The Relationship Between The Big Five Personality Psychology Essay

Published Date: 23 Mar 2015

Personality traits as the characteristics of a person with consistent patterns of behaviour are undoubtedly playing a significant role in determining a student's success in pursuing his or her tertiary education. Therefore, this research aims to investigate and examine the effects of personality traits on the students' performance in UTAR Kampar Campus, Perak, Malaysia. As proposed by Costa and McCrae (1992), the Big-Five Personality Traits model has been used in this study whereby the model categorizes human's personality into five factors, namely the Conscientiousness, Agreeableness, Openness to Experience, Extraversion, and lastly Neuroticism. Target population for this research is approximately 408 students and a questionnaire encompassing 3 sections was presented to the respondents. The study subjects were 200 accounting students in UTAR Kampar Campus. For data analysis, the Pearson Correlation Analysis and Multiple Regression Analysis are adopted to determine the relationships between the variables. Overall, completion of this study will provide a guideline as to which traits would be favourable for students to harness so as to achieve better results in tertiary education. Lastly, this study also confirmed the validity and reliability of the Big-Five Personality Traits model as proposed by Costa and McCrae (1992).

CHAPTER 1: INTRODUCTION

1.0 Introduction

This chapter gives a general idea of this research, where it comprises of seven sections. The research background briefly explains what are personality and students' performance. The problem statement is addressed in the second section. Research objectives and research questions are also highlighted in this chapter and lastly, significance of the study, outline of chapter, and conclusion is followed by.

1.1 Research Background

When someone talks about students' performance, what is it actually refers to? Past studies broadly defined students' performance as a pedagogical terminology used to determine learners' achievement in formal education while to measure a students' performance is only by examinations (Tope, 2011). Apparently, it gives rise to the question of whether what is the definite measurement of it? Is examination the only way to measure a students' performance? Does the employers nowadays only concern about students' academic achievement? While most employers actually view that way, it would be unfair to only compare a person's past achievement in a sole aspect. Nonetheless, some employers nowadays are different whereby they perceive that a successful student should include academic success, secured position in career field, and ability to apply knowledge and skills in real life (Dean, 1998). Therefore, quantitative measurement such as Grade Point Average (GPA) may not be the only factor to determine a student's performance whereby other qualities such as communication skills, leadership, and team performance should also be considered (Sansgiry, Bhosle & Sail, 2006).

Due to aggressive competitions in business world, employers demand higher requirements in fresh graduates (Maurin, Thesmar & Thoenig, 2002; Koda, Yuki & Hong, 2011). The criterion includes time management, teamwork and leadership ability (Weligamage & Siengthai, 2003). Students possessing such skills are definitely in a better position of securing job opportunities. Therefore, higher attention should be placed on graduate students' personality as it affects their employability which in turn influences unemployment rates in Malaysia (Ismail, 2011).

On the contrary, when people talks about human personality, what is the first thought that comes into your mind? Most people think that it is the different attitudes or habits that each individual possess. This is known as the individual differences or national characters that differentiate people (Mooradian & Swan, 2006). Previous studies clarified that there indeed exist differences in individuals' attitude from different countries (Lynn & Martin, 1994). A simple example of this is where some people would tend to be more sociable and talkative, and some would be the exact opposite (Wilt & Revelle, 2008). Such difference would somehow cause different outcomes in one's education with their future career at stake. While it is difficult to completely define the term personality, Pervin's study defined it as the characteristics of a person with consistent patterns of behavior (Saleem, Beaudry & Croteau, 2011). Apart from that, recent studies presented that personalities can be branched out to other perspectives such as the biological model of the Big-Five personality traits (DeYoung, Hirsh, Shane, Papademetris, Rajeevan & Gray, 2010), and the Dark Triad personality traits (Jonason, Webster, Schmitt, Li & Crysel, 2012).

Personality is becoming an important factor in various situations (Caspi, Roberts & Shiner, 2005) where at working place, a right personality allow workers to interact well with colleagues and clients thus expanding their network span; at university, students are more sociable thus studies in a comfortable environment (Bester, 2007). Furthermore, many human-made issues and criminal cases can be related to the decline in one's personality, in other words, lacks of Personality Development (PoliÅ¡enský, 2006). Then, Jinnie (2011) stated that a person with good personality will enhance his or her communication skills and anger management. Meanwhile, in management's perspective, a positive personality will aid in clearing workers' negative conditioning, anxiety, and depressions in solving problems (Morton, 2011; Brunello & Schlotter, 2011). Therefore, personality is an important determinant of career choice (Holland, 1976).

In relation to students' performance at school or university, prior studies have identified the five personalities that affect students' behavior where this in turn affects their performances. In this case, the Big-Five personality model is an effective way to predict an individual's behavior as it has been widely used and proved to be convincing (Noftle & Shaver, 2006; Robbins, 2007).

1.2 Problem Statement

Students' performance has been a questionable factor on the employability of fresh graduates and how well are they satisfying the qualities that the employers looking for (Marshall, 2010). According to Department of Statistic in Malaysia, unemployment rate among fresh graduates has been rising from 2.6% in 1996 to 3.1% in 2011. The Prime Minister of Malaysia also indicated that the highly skilled workforce in 2010 is only 23% where this percentage is still far from the minimum requirement, compared with some of the developing countries (Ramakrishnan & Yasin, 2011).

In the past decade, there are various studies being carried out by the researchers to examine the effect of students' performances using the Big-Five personality traits. Gray and Watson (2002) investigated the connections between personalities and sleep that have the combined effects on students' academic outcomes in United States. According to Komarraju, Karau and Schmeck (2009), personality traits are important to improve students' self-motivation in attaining higher academic honors. Besides, a study in Iran investigated that students' distinct characteristic and personality is one of the variables that affect their academic achievement (Hakimi, Hejazi & Lavasani, 2011). On the other hand, Taher, Chen and Yao (2011) studied the relationship between MBA students' performance and their personality traits whereas Kalshoven, Hartog and Hoogh (2010) carried out a study on individual leadership skill and used only three out of five of the big-five personality traits (conscientiousness, agreeableness, and emotional stability).

