dependent variable and independent

variables.

Here ANOVA table construct like bellow:

Source

of

variatio

n

Sum of

square

Degree

of

freedo

m

Mean sum

square

F-test

statistic

s

Regressi

on

Error

SSR=(yi-

ŷi)2

SSE=(yi-

ŷi)2

k-1

n-k

MSR=SSRk−1

MSE=SSEn−k

F=MSRMSE

Total SST=SSR+SS

E

n-1

Hypothesis testing for the above two models

Step 1: state the hypothesis and identify the claim

Ho: the two regression models have the same

intercept

H1: the two regression models have different the

intercept

Step 2: compute the test statistics.

Step 3: Decision-making base on the test statistics.

Reject Ho where Fcal > Ftab (n, n-k-1) and p-value

less than the significant level of α, accept otherwise.

Remark: since the dummy variables are non-stochastic (at

least somewhat error free), they pose no especial problems

in the application of OLS. However, care must be exercised

in transforming data involving dummy variables. In

particular, the problem of autocorrelation and

multicolinearity need to be handled very carefully.

Model assumptions:

yij are normally and independently distributed

The model errors are normally and independently

distributed with mean zero and constant variance δ2.

Homogeneity of the variance (all treatments have equal

variances)

Linearity (Additive assumption)

Hypothesis testing:

H₀: µ1=µ2=…=µa (there is no difference between means of

treatments)

H1: there is at least one difference among the means of a

treatment.

From random samples of equal or unequal size the following

test statistic is computed make decision about the

hypothesis of equality of means.

Fcal=MST

MSE, which has F distribution with degree of freedom

(a-1) and (an-1). Reject H0, ifFcal>Fα¿ ¿, otherwise it is

accepted, or if P-value is less than α-level of

significance, reject Ho otherwise accept. If the null

hypothesis is rejected we make pair wise mean comparison

LSD, Turkey’s tests, etc.

3.7.2.2 Multiple linear regressions model

Multiple linear regression analysis is used to estimate

models to describe the distribution of a response variable

with the help of a number of independent (predictors). In

multiple linear regression analysis, we have several

explanatory variables (X’s) and one dependent variable Y,

and assume that the relationship between Y and X’s is

linear, then the model that indicate the linear relationship

between Y and X’s is given by:

yi=β0+β1x1i+…………+βkxki+εii=1,2…………n

Where,yiis theith observation of response variable.

x1i………..xkiare predictor variables corresponding to ith

observation

β0is the value of mean response when all predictor variables

are set to zero

β1………….βkis the change in average response for each unit

change in corresponding predictor variable

Model Assumptions are:

The relationship between yi¿xkiis linear.

The random errors are independently and normally

distributed with mean 0 and variance δ2.

There is no multicolinarity between predictor

variables.

Parameters of the model are estimated by fitting model to

the data using least square method or maximum likelihood

method; often coefficients are estimated by minimizing sum

of squares of the error with respect to parameters (Ordinary

Least Square method).Thus, the matrix notation of least

square estimator for parameter β is: β̂OLS=(X'X)−1X'Y.

To see significance of each independent variable (relative

importance) on dependent variable the hypothesis that could

be tested is given as H0:βi=0, for i=0,1,2...k with two tailed

alternative. T statistics is used for comparing with

critical value from T table or with its corresponding

probability of rejection (Type I error=α). To test

significance of overall model one test the null hypothesis

stated as: H0:β1 = β2= β3 = . . . = βk = 0 , meaning all explanatory

variables X1, X2,...,Xk do not have effect simultaneously on

the dependent variable versus alternative hypothesis stated

as at least one of the βi’s is different from zero. To test

this hypothesis we use F-test; the test statistic is given

asFcal =

SSR/ (k−1)SSE/ (n−k )

The decision to test overall significance of model is

rejecting H0 if Fcal>F(k−1, n−k )

(α) , where, α is level of

significance, k-1 and n-k are degree of freedoms

corresponding to SSR and SSE respectively, or we reject H0

if p-value is less than α.

