Essay: Fluid Intelligence and Age

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According to Kail, (1993) as children grow, their analogical reasoning performance increases since the concept is dependent on neurological maturation and therefore canto process information quicker. The nature of this developmental trend was clearly verified in a study by Hale (1990) who tested four age groups (10, 12, 15, and 19 year olds) on a battery of four different processing speed tasks. The results revealed that the increase in speed with age was not specific to any one task but rather appeared to be global in nature. That is, across all tasks, the time required by children of a particular age group was nearly proportional to the time required by the young adult group (e.g. in all conditions, those who were 12 years old were about 50% slower than young adults). Hale (1990) suggested that the systematic decrease in this proportion with age provided proof for a global developmental trend in fluid intelligence.

Contrary to earlier ideas, analogical reasoning is not late-developing (Holzschneider et al., 2012) for ample evidence exists that by as early as age 3, children demonstrate analogical competence in solving analogical problems Persson et al., (2006).Cognitive performance naturally drop as persons’ age (De Chastelaine et al., 2011). However, study suggests that through a variety of cognitive training programs and aerobic exercise, cognitive performance and related brain function in adults can be enhanced (Belleville et al., 2011; and Anguera et al., 2013). Moreover specific cognitive abilities, even the higher level construct of fluid intelligence has lately been revealed to be trainable through working memory training (Jaeggi et al., 2008), but so far training-induced improvements in fluid intelligence and associated brain function have been demonstrated only in young people Sternberg (2008) and Melby-Lervag and Hulme, (2013).

There is increasing proof that meditation could possibly decrease age-linked decline in cognition and brain function (Gard et al., 2014). For instance, older mediators have been shown to outdo age-matched participants on an attention blink task (Van Leeuwen et al., 2009) and on tasks measuring attention, short-term memory, perceptual speed, and executive functioning (Prakash et al., 2012). However, a limitation of these studies is that, intervening variables of gender, age, education level, and other important factors like physical exercise and cognitive involvement were not controlled (Fotuhi et al., 2012; Wilson et al., 2012). Furthermore, these investigations concentrated on specific cognitive functions, general attention, as opposed to higher level constructs such as fluid intelligence, which could have broader implications due to its high predictive value of real life behavior (Deary, 2012). Krawczyk et al., (2010) adds to a growing list of studies that have shown that analogical reasoning critically depends on neurological maturation of a child. Notably analogical reasoning, permits researchers to make sense of and make inferences about novel circumstances and information with respect to things already known and understand (Dunbar & Blanchette, 2001) and therefore, theories of the development of analogy in children have frequently focused on relational knowledge as a necessary precondition for analogy (Goswami, 2001).

2.1.3 Fluid intelligence and gender

According to Lynn R and Irwing P (2005), the general intelligence of males is higher than that of females since they have bigger brain size in comparison to their bodies. The researchers analyzed the data of respondents that were obtained after administering the Raven standard progressive matrices, which showed that the mean IQ of men exceeded that of women by up to 5 points. Douglas N and Rushton J (2006) carried a study of males aged 17 – 87 years and found out that they had an average of 3.63 IQ points in excess of their female equivalents on the scholastic Assessment Test.

Irwing P (2012) found a 3 point IQ advantage for males in general intelligence from subjects aged 16-89 years in the USA on the WAIS111 test favoring men on the information, arithmetic and symbol search and favoring women on the speed.

Another study by Lynn R (2006) in Sudan, found 16-18 years old males scored 5 points higher on the Raven Standard Progressive Matrices. Liu J (2015) also found 3 points higher in male scores on the WISC which is the children version of the WAIS 111.

Colom R (2000) conducted a large study of 10,475 adults on five IQ tests taken from the primary Mental Abilities and found negligible or no significance sex differences. Colom R (2002) found out that males’ IQs were 3,16 points higher on the WAIS 111 test, but that there was no difference on the general intelligence facto (g) and therefore explained the differences was due to non-g factors such as specific group factors and test specificity. Another study by Colom R (2002), using 4,072 high school graduates, found out that females outperformed males on the inductive Primary Mental Abilities reasoning test, males outperformed females on the Raven Standard Progressive Matrices and there was no difference on the Culture-Fair Intelligence test and therefore concluded no difference in general intelligence. Using multi-group covariance and mean group structure analysis Sluis S (2006) found that g factor couldn’t explain any sex differences on the WAIS111. A study conducted by Flynn J and Rossi L (2011) found that men and women achieved roughly equal IQ scores on Raven Standard Progressive Matrices after reviewing standardized samples from five modern nations.

