The Correlation of and with Academic Performance of Undergraduate Students at the University of New York in Prague Thesis By Mária Majerová

Submitted in Partial Fulfillment of the Requirements for the degree of Bachelor of Arts In Psychology

State University of New York Empire State College 2017

Reader: Ronnie Mather, Ph.D.

Acknowledgements:

Primarily, I would like to thank my mentor, Ronnie Mather, Ph.D. for his time, support and guidance as well as the fruitful advice provided. Secondly, my great thanks goes to professor Aguilera, who was there for me from the beginning of my statistical analyses.

Moreover, I am also forever grateful to mum, who was always there for me and supported me through the entire process of this thesis as well as my whole university studies. I would also like to thank my friends, who me that with enough persistence one can accomplish anything as well as stood by me through my bachelor’s studies.

Table of contents Abstract...... 3 1 Introduction...... 4 2 Literature review ...... 7 2.1 Intelligence...... 7 2.1.1 Theories of Intelligence...... 8 2.1.2General/ non-verbal Intelligence...... 9 2.1.3 Measuring Intelligence …………………...... 9 2.1.4Genera Intelligence and academic performance……...... 13 2.2 Creativity...... 15 2.2.1 Theories of Creativity...... 17 2.2.2 Domain- specific Creativity ...... 19 2.2.3 Measurements of Creativity...... 20 2.2.4 Creativity and academic performance...... 21 2.3 Academic Performance...... 22 2.4 Creativity, intelligence and academic achievement ……………………………. 22 2.5 Hypothesis to be examined...... 24 3 Methodology ...... 26 3.1 Research Design………………………………………………………………… 26 3.2 Participants...... 26 3.3 Materials ...... 27 3.3.1 K-DOCS………………………………………………………………………. 28 3.3.2 Beta III………………………………………………………………………… 29 3.4 Procedure ...... 31 3.5 Data analysis ...... 32 3.6 Protection of Human Rights……………………………………………………...33 4 Results…...... 34 4.1 Introduction………………………………………………………………………34 4.2 Results of application of method…………………………………………………34 4.3 Results of the descriptive statistics……………………………………………….34 4.4Normalityassessment of the main variables……...... ………….…………………36 4.5Test of hypothesis………………………………………………………………....37 4.6 Discussion...... 39 5 Conclusion...... 45 6References...... 48 AppendixA (informed consent) ...... 53 AppendixB (questionnaire)...... 55 Appendix C (table & figures )…...... 57

Abstract

The aim of my thesis is to determine whether the five domains of creativity (Self/

Everyday, Scholarly, Performance, Mechanistic/ Scientific and Artistic creativity) and non-verbal general intelligence can explain the variance of academic performance. It was hypothesized that intelligence is a good predictor of academic performance.

Moreover, that self/ everyday, scholarly and scientific creativity are also positively correlated with academic achievement. Conversely, artistic and performance creativity is hypothesized to either not be related to academic achievement, or to be inversely related. To measure the construct of creativity Kaufman's Domains of Creativity Scale

(K-DOCS) was administered to students and consequently, each domain was correlated with the academic performance. The IQ of students was measured as general non-verbal intelligence through the Betta III IQ test. Subsequently, the IQ test scores were also correlated with academic performance. The variable of academic success was measured through students’ grade point average (GPA). Former research findings indicated that general non-verbal intelligence did indeed predict academic performance. However, the previous findings of creativity and academic performance association were varied but still indicated a positive significant correlation. Conversely, the results of this thesis indicate that IQ was not associated with academic performance significantly. Moreover, the only domain of creativity that was statistically significantly associated with academic performance was scholarly creativity.

I. Introduction

Previous studies have been able to identify that both independent variables, creativity, and general intelligence are functionally useful in different areas of human experience.

For example; work, education, and entrepreneurship (Csikszentmihalyi, 1996 &

Gottfredson, 1997 & Bilton, 2007). Moreover, other research stresses that general intelligence is enormously important in everyday life activities (Gottfredson, 1997).

Still, some believe that without creativity, humanity would not progress

(Csikszentmihalyi, 1996). Prior studies have been able to identify that the independent variable, creativity, is functionally useful in different areas of human experience. For example, Bilton (2007) argues that it is essential for managers of business to be high in creativity as the global markets nowadays are complex, stratified and unpredictable.

Moreover, Bilton also argues that creativity is one of the most important traits to create strategies that will promote a positive change (Bilton, 2007).

According to Csikszentmihalyi (1996), one of the prominent positive psychology figures, if it were not for creativity humans could not advance, and therefore it’s the one quality that lets us grow as a society. Creativity has the power to change society, and make an individual go over his or her comfort zone and explore risks (Csikszentmihalyi,

1996). Furthermore, people who are high in creativity are adaptable not only regarding novelty but also in the adaptation of the known facts to fit novel solutions

(Csikszentmihalyi, 1996). This all leads to considering creativity as a practical, valuable, and needed characteristic for the individual as well as society. However, it is also one that can be improved or diminished by our environments (Csikszentmihalyi,

1996). Csikszentmihalyi postulates that environment is a great influencer of creativity and that too much rigidity in a field slowly disempowers creativity by discarding

4 novelty (1996). Therefore, it is essential to study creativity in an undergraduate university context for two . Firstly, to learn if creativity is a trait that can explain the variance in student’s academic performance. Secondly, if creativity does not account for the variance of student’s academic performance while being such an essential quality at both the individual and societal level, maybe the educational system does not give creativity its due value.

Similarly, Gottfredson (1997), a life-long researcher of intelligence, argues that a level of general intelligence is necessary for people to be successful in any career, where higher cognitive processing is needed. This may be true of undergraduate study as well.

Additionally, Gottfredson postulates that intelligence is necessary for elevating the quality of human life; since, with more complex thinking every aspect of life is also improved, because people are more aware of their health, possibilities in work, as well as free time and family life (1997). Consequently, from the previous research, it is clearly the case that both IQ and creativity have been identified to be crucial for our lives, as well as the society we live in. However, do these two variables also influence the academic performance at the undergraduate level of university education at the

University of New York in Prague?

Even though there have been studies concentrating on intelligence and creativity in relation to academic performance, the body of evidence is insufficient in the area of undergraduate studies and missing altogether in the Czech context. What I aim to do in my study is to determine if both of these independent variables also meaningfully predict student’s CGPA at the undergraduate level. This thesis has a correlational design and aims to answer the following research questions: Can creativity and IQ

7 combined predict academic achievement? If yes, what range of variation in academic performance can be explained by creativity? What range of variation in academic performance can be explained by intelligence?

1. Literature Review

1.1 Intelligence

One of the most common definitions of Intelligence is that it is a capability of cognitive nature, and acts as the basis for thinking, , and everyday problem-solving, with varying degrees of alertness or preparedness (Ruiz, Bermejo, Ferrando, Prieto, & Sainz,

2014). Thus, it would be fair to define intelligence as a capability to use experience for learning, and to be able to form and choose environments, as well as an individual’s adaptability to these environments (Sternberg, 2012). It is a construct that fluctuates through different ages, as well as cohort groups, cultural and ethnic groups (Sternberg,

2012). For example, there is substantial evidence that people of Asian origin tend to score highest, followed by Caucasian and African American (Sternberg, 2012).

Conversely, race is socially constructed, and therefore it is problematic to interpret these findings (Sternberg, 2012 & Gottfredson, 2005). Additionally, Flynn has revealed that every ten years the average IQ improved roughly 3 points during the 20th century, therefore intelligence is also influenced by the growth in society (Sternberg, 2012).

