Georgi Stoilov Econometrics

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Introduction to Econometrics

BS2220

Dr. Kai Sun

Report

Analysis of the impact of academic and non-

academic activities on first year students

performance

Candidate Number: 150851


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Analysis of the impact of academic and non-academic activities on first year

students performance2011

BS2220: Introduction to Econometrics | Candidate No. 150851

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Contents:

1. Introduction:

1.1. Research Question and Importance of the Study

1.2. Existing Studies

1.3. Report Structure

2. Conceptual Framework:

2.1. Theories -

2.2. Literature review

2.3. Relationship prediction -

3. Data and Methodology:

3.1. Source and Explanation of Data

3.2. Data Analysis

3.3. Correlations

3.4. Regression Model

3.5. Estimation Strategy

4. Econometrics Results:

4.1. Estimated Coefficients

4.2. Goodness of Fit and Diagnostic Tests

5. Conclusion:

5.1. Implications

5.2. Suggestions

6. References


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Introduction

Research Question and Importance of the Study

The main aim of this study is to examine and analyze the main factors that reflect on

first year students academic performance. The UK is renowned for its excellent

higher education and attracts thousands of new students each year. Many of them

originate from different cultures and backgrounds and are used to lead different

lifestyles. This may include after-school engagements with academic and non-

academic clubs and societies, part-time employment and socializing with friends. The

core purpose of this study is to investigate how many this differences in students

lifestyle and background influence on their academic performance. It is also crucial to

identify which determinants are statistically significant and have negative or positive

impact on students performance, so that both students and the University can put

their efforts on them to increase students efficiency and overall performance. In

order to achieve the goal of our study we have gathered data from a survey,

conducted online by hundred and seventy students doing the BS2220 course at

Aston University, comprising of seventy-nine questions in four areas. For this

research I have primarily focused on three main categories as I consider them most

highly correlated with the academic performance of first year students: (i) Studying

and proximity to the universitys educational facilities (ii) Time spent on leisure

activities and part time jobs; (iii) Using the universitys educational facilities.

Existing Studies

This topic is not new and prior studies have been conducted to identify the variables

related to academic success of students in a variety of countries. With the increasing

diversity of students attending university, there is a growing interest in the factors

predicting academic performance. In a study conducted in Australian university,

McKenzie & Schweitzer (2001) identified previous academic performance as the

most significant predictor of academic performance. Other predictors of university

grades, found by the study, were integration into university, self efficacy andemployment responsibility. In other more recent research Lebcir, Wells and Bond

(2008) suggest that the factors: level of details given in lectures, speed of lectures,

academic internet sources, English Language Skills, group or individual assessment,

the qualitative/quantitative content of assessment are important drivers of the

academic performance of international students.

Report Structure

The first section of this analytical paper introduces a conceptual framework and

literature review of previous research in the area and finishes with a prediction for theoutcomes of the examined question. The second section presents the means of data


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gathering and its explanation followed by presentation of the research methodology.

The Econometrics results section provides evaluation of the performance of the

regression model and examines it for violations. At the end a conclusion summarises

the main findings and gives suggestions for better future performance, based onthese findings.

Conceptual Framework

Theories

In order to identify the relationship between students lifestyle and their academic

performance it is necessary first to give definition to the concept of academic

performance. Academic performance can be defined as the general level of grades

a student acquires in exams and course-works throughout the academic year.

Researchers have explored the study environment as a potential factor of academic

performance outcome (Dorman, Fraser & McRobbie, 1997). It is also essential to

understand the concept of lifestyle. According to Michman, Mazze, and Greco

(2003) lifestyle emerges from various social influences, as well as an individuals

personal value system and personality. Students spend their time influenced by their

activities, interest and options available. After we defined these variables we can

identify the key relationships between different student lifestyles and academic

results.