According to Lynn R and Irwing P (2008), proposed that since working memory ability correlate highest with g factor, researchers would have no choice but to accept greater male intelligence if differences on working memory tasks are found. As a result, a neuro-imaging study done by Schmidt (2009) conducted an investigation into this proposal by measuring sex differences on a n-back working memory task. There was no sex difference in working memory capacity. A study done by Haier R and Burgaleta M (2012) found that bigger brain size don’t indicate greater male g factor than females but instead found that grater male brain size was instead associated with greater visual spatial abilities but not with general intelligence

2.1.4 Assessment of learners’ fluid intelligence

Psychologists had challenges in assessing reasoning and problem solving using conventional intelligence tests and Raven Standard Progressive Matrices test was specially designed to address this challenge (Reynolds & Kamphaus, 2003). Initially developed as an assessment tool for evaluating the mental ability of military recruits in the United Kingdom independent of their educational background and on the basis of this background, the RSPM tests epitomizes the most successful efforts to present inductive reasoning and analogical tasks in non-verbal format (Kaplan & Saccuzzo, 2001). The linguistically minimized nature of the RSPM tests is deemed important for it allows an evaluation of intellectual ability without substantial influence by language, educational background, and cultural factors, and therefore the test is considered significantly culture-reduced, less prejudiced and reasonable measure across different races (Kaplan & Saccuzzo, 2001; Grieve & Viljoen, 2000). The RSPM test is probably the most known, most researched, and most widely used of all culture – reduced tests and its acceptance is evident from the fact that it has been applied in over 1000 studies (Rven, Raven, & Court 1998). In comparison to other intelligence tests, the RSPM is considered to be the single best psychometric measures of general intelligence; outside of multi-domain IQ tests to similar the Wechsler scales (Kunda & Goel, 2011).

The RSPM is a psychometric non-verbal multiple choice test that evaluates the global index of intelligence (Raven, Court, 2008; Huepe et al., 2011). It is a standardized intelligence test consisting of geometric analogy puzzles in which a matrix of geometric figures are presented with one entry missing, and the right missing entry is selected from a set of answer choices. There are currently three versions of the Raven Matrices: the Raven Advanced Progressive Matrices (APM), developed as a more difficult test than the SPM for persons in high IQ ranges, the Standard Progressive Matrices, and the Colored Progressive Matrices (CPM), intended as a simpler test than the SPM to be used with children, the elderly, or other individuals within the lower IQ ranges (Raven et al., 2003). All the three versions of the Raven Matrices are usually used in clinical, educational, occupational, and scientific situations (Soulières et al., 2009). Recently, the RSPM has found application in other areas of psychology, and in medicine for example Neuro imaging and behavioral studies suggest that humans use qualitatively different strategies on the RSPM regarding what types of mental representations are used, specifically in terms of visual versus verbal strategies (Soulières et al., 2009).

Spearman (1946) viewed the three Raven progressive matrices as the best non-verbal pure tests of g, an observation endorsed by British psychometricians like Vernon (Vernon & Parry, 1949) and Jensen (1980). The current studies on this standard test have showed that it tests two unique process namely perceptual/figural and analytical/analogical (Van der Ven & Ellis Ellis, 2000). Lynn, Allik & Irwing (2004) administered Raven tests to 2,700 Estonian 12-18 year olds, and after analysis of scores found from the respondents, confirmed Van der Ven and Ellis classification of perceptual and analytical components of Gf. Spearman continued to regard Raven’s as possibly the best of all non-verbal tests of g (Spearman, 1946), an opinion accepted by other British psychometrics such as Vernon, who regarded Raven’s as nearly pure g test (Vernon & Parry, 1949). This strong assertion was later endorsed by Jensen (1980). Jensen continued to claim that perhaps the most distinguishing feature of Raven’s test is its very low loadings on any factor other than g (Jensen, 1998). But there now seems to be good indication, not only from factor analytic studies, that the many types of the Matrices tests at least two different processes, one that has been variously called figural, perceptual or Gestalt, the other generally termed analytic or analogical (Van der Ven& Ellis, 2000). Rasch analysis of Sets A to E of RPM concluded that Set A and the first half of Set B tested the perceptual process, while the second half of Set B and Sets C to E measured the analytic process—though other processes were involved in the solution of half the items in Set E (Van der Ven& Ellis, 2000). Lately, Lynn, Allik, and Irwing (2004) factor analyzed the RPM scores obtained by some 2700 Estonian 12- to 18-year-olds, and established van der Ven and Ellis’s identification of a perceptual factor tested by Set A and early items in Set B. However, they also found that van der Ven’s analytic factor might be divided into two distinct factors. Set II of RAPM does not contain easy figural items, and some confirmatory factor analyses of RAPM have established that a two-factor solution does not provide a better fit than a one-factor solution (Abad, Colom, Rebollo, & Escorial, 2004; Arthur & Woehr, 1993). However, Dillon, Pohlmann, and Lohman (1981) stated a two-factor solution, and, as noted above, in their factor analysis of RPM, Lynn et al. (2004) concluded that Van der Ven and Ellis (2000) analytic factor must be divided into two: the later items in Set B, and all but the last items in Sets C and D loaded onto one factor, while these last items in Sets C and D and nearly all items in Set E loaded onto a second.