It is also possible to look at intelligence from the biological perspective; intelligence correlates with different physiological and structural properties of the brain. For example, the function of the pre-frontal cortex, brain size, neocortex size and its function, are all correlated with the measures of intelligence covered by different intelligence assessments (Sternberg, 2012). Moreover, a heightened function of the

temporal, parietal, occipital cortex and striatum were shown in individuals with higher intelligence (Sternberg, 2012). Another positive correlation is that of intelligence and velocity of conduction of neural impulses, as well as lower general metabolism of glucose while solving a problem (Sternberg, 2012). The more intelligent the individual, the higher the particular area's level of metabolism of glucose, which seems to signify that individuals that score higher on IQ test might just have learned how to efficiently utilize their brain capacity (Sternberg, 2012).

2.1.1 Theories of intelligence

As might be suggested by the flexible nature of intelligence, the theories of intelligence are manifold, ranging from psychometric systems to biologically based theory

(Sternberg, 2012). Sternberg’s Triarchic theory of intelligence is one of the most influential, and claims that intelligence can be defined by three main skill sets: practical skills, creative skills and analytical skills, with new versions also including a set of wisdom skills (Sternberg, 2012)

Another notable theory of intelligence is Gardener’s theory of multiple .

This theory denies the existence of general intelligence, and instead proposes eight different types of intelligence an individual can possess (Sternberg, 2012). The eight intelligences are comprised of linguistic, mathematical, spatial, musical, bodily- kinesthetic, naturalistic, interpersonal and intrapersonal intelligence (Sternberg, 2012).

This intelligence theory is commendable in that it tries to find equality between different abilities a human being can possess, however, it is deficient, in that each type of intelligence will predict different life pursuits, and so it is unable to account for the general life outcomes of individuals.

Yet another of the most prominent theories of intelligence is the Cattell, Horn and

Carroll Theory (CHC theory), which posits that intelligence consists of three different strata that are hierarchically associated. Stratum I, II and III contain narrow, broad capabilities and general capability, respectively (Sternberg, 2012). Throughout my research, the Stratum III- general ability, or non- verbal ability, will be measured because it is best in predicting general life outcomes as well as educational achievement, and is culturally neutral. General intelligence can be both fluid; a capability for handling new things, and of rapid and flexible thinking; and, crystallized; the learned supply of information that is essential for adapting to life’s situations

(Sternberg, 2012). The former will be used in my research, again because in crystallized intelligence culture plays an important role, and the researched sample of UNYP students is likely to comprise many different cultures.

2.1.2 General intelligence

The definition of general non-verbal intelligence stems from the CHC theory but also from IQ testing. discriminate capabilities predominantly depending on their generalizability, or the scope of tasks in which the usefulness of the capabilities can be proven (Gottfredson, 2005). Through this kind of discrimination, general non- verbal intelligence is highest in generality, since it explains a third to half of variance in any comprehensive omnibus of mental tests (Gottfredson, 2005). The first operational definition of general intelligence was suggested by Spearman, resulting from factor analyses as the general factor through numerous assessments or common variance shared by an inter-correlation of a compilation of cognitive tests (Irwing & Lynn, 2005)

(Spearman, 1904). The logic behind the general intelligence ability factor is that the broad capability is the fundamental basis of many different specific abilities, and that is why the operational definition requires that the non-verbal general intelligence resulting

from different IQ tests converge as one general ability factor (Gottfredson, 2005). But what is the best way to measure verbal intelligence?

2.1.3 Measuring Intelligence

While “proper” IQ testing originated in the late nineteenth century, societies as early as ancient China developed tests that could measure different mental abilities in order to formulate if one is suitable for earning a government position (Cianciolo & Sternberg,

2008). It can be said that the originator of earnest IQ testing was Francis Galton. For him, intelligence was the individual’s ability to wield effort, and his or her sensitivity to different environmental conditions (Cianciolo & Sternberg, 2008). His view was biologically based concerning the acuteness of senses being important in intelligence, because the better the senses, the better the ability to perceive. Consequently, this in his logic results in having more material to derive from, in order to handle each situation, thus higher intelligence (Cianciolo & Sternberg, 2008).

Later Cattell developed 50 tests, which were based on Galton’s assumptions about intelligence. However, these turned out not to be either related to one another, or to success when performing complex intellectual tasks (Cianciolo & Sternberg, 2008). The subsequent great expander of intelligence testing was Alfred Binet, who unlike his predecessors tried to develop intelligence testing with the aim of distinguishing individuals who could excel or succeed scholastically, and those who could not

(Cianciolo & Sternberg, 2008). Moreover, he and his colleague Simon postulated that at the core of intelligence are well-developed judgment skills, such as the ability to focus on steps needed to perform a given task, the capability to adapt while performing a task and to self-monitor his or her own performance (Cianciolo & Sternberg, 2008).

In order to test these judgment skills, Binet devised a test that assessed “verbal skills and social comprehension” through multiple tasks, with each following assignment being harder, and aimed to measure different typical level of performance at a particular chronological age (Cianciolo & Sternberg, 2008, p. 34). Mental age was derived by the uppermost level task that an individual successfully completed (Cianciolo & Sternberg,

2008). Developing on this idea, William Stern operationalized IQ as a person’s mental age divided by his or her chronological age and multiplied by 100 (Cianciolo &

Sternberg, 2008). Stanford-Binet Intelligence Scales are still in use today, testing different abilities such as reasoning, general , working , etc.

However, like other modern IQ measures, these scales measure the deviation IQ; a variable indicating the performance of an individual in relation to other individuals who have the same chronological age (Cianciolo & Sternberg, 2008).

We can be thankful to the military for the efficiency of current IQ tests that have objective scoring techniques, short standardized instructions, and time-limits (Cianciolo

& Sternberg, 2008). David Wechsler advanced on these ideas after experience in the military and developed tests that measure “Comprehension Index,” “Perceptual

Organization Index,” “Processing Speed Index,” “ Index.” Moreover,

Wechsler’s tests were multiple, each aimed at a different age group, and were founded on the practical utility to better place individuals on various educational, as well as professional positions, based on their intelligence (Cianciolo & Sternberg, 2008, p. 37).

From the need to identify and assess the capabilities that allow and determine the scope of intelligence skills in any cultural context, which individuals are able to perform, arrived the one-factor tests, such as Raven’s Progressive Matrices, Cattell Culture Fair

Test or Beta III (Cianciolo & Sternberg, 2008). All of these measure the concept of general non-verbal intelligence. However, there are also different multiple factor tests measuring multiple cognitive abilities, regarding various aspects. These are considered to contribute to intelligence, for example working memory, auditory pattern recognition, reasoning, spatial ability or verbal ability and other abilities depending on which hierarchical theory of intelligence they are testing (Cianciolo & Sternberg, 2008).

One of the most prominent measures of general intelligence is the Raven’s Matrices

(RM). This was considered as the best measurement of g as early as the 1980’s.