However, there are still some deficiencies in the past empirical researches. The study in Gray and Watson (2002) only focuses on the university's students in their country, therefore no firm explanations that the results of the past research are valid in Malaysia. Besides, Komarraju et al. (2009) pointed out that the personality of the students is being influenced by the environment as the research was conducted in various universities. In addition, Hakimi et al. (2011)'s study is very limited due to the area of research were not conducted in Asia. This indicates that little study has been done on the students in Malaysia. Moreover, the study of Taher et al. (2011) also ignored the other aspects of students' performance other than their scores and grades. Lastly, Kalshoven et al. (2010) did not include all of the Big-Five factors of personality traits in their research and this implies that the research's results and measurements of personality traits may not be accurate. Therefore, this research is carried out to fill the gaps of past researches by investigating the personality traits as the one of the important factors that affects the several aspects of university students' performance in Malaysia.

1.3 Research Objectives and Research Questions

Table 1.1: Research Objectives and Research Questions

Research Objectives

Research Questions

General Objective

In general, this research aims to investigate and examine the relationship of personality traits on the students' performance in UTAR.

General Question

Do personality traits relate to the students' performance in UTAR?

Specific Objectives

To examine the relationship between conscientiousness and students' performance in UTAR.

To examine the relationship between agreeableness and students' performance in UTAR.

To examine the relationship between openness to experience and students' performance in UTAR.

To examine the relationship between extraversion and students' performance in UTAR.

To examine the relationship between neuroticism and students' performance in UTAR.

Specific Questions

Is there any relationship between conscientiousness and students' performance in UTAR?

Is there any relationship between agreeableness and students' performance in UTAR?

Is there any relationship between openness to experience and students' performance in UTAR?

Is there any relationship between extraversion and students' performance in UTAR?

Is there any relationship between neuroticism and students' performance in UTAR?

Source: Developed for the research

1.4 Significance of the Study

1.4.1 Theoretical Perspective

From an educational perspective, this research can serve as a basic guideline for future researchers as it is a modified research model in terms of measuring students' performance qualitatively. This research also confirmed the previous theory of Five Factor Model of personality traits by Costa and McCrae (1992), thus adding credibility towards the theory.

1.4.2 Practical Perspective

From country-wide perspective, students, as the citizens of Malaysia, developing their personality and improving their performance would boost overall capability and reduces unemployment rates in Malaysia as more students will be employed.

From the industry perspective, findings from this research may contribute to the employer by providing the basic information on which type of employee's personality is preferable. Although it may not be of huge importance, employers should not overlook this aspect in recruitment as employees with great personality will definitely enhance the organization's intellectual use of available human resources.

1.5 Chapter Layout

Chapter One covers the introduction of this research which includes research background, problem statement, research objectives and questions, and lastly the significance of this study. Chapter Two explores the core theories by reviewing literatures of past empirical studies and developing theoretical framework and hypotheses. Chapter Three explains the research methodology adopted whereby it includes the research design, population and sampling procedures, data collection and analysis techniques, and variables and measurements. Chapter Four presents the analyzed data and results from the target respondents detailed in descriptive and inferential analysis, and scale measurements. Chapter Five briefly concludes this research as it summarizes the findings from this research and also providing implications, limitations, and recommendations for this research.

1.6 Conclusion

This chapter has acknowledged the problem statement, research questions and objectives, significance of the study and the outline of the research project. In Chapter Two, it would then provide the relevant literature review.

CHAPTER 2: LITERATURE REVIEW

2.0 Introduction

The previous chapter has highlighted the introduction of this research project. This chapter will touch on the literature review of the research. Literature review provides a comprehensive review on the secondary sources of data done by previous authors or researchers such as books, journal articles, thesis papers, research projects, and reports. This chapter embodies five sections. Firstly, Section 2.1 is the review of literature whilst Section 2.2 reviews the prior empirical studies. Then, Section 2.3 shows the proposed theoretical conceptual framework. After that, Section 2.4 is the development of hypotheses. Lastly, Section 2.5 summarizes this chapter to provide a general understanding to the readers.

2.1 Review of the Literature

Based on the prior researches, it has been widely accepted that the Big Five Personality Traits by McCrae and Costa (1992), and Digman (1990) can determine one's individual characteristics (Moghaddam, Peyvandi & Wang, 2009). In this part, this research will define and explain thoroughly each of these traits to provide for the development of hypotheses for this research and lastly, relating each of them with the student's performance.

2.1.1 Conscientiousness

Conscientiousness is defined by John and Srivastava (1999) as individual differences in the propensity to be goal directed (Savelyev, 2012). According to Barrick and Mount (1991), traits of conscientiousness includes being dependable, responsible, and organized (Trinh, 2002). More specifically, individual who is measured as high in conscientiousness is determined and strong-willed (Bruck & Allen, 2002).

2.1.2 Agreeableness

According to Nettle and Liddle (2008), Digman's and Graziano's study suggested that agreeableness is associated to a person's warmth, friendliness and conformance with others. It is also supported by Jans's and Rabinowitz's study where traits of agreeableness include unselfishness, friendliness and modesty (Bozionelos, 2003). Thus, people who is high in agreeableness is likely to get along well with others (Judge, Livingston, & Hurst, 2011).

2.1.3 Openness to Experience

Yamagata's study defined openness to experience as people who are intellectually curious (McCrae & Sutin 2009) and individuals who are measured high in openness are curious for inner and outer world (Bruck & Allen, 2002). Conversely, low in openness to experience usually have a narrow and common interest and likes to enjoy routine activities (Flynn, 2005).

2.1.4 Extraversion

Traits of extraversion can be represented by sociability, assertiveness, and social dominance (Bozionelos, 2003). Judge's and Wastson's study also supported that viewpoint as it refers extraversion to sociability (Chan, 2007). In other words, it is a tendency to search for stimulation and to enjoy mingling with other people.

2.1.5 Neuroticism

Defined as a tendency to experience unpleasant emotions easily, neuroticism continuum ranges from calm and composed to nervous and anxious (Greenberg & Baron, 2008). Eysenck (1967) defines that neuroticism accounts for a low tolerance for stress (Norris, Larsen & Cacioppo, 2007). In other words, neurotic people respond poorly to environmental stress, and often interpret ordinary and minor situations as threatening and difficult (Hettema, Neale, Myers, Prescott & Kendler, 2006).