Literature review

Throughout years a large amount of researches have been carried out in various

universities worldwide in an attempt to most accurately identify the

variables, determining students performance. Attendance in lectures and tutorials is

considered to be essential variable in determining students academic results. This

was proven in a research carried out by Walbeek, C. (2004) that students, attending

all lectures and tutorials, are expected to achieve 7.3 percent more than their

colleagues, who have not attended any lectures. Other supporting evidence is the

study of E. Burd & B. Hodgson (2004) which identifies a strong correlation between

lecture attendance and academic performance. I am also interested in therelationship between the usage of University facilities and services and academic

results. In particular I am interested in the usage of mathematics tutorials, as Aston

University provides valuable mathematics support, through its Learning Development

Center, for students who are not very good with numbers and aims to improve their

understanding and knowledge of the subject thus increase their academic

performance. Studies among academics, including Mark Fenster (1998) and others

have shown that there is a positive correlation between the amount of mathematics

taken and exam result. Another component of students lifestyle is the time they

spend watching television. Chernin & Limburger (2005) have found in their research

a negative relationship between the amount of television watched and academic


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performance. Additionally, part-time work is also a segment of students lifestyle,

especially for the students, who cover their living expenses on their own, without any

financial support from their parents. The Cire Rochford, Michael Connolly &

Jonathan Drennan (2009) research, conducted on students doing nursery, supportthe theory, that part-time employment has a negative correlation with academic

overall result.

Relationship Prediction

Based on previous studies, the findings listed above and my personal preferences I

will choose six variables to find out what determines academic performance based on

our sample. In this paper I will study the relationship between academic performance

and dwelling location, mathematics tutorials and study skills workshops taken, time

spent watching TV, part-time employment during university and alcohol consumption.

I predict that students, who live on campus, have higher attendance of study skills

workshops and mathematics tutorials, spend less hours watching TV and doing part-

time work and consume smaller units of alcohol are tend to have higher grades. In

my research paper I am going to test these assumptions using correlations,

regression analysis and other statistical methods.

The Data and Methodology

Source and Explanation of Data

In order to complete this investigation, a survey has been conducted on hundred and

seventy students from Aston University, Birmingham, taking the BS2220 module

Introduction to Econometrics taught by Dr. Kai Sun. The questions in the survey

were divided into four main groups; students personal information, lifestyle, family

background and academic related information. The cross-sectional data collected

from thirty eight questions in the survey represents students from diverse ethnicity

and cultural backgrounds, which is something normal for Aston University and all

other UK universities as whole. The sample size examined seems to be relatively

small comparing to the scale of Aston University but I presume that the quality of the

students in it is relatively high, as the Econometrics module is considered to be oneof the most complicated and demanding modules throughout the Business School. A

lot of the variables in this dataset are highly correlated with students academic

performance, for example, proximity of accommodation to university, prior academic

achievements, after school engagements, such as part-time jobs, clubs, societies

and sport activities, number of lectures and tutorials missed, time spend studying and

revising prior and during exam period and time spend on leisure activities, such as

TV watch, Facebook socializing and alcohol consumption. For the matter of my

investigation I will analyse a set of six variables, which I think have a high impact on

students performance and cover all aspects listed above.


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Data Analysis

The outcomes of some questions in the survey conducted were initially described

with characters in the dataset, or non-numeric values, but they have been convertedinto numeric functions. For example, using the destring and encode commands in

STATA we can create dummy variables, which convert the character variables into

numeric functions, so that all answers yes become 1 and no become 0. The

data coding was completed in advance and the data was ready to be analysed. The

summary of the dependant variable and some of the independent variables

conducted in STATA showed the following results:

Variable Description Observations Mean Standard Deviation Min Max Measurment

First Year Average Score 79 64.71139 12.60914 2.2 84 Percentage

Uni Entry Score 79 81.60253 29.1013 3.6 300 Percentage

Lecture Hours Missed 79 16.08861 24.62862 0 145 Hours

Use of Study Skills Workshop 79 .2025316 .4044543 0 1 Yes=1 No=0

Prior Exam Period Library Study 79 18.26582 39.88178 0 300 Hours

Exam Period Library Study 79 20.18987 22.81816 0 105 Hours

Living on Campus 79 .2151899 .4135799 0 1 Yes=1 No=0

Part of Non-Academic Society 79 .7341772 .4445932 0 1 Yes=1 No=0

Part-Time Job 79 9.563291 26.34709 0 160 Hours

Money Spent on Phone 79 25.15823 12.92943 5 65 Pounds

Alcohol consumption 79 5.93038 11.92061 0 75 Units

Facebook Usage 79 6.772152 11.99834 0 75 Hours

Hours Watching TV 79 5.898734 6.826785 0 26 Hours

As we can see from the table above, there are some extreme and unrealistic results