Additionally it has the highest g loadings, and currently is also broadly recognized as the best assessment of Spearman’s g, as stated by Spearman and established by subsequent studies (Irwing & Lynn, 2005) (Raven, 2000). RM also tests the individual’s intellectual ability, excluding past explicit learning, and that is what valid IQ tests are supposed to measure (Cianciolo & Sternberg, 2008). Raven assesses two central constituents of g recognized by Spearman; the educative capability, and the reproductive capability. The former is concerned with being able to arrive at meaning from confusion and to deal with complexity by creating higher-level schemas. The latter is the capability to grip, reproduce, and be able to recollect explicit material (Raven,

2000). Likewise, RM factor analysis in a sample of 2, 735 students revealed three factors and one higher order factor-g, with g correlating (.99) with total scores. This again provides evidence that RM measures g reasonably (Irwing & Lynn, 2005).

Because of these qualities, RM would be the most adequate measure for my research.

However, because of its unavailability for use by undergraduate students, and the training needed to use, and the long time required for the RM intelligence administration. Beta III was chosen as the second best option to use for measuring

general non-verbal intelligence in the sample of this study, because of its ability to measure general intelligence resulting in one factor and it’s cultural fairness.

Beta III was first established by the army to assess the general intelligence of military recruits. We can be grateful to Kellogg and Morton (1934) who edited the measurement for general citizen usage, using Wechsler’s procedures in their 1946 modification

(Grubb, Whetzel, & McDaniel, 2004). It is a revised version of the Revised Beta

Examination. Beta III is also a culturally neutral assessment of IQ, since it bases its measure of general intelligence on non-verbal abilities, and therefore the barrier is lifted and is thus well suited for this particular sample. Another why

Beta III is a suitable choice for my sample is its straightforward administration and scoring, as well as its time-efficiency, since undergraduate students often do not have much time. Moreover, Beta III has the ability to assess a vast range of participants in a time-efficient manner. The validation of Beta III has been conducted on other validated intelligence tests such as WAIS-III, Raven’s, Standard Progressive Matrices, ABLE-II,

Bennett Mechanical Comprehension Test and others. The reliability and validity have been established on a standardization sample of 1,260 adult individuals. This sample was stratified by age, the level of education, culture, gender, and area based on 1997 census data (Grubb et al., 2004). Because of the Beta III’s single score of non-verbal intelligence, intelligence will be more easily compared and correlated to the other variables studied in my research, CGPA and creativity. Moreover, since the Raven’s

Matrices’s standard time of completion is about an hour, it would be too long to ask from my participants and therefore the shorter, 16 minutes lasting Beta III is more suitable.

2.1.4 General Intelligence and Educational Performance

Gottfredson believes that the more complex the work, the higher the usefulness of general intelligence (1997). Since educational success concerns highly complex processes, such as; planning, the division of labor and , general nonverbal intelligence can thus be inferred to predict academic success strongly.

Moreover, Gottfredson’s paper states how different IQ levels interact with various learning skills. People with lower general intelligence up until the threshold of IQ of 95 need to be explicitly provided with instructions and also need continual guidance in their learning. However, individuals whose IQ is over 110 can more readily learn autonomously, are more self-directed, synthesize different, and more, abstract information and infer conclusions from this information (Gottfredson,1997;

Gottfredson, 2005). Additionally, Gottfredson believes that students with lower IQ need to have their study materials comprised of smaller parts that add up together. Whereas students with higher IQ can comprehend incomplete and complex instructions as well as study materials, and through the processes of assembling and reassembling of deep- rooted and new information are able to find a characteristic meaning (Gottfredson,

2005).

Likewise, IQ score is also a progressively central predictor of performance in increasingly higher-level professions; for example, the minimum IQ of attorneys, doctors, engineers or professors is generally 115, with the average being between 125-

130 (Gottfredson, 2005). However, studying the correlation in the sample population of university students, the range of possible IQ scores is usually smaller and thus the correlations might not be as high, even when the importance of IQ does not diminish

(Gottfredson, 2005). Thus, the more varied ranges of IQ between elementary or high school students allow for greater correlations than the smaller range of IQ in university students. Also through personality studies extraversion has been correlated significantly with general non-verbal intelligence, and extraversion is also quality privileged in our society and thus useful for academic as well as professional success (Luciano, Leisser,

Wright, & Martin, 2004).

Moreover, general intelligence has been shown to be a stable and universal forecaster of academic success through investigations lasting more than a century (Ruiz, Bermejo,

Ferrando, Prieto, & Sainz, 2014). Gottfredson (2005) reviewed a large amount of studies and inferred a .60 median correlation of academic performance on standardized assessments and assessment scores of IQ, which showed that academic performance and

IQ were powerfully related and that the sizeable variance of academic achievement variance can be accounted for by IQ variance. However, this is only a correlation as the third variable problems, as well as the causality issue, have not been studied thoroughly yet (Pesta & Poznanski, 2008).

Likewise, Gottfredson (2005) still states that the sole, or at least the primary cause of individual differences in educational success is general intelligence. One particular analysis examining the relationship between educational success and IQ is that of

Lozano, Gordillo, & Perez (2014) which had a sample of 174 university students of a high range of ages. This analysis revealed that intelligence was positively related to academic performance, with the correlation being positive and significant (.440, p <

.01). Furthermore, non-verbal intelligence had a significant contribution in predicting and explaining academic achievement (Lozano, Gordillo, & Perez, 2014).

2.2 Creativity

In 1922, D. Simpson introduced the construct of creativity, and stated that it reflects the capability to produce novel ideas, and not to think stereotypically (Mynbayeva,

Vishnevskaya & Sadvakassova, 2016). Another definition of creativity is the potential to generate something that has the features of usefulness and novelty. (Benedek, Jauk,

Sommer, Arendasy, & Neubauer, 2014). This concept can be looked at from the domain-specific or domain-general perspective. The former view postulates that individuals can have different types of creativity in specific capacities that do not have to correlate with each other, and the latter perspective suggest that there is a creativity factor that will be true across different domains (Mourgues, Tan, Hein, Elliott, &

Grigorenko, 2016). Other operational definitions of creativity are based on the characteristics that people high in creativity should possess, such as; exceptional observation skills, avoidance of perceptual sets, perceiving new techniques, truths and many others (Mynbayeva, Vishnevskaya & Sadvakassova, 2016).

Divergent thinking is another standard criterion for creativity. The term divergent thinking was first suggested by Guilford as a cognitive ability to see many solutions or directions and has been correlated with real-life creativity tasks (Mynbayeva,

Vishnevskaya & Sadvakassova, 2016) (Benedek, Jauk, Sommer, Arendasy, &

Neubauer, 2014). Divergent thinking is an individual’s ability to think “out of the box” and see possibilities and solutions others would not have thought of. However, some argue that too much divergent thinking can have adverse effects on creativity since it seems to be a trait that can spur irrelevance, and therefore the creative idea is not useful anymore (Kozbelt, Runco, & Albert, 2010).

Creativity is a complex construct that has more than 60 different definitions, and thus,

Taylor conducted an analysis of definitions and divided them into six types. Each type differs in the approach of researchers to the creativity problem its several dimensions and definitions: Gestalt, innovative, problem, psychoanalytic, esthetic concepts and other non-related concepts (Mynbayeva, Vishnevskaya, & Sadvakassova, 2016). From all of these different perspectives and the literature review of the concept of creativity

Vishnyakova extrapolated a definition as follows: “creativity is a set of intellectual and personal abilities enabling the individual to independently bring forth problems, generate multiple original ideas and produce innovative solutions" (Mynbayeva,

Vishnevskaya, & Sadvakassova, 2016, p. 409). This research will use domain-specific creativity as a predictor of the creativity variable, as in terms of academic achievement more interesting and novel finding might arise.