2.1.6 The Relationship between the Big-Five Personality

Traits and Students' Performance

Chamorro-Premuzic and Furnham (2003) indicated that conscientiousness has a strong direct positive relationship with students' academic performance and this is also supported by many existing literatures (Conrad, 2006; Zyphur, Bradley, Landia & Thoresen, 2008). However, some showed otherwise as a mediating factor exist (Conrad & Patry, 2012). The trait is also able to influence a person's behavior which in turn affects their academic behaviors such as self-efficacy and learning styles (Bong, 2008). Additionally, Laidra, Pullmann and Allik (2006) studied the relationship of personality traits and general intelligence with the students' academic performances. The study showed that Conscientiousness has a positive association with academic performance.

H1: There is a positive relationship between conscientiousness and student's performance.

Farsides and Woodfield (2003) indicated that agreeableness is negatively correlated with absence for classes. Furthermore, high attendance in classes and seminars has a positive contribution on students' performance (Arulampalam, Naylor & Smith, 2008). Nevertheless, Nyugen, Allen & Fraccastoro (2005) proposed that there was indirect relationship between agreeableness and students' performance as the learning style is the mediating factor (Chamillard & Sward, 2005). Students with this trait are able to interact and learn well with others especially in groups; thus, agreeableness also enhanced team cohesion which in turn affected their performance positively (O'neill & Kline, 2008). Thus, agreeableness is positively correlated with students' performance (Taher & Jin, 2011).

H2: There is a positive relationship between agreeableness and students' performance.

Duff, Boyle, Dunleavy and Ferguson (2003) revealed that openness has positive effects on learning approach (deep approach) and such approach is positively related to students' performance. Learning approach plays an important role in linking the Big-Five traits with students' performance (Cano &Berben, 2009; Chamorro-Premuzic et al., 2006). This is further supported by Chamorro-Premuzic, Furnham and Lewis (2006). Furthermore, Furnham, Monsen and Ahmetoglu (2009) proposed that openness had significant relationship with students' performance provided the mediator is deep approach.

H3: There is a positive relationship between openness to experience and students' performance.

According to Furnham, Zhang and Chamorro-Premuzic (2006), extraversion can be recognized as a person's interpersonal and intrapersonal skills. Furthermore, extraversion is made up of two central components; affiliation which was having warm personal relationship and agency which was being socially dominant (Bono, 2004). In addition, the most common definitions of extraversion are ascendance and sociability (Mooradian & Swan, 2006). Therefore, extraverted individual are more likely to have a desire to work with others and more confident in their ability to work effectively within a team structure. It is essential when an individual is joining a group study and will enhance student performance (Morgeson, Reider, & Campion, 2005). In spite of this, students with high extravert personality prefer to be sociable and active in extra-curricular activities rather than focusing on their studies. Thus, students' performance would be adversely affected. Chamorro-Premuzic and Furnham (2003) researched on the relationship of personality traits and students' academic performance at University College London and as a result, extraversion was negatively related with students' academic performance.

H4: There is a negative relationship between extraversion and students' performance.

Neuroticism may affect a person's ability resulting in poorer academic performance (Chamorro-Premuzic & Furnham, 2006; Lievens, Ones & Dilchert, 2009). As cited under Ahmad and Rana (2012), many neurotic students suffered higher percentage of failures in examinations because this trait shifts the students' concentration away from study due to negative emotions. On the other hand, students who obtained high grades were less associated with anxious emotions (Al-Qaisy & Khuffash, 2012; Al-Qaisy, 2011), this suggested students with high grades are good in managing their stress. Nevertheless, Poropat (2011) conducted a research about the use of personality in predicting academic performance and proved that there is significant negative correlation between neuroticism and academic performance.

H5: There is a negative relationship between neuroticism and students' performance.

2.2 Review of Relevant Theoretical Models

2.2.1 The Big-Five Personality

The earliest founders of The Big-Five Theory are Tupes and Christal (1961) as they established the five factors of personality traits that we know today (Busato, Prins, Elshout & Hamaker, 1999). Unfortunately, their study was published in an obscure Air-Force publication that was not read by many people, therefore the theory was not widely-known at that time (Locklair, 2011). According to Goldberg (1993), other early explorers of The Big-Five include Borgatta (1964) and Smith (1967) who continued the founders' work. The first version of this theory is called The Big-Five, introduced by Warren Norman in 1963 (Boeree, 2006). The Big-Five Personality Traits is a comprehensive research which analyzes human personality together with their traits (Digman, 1990).

Particularly, a five-dimensional personality traits model is proposed by McCrae and Costa (1992) after studying on the Five-Factor Model (FFM) and its applications. To further understanding human's personality, this study categorized human's personality into five main factors, namely the Conscientiousness, Agreeableness, Openness to Experience, Extraversion, and Neuroticism. FFM model dominated the personality field over the past two decades due to its famous recognition as a comprehensive description of personality traits and provided a major degree of convergence in the trait-factor analytic psychology (Nikolaou & Robertson, 2001).

This study has come across many researches about the Big-Five personality model. Thus, it shows that this model can be used in many areas as it gives us the fasters and accurate way to identify a person's attitudes and behaviors (Pickens, 2005). However, the Big-Five personality model has also been discovered to have effects on social or friendship networking behavior (Wehrli, 2008); in addition that it is also a good predictor for employee's job performance (Hurtz & Donovan, 2000; Fietze, Holst & Tobsch, 2010). Nevertheless, this model can also be applied to almost everyone in this world every individual possess all five personalities of the model to a greater or lesser extent (Soto, Gosling, John, & Potter, 2011). Apart from that, the Big-Five model is also applied to the research done by Distel, Trull, Willemsen, Vink, Derom, Iynskey, Martin and Boomsma (2009); studying the five personality traits and nature of personality disorders. Furthermore, in the aspect of employment, studies on the five personalities are also conducted relating to executive mangers' job recruitment and fresh graduates (Dykeman & Dykeman, 1996).

The Big-Five includes Conscientiousness, Agreeableness, Openness to Experience, Extraversion, and Neuroticism. As there are many personality-related-researches also utilized this Big-Five model, this study exploit this advantage and uses this model to give us the faster and accurate way to identify a person's attitudes and behaviors (Kumar, Bakhshi & Rani, 2009). Hence, this theory will be applied in this research as it is a widely-used theory in evaluating people's personality (Brown & Taylor, 2011; Wood, Linley, Maltby, Baliousis & Joseph, 2008).