The coefficient of determination shows how the explanatory

variable explains the dependent variable or measure goodness

of fit of the model. The larger value of the R2 indicates

the model is good, where R2=1−SSESST

Model Adequacy Checking

Model adequacy diagnostic procedures are intended to check

how well the assumptions of multiple linear regressions are

satisfied. Violation of these assumptions cast doubt on the

validity of the conclusions drawn on the basis of the

results. Here bivariate and scatter plot is used to test

linearity, P-P plot is used to test normality, and to test

multicollinearity variance inflation factor is used. The

assumption about autocorrelation among residuals is not

tested since our model is crossectional model. If one of the

assumptions is violet remedial are taken; like

transformation could be used to change the data in suitable

form for analysis.

Test of presence of multicolinarity

Multicolinsarity refers to the existence of high (perfect)

linear relationship among regress ores can test the presence

of multicolinarity by variance inflation factor,

If value of VIF less than 5 then there is no multicollinarity in the data .as we can see the value are ofVIF from Minitab out put on value are less 10,this indicatesthat there is no multicolinarity in the data

Homoskedasticity (Constant Variance)

The constancy of the variance of the dependent variable

(error variance) can be examined from plots of the residuals

against any of the independent variables, or against the

predicted value Assumption of Homoskedasticity

4. RESULT ANDDISCUSSION

4.1 DISCRIPTIVE ANALYSIS RESULT

From the table 4.1 among 124 students 28.2% are female and

71.8 % are male.

About 56.45% were chewing chat which is about 70 and 43.5%

respondents are non chewing chat which is about 54. Hence

majority of the respondents were found that chewing chat.

According to above result in table 4.1, I can conclude that

most of the respondents are drink alcohol (57.3%) which is

about 71 respondents when I compare those of respondents who

no drink alcohol (42.7%) from 53 respondents out of 124

samples. This implies that respondents are more participated

with drinking alcohol is more than the respondents of who

nondrinking alcohol.

According to the table 4.1, I can conclude that most of the

respondents are not smoke cigarette (69.4%) which is about

86 respondents when I compare those of respondents who

smoking cigarette (30.6%) or only 38 respondents out of 124

samples. This implies that the respondents are more

participated out of smoking cigarette is more than the

respondents of smoking cigarette.

The table 4.1 for sex, chewing chat, drinking alcohol and

smoking cigarette.

Codenameforcategoricalvariable

Frequency

Percent

ValidPercent

CumulativePercent

Sexofstudents

Female 3528.22581

28.22581

28.22581

Male89

71.77419

71.77419 100

Chewingchat

No 5443.54839

43.54839

43.54839

Yes 7056.45161

56.45161 100

Drinkalcohol

No 5342.74194

42.74194

42.74194

Yes 7157.25806

57.25806 100

Smokingcigarette

No 8669.35484

69.35484

69.35484

Yes 3830.64516

30.64516 100

. 4.2 INFRENTIAL ANALYSIS

The inferential analysis to examine the factors of our

questionnaire associated with addiction. degree of freedom

and levels of significance are reported. Next, we performed

academic performances as the dependent variable and time

students spent on the study as a independent variable. In

all calculations, p values under 0.05 were considered

significant. All figures and graphs were produced with SPSS

13.0

From Table 4. 3 the hypothesis equality of sex is rejected,

since p-value (0.004) is less than level of significance

(α=0.05). This tells us sex is related to CGPA in Arba

Minch, because the mean difference in sex is statistically

significant.

Table 4.3 t-test sex for CGPA

Independent Samples Test

3.164 122 .002 .307 .097 .115 .499

3.001 56.145 .004 .307 .102 .102 .512

Equal variancesassumedEqual variancesnot assumed

cgpat df Sig. (2-tailed)

MeanDifference

Std. ErrorDifference Lower Upper

95% ConfidenceInterval of the

Difference

t-test for Equality of Means

From result of Table4.4 the hypothesis equality of chewing

chat is rejected, since p-value (0.000) is less than level

of significance (α=0.05). This tells us chewing chat is

related to CGPA in Arba Minch, because the mean difference

in chewing chat is statistically significant.