2.2.1 Theories of Creativity

One approach to creativity is to look at it through the four or six päs of creativity

(Kozbelt et al., 2010). Firstly process, where one looks at what constitutes the creative process. Secondly, the product where the creativity of individual is judged according to their creative accomplishments. Thirdly, personality, in this approach, the creative nature is studied, for example, it has been found that the trait openness to experience is associated with creativity. The fourth p is place or press, where environmental factors are studied that are either detrimental, or inducing, for creativity. The last two p’s were additionally added and are called persuasion (creative person can shift someone’s perspective to a novel one) and potential (the innate ability to be creative, which can be fulfilled in life depending on circumstances) (Kozbelt et al., 2010). Since creativity is

such a varied construct there are another ten types of creativity theories: Psychometric,

Economic, Developmental, Cognitive, Stage and Componential Process, Typological,

Problem Solving and Expertise based, Problem Finding, Systems and Evolutionary theory (Kozbelt et al., 2010). However, since each theory is very broad concerning different p’s of creativity only some of them will be described.

Developmental theory tries to comprehend the origins of creativity, through finding environments that are influential to the growth of creativity (Kozbelt et al., 2010). For example, it has been found that creativity flourishes in those individuals, who as children have lived through varied experiences, with creative parents, who allowed for independence of these children with not too little or too many restrictions (Kozbelt et al., 2010). This theory looks on the effects of nurture in creativity development, which may explain why particular individuals would be high or low in particular domain of creativity in my sample. The economic theory of creativity also distinguishes factors, which affect creativity, however, at the societal level. For example, it looks at factors such as larger vs. smaller groups, where findings indicate that the larger the group, the more inhibition on creativity since there are higher costs of showing the difference in thinking (Kozbelt et al., 2010). Another finding was that tolerance in a market or society for unconventionality is necessary, but not sufficient, for creativity to flourish

(Kozbelt et al., 2010). This can be applied later in my research when looking at the university environment and creativity.

Another theory that is critical to my research, because it shows some similarities between my independent variables, is the cognitive theory of creativity. This theory states that differences between individuals’ constitute a significant part of

differences in creativity (Kozbelt et al., 2010). The cognitive abilities most often emphasized in this theory are abilities like attention, memory, divergent thinking, or remote associations (Kozbelt et al., 2010). These qualities have also been often associated with general intelligence, especially the former two.

The last theory of creativity, Systems theory, is the most complex one, looking at creativity “not as a single entity, but as emerging from a complex system with interacting subcomponents” (Kozbelt et al., 2010, p. 38). For example, one of the systems theories states that creativity emerges through three reciprocally connected mechanisms. The first, being the existing knowledge/ skills in given discipline at a given time, while the second being the person who has acquired the knowledge/ skills and is able to reproduce different variations of his or her acquisitions. Finally, the third are the other individuals in a given domain, who make decisions about the value of the products (Kozbelt et al., 2010). Another sub-theory in the systems theory of creativity is the theory of influential systems, which states that interpretations of creativity between levels of system regulate the restrictions vs. freedom in creative behavior.

2.2.2 Domain specific Creativity

There are two diverging views in creativity research, either it is argued that creativity is domain general, or that it is domain specific. This two opposing views can be looked at through the notion of transfer (Baer, 2012). If creativity is domain general, then when a creative skill is acquired in one area it should also be applicable to other areas and vice versa. The current findings indicate that there are too low an inter-correlation between domains of creativity for it to be explained by a single creative ability (Baer, 2012)

However, it is important to look at domain-specific creativity from the approach that does not state that creativity in different domains cannot be related, only that single domain cannot predict creativity in other fields (Baer, 2012). On the contrary, some of the most prominent figures in the study of creativity believe that individuals creative in one context are likely to be creative in another. Consequently, the domain- specificity approach acknowledges some creative potential that can be learned to be express in different domains (McKay, Karwowski, & Kaufman, 2016). Moreover, domain specificity can be further distinguished into comprehensive domains such as musical, numerical or dramatic creativity, or more narrow domains, such as writing poems, singing or writing a thesis

(Baer, 1998). In this research, the broad domains are used as factors, whereas the narrower ones are items of the five factors.

2.2.3 The Measurements of Creativity

The measurement of creativity varies as well as its concepts from specific, general up to performance tests, past achievement tests and finally, self-reported measures of creativity. Moreover, it can be divided into five broad categories: creative products, creative , creative traits, and creative behavior and accomplishments (Silvia.

Wigert, Reiter-Palmon, and Kaufman, 2012). This research will use a self-reported measure of creativity, specifically Kaufman's Domain Specific Questionnaire (K-

DOCS) (Kaufman, 2012). This measure of creativity has been chosen because of its availability, time efficiency, and psychometric properties. The K-DOCS five domains version to be used has high internal consistency, indicated by the coefficient alpha of

.80, and it has also shown test-retest reliability (Kaufman, 2012). K-DOCS covaries highly with the other three assessment tools for creativity that were tested in a study conducted by Silvia et.al. (2012). Since the specific domain questionnaire that will be

used in my research is measuring self-reported beliefs about creativity in the different domain, it is important that it covaries with measurements of creative performance, which it does (Silvia. Wigert, Reiter-Palmon, and Kaufman, 2012). Thus, it can be inferred that beliefs about creativity are correlated with actual creative abilities, which is what I aim to study (Silvia. Wigert, Reiter-Palmon, and Kaufman, 2012). Through K-

DOCS, the construct of creativity will be measured domain-specifically within five domains: self or everyday creativity, academic creativity, performance creativity, scientific creativity and finally artistic creativity (Kaufman, 2012).

2.2.4 Creativity and Academic Performance

According to Sternberg creativity is a contributor to academic achievement because it consists of reproductive skills that are essential to learning (Mourgues, Tan, Hein,

Elliott, & Grigorenko, 2016). However, the literature review reveals that the relationship between creativity and academic performance across studies is inconsistent; sometimes the correlations are high or moderate, or even non-existent, depending on the given study. As creativity is defined in many ways, these correlational studies have a wide range of variability. However, these two variables are usually correlated positively, depending on the instruments used (Mourgues, Tan, Hein, Elliott, &

Grigorenko, 2016)

For example, one study showed that creative performance could predict educational achievement by years (Mourgues, Tan, Hein, Elliott, & Grigorenko, 2016). Another study found that the Aurora creativity test was able to predict performance on the

General Certificate of Secondary Education assessment four years before administration

(Mourgues, Tan, Hein, Elliott, & Grigorenko, 2016). However, the correlational

coefficients were small to medium but still significant (Mourgues, Tan, Hein, Elliott, &

Grigorenko, 2016). Yet another study of 1013 university students, found that the creativity component had significant contributions to predict GPA above and beyond

SAT’s (Mourgues, Tan, Hein, Elliott, & Grigorenko, 2016).

Moreover, Nami, Marsooli, and Ashouri (2014) conducted a study of 72 students, testing creativity with the Torrence Tests of Creative Thinking (TTCT) assessment and found significant correlations between academic performance and creativity to flexibility, and initiative components. TTCT was also used in a study on this subject conducted by AI (1999), in a sample of 2,264 students with two other less well-known creativity tests. The results showed that creativity was related to academic achievement

(Ai, 1999). Moreover, it was found that higher scores on the Creative Achievement

Questionnaire correlated with higher cumulative grade point averages (Silvia. Wigert,

Reiter-Palmon, and Kaufman, 2012). However, a longitudinal research showed opposing results, that indicated a very low correlation between creativity and academic performance in some subjects and even a negative correlation between creativity and academic performance (-.12) in the subject of physics (Naderi, Abdullah, Aizan, Sharir,

& Kumar, 2010).