2.3 Proposed Theoretical/ Conceptual Framework

Figure 2.1: Relationship between the Big Five Personality Traits to Students' Performance

Big 5 Personality Traits

Independent Variables Dependent Variable

Conscientiousness

H1

H2

Agreeableness

H3

Students' Performance

Openness to Experience

H4

H5

Extraversion

Neuroticism

Sources: adapted from Taher et al., 2011; Anwar, Shahzed & Ijaz-ul-Rehman, 2011; Chen, Tsai & Chen, 2009

Based on the literature review, a conceptual framework has been developed and shown in Figure 2.1. This research is conducted to test the relationship between the independent variables and dependent variable. Whereby, independent variables comprises of conscientiousness, agreeableness, openness to experience, extraversion and neuroticism, whilst dependent variable is students' performance. Previous researches (Nguyen, Allen & Fraccastoro, 2005; Chowdhury, 2006) showed that the five factors have produced distinct results. From the proposed conceptual framework, hypotheses will be developed and it is to be proven after completion of a thorough empirical study investigating whether personality traits are related to students' performance.

2.4 Hypotheses Development

Based on the review of the prior empirical studies discussed at 2.1.6, a summary of the hypotheses development is presented as below.

Table 2.1: Development of Hypotheses

Personality Traits

Hypotheses

Conscientiousness

H1: There is a positive relationship between conscientiousness and students' performance.

Agreeableness

H2: There is a positive relationship between agreeableness and students' performance.

Openness to experience

H3: There is a positive relationship between openness to experience and students' performance.

Extraversion

H4: There is a negative relationship between extraversion and students' performance.

Neuroticism

H5: There is a negative relationship between neuroticism and students' performance.

Source: Developed for the research

2.5 Conclusion

A review of literature has been carried out in this chapter and it has been discovered that there are different opinions from researchers regarding the relationship between the Big-Five Personality traits with students' performance. Besides, an understanding on the Big-Five Personality traits has been done for a clear explanation and how can it be associated with students' performance. Then, a conceptual framework has been proposed to show the relationship between each trait with students' performance. Finally, hypotheses have been developed based on the literatures reviewed and the discussion for relevant methods to be used in this study will be conducted in the following chapter.

CHAPTER 3: METHODOLOGY

3.0 Introduction

This chapter will address an overview of the research methodology. At the beginning of this chapter, the research design in term of quantity methodology and deductive research approach will be described. Next, the population, sample and sampling procedures would be explained. After that, data collection methods that have been applied which are primary and secondary data collection are discussed. In addition, variable and measurement were also being presented in this chapter. Lastly, data processing and its analysis would be presented to summarize the findings.

3.1 Research Design

The main purpose of this research is to examine the relationship between Big-Five Personality Traits and students' performance in UTAR. Primary data collection and survey method were used in this research in which questionnaires will be self-administered because it is affordable and time saving.

Based on the purpose of this study, a deductive research approach was adopted. Besides that, a quantitative methodology was employed because it clearly and precisely specifies both variables of the study (Alaxei, 2002). Moreover, this type of research is a descriptive study because it describes and documents on a phenomenon (Johnson, 2001). The research is based on a cross-sectional study due to the time constraint by academic purposes. Hence, investigation was limited to a subset of population only.

3.2 Data Collection Methods

3.2.1 Primary Data Collection

For this research, a self-administrated questionnaire is preferred because it provides convenience to both the researchers and respondents. As an effort to establish the validity and reliability of the survey questionnaire, a total of 30 questionnaires were distributed for a pilot test to check the questionnaire's understandability (Black, 2008). After that, questionnaires were distributed to 270 respondents whereby they are expected to complete it under researchers' supervisory within twenty minutes and returned to ensure no missing questionnaires.

3.2.2 Secondary Data Collection

Secondary data collection involves the literatures review on related past studies (Daas & Toth, 2012). This method is vital because relying on primary data collection is not adequate to complete this research (Sandall, Schwartz & Lacroix, 2004). Besides, review on literatures also allows the researchers a better understanding on the topic and able to prove the hypotheses developed earlier (Bailey, 2006).

3.3 Sampling Design

3.3.1 Target Population

Target population is a complete collection group of objects or people that are specifically identified for an investigation (Wang, 2007). Final year accounting undergraduates in UTAR, Kampar Campus were the target respondents for this research as there is no personality-related study addressing the wide aspects of students' performance carried out in this university, hence this university is targeted to examine the relationships between the five factor model of personality traits and students' performance. Previous personality-related researches only studied the relationships between personality and perceived benefits on e-ticketing behaviour, and also personality and social networking behavior. Undergraduate students tend to have a higher proficiency in English language, thus they would understand this research's survey better. Moreover, another reason is that labor market is new to fresh accounting graduate as they will be joining the labor force soon and thus it is emerging trend for the employers to seek high performances, vital interpersonal skills and personality development among the undergraduates (Lim, 2011; Sirat, Chan, Shuib, Rahman, Kamil, & Singh, 2012). Besides, Lim (2007) found out that the accounting students in Universiti Utara Malaysia (UUM) would have higher unemployment probability. This highlights that there might be a chance of UTAR accounting students facing the same problem since the competition for best and talented accounting graduates is getting more intense especially in large accounting firm (Brundy & Norris, 2011). Therefore, it is interested to investigate the connection between personality traits and students' performance in which will affects their employability in future.

3.3.2 Sampling Frame and Sampling Location

According to Thompson (1999), sampling is necessary as it is too expensive and impractical to study on every single element in the population. However, sampling frame for this research could not be obtained as the students' full details cannot be disclosed due to the university's privacy policy. Final year accounting undergraduates in UTAR, Kampar Campus were the target respondents for this research because it is convenient and easier to be contacted for researchers (Loh, 2011). The population size was estimated as 408 students. Moreover, UTAR already a well-established university and the student population were increasing significantly (Loh & Hew, 2012). The main campus for UTAR had switched from Setapak, Kuala Lumpur to Kampar (Koh, 2007). According Yeoh (2012), the student enrolment grew from 411 students in year of 2002 to 21,000 students in 2012, of which around 15,000 students are allocated at Kampar. Hence, students in UTAR Kampar Campus represented the majority of UTAR students in Malaysia.

3.3.3 Sampling Elements

The respondents of this study were all final year accounting undergraduates in UTAR, Kampar Campus where they were mostly aged from 21 to 24 years old.

3.3.4 Sampling Technique

Non-probability sampling was used for this research as the sampling frame cannot be determined. Meanwhile, by using non-probability sampling, a convenience sampling technique is applied in this research because it is more cost effective and time saving and this sampling technique is easier to reach the target respondent in comparison to other sampling techniques (Saunjoo & Claydell, 2004).