There seems to be good evidence that the RSPM tests measure at least distinguishable processes, variously called perceptual/figural and analogical commonly referred to as analytical reasoning. A Rasch analysis of sets A to E of RSPM concluded that that set A and the first half of set B measured perceptual process, and half of set B and sets C to E measured the analytic process (Van de Ven & Ellis, 2000). Lynn, Allik, and Irwing (2004) factor analyzed the RSPM scores obtained by some 2700 Estonian 12- to 18- year- olds, confirmed Van der Ven and Ellis’s identification of a perceptual factor measured by set A and early items in set B. Lynn et al,. (2004) reported that, at age 17, when there was an overall male superiority in total RSPM score, male obtained higher scores than females on their perceptual factor and on one of their analytical factors, but not on the other though in subsequent analysis did not show significance sex difference in performance.

2.1.5 Attitude and examination cheating

(Udokang and Okoro (2004) and Amoo and Rahman (2004) were able to show a positive correlation between positive attitude towards examination cheating and the actual participation in examination cheating, and this study was authenticated by Adebayo (2010). Centra (1970) found that those with lenient attitudes towards academic cheating inclined to be less academically motivated.

The menace of examination cheating has been reported in every country of the world though the cases differ in magnitude (McCabe, Trevino & Butterfield (2001). The searchers further concluded that cheating in academic institutions in America was increasing in the last 30 years. Recent data on plagiarism and examination cheating in Africa and the rest of the world is becoming a concern to educational scholars, stakeholders, psychologists, and the public in general (Nabi, 2012; Herst-Bayliss, 2013). The literature on academic dishonesty indicates that cheating is practiced by learners at all levels of schooling, with many cases reported in high schools (Godfrey & Waugh, 1998) and the academic cheating is on the rise (Murdock, Miller, & Goetzinger, 2007). The researcher was validated by McCabe et al., (2001) by reporting that between 80% to 95% of high school students acknowledged to cheating at least once and 75% admitted to cheating four or more times. Examination malpractice was found to discourage hard work upon which the existence of the individual and that of the society are founded (Okoro, 2001). Jacob and Lar (2001) pointed out that examination malpractice makes the students to lose the motivation to work hard in their studies since it is not who work hard that get the best results under a system where examination malpractice is prevalent.

Examination is an instrument for testing and judging the standard of education in all countries of the world, and it is used for selection, certification and reporting of progress to parents and policy makers, monitor the performance of the educational system and as an accountability measure in schools. As an integral part of the curriculum process, it is thus an important tool in the teaching-learning process therefore examination malpractices will make this objective to be realized impossible Bello, Kolajo & Uduh, (2010).

According to Ossai, (2013), Poor study habits and high examination anxiety are positively correlated with tendency to engage in examination malpractices. For instance, McCabe, Trevino and Butterfield (2001) concluded from a meta-analysis of a decade of research on cheating in academic institutions in America that cheating is increasing in the last 30 years. Walker (2012) further corroborated the McCabe et al (2001) research by reporting that between 80 and 95 percent of high school students admitted to cheating at least once and 75% admitted to cheating 4 or 5 times. In a recent research done by Murdock, Hale, and Webber (2001) on a survey among American High School students, 80% of them admitted to have cheated at least once in their school life.