However, there were no studies performed specifically on the Kaufman's Domains of creativity with associations to academic performance, most likely because this measure is a quite novel measure of creativity (Kaufman, 2012). Moreover, only the domain- general, one-factor creativity was used in the previous studies. Therefore, it was only inferred from the theory that the domains more related to education (Scholarly, Self/

Everyday, Scientific) should be positively correlated to academic performance, while

the other (Artistic, Performance) should not have an association or could even be inversely related.

2.3 Academic Performance

The variables of creativity and intelligence will be further correlated with the cumulative grade point averages (CGPA) of the sample of students as a criterion variable for academic performance. The educational performance can be defined as the conduct that has been performed throughout the entire studies at a given level and its outcomes (Yusuf, 2002). Since CGPA is the most objective value that estimates the student’s learning behavior and outcomes, it is appropriate to use it as a measure of academic performance (Yusuf, 2002). The CGPA calculation is completed through the division of the total number of GPA by the total number of attempted credits (Naderi,

Abdullah, Aizan, Sharir, & Kumar, 2010). Moreover, at UNYP students are provided with their CGPA in their transcripts, and therefore it will be easy for a given sample to present their CGPA for this research.

2.4 Creativity, General intelligence and Academic Performance

Creativity and intelligence themselves have been positively correlated in some studies

(Benedek, Jauk, Sommer, Arendasy, & Neubauer, 2014) (Mourgues, Tan, Hein, Elliott,

& Grigorenko, 2016) (Ruiz, Bermejo, Ferrando, Prieto, & Sainz, 2014). This positive relationship has been hypothesized to have a reciprocal nature. Because of intelligence being the necessary condition for creativity up to a certain threshold of IQ, becoming less relevant over the scores of IQ that are higher than 120. Similarly, certain aspects of creativity such as divergent thinking, or ideas generation are prerequisites for intelligence (Benedek, Jauk, Sommer, Arendasy, & Neubauer, 2014).

The literature review shows that there is sufficient evidence for general intelligence predicting academic achievement on lower than university level. However, the evidence is not so telling for these higher institutions. Moreover, creativity has been found to be mostly positively related and able to predict academic success, however, the different facets of creativity specific to this research have not yet been studied. Therefore, it is important to answer the questions if IQ is a good predictor of academic performance and subsequently, if creativity correlates with academic achievement and, if yes, which domains are the strongest predictor from the five domains used. Moreover, this type of correlational study has never been performed on a sample of students in the Czech

Republic before, and, therefore valuable information may be gained about the cultural and educational systems needed for IQ and Creativity to flourish in individuals. Based on previous research, it is hypothesized that IQ will be a good predictor of academic success in this sample of undergraduate students. Moreover, that academic, scientific and everyday creativity will also be positively correlated with academic achievement in the aforementioned sample. Conversely, artistic and performance creativity is hypothesized to either not be related to academic achievement, or to be inversely related.

2.5 Hypotheses

The purpose of this research is to investigate general intelligence (Beta III IQ), the five domains of creativity (Self/ Everyday, Scholarly, Performance, Mechanistic/ Scientific and Artistic creativity) and their relation to academic performance (CGPA). The research question to be answered is the following: Do creativity and intelligence predict

academic performance significantly? In order to answer this research question, it was hypothesized at the beginning of the study that:

H01: None of the predictors (Beta III IQ, Self/ Everyday creativity, Scholarly creativity,

Performance creativity, Mechanistic/ Scientific creativity and Artistic creativity) is useful in predicting the academic performance.

H02: General intelligence measured as Beta III IQ does not contribute to the model.

H03: The domain of creativity Self/ Everyday creativity does not contribute to the model. 2

H04: The domain of creativity Scholarly creativity does not contribute to the model.

H05: The domain of creativity Performance creativity does not contribute to the model.

H06: The domain of creativity Mechanistic/ Scientific creativity does not contribute to the model.

H07: The domain of creativity Artistic creativity does not contribute to the model.

Ha1: At least one of the predictors; (Beta III IQ, Self/ Everyday creativity, Scholarly creativity, Performance creativity, Mechanistic/ Scientific creativity and Artistic creativity) is useful in predicting the academic performance.

Ha2: General intelligence measured as Beta III IQ contributes to the model.

Ha3 the domain of creativity-, Self/ Everyday creativity does contribute to the model.

Ha4: The domain of creativity, Scholarly creativity does contribute to the model.

Ha5: The domain of creativity, Performance creativity does contribute to the model.

Ha6: The domain of creativity, Mechanistic/ Scientific creativity does contribute to the model.

Ha7: The domain of creativity, Artistic creativity does contribute to the model.

The above group of hypotheses states the effect of general non-verbal intelligence and the five domains of creativity. It is assumed that general intelligence, Self/ Everyday creativity, Scholarly creativity, Performance creativity, Mechanistic/ Scientific creativity and Artistic creativity do not predict academic performance as measured by criterion variable of CGPA.

3. Methods

3.1 Research Design:

The design of this study is qualitative, non-experimental descriptive and correlational, as it studies the relations between researched variables. The research determines non- verbal intelligence, the creativity of participants, and consequently investigates a relation between both of these independent predictor variables and the dependent variable academic performance quantified through CGPA. This design was chosen because a quantitative descriptive study is best at generalizing a sample to given populations, as well as for a vast amount of data that these two questionnaires provide.

The study was conducted through providing to participants a questionnaire, which assesses different domains of creativity: K-DOCS. To measure general intelligence, a

Beta III assessment was employed. Additionally, it was mandatory to also provide cumulative grade point average. Finally, participants were also asked to present demographic variables, namely; their gender, nationality, age, and major. After the assessments had been administered and data collected SPSS data analyses - multiple regression was run on the scores of participants to discover if GPA correlates with the five domains of creativity and general intelligence of the given sample. Moreover, the results of the survey were later compared to previous research finding described in the literature review.

3.2 Participants

The participants were chosen under the condition of being students of University of

New York in Prague, as that was the target population of this study. They were recruited by the researcher and the sample size of this study was 51 participants total.

However, there were only 49 participants qualified for the study, as 2 participants were removed from the study because of missing data from the domains of creativity assessment. Participation in this research was voluntary and anonymous; the aspects of the individual that were used for the purposes of the research were CGPA, gender, age, and nationality. The sample was not equal by gender as it was comprised of 70.59 % female participants, and only 20. 41% male participants (viz. Figure I.). The age range was from 18 to 36 years old, with a mean age of 22.20 years (SD=2.829). There were total of 14 nationalities (Slovak, Czech, Russian, American, Canadian, Italian,

Azerbaijani, South Korean, Norwegian, Kazakhstani, Israeli, Belarussian, Iranian and

Uzbek) in the study. However, only three had enough representation to form separate groups, namely: Slovak nationality with 27.45% having the highest representation, followed by Czech nationality with 19. 61% and Russian nationality with 15.69%. (viz.

Figure II & III). The rest of the participants’ nationalities were grouped under the variable Other 37,25%. The sample of participants was further differentiated by four different majors of undergraduate studies as follows: Psychology (49.02%), Business

Administration (29.41%), & Mass Media (11.76%), and International

& Economic Relations (9.80%).