Table 4.3 t-test chewing chat for CGPA


-5.653 122 .000 -.461 .081 -.622 -.299

-5.710 117.952 .000 -.461 .081 -.620 -.301



MeanDifference



Difference


From result of Table 4.5 the hypothesis equality of alcohol

is rejected, since p-value (0.002) is less than level of

significance (α=0.05). This tells us alcohol is related to

CGPA in Arba Minch, because the mean difference in chewing

alcohol is statistically significant.

Table4.5 t-test alcohol for CGPA


-3.103 122 .002 -.274 .088 -.449 -.099

-3.113 113.376 .002 -.274 .088 -.449 -.100



MeanDifference



Difference


From result of Table4.6 the hypothesis equality of smoking

cigarette is rejected, since p-value (0.002) is less than

level of significance (α=0.05). This tells us smoking

cigarette is related to CGPA in Arba Minch, because the mean

difference in smoking cigarette is statistically

significant.

Table4.5 t-test smoke cigarette for CGPA


.883 122 .379 .087 .098 -.108 .281

.844 64.050 .002 .087 .103 -.118 .292


cgpat df

Sig.(2-tailed)

MeanDifference



Difference


As we observed from table 4.7 the p-value less than the α

level of significance we reject the null hypothesis,

Therefore from the above table output indicating that p

value< 0.05 or (0.000<0.05) reject Ho , since we can

conclude that all the above variables have p-value less than

α level it is statistically significant effect on CGPA

(student academic performance) at 5% level of significance

4.2.2. ONE-WAY ANOVA

Table 4: One Way ANOVA Results of Students academic

performance on Different Independent Variables

Sum of Squares df

Mean Square F Sig.

DEPARTEMENT

Between Groups 3.531165 6

0.588528

2.491985

0.026382

Within Groups 27.63167 117

0.236168

Sum of Squares df

Mean Square F Sig.

AVERAGE TIME


0.996981

4.246706

0.006861


0.234766

MONTHLY INCOM

Sum of Squares df

Mean Square F Sig.


0.392172

1.626784

0.107758


0.241072

cgpa

CHEWING CHAT

Sum of Squares df

Mean Square F Sig.


6.467909

31.95331

1.06E-07


0.202417

Total 31.16284 123

DRINKING ALCHOL

Sum of Squares df

Mean Square F Sig.


2.279985

9.630565

0.002379


0.236745

Total 31.16284 123 SMOKING CIGARET

Sum of Squares df

Mean Square F Sig.

Between 0.198085 1 0.19808 0.7804 0.3787

Groups 5 46 42Within Groups 30.96475 122

0.253809

31.16284 123

4.2.2Multiple Linear Regression Analysis

Multiple regression analysis has been viewed only as away to

describe the relationship between a dependent variable and

several independent variables.

Ŷ=β0ˆ+β1

ˆX1+β2ˆX2+β3

ˆX3+β4ˆX4+β5

ˆX5+β6ˆX6+β7

ˆX7+β8ˆX8

Co linearity statistics tells as whether multicollinearity

exists in in the model or not. From the result, we see that

the variance inflation factor (VIF) is very small; hence, I

can conclude that there is no problem of multicolinearity

presents in the data. Of course, Tolerance of all variables

is closer to one, which implies that the variables are not

collinear with each others.

β0= 2.298 indictes that the value of CGPA when the

explanatory variables (assume are zero. βo is posative

indicate that there is effect of independent variables on

the dependent variable ( CGPA )

β1 = 0.335 is greater than zero this indicates that there is

positive (direct) relationship between sex of student and

CGPA .

β2 = 0.080 is greater than zero this indicates that there

is positive (direct) relationship between CGPA and

department of the students.


positive(direct) relationship between CGPA and average time

.

β4 = 0.001 greater than zero this indicates that there is

positive (direct) relationship between CGPA and monthly

income.

Β5 = -0.421 is less than zero this indicates that there is

negative (indirect) relationship between chewing chat and

CGPA.