3.3.5 Sampling Size

The population size was estimated to be 408 students, thus a minimum of 196 samples (based on 95 percent confidence level) is required to represent the characteristics of population (Saunders, Lewis & Thornhill, 2009). Total 270 sets of questionnaires were distributed personally to the target respondents of final year accounting undergraduates in UTAR, Kampar Campus. Among the feedback, there were 49 sets contained incomplete data. Therefore, only 221 questionnaires were qualified for data analysis purposes.

3.4 Research Instrument

3.4.1 Questionnaire Survey

A self-administrated questionnaire was chosen as a data collection instrument in this study where it can be distributed out by hand, mail and internet (Shilubane, 2009). It was proven that questionnaire are easier to manage and control compared to face-to-face interviews as the respondents would be more responsive to the questions (Eiselen & Uys, 2005). Therefore, self-administrated questionnaire was chosen as our primary data collection method because it provides convenience to both the researchers and the respondents since most of the respondents are well-acquainted with the concept of questionnaire.

3.4.2 Pilot Test

As an effort to establish the validity and reliability of the survey questionnaire, a pilot test is needed in this study whereby it comprises of 30 sets of questionnaires (Black, 2008). Pilot testing helps to check the survey questionnaire on its understandability, wording and clarity of instructions given. The final questionnaire was then being distributed to 270 respondents in UTAR Kampar's final year accounting students.

3.5 Constructs Measurement

3.5.1 Nominal Scale

Nominal scale is used to measure the variables that are unable to rank in different level. Therefore, nominal scale was applied on Section A of demographic profile in this study's questionnaire which included gender, marital status, race, origin and course of bachelor degree undertaking by the respondents.

3.5.2 Ordinal Scale

In this research, ordinal scale was used to measure the category of age in the range of 20 years old and below, 21-24 years old and 25 years old and above.

3.5.3 Interval Scale

Interval scale of measurement was applied in Section B and Section C to measure the five dimensions of independent variables of this research. This scale of measurement served to collect information from the target respondents in order to measure the level of agreement or disagreement with five types of scale rates ranging from one (1) strongly disagree, two (2) disagree, three (3) neutral, four (4) agree, and to five (5) strongly agree on dependent variable (students' performance) and independent variables (Agreeableness, Openness to Experience, Conscientiousness, Extraversion and Neuroticism).

3.5.4 Independent Variables

There were five dimensions of independent variable to be measured, namely Agreeableness, Openness to Experience, Conscientiousness, Extraversion and Neuroticism. It was categorized under Section B of the questionnaire. Each construct of this variable consisted of five questions. In this research, five-point ratings scale was used in questions of independent variables. For example, CS4: I see myself as someone who does things according to a plan (Saiful, Fuad & Rahman, 2010).

3.5.5 Dependent Variable

The dependent variable for this research is the students' performance. It was measured by the constructs of Cumulative Grade point Average (CGPA), conflict management, team performance, creativity, communication skills, IT skills, participation in co-curriculum activities and leadership. CGPA can be calculated by dividing the total grade points earned with the total credit hours attempted. Team performance is defined as team member's effort in accomplishing their goals and mission (Bell, 2007). Conflict management includes limiting and avoiding future violence between the conflict parties (Hamad, 2005). Creativity is a process to become sensitive to problems, identify the obstacles, figure out for solutions, and finally communicate the outcomes (Kim, 2006). Communication skill relates to the effectiveness of using body language, eye contact, and tone of voice to present ideas clearly (Danilova & Pudlowski, 2007). Computer literacy refers to an individuals' ability to imply skills and expertise with up-to-date's computer applications (Williams, 2002). Co-curricular activities are out-of-class learning experiences (Ford, Lumsden & Lulgjuraj, 2009). Leadership relates to individuals' performance who holds top position in a society or club (Barker, 2001). For this variable, eight questions were developed and interval rating was used.

3.6 Data Processing

The data was collected from the survey conducted through questionnaire and this research implemented the Statistical Analysis System (SAS) version 9.3 to perform the process of data analysis.

3.6.1 Data Checking

Data checking was completed at the first stage to ensure the completeness and usability of the questionnaire being returned. It is an important step to further the data analysis process because it ensures that no missing or incomplete data on all questionnaires before processing with the results.

3.6.2 Data Editing

Questionnaires with missing or incomplete data were rejected to ensure an accurate result is obtained. Besides, outliers are removed before analyzing the data.

3.6.3 Data Coding

Data coding is a process of recording all the data by using a specific numerical code. Therefore, all the answers from the survey questionnaire can be differentiated easily and categorized into a limited number of categories. This can reduce the probability of typing error during the process of entering the data into the program and also time saving. For Section A, the demographic profile of respondents, the data was coded with a specific number. For an example, question 3 marital status, single was coded as '1' and married was coded as '2'. On the contrary, for Section C and D, all answers were coded as '1' for Strongly Disagree, '2' for Disagree, '3' for Neutral, '4; for Agree and '5' for Strongly Agree.

3.6.4 Data Entering

Before transferring all the data into data software for further analysis, the data were coded into specific categories. Before the data was entered, it was double checked to ensure that there are no discrepancies with the data collected from the survey questionnaire.

3.6.5 Data Transcribing

Data transcribing is a process of transferring the data collected from the questionnaire in to Statistical Analysis System (SAS) version 9.3 to be analysed.

3.7 Data Analysis

All the data collected were entered into the program of Statistical Analysis System (SAS) version 9.3. SAS software was used to analysis descriptive statistic, reliability test, normality test, inferential analysis, Pearson Correlation Coefficient and Multiple Regression Analysis for this research.

3.7.1 Descriptive Analysis

Descriptive analysis basically refers to a technique commonly used by researchers in their research so that the data collected were managed to be understood easily (Best & Kahn, 1998; Gomez, 2002; Zikmund, 2003). In short, such analysis allows the researchers to describe and detect the characteristic of respondents. Mean, standard deviation, maximum and minimum were also collected for the interval scale of independent and dependent variables.

3.7.2 Scale measurement

3.7.2.1 Reliability Test

The reliability test is used to determine the consistency of results produced by questionnaires (Miller, 2000). By using SAS, the reliability of data collected has been assessed, by using the Cronbach's Alpha reliability test that varies from 0 to 1. Tavakol and Dennick (2011) considered that a minimum alpha of 0.70 is acceptable but preferably closer to 0.90 to be more reliable. In short, alpha measure that is equal or greater than 0.70 is considered reliable (Spiliotopoulou, 2009).