3.3 Materials

For the purpose of this thesis, two standardized questionnaires were used, because of their availability as well as of their ability to easily obtain the data from the population sample of undergraduate students at university level. Additionally, participants were asked about demographical variables, namely their gender, nationality, age, major and

CGPA. The two standardized measurements K-DOCS and Beta III and their psychometrics are described closely below.

3.3.1 K-DOCS

Through K-DOCS the construct of creativity will be measured domain-specifically with five domains: self or everyday creativity, academic creativity, performance creativity, scientific creativity and finally artistic creativity (Kaufman, 2012). The assessment was construed to provide a self-assessment that will deliver behaviourally-based creativity measure (Kaufman, 2012, p. 299).

K-DOCS was standardized using a sample of 2,318 undergraduate university students at the public state University of California, and thus it perfectly fits my chosen population sample (Kaufman, 2012). Kaufman (2012) ran a factor analysis on the responses of the participants, whom he randomly assigned into two groups, to allow for a certainty of the final factor structure on the original 94-item scale. Items were chosen and amended from the former version of K-DOCS, but adaptations from Ivcevic and Mayer (2009) and Carson et al. (2005) were added to improve the quality of the assessment. Finally, of the 94 initial items, only 50 were kept after eliminating the items that loaded on many factors or did not load on any one of these five factors (Kaufman, 2012).

Kaufman ran the principal factor analysis to detect a simple structure through using

orthogonal varimax solution with Kaiser normalization (2012). Five factors with eigenvalues 2.0 and 18 with values were revealed by Kaufman and “1.0. Each of the first five factors had anywhere from seven to 14 variables with loadings .45” (Kaufman,

2012, p. 300). The five domains reached were Self/Everyday, Scholarly, Performance,

Mechanical/Scientific, and Artistic Creativity” all of which had strong alpha coefficient reliabilities as well as congruence coefficients (Kaufman, 2012).

The Self/Everyday factor reflects inter- and intra-personal creativity and has a coefficient alpha of .86 and is measured by statements 1-11 from K-DOCS. The second domain is the Scholarly factor with the coefficient alpha of .86 and reflecting “creative analysis, debate, and scholarly pursuits” (Kaufman, 2012, p. 300). This factor is computed using statement 12-22 from K-DOCS. The third domain, performance creativity, reached a coefficient alpha of .87 and includes the “public presentation dimension,” which is assessed through statements 23-32 (Kaufman, 2012, p. 300). The mechanistical/ scientific domain which assesses creativity in scientific and technical matters had an alpha of .86 and determined by statements 33-41 of K-DOCS. Finally, the fifth domain, artistic creativity had an alpha coefficient of .83, and reflects artistic pursues such as dancing or song-writing (Kaufman, 2012). The last domain is computed through statement 42-50.

A good internal consistency is implied since for every one of the five scales used all items are above .80. The test-retest reliability was measured trough second administration of K-DOCS to a sample of 132 individuals two weeks afterward

(Kaufman, 2012). The results yielded a good test-retest reliability with correlation coefficient all above .76 (Kaufman, 2012). Bivariate correlations were run with the Big

Five personality factors and the results yielded showed a consistence with the previous research, providing evidence of convergent validity (Kaufman, 2012).

3.3.2 Beta III

This assessment of intelligence was chosen for usage because of its cultural fairness, quickness. It is also language free IQ assessment since it is non-verbal test of intelligence, it can be readily applicable in a multicultural setting such as UNYP, where most participants have English as their second language. This provides for better reliability of the final data. Additionally, it is easy to administer and collect data from.

The whole administration procedure last around 30 minutes, including the time of delivering of instructions (Grubb et al., 2004).

Beta III is intended to estimate five forms of non-verbal intelligence for age range from

16- 89 years. The five forms of general intelligence are the following: visual information processing, processing speed, spatial and nonverbal reasoning, aspects of fluid intelligence (Grubb et al., 2004). Followingly, this measurement entails five subtests: Coding, Picture Completion, Clerical Checking, Picture Absurdities, and

Matrix reasoning, from which all combine to form a global general mental ability score

(Grubb et al., 2004).

The Coding subtest involves quickly coding ciphers with numbers paired to given ciphers. Subsequently, Picture Completion entails drawing absent parts of a present illustration. The Clerical Checking subtest involves deciding if pairs of objects presented are the same or different. The Picture Absurdities part of assessment requires choosing one wrong or foolish illustration out of four illustrations. Finally, in the

Matrix Reasoning, a pattern of symbols is provided, and missing illustration has to be chosen.

Psychometrically it has been extensively studied and validated. In 1997, Beta III was normed using a representative sample of population sample of 1,260 adult individuals.

The sample was comprised of citizens of US, as well as, minorities such as prisoners

(N=400) and exceptional individuals with mental retardation (Dumont & Willis, 2007).

It was validated through usage of other standardized intelligence tests such as Raven’s

Standard Progressive Matrices, WAIS-III, Beta II, Bennet Mechanical Comprehensive

Tests and others (Dumont & Willis, 2007). For example, the convergent validity was tested through correlation with WAIS- III (N=182), yielding a result of rs= .67, .80, .77

(Grubb et al., 2004). The test-retest reliability is “r=.89; and when adjusted for range restriction, r=.96” (Grubb et al., 2004). However, this IQ assessment was never validated on undergraduate student sample.

3.4 Procedure

The data- collection procedure was to use social network (Facebook) as a recruitment tool, where the main information about research was provided, and undergraduate students of UNYP were asked if they are willing to participate. If their answer was positive, a Google- excel document was sent to them through the social network, they were they were supposed to fill in their names to a free field with the specific time and date, when they were able and free to come to the room in library to take the Beta III IQ test, and pick up their K-DOCS to fill in at home. Consequently, to bring it back to a box, which was previously placed at the library and label with the researcher’s name.

The time was always set at approximately the same time of the day, afternoon, so that all the participants have equal conditions.

When meeting with the participants, either in groups or individually, firstly the research with its purpose and limitations was explained, and then the instructions were given verbally for the Beta III assessment while written instructions (viz. Appendix A:

Kaufman Domains of Creativity Scale) were provided for the K-DOCS questionnaire.

Secondly, the participants were given the informed consent to read and sign, and consequently return to me. The next step was the administration of the Beta III and handing over the K-DOCS, both tests with the same assigned number for each participant in order for the data to be organized. Consequently, the start of the Beta III completion was announced and the participants were explained as well as asked to practice what they should do in the first task- Coding. After this the real coding task was started and the participants were asked to start writing. The researcher measured the time for this task by stopwatch and when two minutes passed participants were asked to stop writing. The same procedure was repeated with task II- Picture Completion, task

III- Clerical Checking, task IV- Picture Absurdities, and task V- Matrix reasoning. The only difference being the different time ranges for each task. The time participants had for Picture Completion was two minutes and 30 seconds, for Clerical Checking they had again only two minutes and the Picture Absurdities task lasted 3 minutes. Finally, for

Matrix Reasoning 5 minutes of time was allowed. Consequently, the participants were told to stop writing and return their completed Beta III test to the researcher.

The compensation for participation was provided by giving participants a reward of their choice from a wide range of sweets: Fit bars of various flavors, Bebe Brumiks,

Horalkas, Lion bars and other sweets.