The Centre for Academic Integrity (2005) established that on many campuses, over 75% of students admit to some form of cheating. McCabe (1999) carried a survey of 2,100 students and about one third of the students admitted to test cheating and half admitted to one or more occasions of cheating on the assignments. According to Whitley, Nelson and Jones (1999), academic deceit is a major challenge in American tertiary colleges and universities. Al-Qaisy (2008) claimed that academic cheating was a prevalent problem in higher education. Several other studies also pointed to the high occurrence of academic cheating both in America and the world at large (Cole & Kiss, 2001; Jordan, 2001).The degree of the problem of academic cheating is also demonstrated in the bulk of research works associated with it and the fact that literature is full of a myriad of proposals on how it can be tackled Jordan, (2001). About the prevalence of examination malpractice in Nigeria, many related negative effects have been reported by authors and some claiming that it has constituted a serious threat to educational system Ogbonna, (2001) and Okorodudu, (2010).

2.1.6 Students’ Self-Esteem and examination cheating

There are many psychological variables that influence learners’ academic achievements (Reynolds & Kamphaus 2003) apart from Gf. Students’ attitude towards learning and their self-esteem were demonstrated by Sabella (2010) and Rubie et al., (2004) that they significantly influence the learning outcomes of students. According to Alexander (2001), Self-esteem is the value individuals give themselves, and how worthwhile they are to others and therefore it influences persons’ trusts and association with other people. It is how a person judges oneself as good or bad and therefore is an evaluative attitude towards oneself. Positive self-esteem gives a person the strength and flexibility to take charge of his live and grow from his mistakes without the fear of being rejected. Positive self-esteem can be manifested through the syndromes such as: optimism, good self-care, non-blaming behavior and low self-esteem may be known through signs like: negative opinion of life, fear of being mocked, and fear of taking any danger, James (2002).

James (2002) indicated that high self-esteem is not only considered good for persons who have it but it is also good for societies as a whole since high self-esteem can avert young people against susceptibility to a wide range of socials ills Rosenberg (1965). According to and Skehan (1989) and Brown (2000) the personality characteristics such as emotion, motivation, attitude, and anxiety, and self-confidence, and self-esteem are important for learners’ academic achievement. Among these, self-esteem is one of the most influential variables which affect learning either positively or negatively. One of the basic drives in people and can work out a determining influence on a person’s life, for bad or good (Dörnyei 2005). Dörnyei (2005) further affirmed that the concept of self- confidence is strongly associated with self-esteem, and both share a general importance on the individual’s perception of his or her abilities as a person. Glenda & Anstey (1990) explained that many researchers used the terms self-confidence, self-evaluation, self-worth, self-appraisal, and self-satisfaction interchangeably. Self-esteem is a psychological variable in which an individual evaluates him/ herself based on some values and prone to variations depending on the environment (Rubio, 2004).

2.1.7 Students’ self-esteem and academic performance

A study of 838 secondary students in the United States has found a significant relationship between self-esteem and academic achievement for seventh-grade students, but not for ninth-grade Alyes-Martins et al,. (2002). A study conducted by Trautwein et al, 2006 found no relationship between learners’ self-esteem and academic performance.

Ross and Broh (2000) have found in an analysis of data from the National Educational Longitudinal Study in the United States that a sense of personal control affects subsequent academic achievement but self-esteem does not. While self-efficacy and self-esteem are often found to be related, the increasing evidence revealing the positive effect from student self-efficacy for academic success does not likewise demonstrate a direct positive influence from self-esteem on school achievement Ross and Broh (2000). Nonetheless, a positive self-esteem has been viewed as a desirable attribute for students, and therefore studies investigating self-esteem measures often note the important influence of teacher dispositions and school climate in the development of a positive sense of self Helm (2007).

Ouatman and Watson (2001) have also found boys to demonstrate a slightly higher level of self-esteem than girls, but unrelated to grade level during adolescents; whereas Baldwin and Hoffmann (2002) found gender effects to be strongest for younger rather than older adolescents. Some research in England suggests gender patterns, with males demonstrating a closer relationship between self-esteem and academic performance except for competence in the English language (Ireson, Hallam, and Plewis, 2001). Furthermore, a study of urban adolescents in Belgium found that boys’ self-esteem was highly dependent on their sense of mastery, while girls’ was more dependent on relationships, especially parental support Brutsaert 1990).

