BHO2285 Marketing Research 2

Question:

 

Answer:

Introduction:

Background

Not attending lectures properly is a very common behaviour in college students. It has been also seen that in recent years marketing management curses and universities are facing a serious issues regarding this lack of attending attitude for lectures within the students (Hu and Wolniak 2013). This problem has been appeared to be a worldwide problem. From the perspective of the universities, non-attended lectures are the sign of increased drop-out rate. According to Kahu (2013), the attendance of university students is directly proportional to the academic performance of the students while inversely proportional to the pupil attrition. There are many factors that contribute in less attendance in class and the resultant poor academic performance. These factors are classified into teaching styles, different learning styles, interests in topic, and personal background. These personal issues are financial differences, cultural differences, and lifestyle. As stated by Flynn (2014), the professional life and work-life balance also influence the attendance rate of the students. It has been also highlighted that the students of new generation prefer different teaching styles rather than the conventional tutorial techniques. All of these issues are collectively contributing to the lack of attendance in academic lectures.  

Problem Statement

From the above discussion it can be clearly seen that not attending lectures and academic classes have become a major issue over the world. There are several researches and studies that have been already done to highlight the underlying factors behind this lack of student engagement in collage and higher education (Lester, Brown Leonard and Mathias 2013). However, aspects of subject delivery as well as the personal circumstances of students have not been properly examined yet. Both of these factors are very essential in this topic of not attending lectures.   

Research purpose and aims

The aim of this research is to identify the factors that may influence the attendance patterns of the student, especially who are enrolled in management courses considering their motivation towards subject delivery and their personal circumstances. In broader aspects the purpose of this research is to improve the student engagement by understanding their interest and attitude and underlying factors.

Research objectives

As per the discussed aim, the following objectives have been presented for this study. Hence the objectives of this research are:

To determine students’ motivation and attitude towards subject delivery;

To determine aspects of subject delivery that encourage or inhibit students from attending lectures; and

To identify the personal circumstances of students that has the greatest influence on lecture non-attendance.

Methodology

Research design

Research design implies the strategic structure of conducting a research considering the research objectives and the scopes while utilising proper tools and approach of the chosen topic. The major divisions of research designs are experimental study, cross sectional, longitudinal, case study, systematic review, reflection, observation and others (Kumar 2019). Depending on the research scope the selection of the tools differs. As discussed earlier this research examines the psychological factors of the student that influence their attendance in academic lectures. In this case the cross sectional research design can provide adequate opportunities to collect data regarding the perception of the students and their choices of behavioural attributes in a short period of time. Hence, for this study the cross sectional research design has been chosen, where the direct interaction with the target population can be possible through utilising various field based data collection tools.

Target population and sampling

The target population is the target people from which the research has been planned to collect data. The target population of this research is the college students who are enrolled in different courses for different expertises. Sampling process is another major part that determines the process of participant selection (Flick 2015). In this research cluster sampling process has been used where the participants are selected by using a set of inclusive criteria. In this study participants have to be between the ages of 18 and 24 and sign a consent form indicating their willingness to participate in this survey. Participant must be a student enrolled in a College of Business degree program at Victoria University. Apart from that, the participants must be from the courses of Marketing, International Trade, Marketing/International Trade, Marketing/Event Management, Marketing and Applied Economics, Accounting, Banking and Finance, Financial Risk Management, Music Industry/Event Management.

Data collection process

Data collection is the governing part of a research on which the outcome of a research depends. At the same time the data collection process should be determined by the research design and the target population (Novikov and Novikov 2013). For this cross sectional research design, survey questionnaire has been chosen as data collection tool. The survey questionnaire has been distributed among the students of College of Business degree program at Victoria University. The questionnaire consists of several attributes namely the enrolled course name, learning preference, cause of missing lectures, perception about lectures, suggestions for changes, obtained marks in examinations, employment background, past academic qualification, and age.

Data analysis process

The data analysis process is the process of examine the collected data in a systematic order to find the implications from the raw dataset. Data analysis process can be qualitative or quantitative or both. The qualitative data analysis uses non-numerical dataset for analysis and the quantitative data analysis uses numerical dataset for analysis utilising various mathematical and statistical procedures (Choy 2014). In this research the collected data are highly quantitative. Therefore, the statistic based quantitative data analysis has been executed. For this data analysis the major statistical methods are descriptive analysis (using mean, standard deviation and percentage distribution), Pearson Correlation analysis, and independent sample T-test competitive mean analysis (Barnham 2015). In order to execute these statistical data analysis the SPSS software has been used where the raw data has been initially collected in Microsoft excel workbook .

Results and discussion:

Descriptive analysis

Q1
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Marketing	6	8.0	8.0	8.0
	International Trade	1	1.3	1.3	9.3
	Accounting	49	65.3	65.3	74.7
	Banking and finance	1	1.3	1.3	76.0
	Financial Risk Management	18	24.0	24.0	100.0
	Total	75	100.0	100.0

From the above percentage distribution analysis it has been found that most of the participants are from Accounting (65%) and Financial Risk Management (24%).