3.5 Analysis

The data was collected from the participants through K-DOCS, Beta III and additional demographic questions, as stated above. The data was then put and analyzed using

SPSS program. Firstly, overall descriptive statistics was performed on all the data, including demographic variables to screen the data. Secondly, the normality tests were executed to determine whether all the variables in our sample were normally distributed. Moreover, multiple regression was run on the five domains computed from the K-DOCS 50 statements, the Beta IQ variable, and CGPA of each participant. This analysis was performed to examine Beta IQ and Kaufman’s five domains of creativity

(Self/ Everyday, Scholarly, Performance, Mechanistic/ Scientific and Artistic creativity) ability or inability to predict academic performance measured through CGPA.

3.6 Protection of Human rights

The consent form, as well as the opportunity sampling, ensured the protection of human rights as the participation, which was voluntary. The confidentiality of the participants was ensured through assigning each participant their number and not using their name, as well as the consent form being separate from the questionnaire in order to disable the possibility of connecting the signature with given person’s answers. Also on the consent form, there was the possibility to state your email address and find out results of the study once it’s finished, by which the transparency is assured. Moreover, the participants were not deceived in any way and were informed of the purpose and

conditions of the study in the consent form so in this study there is no need for debriefing.

4. Results

4.1 Introduction

The results contain the resulting values and tables and figures of the descriptive statistics on demographics of participants and perfectionistic self-presentation, the normality assessment of studied variables, the results of the statistical procedures, and the test of hypotheses.

4.2 Results of the application of the method

The most suitable analyses to determine the relation of the independent variables intelligence and creativity and dependent variable academic performance is multiple regression analysis. Therefore, this procedure was used to test the hypothesis.

4.3 Descriptive statistics of the main variables

51 subjects voluntarily participated in our study, from whom two were excluded because of incomplete data. The mean of CGPA was 3.11 similar to the median score

(Mdn = 3.10). The most frequent retrospective physical aggression score was 3. The scores ranged from 2.04 to 4,00 with rather small standard deviation and variance (SD

=.464 S2 = .216) (see also Table II).

The mean Beta III IQ score in participants was a score of average intelligence

(M=100.53) and was similar to their median score (Mdn = 100.00). The most frequent

Beta III IQ score was 91. The scores ranged from 73 to 141. Moreover, the Beta III

general intelligence scores seem to be quite varied according to the very large standard deviation and variance (SD = 14.873, S2 =221.214) (see Table IV).

The mean Self/ everyday creativity was highest from all of the five domains of creativity scores (M=39.06), which was similar to the median score (Mdn = 40.00) The most frequent Self/ Everyday creativity score was 41. The scores ranged from 24 to 47, with the standard deviation and variance (SD = 4.351, S2 = 18.934) signifying that the

Self-Everyday creativity scores were quite varied (see Table V).

The mean score of Scholarly Creativity was 35.69 and median score was 35.00. The most frequent of Scholarly Creativity scores had the lowest value of 31. The scores ranged from 26 to 51. The standard deviation and variance were a bit higher than for

Self/ Everyday creativity (SD = 6.138, S2 = 37,675) (see Table V).

Furthermore, the mean Performance Creativity score was 27.31 similar to the median score (Mdn = 27.00). The mode of Performance Creativity scores was 25. In this dimension of Creativity, the scores ranged from 10 to 46. Additionally, Performance

Creativity score varied quite a bit based on the large standard deviation and variance

(SD = 8.227, S2 = 68,509) (see Table V).

The mean score of Mechanical / Scientific Creativity was lowest from all of the five domains of creativity (M=22,98) as well as the median score of 23,00. While the mode of Mechanical / Scientific Creativity scores was 13. The scores were ranging from 9 to

39. The standard deviation and variance were as follows, SD = 8,179, S2 = 66,895 (see

Table V).

Finally, the mean score of Artistic Creativity was 30,61, similar to the median score of

30,00. The most frequently occurring score of the fifth factor of creativity was 29. The scores were ranging from 16 to 45. The standard deviation and variance signify moderately varied scores of Artistic Creativity (SD = 6,689, S2 = 44,742) (see Table V).

4.4 Normality assessment of the main variables

Cumulative Grade Point Average scores were normally distributed with a skewness of -

.063 (SE = .340) and kurtosis of -.256 (SE =.668) (see table VII). Cumulative Grade

Point Average scores were normally distributed as assessed by Shapiro-Wilk's test (p >

.05). (see Table VI). Moreover, through the visual inspection of the CGPA histogram the scores were normally distributed (see Figure V).

Non-verbal intelligence scores were normally distributed with a skewness of .527 (SE =

.340) and kurtosis of .127 (SE =.668) (see Table VII). Non-verbal intelligence scores were normally distributed as assessed by Shapiro-Wilk's test (p > .05). (see Table VI).

Moreover, through the visual inspection of the Non-verbal intelligence histogram, the scores were normally distributed (see Figure VII).

Self/ Everyday creativity scores were normally distributed with a skewness of -.063 (SE

= .340) and kurtosis of -.706 (SE =.340) (see Table VII). Self/ Everyday creativity scores were normally distributed, as assessed by Shapiro-Wilk's test (p > .05). (see

Table VI). Moreover, through the visual inspection of the histogram, the Self/ Everyday creativity scores were normally distributed (see Figure IX).

Scholarly Creativity scores were not normally distributed with a skewness of .669 (SE =

.340) and kurtosis of -.139 (SE =.668) (see Table VII). Scholarly creativity scores were not normally distributed, as assessed by Shapiro-Wilk's test (p < .05). (see Table VI).

Moreover, through the visual inspection of histogram Scholarly Creativity scores were not normally distributed. (see Figure XI).

Performance Creativity scores were normally distributed with a skewness of .317 (SE =

.340) and kurtosis of -.524 (SE =.668) (see Table VII). Self/ Everyday creativity scores were normally distributed, as assessed by Shapiro-Wilk's test (p > .05). (see Table VI).

Moreover, through the visual inspection of histogram, the Self/ Everyday creativity scores were normally distributed (see Figure XII).

Mechanical/ Scientific Creativity were normally distributed with a skewness of .235

(SE = .340) and kurtosis of -1.039 (SE =.668) (see Table VII). Mechanical/ Scientific

Creativity scores were normally distributed, as assessed by Shapiro-Wilk's test (p >

.05). (see Table VI). Moreover, through the visual inspection of histogram Mechanical/

Scientific Creativity scores were normally distributed (see Figure XV).

Artistic Creativity scores were normally distributed with a skewness of .210 (SE = .340) and kurtosis of -.261 (SE =.668) (see Table VII). Artistic Creativity scores were normally distributed, as assessed by Shapiro-Wilk's test (p > .05). (see Table VI).

Moreover, through the visual inspection of histogram Artistic Creativity scores were normally distributed (see Figure XVII).

4.5 Test of hypotheses

A multiple regression was run to predict academic performance from domains of creativity (Self/ Everyday, Scholarly, Performance, Mechanistic/ Scientific and Artistic creativity) and general non-verbal intelligence. The assumptions of linearity, independence of errors, homoscedasticity, unusual points and normality of residuals were met. Domains of creativity (Self/ Everyday, Scholarly, Performance, Mechanistic/

Scientific and Artistic creativity) and general non-verbal intelligence did not statistically significantly predict perfectionistic self-presentation, F (6, 42) = 1.235, p = .308, adj. R2

= .028. Therefore, the first null hypothesis that states that none of the predictors (Beta

III IQ, Self/ Everyday creativity, Scholarly creativity, Performance creativity,

Mechanistic/ Scientific creativity and Artistic creativity) is useful predicting academic performance cannot be rejected.