β6 =-0.276 is less than zero this indicates that there is

negative (indirect) relationship between drinking alcohol

and CGPA.


positive (direct) relationship between smoking cigarette and

CGP.

Coefficientsa

2.298 .144 15.911 .000.335 .074 .300 4.526 .000 .921 1.086.080 .017 .316 4.643 .000 .874 1.144.057 .039 .097 1.456 .148 .908 1.101.001 .000 .267 4.130 .000 .968 1.033

-.421 .067 -.416 -6.300 .000 .931 1.074-.276 .066 -.273 -4.198 .000 .961 1.041.122 .070 .112 1.736 .085 .972 1.029

(Constant)sex of studentsdepartmentsavreage timemonthly incomechewing chatdrink alcoholsmoke cigarette

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig. Tolerance VIFCollinearity Statistics

Dependent Variable: cgpaa.

From the result, R-square and Adjusted R-square is 0.566and0.522 respectively. Besides this, i have to take in toaccount that the value of R-squared adjusted is 52.2%, whichindicates 52.2 of the variability of the academicperformances of the students explained by the factorsincluded in the model.

R-sqr (adj) is 0.522 which indicates 52.2% of the

variability of the CGPA of the students is explained by the

factors included in the model. The table shows that R-

squared =0.566 that means 56.6% explanatory variables

explains the response variables and residual 75.3% the

predictor variable fit model.

Model Summary

.753a .566 .522 .350Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), level of chemistry students,smoke cigarette, monthly income, drink alcohol, sex ofstudents, chewing chat, avreage time, level of geologystudents, departments , level of metrology students,level of physics students

a.

From the result, I can decide that whether accepting or

rejected HO. This is depend on p_value and α =0.05 level of

significance. Since p-value=0.000 is less than F-value which

12.703 at given level of sign.

From the decision rule it can be conclude that the model of

the estimated (ANOVA) model are significance. This also

indicates that there is a linear relationship between

dependant and independents variables. Therefore, I have

enough evidence to reject the null hypothesis.

From the below decision, I can decide whether accepting or

rejected HO: This is depending on p_value and α =5% level of

significance. Since the p-value=0.000 is less than α=0.05

level of significance. Therefore, there is no significance

evidence to accept the null hypothesis.

From the below decision rule, it can be concluding that the

academic performances of the students that caused by

addiction are different from the academic performances of

students that do not caused by addiction. That means two

regression model have different intercept.

ANOVAb

17.086 11 1.553 12.703 .000a13.084 107 .12230.170 118

RegressionResidualTotal

Model1

Sum ofSquares df Mean Square F Sig.

Predictors: (Constant), level of chemistry students, smoke cigarette, monthlyincome, drink alcohol, sex of students, chewing chat, avreage time, level of geologystudents, departments , level of metrology students, level of physics students

a.

Dependent Variable: cgpab.

Dependent variable: Academic performance of students

Normality check of the response variable: Cumulative grade

point.

The p.p plot of regression standardized residual is

normal .it shows that there is a linear relationship between

response &explanatory variables and there is no outliers, if

there is no outliers, no hetroscedacity problem exist;

The error term is normally distributed mean zero andvariance sigma square.

i.e. ~ N (0, б2)

1.00.80.60.40.20.0

Observed Cum Prob

1.0

0.8

0.6

0.4

0.2

0.0

Expected Cum

Prob

Dependent Variable: cgpa

Norm al P-P Plot of Regression Standardized Residual

This scatter plot of regression Residual Versus theFitted values shows that there is Homosecandcity ofvariance relationship between the academic performancesof students and observed (explanatory) variable.

This scatter plot of regression Residual Versus the

Fitted values shows that there is Homosecandcity of

variance relationship between Average family size

(person per households) and observed (explanatory)

variables.

5. CONCLUSSION ANDRECOMMENDATION

5.1 CONCLUSSIONSBased on above statistical analysis we can forward followingconclusion as below;

Smoke cigarette has a positive effect on the academic

performance of students.