 

Q2
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Graphs, chart, symbols	25	33.3	33.3	33.3
	Small group discussion	14	18.7	18.7	52.0
	Text and reading	7	9.3	9.3	61.3
	Experiencing in Simulation	9	12.0	12.0	73.3
	Face to face lecture	20	26.7	26.7	100.0
	Total	75	100.0	100.0

 

From the above percentage distribution analysis it has been found that most of the participants (33%) like to learn through graphical presentation, chart and symbols. Another large number of participants supported that they like to learn from face to face lecture (26%). As an alternative process almost 19% students want Small group discussion and another 12% students want Experiencing in Simulation.

 

Forms of assessment
	Frequency	Percent
Test in class	42	56.0
Tests on-line	25	33.3
Light Assignments	21	28.0
Complex Assignment	15	20.0
Reflective Journals	3	4.0
Work based	13	17.3
Others	1	1.3

 

From the above percentage distribution analysis of preferred assessment style it has been fund that most of the participants (56%) likes class test. Another large number of participants like online test (33%). About 28% students want Assignments that can be written using books/journals as an information source.

 

Q9
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Matriculation	33	44.0	44.0	44.0
	Diploma/Advanced	35	46.7	46.7	90.7
	Degree	3	4.0	4.0	94.7
	Full time work	2	2.7	2.7	97.3
	Looking for work	2	2.7	2.7	100.0
	Total	75	100.0	100.0

 

Q10
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Full time	72	96.0	96.0	96.0
	Part time	3	4.0	4.0	100.0
	Total	75	100.0	100.0

 

 

Statistics
		Q11	Q12	Q13
N	Valid	75	75	75
	Missing	0	0	0
Mean		18.92	42.67	21.71
Std. Deviation		3.627	17.482	1.707

Correlation

The Pearson correlation has been done to find the correlation of paid working hours and the distance from the education centre with the underling behavior towards attending academic lectures (Appendix B). In the following table only those correlations are presented that have 2 tailed significant value less than 0.05.

Correlations
		Q11	Q12
Q4.5	Pearson Correlation	.107	-.301**
	Sig. (2-tailed)	.361	.009
	N	75	75
Q4.6	Pearson Correlation	.133	-.336**
	Sig. (2-tailed)	.256	.003
	N	75	75
Q4.19	Pearson Correlation	.035	-.259*
	Sig. (2-tailed)	.768	.025
	N	75	75
Q4.20	Pearson Correlation	.197	-.233*
	Sig. (2-tailed)	.090	.044
	N	75	75
Q4.21	Pearson Correlation	.020	-.310**
	Sig. (2-tailed)	.867	.007
	N	75	75
Q5.9	Pearson Correlation	.265*	-.096
	Sig. (2-tailed)	.022	.413
	N	75	75
Q6.1	Pearson Correlation	.411**	.000
	Sig. (2-tailed)	.000	.999
	N	75	75
Q6.6	Pearson Correlation	.288*	-.108
	Sig. (2-tailed)	.012	.358
	N	75	75
Q6.16	Pearson Correlation	.300**	-.099
	Sig. (2-tailed)	.009	.400
	N	75	75
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).

 

From the above correlation it has been found that there is a significant correlation between “lacked the motivation to attend the lecture” and “duration of travel to Victoria University”. Hence it has been interpreted that student who travels long distance lose their interest and motivation towards lectures.  There is significance connection between “I was feeling tired” and “duration of travel to Victoria University”. It can be interpreted that long distance travel for college makes the students tired. It has been also found that students who travel long distance to attend collage are more supportive to the fact that “The information (e.g., topics) delivered was boring” and “couldn’t understand the lecturer”. From the second phase correlation it has been found that “hours of paid employment” has significant correlation with “I can’t pass the unit without attending lectures”. It can be interpreted that students who are greater hours of paid employee have more fear of failure because of not attending lectures. From the correlation of   “hours of paid employment” and “prefer if lectures were recorded and available on VU Collaborate” it can be integrated that students who are greater hours of paid employee find it difficult to remain focused during lectures. At the same time, from the correlation it has been also found that students with longer hours paid employment feels that  “lectures is an implicit part of education”.

Test

Independent Samples Test
Q10		Levene's Test for Equality of Variances		t-test for Equality of Means
		F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
									Lower	Upper
Q7	Equal variances assumed	8.728	.004	1.328	73	.188	13.207	9.945	-6.612	33.027
	Equal variances not assumed			.584	2.027	.618	13.207	22.623	-82.882	109.297
Q8	Equal variances assumed	.032	.859	-.288	73	.774	-2.528	8.790	-20.046	14.991
	Equal variances not assumed			-.366	2.291	.745	-2.528	6.897	-28.870	23.814

The significant value between studying type and the percentage of tutorial attended is 0.004 which is very less than 0.05 (probability test value). Hence it can be clearly interpreted that the there is a significant difference between part time and full time students when it comes to attending tutorials. The positive t value also indicates that students with full time engagement are more attentive that part time students.