Table 3.1 : Rule of Thumb for Evaluating Alpha Coefficients

Alpha Coefficient Range

Strength of Association

< 0.6

Poor

0.6 to < 0.7

Moderate

0.7 to < 0.8

Good

0.8 to < 0.9

Very Good

≥ 0.9

Excellent

Source: adapted from Hair, Money, Samouel & Page 2007

3.7.2.2 Normality Test

Normality test is essential to test whether the data is normally distributed. For this research, the questionnaires' data was entered and normality test will be conducted by using the SAS as well. This must be done before conducting the inferential analysis as the data cannot be applied if it is not normally distributed. Therefore, Kolmogorov-Smirnov and Shapiro-Wilk Tests were used to examine whether the result from the questionnaires shows a normal distribution (Ã-ztuna, Elhan & Tuccar, 2006). It can be assumed that the data is normally distributed only if the p-value from the tests is more than 0.05 (Pandurangi, 2012).

3.7.3 Inferential Analysis

3.7.3.1 Pearson Correlation Coefficient

Pearson correlation coefficient is a technique that identifies the strength of linearity relationship between the variables by measuring correlation coefficient, whereby its value(r) ranges between -1 and +1 (Saunders et. al., 2009). Correlation does not imply causation as it only means that the two variables tested are related (Higgins, 2005). This analysis is preferred for this research as it investigates the relationships between both variables having an interval scale measurement (Saunders et. al., 2009). Besides, it tests the directional relationship between conscientiousness, agreeableness, openness, extraversion, and neuroticism with the students' performance (Saunders et. al., 2009).

Table 3.2 : Rule of Thumb for Pearson Correlation Coefficient

Coefficient Range

Strength of Association

± 0.91 to ± 1.00

± 0.71 to ± 0.90

± 0.41 to ± 0.70

± 0.20 to ± 0.40

± 0.00 to ± 0.20

Very Strong

High

Moderate

Small but define relationship

Slight, almost neligible

Source: adapted from Hair, Money, Samouel & Page 2007

3.7.3.2 Multiple Linear Regression (MLR)

Multiple Linear Regression is a statistical technique that analyzes the linear relationship between the variables by estimating coefficients for the equation of a straight line (Choon, Lau & Tan, 2010). It is also able to deal with an arbitrarily large number of explanatory variables (Sykes, 1992). MLR was used in this study because there is only a single metric dependent variable and multiple metrics of independent variables. The hypotheses are significant if the p-value is less than 0.05 (Poole, 2001). A p-value is calculated to assess whether trial results are likely to have occurred simply through chances (Davies & Cromble, 2009).

General Equation

Equation for this research

Y = β0 + β1X1 + β2X2 + β3X3 + ….

+βnXn

Student Performance (SP) = β0 + β1*Conscientiousness (CS) + β2*Agreeableness (AG) + β3*Openness to Experience (OE) - β4*Extraversion (EX) - β5*Neuroticism (NE)

3.8 Conclusion

This chapter described the research methodology adopted for this research in detail. In the following chapter, this research will discuss the questionnaires data analysed using the SAS.

CHAPTER 4: DATA ANALYSIS

4.0 Introduction

This chapter begins with result of pilot test that had been conducted, descriptive analysis which comprises of demographic profile of the respondent and central tendencies measurement of constructs, followed by scale measurement and inferential analysis, and lastly the conclusion for this chapter. SAS version 9.3 software, Pearson Correlation Analysis and Multiple Regression Analysis were used to perform the analysis.

4.1 Pilot Test

Table 4.1 : Result of Reliability Analysis for Pilot Test

Cronbach's Alpha

Number of items

Conscientiousness

0.86

5

Agreeableness

0.88

5

Openness to Experience

0.82

5

Extraversion

0.81

5

Neuroticism

0.85

5

Students' Performance

0.72

8

Source: Developed for the research

Based on the Table 4.1, the questionnaire tested is consistent and reliable to be analysed as all the variables have a Cronbach's alpha value of more than 0.7. Achieving values of more than 0.7 were considered the pilot test had passed the reliability test and the items represent a measure of high internal consistency (Bahri Yusoff, Abdul Rahim & Yaacob, 2009).

4.2 Descriptive analysis

4.2.1 Demographic Profile of the Respondents

4.2.1.1 Gender

Table 4.2 : Frequency Table for Gender

1=male 2=female

Gender

Frequency

Percent

Cumulative Frequency

Cumulative Percent

1

2

95

126

42.99

57.01

95

221

42.99

100.00

Source: Developed for the research

Figure 4.1 : Percentage of Respondents Based on Gender

Source: Developed for the research

Based on Table 4.2, it can be seen that the gender of respondents consist of 95 males (42.99%) and 126 females (57.01%).

4.2.1.2 Age

Table 4.3 : Frequency Table for Age

1= 20 years old and below 2= 21-24 years old 3= 25 years old and above

Age

Frequency

Percent

Cumulative Frequency

Cumulative Percent

1

2

3

6

213

2

2.71

96.38

0.90

6

219

221

2.71

99.10

100.00

Source: Developed for the research

Figure 4.2 : Percentage of Respondents Based on Age

Source: Developed for the research

Table 4.3 indicates that the age of respondents in this study consists of 3 stages which are 20 years old and below, 21 to 24 years old, and 25 years old and above. The respondents who are 20 years old and below have 6 people and the percentage is 2.71%; the respondents who are 21-24 years old have 213 people and the percentage is 213%; and lastly the respondents who are 25 years old and above have 2 people and the percentage is 2%.

4.2.1.3 Race

Table 4.4 : Frequency Table for Race

1= Malay 2= Chinese 3= Indian

Race

Frequency

Percent

Cumulative Frequency

Cumulative Percent

1

2

3

1

209

11

0.45

94.57

4.98

1

210

221

0.45

95.02

100.00

Source: Developed for the research

Figure 4.3 : Percentage of Respondents Based on Race

Source: Developed for the research

Table 4.4 shows that the race of respondents in this study consists of Malay, Chinese, and Indian. Therefore, the respondents in this study consists of 1 Malay (0.45%); 209 Chinese (94.57); and 11 Indian (4.98%).