Pullmann & Allik, (2008) did a research on self-esteem and academic performance and concluded that high self-esteem facilitates academic achievements and low self-esteem does not necessarily signal poor academic performance. . However, research finding from (Marsh & O’Mara, 2008) suggested that prior self-esteem has small positive effect on subsequent educational attainment.

According to Sar Abadani Tafreshi, (2006), there is a significant difference in self esteem between males and females, however Hossaini, (2002), found that gender is not a predictor of self-esteem after the study that involved 240 pre-university students.

Some research has been reported on the relationship between self esteem and academic achievement among the students in Iran. (Zeinvand, 2006) studied the relation between self esteem, social support and student’s educational progression in a high school in Dareh Shar, a city in Iran. 72 students (37 boys and 35 girls) were classified based random method. The research data were collected through Cowper Smeit’s questionnaire of self esteem. The data analysis showed no significantly relationship between self esteem and academic achievement. However, the research depicted the significant differences in boy and girls. The t-test revealed that self esteem is more in boys than in girls.

It has been reported that there is a significant difference in self esteem between male and women (Tafreshi, 2006). (Miraei, 2005) did a research entitled ‘’the result shows that the rate of relation between self esteem and academic achievement. Finding has shown no significant difference. Additionally, (Emamzadeh, 2004) did a research in order to compare the social skills and self esteem and academic achievement among 261 students (boys and girls) in Orumieyeh city. Self Esteem test (Popo) and Mathematics test were used to evaluate through descriptive statistic methods and t- test. The result showed that there was no significant relationship between self esteem and academic achievement.

A recent Meta-analyze study (PourSina, 2003) reported differing results. In this research, entitled ‘’ the analysis of self esteem depression and academic achievement of boy students in Tehran’’ included 192 secondary school students. Cowper Smiths self esteem test was used for data collection and CGPA for the academic achievement. The result showed that there is a significant difference among the students. In another investigation, (Amini, 2004) conducted a research in order to study the role of self efficiency, self regular and self esteem in high school students’ academic achievement. 500 students (300 girls and 200 boys) participated in this study in Share Kord. The result showed both positive and significant relationship between self esteem and academic achievement. Additionally, (Mefteh, 2002) also conducted the same study on 378 students (boys and girls) in secondary school based on randomly sample through Cowper Smiths of self esteem questionnaire. The research demonstrated that there is a significant relationship between self esteem and the students’ CGPA.

2.1.8 School Ecological Contexts and examination cheating

Depending on the school environment, it can either open or close the doors that lead to academic performance of learners (Barry, 2005). The school sets the limits of a learning experience and students’ academic achievement is greatly influenced by the type of school they attend, the School factors like structures, administration, teaching and non-teaching staff (Hoy, Kottkamp & Rafferty, 2003). Considerable research has been conducted on teaching skills, socioeconomic conditions, and student achievement (Hoy, Kottkamp & Rafferty, 2003).Crosnoe (2004) suggest that class size is important structural components of the school and Bandele (2003) noted that the importance of physical facilities cannot be relegated. Private schools tend to have smaller class sizes, more access to better resource base and this has been shown to enhance academic achievement (Eamon, 2005).

Teachers’ experience is another indicator of students’ academic performance since students who attend schools with a higher number of teachers with full credentials are likely to perform better and vice versa (Bali & Alverez, 2003). The education literature has not reached an agreement on the influence of many elements of school quality on student outcomes despite the large number of published studies (Hanushek, 2007). Akinfolarin (2008) identified facilities as a major factor contributing to academic performance in the schools system. Various studies done by Ayodele (2000) and Vandiver (2011) revealed that a positive relationship exists between availability of facilities and student academic performances.

2.2 Theoretical literature review

2.2.1 General intelligence and fluid intelligence

According to Schmidt (2002) the first scientific attempts at defining the construct of intelligence was Spearman (1904) in the hypothesis of general intelligence “g”. Spearman used factor analysis to show that the positive correlation along a diversity of mental tests resulted from a common fundamental factor of common intelligence (Johnson, Gottesman, Mcgue, Krueger, & Bouchard, 2004). Spearman concluded that the degree of relationship between any two tests depended on the amount of general factor operating in each. This correlation is said to be very high in test materials used to assess word meanings, mathematical reasoning, sentence completion, reasoning by analogy and perception of relationships in geometric forms and picture completion (Johnson, Bouchard, Krueger, Mcgue, & Gottesman, 2004). According to Cattell (1963) and recounted by Schmidt (2002), general intelligence consists of two cooperating factors; crystallized and fluid intelligence. Crystallized intelligence encompasses previously learned knowledge and skills and fluid intelligence encompasses analytical and perceptive knowledge (Schmidt, 2002).