Independent Samples Test
Q313		Levene's Test for Equality of Variances		t-test for Equality of Means
		F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
									Lower	Upper
Q7	Equal variances assumed	10.449	.002	-2.335	73	.022	-8.983	3.846	-16.649	-1.317
	Equal variances not assumed			-2.143	43.310	.038	-8.983	4.193	-17.436	-.529
Q8	Equal variances assumed	5.336	.024	-1.542	73	.127	-5.288	3.429	-12.122	1.547
	Equal variances not assumed			-1.422	44.524	.162	-5.288	3.719	-12.781	2.205

 

The significant value between age of the students and the percentage of tutorial attended is 0.002 which is very less than 0.05 (probability test value). Hence it can be clearly interpreted that the there is a significant defence between older and younger students when it comes to attending tutorials. The negative t value also indicates that older student students (more than 22 years old) are more attentive than the younger student (Less than 22 years old).

Interpretation of findings

From the above descriptive study, correlation test and t tests it has been found that travelling time to lecture class is a problematic issue for a large number of students. Apart from that it can be interpreted from the above findings that large number students prefer online test and online lectures. Long distance travel for attending lectures makes the students tired. It has been also found that students who travel long distance to attend collage are unable to understand the lecture properly and they find the lectures very boring. It has been also found that the students who are greater hours of paid employee find it difficult to remain focused during lectures. After the data was interpreted, it can be stated that there is a strong difference between the students who are doing part time jobs and who are attending the full time education process. According to this study it can also be stated that students who are younger than 22 years of age are less attentive than students who are above the age of 22

Limitation:

These study has 2 limitations and both of these limitations are associated with the data design method. The primary limitation of this experimental study was the normal survey procedure being followed for the data collection procedure. This process involved the use of a series of selected questions for the data collection purpose. Therefore, the, the sections considering other aspects remains uncovered since the students are never asked for those answers. No secondary data was also collected from the available databases for this study procedure. As a result primary data was the only choice for the data interpretation process performed here. Hence this study could have some instrumental bias with some confounding factors.

Conclusion and Recommendation:

Conclusion

On a concluding note, it can be stated that correlation and t test studies provided a significant justification about the fact that students attending part time jobs are less attentive than the students engaged in full time study processes. It has been found that four major attributes contribute to the attendance in lecture namely the age of the students, part time or full time engagement as students, travelling time for lectures and involvement in paid employment. Academic career can therefore be stated to be better for the students attending the full time lecture than the students busy with part time jobs. Apart from that, students who travel long for attending class feel tired during lectures and they prefer alternative education system more. At the same time, students who are longer hours of employment commitment have more preference on availability of lecture content on their online university’s portal.

Future research

Future researches will focus on collecting more sources of data in the form of both primary and secondary data. Secondary data will add more significance to the statistical study performed in the experiment. Addition of the statistical significance will reduce the chances of bias in the experiment. Another factor important for the future researches is to provide a scope for more questions to be included in the survey process. This factor will ensure that the participants get enough scope to express them during the survey.

References:

Barnham, C., 2015. Quantitative and qualitative research: Perceptual foundations. International Journal of Market Research, 57(6), pp.837-854.

Choy, L.T., 2014. The strengths and weaknesses of research methodology: Comparison and complimentary between qualitative and quantitative approaches. IOSR Journal of Humanities and Social Science, 19(4), pp.99-104.

Cole, J.S. and Kim, Y.M., 2013. Student veterans/service members’ engagement in college and university life and education. Washington: American Council on Education.

Flick, U., 2015. Introducing research methodology: A beginner's guide to doing a research project. Sage.

Flynn, D., 2014. Baccalaureate attainment of college students at 4-year institutions as a function of student engagement behaviors: Social and academic student engagement behaviors matter. Research in Higher Education, 55(5), pp.467-493.

Hu, S. and Wolniak, G.C., 2013. College student engagement and early career earnings: Differences by gender, race/ethnicity, and academic preparation. The Review of Higher Education, 36(2), pp.211-233.

Kahu, E.R., 2013. Framing student engagement in higher education. Studies in higher education, 38(5), pp.758-773.

Kumar, R., 2019. Research methodology: A step-by-step guide for beginners. Sage Publications Limited.

Lester, J., Brown Leonard, J. and Mathias, D., 2013. Transfer student engagement: Blurring of social and academic engagement. Community College Review, 41(3), pp.202-222.

Novikov, A.M. and Novikov, D.A., 2013. Research methodology: From philosophy of science to research design. CRC Press.

Quaye, S.J. and Harper, S.R. eds., 2014. Student engagement in higher education: Theoretical perspectives and practical approaches for diverse populations. Routledge.

Yilmaz, K., 2013. Comparison of quantitative and qualitative research traditions: Epistemological, theoretical, and methodological differences. European Journal of Education, 48(2), pp.311-325.