Among 124 respondents 56.5% of the students caused by

chewing chat and the other 43.5% of the students do not

caused by chewing chat.

Among 124 respondents, 56.5% of the students use chewing

chat which is 70and 57.3use drink alcohol which is about

71and 30.6 smoke cigarette which is about 38 .The

respondent do not use the above stimulants 43.5% not

chewing chat which is about 54 and the respondent do not

drink alcohol42.7% which is about 53and also the

respondents who not smoke cigarette is 69.4% which is

about 86. The academic performances of the students that

use stimulants are less than the academic performances

of the students that do not use stimulants. That means

the students who do not use stimulants (not addicted)

get a good academic result then those who are addicted.

Drinking alcohol has an effect on the academic

performance of students or stimulants of alcohol have

independent on academic performances of students

The magnitude of the ANOVA indicates CGPA affect by somevariables. About the factors of alcohols, sex, chewingchat, smoking cigarette, average time, monthly income,and department was statically significant in regressionmodel.

5.2 RECOMENDATIONS Based on the result and discussion, the following

recommendations are forwarded;

Since from the addiction chewing chat has a side effect

on students academic performances, the students should

not use chewing chat for increasing reading and

understanding to improve their academic performances

In order to avoid the impact of addiction, the students

should be developing different techniques in order to

avoid the impact of addiction on their academic

performance by learning and advice to use time properly

and systematically and increasing their CGPA.

All students and Governments or other educated person

should give attention for impact of addiction on academic

performance because; this factor has a great effect on

academic performance of the students.

Overall, Addiction is a serious impact on student’s

academic performances. There is a need for a campaign to

inform parents, teachers and state officials about the

dangers of the addiction.

REFERENCE

1. Adam Kediri Gelato, (2005), “Introduction to basic statistics& its

applications”, 1st edition, Ethiopia printing press.

2. Cochran, W.G (1977), “Sampling techniques”, 3rd edition, John Wiley &sons,

inc.., New York.

3. D.N. Gujarati (2005), “Basic Econometrics”, 3rd/4th editions. (McGraw-Hill:

New York.

4. George. W. (1881), “Statistical Methods”, 7th edition, Iowa state university.

5. Gubta S.C, Kapoor U. K. (1995),”Fundamentals of applied statistics”, 3rd

edition, Sultan Chanda and Sons. , New Delhi

6. Lwanga SK and Lemeshow.S (1991). The impact of addiction on society in

Health studies: A practical Manual. Geneva: World Health Organization.

7. http://www.google.com.et/, the impact of addiction on students on their

academic performance.

Arab MinchUniversity Collegeof Natural Science

http://www.google.com.et/

Departmentof Statistics

Questionnaires

Dear respondent this questionnaire is prepared to collectdata about the impacts of addiction on academic performance,in Arba-MinchUniversity the case study in Abay campusstudents. So, I would like to thank your collaboration andwillingness to give me correct information kindly.

Instruction: please give responses to the question by mark athick (√) marks on the boxes are provided and write thenecessary information in the space provided.

1. Sex Male Female

2. Your department ________________________

3. Average time of study per day

2:00-3:00hrs 3:00-5:00hrs 5:00-7:00hrs

>7:00hrs

4. Your CGPA ___________________

5. Your average monthly income in Birr________________

6. Do you chewing chat? Yes No

7. When you start chewing chat?

In elementary in high school in university

8. Who gives you the financial supported

Family Relative Friend Others

9. If your answer is “yes” in question 6 why you use chewing

chat?

Spending time To improve understanding

For refreshment By influencing of friends

others

10. After you have chew chat what is your motivation for

reading

Increase decrease no changed

11. Do you drink alcohol? Yes No

12. If you drink alcohol, how often do you drinking alcohol

Usually always sometimes

13. Do you smoke cigarette? Yes No

14. When you start smoking cigarette?

In elementary in high school in University

15. If your answer in 13 is “yes”, how much cigarette you

smoke per day?

One cigarette Two cigarette Three

More than cigarette

16. How often you smoke cigarette?

Usually always

sometimes

Thank you for your

cooperation!!

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