The fluid and crystallized concepts have their origin in Raymond Cattell’s investment theory (Cattell, 1987). According to this theory, crystallized intelligence (Gc) encompasses the skills and knowledge attained through education and acculturation and rises with age (Park & Reuter Lorenz, 2009). According to Lezak, et al., (2004) Crystallized abilities are relatively intact even in the presence of brain diseases, and can serve as appointer of previous functioning, and give means of assessing the relative age-dependent declining fluid intelligence (Gf). Crystallized intelligence is usually measured by verbal knowledge, such as new terminologies tasks, semantic understanding and solving crossword puzzles (Salthouse, 2012), and therefore Gc is specific and is associated with abilities from different domains and this stored knowledge in human memory is acquired over a long period of time (Hunt, 2000). In Carroll’s (1993) reanalysis of numerous human abilities studies, he stated that the types of tasks that typically load on GC tasks consist of vocabulary, mathematics, and general knowledge content.

According to Huepe et al., (2011) Gf reflects individual’s capacity to think logically and solve problems in new situation without acquired knowledge. According to Salthouse (2012) Gf has two components namely perceptual and analytical reasoning. Analytical abilities are required to solve problems dealing with new information e.g. pattern recognition, puzzle solving and abstract reasoning while the perceptual component is the knowledge that everything a person experiences (thoughts and feelings) is defined by person’s perception. Psychologists are interested in Gf because of its association with chronological age and is theorized to be less affected by variances in socioeconomic status or educational background and thus more of a “culture- fair” indicator of intelligence (Cattell, 1993) Sternberg, (1997) and Taylor, (1994) argue that Gf is a basic inherited capacity related to novelty and flexible thinking and not influenced by the environment.

Perception is the end result of a thought that starts with the senses of sight, hearing, feeling, smelling or tasting, Kail, Salthouse (2012) and therefore, perceptual Intelligence is the knowledge and understanding that everything human experiences are defined by perception and it is an active process that can be controlled (Mann, 2005). However, some researchers like Silverman, (2002) and Wechsler, (2003) argued that perceptual reasoning abilities refer to the ability to visually manipulate images in space by generating, retaining, retrieving, and transforming whole patterns in a flexible and fluid manner and this is the argument advanced that the first 30 items in RSPM tests is a good test for perceptual knowledge of a student. Rising evidence suggests that perceptive fluid intelligence can be enhanced by perceptual learning and training, (Sternberg, 2008; Jaeggi, Buschkuehl, Jonides, &Perrig, 2008; BoyleMO, 2005).

Analogy which is the part of analytical intelligence, requires the maintenance, manipulation, and selective activation (or inhibition) of mental representations for identification, mapping correspondences, and drawing inferences about higher-order similarity relationships (Cho et al., 2010; Krawczyk et al., 2008). Analogical reasoning demands the manipulation, maintenance and selective launch of intellectual representations to map correspondences, recognize correspondences and draw inferences about higher-order relationships (Waltz, Grewal, Lau, &Holyoak, 2000; Morrison, 2004). Solving a problem can be easier by means of an analogy, for a solution to an old problem can also be the solution to a new problem or can help in finding a similar solution to a new problem (Dunbar & Blanchette, 2001). However, analogical reasoning can also impair effective problem solving. In this case, a person is stuck in a certain thinking pattern and finds it difficult to think “outside-the-box”. This might be related to the executive function fluency, because a person with high fluency skills can shift to another thinking pattern more easily (Ionescu, 2012).

2.2.2 Attitude

An attitude comprises of cognitive, affective, and behavioral components though experimental research fail to show clear differences between emotions, thoughts, and behavioral intention linked with a certain attitude (Vogel, Bohner, &Wanke, 2014). There is a relationship between human behaviour and attitude, and attitude influences behaviour Feldman (200). When an attitude is positive, it increases individual’s interests and participation in an activity Amoo (2002). Amoo (2002) and Rahman (2004) claimed that the connection between performance and attitudes is a result of a reciprocal pressure, in that attitudes influence achievement and accomplishment further affects attitude. Udong and Okoro (2004) theorized that the attitude possessed by learners affect their school work and positive self-esteem plays a role in learning. According to Sieman (2009) and Harding (2007) people’s intentions precede behaviour and the bigger the intention the higher the likelihood that a person will engage in that behaviour.