4.2.1.4 Origin

Table 4.5 : Frequency Table for Origin

1= West Malaysia 2= East Malaysia

Origin

Frequency

Percent

Cumulative Frequency

Cumulative Percent

1

2

195

26

88.24

11.76

195

221

88.24

100.00

Source: Developed for the research

Figure 4.4 : Percentage of Respondents Based on Origin

Source: Developed for the research

From Table 4.5, it can be seen that the origin of respondents in this study consists of 195 from West Malaysia (88.24%) and 26 from East Malaysia (11.76%).

4.2.1.5 Nature of bachelor degree

Table 4.6 : Frequency Table for Nature of Bachelor Degree

1=Accounting course 2= Non-accounting course

Origin

Frequency

Percent

Cumulative Frequency

Cumulative Percent

1

2

221

0

100

0

221

221

100.00

100.00

Source: Developed for the research

Figure 4.5 : Percentage of Respondents Based on Nature of Bachelor Degree

Source: Developed for the research

Based on the Table 4.6, there is a total of 221 responses from accounting students were analyzed in this study which comprised 100%. This had confirmed that the responses are qualified to be analyzed as our target respondents are accounting students.

4.2.2 Central Tendencies Measurement of Constructs

Table 4.7 : Central Tendencies Measurement of Constructs: Conscientiousness

Items

Mean

Standard Deviation

Minimum

Maximum

N

CS1

3.5385

0.6358

2.00

5.00

221

CS2

3.4887

0.6001

2.00

5.00

221

CS3

3.3937

0.7347

1.00

5.00

221

CS4

3.3982

0.7415

2.00

5.00

221

CS5

3.4887

0.7840

2.00

5.00

221

Source: Developed for the research

Table 4.7 presents the mean and standard deviation for each of the item for Conscientiousness.

The item with the highest mean value is CS1 with 3.5385. The item ranked second is CS2 and CS5 with 3.4887, followed by CS4 with a mean of 3.3982. Furthermore, CS3 has the lowest mean which is 3.3937.

The item with the highest standard deviation is CS5 which is 0.7840. CS4 has the second highest standard deviation with 0.7415, followed by CS3 which has a standard deviation of 0.7347. The next item is CS1 with standard deviation of 0.6358. The item with the lowest standard deviation is CS2, with 0.6001.

Table 4.8 : Central Tendencies Measurement of Constructs: Agreeableness

Items

Mean

Standard Deviation

Minimum

Maximum

N

AG1

3.5385

0.7228

2.00

5.00

221

AG2

3.5973

0.6781

1.00

5.00

221

AG3

3.4525

0.7826

1.00

5.00

221

AG4

3.5656

0.8850

2.00

5.00

221

AG5

3.5566

0.6959

2.00

5.00

221

Source: Developed for the research

Table 4.8 presents the mean and standard deviation for each of the item for Agreeableness.

AG2 has the highest mean value with 3.5973. The item ranked second is AG4 which is 3.5656, followed by AG5 with a mean value of 3.5566. The next item is AG1 with 3.5385. AG3 has the lowest mean which is 3.4525.

The item with the highest standard deviation is AG4 which is 0.8850. AG3 has the second highest standard deviation with value of 0.7826, followed by AG1 which is 0.7228. Next is AG5 with a standard deviation of 0.6959. The item with the lowest standard deviation value is AG2, which is 0.6781.

Table 4.9 : Central Tendencies Measurement of Constructs: Openness to Experience

Items

Mean

Standard Deviation

Minimum

Maximum

N

OE1

3.4118

0.6520

2.00

5.00

221

OE2

3.4977

0.6440

2.00

5.00

221

OE3

3.4299

0.7513

2.00

5.00

221

OE4

3.6154

0.7577

1.00

5.00

221

OE5

3.4615

0.6840

1.00

5.00

221

Source: Developed for the research

Table 4.9 shows the mean and standard deviation for each of the item for Openness to Experience.

OE4 has the highest value of mean which is 3.6154. The second highest is OE2 which is 3.4977, followed by OE5 with a mean value of 3.4615. Then, OE3 is followed by, with 3.4299. Lastly, OE1 has the lowest mean which is 3.4118.

For standard deviation, OE4 has the highest value of standard deviation which is 0.7577. The second highest value of standard deviation is OE3 with 0.7513, followed by OE5, 0.6840. Then, OE1 is followed by, with a value of 0.6520. Lastly, OE2 has the lowest standard deviation, which is 0.6440.

Table 4.10 : Central Tendencies Measurement of Constructs: Extraversion

Items

Mean

Standard Deviation

Minimum

Maximum

N

EX1

3.2353

0.7500

1.00

5.00

221

EX2

3.2579

0.8589

1.00

5.00

221

EX3

3.1991

0.8822

1.00

5.00

221

EX4

3.2489

0.8978

1.00

5.00

221

EX5

3.3846

0.8536

1.00

5.00

221

Source: Developed for the research

Table 4.10 presents the mean and standard deviation for each of the item for Extraversion.

EX5 has the highest mean value which is 3.3846. The second highest is EX2 which is 3.2579. Next item is EX4 which has the value of mean of 3.2489, followed by EX1 with 3.2353. EX3 has the lowest mean value, with 3.1991.

EX4 has the highest standard deviation value which is 0.8978. The second highest standard deviation is EX3 which is 0.8822 followed by EX2, with standard deviation of 0.8589. The next item is EX5, with a value of 0.8536. EX1 has the lowest standard deviation value which is 0.7500.

Table 4.11 : Central Tendencies Measurement of Constructs: Neuroticism

Items

Mean

Standard Deviation

Minimum

Maximum

N

NE1

3.4389

0.8269

1.00

5.00

221

NE2

3.3258

0.8162

1.00

5.00

221

NE3

3.3032

0.8493

2.00

5.00

221

NE4

3.3122

0.8724

2.00

5.00

221

NE5

3.2579

0.9870

1.00

5.00

221

Source: Developed for the research

Table 4.11 presents the mean and standard deviation for each of the item for Neuroticism.

For mean, NE1 has the highest mean value which is 3.4389. The second highest is NE2 which has 3.3258, followed by NE4 with a mean value of 3.3122. The next item is NE3, with 3.3032. NE5 has the lowest mean value which is 3.2579.