observed from the output. The maximum Average University Entry Score result was

300, which is inconsistent because the variable is measured in percentage and the

maximum possible value is 100. This student maybe has entered his UCAS score by

mistake instead of his average mark. Another student has answered that he has

studied for 300 hours prior exam period in the library, which again is impossible

because of the time constraint of 168 hours per week. In addition, a student has

inputted a figure of 160 hours as a part-time job which again is almost impossible


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considering the fore-mentioned time constrain. These outliers could have resulted

due to students misinterpretation on the survey questions.

Correlations

In the table below we can examine several correlations between the explained

variable Average Score and the explanatory variables. The STATA summary shows

a positive, although very weak relationship of 7% between students pre-university

academic scores and their current performance at university, which indicates that

students who have put continuous efforts in High School tend to do the same in

university and perform better than those with low pre-university grades. Recent UK-

based study by Duff (2004) confirms our theory that good prior academic

achievement, as measured by results in high school, leads to better student

performance at university. According to the findings below, living in campus ispositively associated to students academic performance with a positive correlation of

7%. This can be explained with the fact that students who live closer to university

have easier access to university educational facilities at any time, especially during

examination period, when the library facilities are open twenty four hours. On the

other hand the amount of time students spend on non-academic activities, such as

undertaking part-time work or speaking on the phone to their family and friends show

a negative correlation to their academic performance. These are expressed by

negative coefficients of -0.0721 and -0.1062 consecutively, which means that, the

more time students spend working or talking on the phone, the lower their academic

performance will be. Unexpectedly a positive correlation of around 3% exists

between hours of lectures missed and students grades. One possible reason for this

may be the fact that students utilize the hours missed in lectures on individual study

or maybe due to the small size of the sample the results may not be totally accurate.

Furthermore, there is a positive relationship of 0.0624 between Study Skills

Workshops and first year average results. This indicates that the workshops, offered

by the university, have provided solid background knowledge to students for their

exams.


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Variable

Description

Average

Score

Uni Entry

Score

Skipped

Lectures

Study Skills

Workshop

Library

Study

Live on

Campus

Part-Time

Job

Phone

Bill

Facebook

Usage

Average score 1.0000

Uni Entry Score 0.0679 1.0000

Skipped Lectures 0.0304 -0.0434 1.0000

Study Workshop 0.0624 -0.0806 -0.1280 1.0000

Library Study -0.0774 -0.1276 -0.0413 0.2069 1.0000

Live on Campus 0.0366 -0.1743 -0.1190 0.0427 0.1668 1.0000

Part-Time Job -0.0721 -0.1035 -0.0252 -0.1239 -0.1619 -0.0524 1.0000

Phone Bill -0.1062 -0.0414 0.0290 -0.0111 0.1618 0.0007 -0.1111 1.0000

Facebook Usage0.0040 0.0978 0.0622 0.0189 0.3028 0.0007 -0.1136 0.0867 1.0000

Regression Model

This section describes an empirical regression model for the determinants of

students performance using the cross-sectional data from the BS220 students

survey. The analysis uses a multiple regression model to study the determinants of

the dependant or Explained variable students academic performance which is

measured by the students average score of first year modules at the university. To

determine the significance of the independent variables that could explain the

dependent variable I have executed several regression analyses between the

originally selected ten explanatory variables and our dependant variable.

Nevertheless, due to the small size and accuracy of our sample data most of these

variables were not enough statistically significant at the 95% confidence interval to

explain the dependant variable. Their P values were higher than 5%. However, Iindentified six independent variables to determine first year students performance.


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In my empirical research, I will perform the regression analysis on the subsequent

explanatory variables: live on campus, study skills workshop, use of mathematics

centre, TV watched, part-time employment and alcohol consumption.