Attitudes are directly prejudiced by personal understanding and negative or positive support and also are influenced indirectly by observation and social learning through association (Eby, 1998). Attitudes shaped by direct experience may be enhanced predictors of later behaviour than attitudes formed as a result of secondary experience. Attitude disparity relies on a host of situational and individual factors (Petty, 1998). Attitudes that are altered due to intellectual effort are stronger than those changed with minimal thought and are more predictive of behaviour. Such attitudes are also more persistent and resilient to counter-persuasion than attitudes that are changed by procedures involving little mental effort in evaluating the major merits of the attitude entity (Petty, 1998). Petty et al (1998) consider it valuable to regard attitudes as falling along a continuum, stretching from non- to strong attitudes Wood (2000).

According to Naomi Craver, Kristin Ruzicka, and Allison Watson (2007), theories of attitude formation can be put into four broad categories namely; Learning theories; Social judgment theories, Social learning theories, and Functional theories. Many scholars agree that attitudes are learnt and therefore are fairly predictable (Simmons & Maushak, 2001), and some other scholars do believe that some kinds of attitude may have some biological origin (Eagly & Chaiken, 1993).

Attitudes are psychological constructs that are composed of four interrelated components namely: affective, cognitive, behavioral intention, and behavior itself. According to Martin & Briggs, (1986) the cognitive and affective component significantly depend on learning. Attitudes differ in direction, degree and intensity (the level of commitment the individual has to the position). Attitudes are not directly apparent, but the actions and behaviors to which they contribute may be observed (Bednar & Levie, 1993).

2.3 Theoretical Framework.

This study will be guided by two theories namely, the Theory of planned behaviour (TPB) and they of human cognitive abilities popularly known as Cattell-Horn-Carroll theory, that are briefly described below.

2.3.1 The Theory of Planned Behaviour

The study about the relationship between attitude and behaviour is a growing research initiative within psychology and there are many theories that endeavor to clarify this relationship, however, one theory which stands among them is the theory of planned behaviour (TPB).The theory of TPB was proposed by Icek Ajzen in 1985 having originated from the theory of reasoned action, which was proposed by Martin Fishbein together with Icek Ajzen (1975). The theory of reasoned action was founded on numerous theories of attitude such as learning theories, expectancy-value theories, consistency theories, and attribution theory. According to the theory of reasoned action, if one assess a certain behavior as positive and if others want the individual perform the anticipated behavior (subjective norm), it would result in a higher intention (motivation) and the person is more likely to do so (Hale, Householder & Greene, 2003). The theory of planned behavior includes the same components as the theory of reasoned action, but complements the component of perceived behavioral control to rationalize for barriers outside one’s own control (Hale, Householder & Greene, 2003).This theory helps provide an explanation to academic cheating for it exposes students’ intentions through their attitudes towards cheating for these intentions herald behaviour and the greater the intention the more likely that a person will be involved in certain behavior (Harding et al., 2007; Sieman, 2009)

Three basic components are involved in TPB, namely, attitude towards a specific behaviour (such as cheating in examinations); subjective evaluation/norms of how people view the behaviour (such as parents, teachers, friends, etc.); and perceived ease or difficulty of executing the behaviour or action (examination malpractices). These three considerations determine whether an “intention” to engage in the behaviour will be formed and eventually lead to the demonstration of the “behaviour”. These three basic components of TPB are referred to as “Attitude toward the Behaviour” (ATB), “Subjective Norm” (SN) and “Perceived Behavioral Control” (PBC) respectively according to Ajzen (2006). The phenomenon of examination malpractices is a behavioral variable which could be premeditated or spontaneously carried out when a chance presents itself. In either case, however, attitude towards behaviour (ATB), subjective norm (SN) and perceived behaviour control (PBC) must be positively inclined towards the act. Hence, there are direct connections between ATB, SN, PBC and Behaviour itself (examination malpractices). The main elements of TPB described by Ajzen (2006) are Target, Action, Context and Time (TACT) in defining the behaviour of interest. The “Target” behaviour is cheating in examinations (Examination malpractices). The “Actions” are those observed and reported events in literature as constituting examination malpractices such as impersonation, copying from written materials, spying, etc. The “Context” is the Kenya secondary school examinations. The “Time” entails prior to, during and after the real examinations.