Meanwhile, NE5 has the highest standard deviation with 0.9870. NE4 is the second highest standard deviation, with 0.8724 followed by NE3 which is 0.8493. Then, it is NE1 with a standard deviation of 0.8269. Lastly, the lowest standard deviation value is NE2 which is 0.8162.

Table 4.12 : Central Tendencies Measurement of Constructs: Students' Performance

Items

Mean

Standard Deviation

Minimum

Maximum

N

SP1

2.8326

0.9066

1.00

5.00

221

SP2

3.3665

0.8348

1.00

5.00

221

SP3

3.6833

0.7318

2.00

5.00

221

SP4

3.8326

0.7532

2.00

5.00

221

SP5

3.8462

0.7348

2.00

5.00

221

SP6

3.6154

0.7456

2.00

5.00

221

SP7

3.6787

0.7391

2.00

5.00

221

SP8

3.8462

0.7885

2.00

5.00

221

Source: Developed for the research

Table 4.12 presents the mean and standard deviation for each of the items of students' performance.

For mean calculation, SP5 and SP8 have the highest mean value with 3.8462. The second highest mean value is SP4 which has 3.8326. Next item is SP3 which has the mean value of 3.6833 followed by SP7 with 3.6787. After that, is the SP6 with a mean value of 3.6154 followed by SP2, with a mean value of 3.3665. SP1 has the lowest mean value which is 2.8326.

For standard deviation calculation, SP1 has the highest standard deviation value with 0.9066. The second highest standard deviation value is SP2 which is 0.8348. Next item is SP8 which has the standard deviation value of 0.7885 followed by SP4 with 0.7532. After that, is the SP6 with a value of 0.7456 followed by SP7 with 0.7391. Followed by, is the SP5 with standard deviation value of 0.7348 and lastly, SP3 has the lowest standard deviation value which is 0.7318.

4.3 Scale Measurement

4.3.1 Reliability Test

To determine the consistency and stability of data collected for all the variables tested in the research, Cronbach's alpha value is used (McCrae, Kurtz, Yamagata & Terracciano, 2011). Below are the results of reliability analyses carried out on every item.

Table 4.13 : Result of Reliability Test

Variables

Cronbach's Alpha

No. of Items

Conscientiousness

0.6885

5

Agreeableness

0.7440

5

Openness to Experience

0.7708

5

Extraversion

0.8017

5

Neuroticism

0.6098

5

Students' Performance

0.7951

8

Source: Developed for the research

Based on Table 4.13, generally all the variables tested are consistent and reliable as majority of the variables tested have alpha value of more than 0.7. Thus, the items were considered to represent a measure of high internal consistency (Bahri Yusoff, Abdul Rahim & Yaacob, 2009). However, conscientiousness and neuroticism have lower alpha score with Cronbach's alpha 0.6885 and 0.6098 respectively. In this case, it will still be acceptable because any measure having an intra-class correlation coefficient of at least 0.6 is still at the minimum acceptable level of reliability and considered useful (Abdul-Halim & Che-Ha, 2009; Bruton, Conway & Holgate, 2000).

4.3.2 Normality Test

Table 4.14 : Result of Normality Test

Test

Statistics

p-Value

Shapiro-Wilk

0.9776

0.0014

Kolmogorov-Smirnov

0.0562

0.0875

Source: Developed for the research

Due to the larger data samples of this research (more than 200), Kolmogorov-Smirnov test is used to determine the normality distribution of the results obtained instead of the Shapiro-Wilk test whereby it is more recommended for smaller data testing (sample size less than 50) (Mohd Razali & Yap, 2011). Table 4.14 above shows that the p-value is 0.0875 (p > 0.05), therefore the results of data is normally distributed.

4.4 Inferential Analysis

4.4.1 Pearson Correlation Coefficient

4.4.1 Pearson Correlation Coefficient

Table 4.15 : Pearson Correlation Coefficient

Variable

CS

AG

OE

EX

NE

SP

CS

1

AG

0.2885

1

OE

0.3905

0.1229

1

EX

-0.2280

-0.0507

-0.0096

1

NE

-0.0856

-0.0183

0.0609

0.0731

1

SP

0.6645

0.3276

0.3896

-0.1371

-0.1452

1

Note : CS = Conscientiousness, AG = Agreeableness, OE = Openness to experience, EX = Extraversion, NE = Neuroticism, SP = Students' performance

Source: Developed for the research

Table 4.15 shows the correlation coefficient between independent variables and dependent variable, to determine whether there exists a multicollinearity problem among the independent variables. The highest correlation between the variables is 0.6645 whereas the lowest correlation is -0.1371. All of the independent variables tested have a coefficient of lesser than 0.9. Therefore, this indicates that there is no multicollinearity problem in this research (Saunders et. al., 2009).

There is a moderate strength of association between the variable conscientiousness and student performance with a coefficient value of 0.6645, which is within the range of 0.41 to 0.70 at significant value of lesser than 0.0001. Thus, the result indicates that there is a significant relationship between conscientiousness and student performance.

There is a small but definite relationship between the variable agreeableness and student performance with a coefficient value of 0.3276, which is within the range of 0.20 to 0.40 at significant value of lesser than 0.0001. Thus, the result indicates that there is a significant relationship between agreeableness and student performance.

There is a small but definite relationship between the variable openness to experience and student performance with a coefficient value of 0.3896, which is within the range of 0.20 to 0.40 at significant value of lesser than 0.0001. Thus, the result indicates that there is a significant relationship between openness to experience and student performance.

There is a slight and almost negligible association between the variable extraversion and student performance with a coefficient value of -0.1371, which is within the range of 0.00 to 0.20 at significant value of 0.0418. Thus, the result indicates that there is a significant relationship between extraversion and student performance.

There is a slight and almost negligible association between the variable neuroticism and student performance with a coefficient value of -0.1452, which is within the range of 0.00 to 0.20 at significant value of 0.031. Thus, the result indicates that there is a significant relationship between neuroticism and student performance.

4.4.2 Multiple Linear Regression

Table 4.16 : Model Summary

Root MSE

0.3611

R-Square

0.4922

Dependent Mean

3.5877

Adjusted R-Square

0.4804

Coefficient Variance

10.0639

Source: Developed for the research

By conducting multiple regression analysis, relationships among a set of variables and a set of responses will be determined while accounting for correlations among the responses (Hashim, Murphy, Purchase & O'Connor, 2009). The table above shows that the adjusted value of coefficient of determination (adjuste