Estimation Strategy

Having considered the above explanatory variables, the specification model was

derived as follows:

Acad.Perf. = 1 Live Camp. + 2 Study Work. +3 Math. Cent. + 4 TV Watch + 5

Part-t. Job + 6 Alcohol + i

The explanation of the variables in the model is the following:

1 Live Camp. denotes whether the student live on campus or not

2 Study Work. denotes have the students used the Study skills workshop

service provided by the university

3 Math. Cent. denotes have the students used the Math center service

provided by the university

4 TV Watch signifies the number of hours students spend watching TV

weekly

5 Part-t. Job signifies the number of hours students spend doing part-time

job weekly

6 Alcohol denotes how many units of alcohol the students consume weekly

i is the error term.

This model is used to measure how much does a unit change in one of explanatory

variables leads to a particular unit change in the explained variable. The inclusion of

the i (the residual term) means that we have some unobserved variables, random


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disturbance or the accuracy of our data is not reliable. Seeing as the sample data

observed is quite small compared to the whole population, several problems are

likely to arise such as the problems of multicollinearity. This mainly occurs when the

regressors are more highly correlated with one another than with the dependent

variable. To test for the presence of such abnormalities I will estimate the variance-

inflation factor (VIF). Another possible statistical problem that primarily occurs in

cross-section data is the heteroskedasticity. This exists when the magnitudes (of

error term) continuously increase and decreasing with the number of variables

observed. In order to indentify the presence of this problem I will perform a Whites

General Test using STATA.

Econometrics Results

The estimation results for BS220 survey are reported in the table below. The total

number of observations is 79. By looking at the T values of the variables we can

determine which one of them are significant in explaining the students performance.

In our mode not all of them are significant enough and they explain only 8.60% of the

variation in the dependent variable, which is relatively small. This means that a large

part of around 91.4% of the variation is missing. This can be due to the accuracy of

the survey data and the size of the sample which was quite small to accurately

represent the whole population.


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Variables Regression

Coefficient

Standard

Error

T- values p>t [95% Conf Interval]

Live On Campus .6524029 3.590289 0.18 0.856 -6.504706 7.809512

Study Skills

Workshops

1.39903 3.56374 0.39 0.696 -5.705155 8.503215

Use of Math Center -7.129188 3.15224 -2.26 0.027 -13.41306 -.8453122

TV Watched -.2018369 .225476 -0.90 0.374 -.6513151 .2476412

Part-Time Job -.0422431 .0548477 -0.77 0.444 -.1515801 .0670939

Alcohol

Consumption

.0242731 .1251488 0.19 0.847 -.2252065 .2737527

Cons 67.81386 2.592498 26.16 0.000 62.64581 72.98191

R-squared 0.0859

Prob > F 0.3552

Estimated Coefficients

The coefficients for Live on campus and Study skills workshops are positive which

means that students who live close to university and attend workshops in theLearning Development center will increase their academic performance by 0.65 and

1.4 marks respectively. On the other hand the negative coefficients of the variables

TV watched and Part-time jobs indicate that a one unit increase in the time that

students spend watching television or work tend to decrease their average marks by

0.2 and 0.04 respectively. Unexpectedly the regression analysis outcome shows,

that students who make use of the math center are going to have 7.1 marks lower

than other students. This I consider as either a mistake in the dataset or maybe

students who use the math center are really bad in mathematics and these personal

tutorials will not help them to improve their mark. Finally, the positive coefficient of

alcohol of 0.02 shows that every unit of alcohol consumed will lead to 0.02 increase

in the student mark. This I can relate to the fact that when students go out with

friends more often, they consume alcohol, but they also relax and release themselves

from and stress and therefore are more productive in their exams. Unfortunately

almost all of the variable, except the use of math center, have low T values and

relatively high confidence levels. This means that the variables in the regression

model do not affect the Explained variable significantly and other variables are

responsible for this. I tried various different combinations with all the variables

provided in the data set but the outcomes were the same or even with lower T values

and confidence levels. To solve this problem we may need a larger sample from the

population and conduct further tests in order to achieve satisfactory results.


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Goodness of fit and diagnostic tests

As mention previously, the R-squared value of the regression explains only 8.6% ofour data variation, which is a very small proportion. This determines the need for an

increase of our observations and further testing.