According to the TPB, attitude does not only include the evaluation of a certain outcome but also the approximation of the likelihood of this consequence, and salient information is a necessary precondition for any attitude, Stutzman & Green, (1982). Lynne & Rola (1988) were able to show a strong relationship between attitude towards behaviour and ecological contexts of the participants. The TPB developed by Ajzen (1991) can be used as a model for examining student’s reasons to cheat (Sieman, 2009). The theory is based on the principle that human beings are rational and are capable of making logical decisions beforehand engaging in behaviors by considering up likely costs and implications against expectations of positive or negative outcomes (Harding, Mayhew, Finelli, & Carpenter, 2007).

Harding et al. (2007) added the component of moral obligation to the TPB. These elements in the table below along with the findings from the study done by Harding et al. (2007) which used this theory to explore undergraduate students’ decisions to cheat.

Table 1: Table of the theory of planned behaviour

Elements of the theory of planned behaviour (TPB)

Attitude towards behavior The individual’s overall evaluation and inclination to respond either favorably or unfavorably towards behavior eg a student who has a positive attitude towards cheating would be more likely to cheat (Harding et al., 2007).

Subjective Norms An individual’s perception about a behaviour which is influenced by the judgment of others eg a student who has parents that support the idea of cheating would be more likely to involve in cheating (Harding et al., 2007).

Perceived Behavioral Control The extent to which a person feels able to endorse the behaviour based on past experiences and difficulties for example a student with a history of successful cheating will vie themselves as an effective cheater and will be most likely to engage in cheating in the future (Harding et al., 2007 and Sieman, 2009).

Moral obligation Harding et al. (2007) added another element to Ajzen’s original model; labeled as moral obligation and it signifies individual’s personal pressures (shame and guilt) to either perform or not perform behaviour and a student who has some moral duty is less likely to cheating

2.3.2 The Theory of Human abilities (Cattell-Horn-Carroll theory)

The theory of human abilities commonly known as Cattell-Horn-Carroll (CHC) theory is the second theory to be considered in this study. The historical development of the theory is that Cattell built upon Spearman’s general intelligence, “g” to posit two kinds of general intelligence; Fluid intelligence (Gf) – the ability to solve new challenges by use of reasoning – believed by Cattell to be mainly a function of biological and neurological factors, and Crystallized intelligence (Gc), – a knowledge-based ability that is greatly dependent on education and acculturation (Horn & Cattell, 1967, Flanaganet al., (2007).Theory and research in the area of cognitive abilities has changed dramatically in the past 20 years due to the advent of Cattell-Horn-Carroll (CHC) theory (McGrew, 2009) for it has provided a strong theoretical and empirical foundation to the understanding of cognitive abilities, which has guided both research (Keith & Reynolds, 2010) and practice (Alfonso, Flanagan, & Radwan, 2005). This theory has provided a strong foundation for the development and revision of many tests, including the Woodcock-Johnson intelligence test, the third edition (McGrew & Mather 2007). Moreover, CHC theory provides a taxonomic structure which allows classification of the empirically-supported cognitive ability tests.

Carroll (1993) fashioned a model of intelligence that was based on the “three strata”. The first stratum included narrow abilities, which are specific skills typical of tasks used on common psychological tests (Keith & Reynolds, 2010). Dozens of narrow abilities were recognized in this first model (over 60), and these continue to be clarified with further research (Keith & Reynolds, 2010; Schneider & McGrew, 2012).

The second stratum consisted of broad abilities and according to Carroll (2005) they were about 10. Finally at stratum three there was a general factor “g” which is highly related to many academic, social, and psychological measures (Jensen, 1998). Overall, Carroll’s three stratum theory can be viewed as a synthesis of previous theories into a comprehensive taxonomy of cognitive abilities based on extensive empirical analysis Carroll, 2005). The three stratum theory brings together aspects of Horn and Cattell’s Gf-Gc theory (Horn & Blankson, 2005), in which several broad abilities explain the correlations among more narrow abilities in a consistent manner, Thurstone’s (1938) primary mental abilities, and Spearman’s (1904) two-factor theory which included a general factor (g) and specific factors. Schneider & McGrew (2012) provided revised the three stratum description based on current research in order to help clarify the definitions of the CHC abilities.

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