I have also performed the VIF and White test to detect if our expression model

contains any multicollinearity or heteroskedasticity. The output shown below indicates

that the model is free of any multicollinearity as the VIF values for all the variables

are less than 10. The Explanatory variables used in our expression model are not

highly correlated to each other more than to the dependant variable.

The White Test has also been performed in order to check whether there is problemof Heteroskedasticity. From the table below we can see that the value of probability >

chi 2 is 0.5102, which is much higher than 0.1, therefore we accept the hypotheses of

Homoskedasticity, and conclude that our expression model used in our data is free of

any Heteroskedasticity; i.e. there is no variability in the magnitude of our error term.

Variable VIF 1/VIF

TV Watched 1.17 0.851935

Alcohol Consumption 1.10 0.906962

Live On Campus 1.09 0.915507

Part-Time Job 1.03 0.966622

Study Skills Workshops 1.03 0.971603

Use of Math Center 1.03 0.971867

Mean VIF 1.03


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White's test for Ho: Homoskedasticity

Against Ha: unrestricted heteroskedasticity

chi2(12) 23.16

Prob > chi2 0.5102

Conclusion

The core purpose of this paper was to test the relationship that exists between First

year students results and a set of different lifestyle variables. We have used a

multiple regression linear model to estimate the coefficients of our independent

variables and see how they influence the dependant variable. Using this information

presented in our data set, we tried to get the most accurate results that are useful in

understanding the factors that affect students performance.

The results revealed that First year academic performance is dependent on students

amount of TV watched and part-time job hours per week, because it represents theamount of time forgone watching TV or working, thus the opportunity cost of which is

studying, if we assume that this is the best students opportunity forgone. The data

has also revealed that attending study skills workshops and living on campus have

increased individuals abilities to perform better on test. However, due to the

inaccuracy in some parts of our data I am uncertain about the validity of this

conclusion. Furthermore, the results also indicate that the utilization of the math

centre service by students affect negatively their performance, which is probably dueto the failure of our data.

Based on the above mentioned econometrics results the following implication are

expected to Improve first year students performance:

University governance should put high emphasize on providing easily

accessible workshops, but also promote awareness about the services among

the students


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University accommodation governance should charge lower accommodation

fees for its students in order to attract them to live on campus and enable them

to use university and library facilities more often

It is strongly recommended for students to spend less time watching TV,

because that would provide them will spare time for more beneficial activities

such as studying or sports

To reach a more accurate conclusion a larger data sample is required, as this will

enable us to carry out more complex and reliable statistical analysis. This sample

was taken only from students studying BS2220 module, and is therefore only

representative of segmental students. I would recommend the survey to be stretched

to all first year students in Aston University giving us a better indication of how the

variables that have been chosen actually affect students educational success. Many

of the results we have found may be coincidental and anomalous. However, using a

greater sample size will eliminate this doubt and give us a more accurate result.


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References:

1. McKenzie, K. & Schweitzer, R. (2001) Who Succeeds at University? Factors predicting

academic performance in first year Australian university students. Higher Education

Research & DevelopmentVol. 20(1): pp.21-33.

2. Lebcir, R. M., Wells, H. & Bond, A., (2008) Factors affecting academic performance of

international students in project management courses: A case study from a British Post 92

University International Journal of Project Management pp.5-10.

3. Dorman, J. P., Fraser, B. J., & McRobbie, C. J. (1997) Relationship between school-level and

classroom level environments in secondary schools. Journal of Educational Administration

Vol. 35(1): pp.74-91.

4. Michman. R., Mazze, E., & Greco, A. (2003) Lifestyle Marketing: researching the new

American consumer, 1 edition. London: Praeger Publishers.

5. Burd, E. & Hodgson, B. (2004) Attendance, and Attainment: a five-year study, CETL- Active

Learning in Computing, South Rd, Durham.

6. Rochford, C. & Connolly, M. & Drennan, J. (2009) Paid part-time employment and academic

performance of undergraduate nursing students Vol. 29(6): pp.601-60.


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Appendix

Commands used:

.sum

.cor

.reg

.vif

.esta imtest, white

Log file


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BS2220: Introduction to Econometrics | Candidate No 150851

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