The predictor general intelligence did not add statistically significantly to the prediction, p = .559. Thus, we cannot reject the second null hypothesis stating that General intelligence measured as Beta III IQ does not contribute to the model.

The predictor Self/ Everyday creativity did not add statistically significantly to the prediction, p = .374. Thus, we cannot reject the third null hypothesis that states that the domain of creativity Self/ Everyday creativity does not contribute to the model.

The predictor scholarly creativity is the only one that did add statistically significantly to the prediction, p = .028. Thus, the fourth null hypothesis that states the domain of creativity- Scholarly creativity does not contribute to the model can be rejected and the

fourth alternative hypothesis stating that the domain of creativity- Scholarly creativity does contribute to the model can be accepted.

The predictor Performance creativity did not add statistically significantly to the prediction, p = .120. Thus, we cannot reject the fifth null hypothesis that states that the domain of creativity Performance creativity does not contribute to the model.

The predictor Mechanistical/ Scientific creativity did not add statistically significantly to the prediction, p = .942. Thus, we cannot reject the sixth null hypothesis that states that the domain of creativity Mechanistic/ Scientific creativity does not contribute to the model.

The predictor Mechanistical/ Scientific creativity did not add statistically significantly to the prediction, p = .728. Thus, we cannot reject the seventh null hypothesis that states that the domain of creativity Artistic creativity does not contribute to the model.

Regression coefficients and standard errors can be found in Tables VIII, IX, X, XI. .

4.6 Discussion

Overall, 51 undergraduate UNYP students participated in this thesis. However, the analysis analyses on 49 students of the sample (29 % males, 71% females) and their age ranged from 18 to 36 years. This thesis examined the association between non-verbal general intelligence, the five domains of creativity (Self/ Everyday, Scholarly,

Performance, Mechanistic/ Scientific and Artistic creativity) and academic performance. Regarding descriptive statistics of the sample, the mean as well as median

Beta IQ score was approximately 100, which suggests that the sample as a whole had an average IQ. This finding is contrary to previous theories, which indicated that persons with IQ over 110 can more easily study autonomously, have more a self-directed nature, and are better in merging diverse and abstract information and inferring conclusions from this information, which are all traits that would, in theory, improve academic performance (Gottfredson, 2005).

Moreover, the main result of this thesis is that there is not a statistically significant association between the independent variables; creativity and intelligence, and the dependent variable academic performance as revealed through the multiple regression analyses. Therefore, we could not reject the null hypothesis and consequently could not accept the alternative hypothesis.

This finding goes against the original hypothesis of the researcher, which stated that non-verbal intelligence as well as some domains of creativity (Self/Everyday, Scholarly,

Scientific), will have a statistically significant association with academic performance.

This hypothesis was based on the previous theories, which state that since academic performance concerns highly complex processes, such as; planning, the division of work and problem-solving. The general nonverbal intelligence, which is conducive to the above academic performance enhancing processes should be able to explain significantly the variance in academic performance (Gottfredson, 1997). Moreover, that creativity, should also be a contributor to academic achievement because it entails the reproductive skills that are crucial to learning, especially at higher levels of study, especially regarding the domains of creativity related to academic performance

(Mourgues, Tan, Hein, Elliott, & Grigorenko, 2016). However, it is important to

mention that the sample studied might not have been large enough for statistically significant associations to arise.

Regarding the analyses of the individual effect of general non-verbal intelligence on academic performance, it was also insignificant. This finding does not allow for the second null hypothesis to be rejected, and makes it impossible to accept the second alternative hypothesis stating that general intelligence is contributing to the model; higher intelligence is related to better academic performance. This result is again inconsistent with previous studies that recognized general intelligence as a good predictor of academic achievement (Ruiz, Bermejo, Ferrando, Prieto, & Sainz, 2014)

(Lozano, Gordillo, & Perez, 2014). Even though the previous theories state that general non-verbal intelligence should be one of the best predictors of academic achievement, for example, the meta-analyses of Gottfredson (2005), but also the fact that general intelligence was operationalized in order for sorting students for educational institutions

(Cianciolo & Sternberg, 2008). The reason why the results of this thesis do not correspond to previous findings might be that the Beta III IQ test was used, which is not as sensitive in measuring the higher scores of IQ as for example, Raven’s matrices are

(Grubb et al., 2004).

Additionally, regarding the analyses of the individual effects of domains of creativity, only Scholarly creativity was found to be statistically significantly associated with academic performance. This finding indicates that it is possible to accept the second alternative hypothesis suggesting that scholarly creativity is contributing to the model; the higher the scholarly creativity the better academic performance. Since previous research has not been unified concerning creativity and it’s associations to academic

performance, but still, has been more inclined to moderate positive correlations between the domains of creativity that are associated with education (in this study- Scholarly,

Self /Everyday and Scientific) as well as general creativity this is the only result in line with the earlier studies (Mourgues, Tan, Hein, Elliott, & Grigorenko, 2016) (Silvia.

Wigert, Reiter-Palmon, and Kaufman, 2012).

Contrariwise, the multiple regression of either Self/ Everyday or Scientific creativity did not reveal a statistically significant association with academic performance, and thus the third and the sixth null hypothesis were accepted and it was not possible to accept the third or sixth alternative hypothesis, which states that self/everyday and scientific creativity do contribute to the model; the higher the two domains of creativity the better the academic performance. As stated above this finding is contrary to the conclusions from previous studies, and the hypothesis based on them, that the domains associated with education should have some association with academic performance (Mourgues,

Tan, Hein, Elliott, & Grigorenko, 2016) (Silvia. Wigert, Reiter-Palmon, and Kaufman,

2012).

Regarding the analysis of Artistic and Performance creativity, the findings also showed that there was not a statistically significant association, which meant that the fifth and seventh null hypothesis was not rejected. That is, the fifth and seventh alternative hypothesis could not be accepted, stating that artistic and performance creativity contribute to the model; the higher these two domains the greater the academic performance measured by CGPA. These results confirm the hypothesis that the domains of creativity, which are unrelated to education, should not have an association with academic performance.

Since all of the alternative hypotheses except for the second one could not be accepted, it must be concluded that general non-verbal intelligence and the five domains of creativity studied do not statistically significantly predict academic performance. Thus, the original hypothesis, that general non-verbal intelligence is associated with academic performance, did not hold in this study. Moreover, the hypothesis that the domains of creativity related to education (Self/ Everyday, Scholarly and Scientific) are associated with academic performance can only partially be accepted, as only scholarly creativity was associated with academic performance. Finally, the last hypothesis that Artistic and

Performance Creativity will not be related to academic performance can be accepted as the result do not indicate a statistically significant association. Moreover, this research had its limitations, and these might also be related to the results, which are inconsistent with previous findings.

One of the limitations that could have affected the findings was the sample size of only

49 analyzable participants, which was too small for a population of approximately 800

UNYP students, as of 2012 (“About UNYP | University of New York in Prague,”

2012). According to the table, which determines a proper sample size for a specific population size for a population size of 800 people, the sample size should be at least

213 participants, my sample size being only a fourth of the advised sample size for statistically significant results (Bartlett, Kotrlik, & Higgins, 2001). Moreover, sample size is known to be able to” influence the detection of significant differences, relationships or interactions” and therefore could have affected the results of this study

(Bartlett, Kotrlik, & Higgins, 2001, p. 43).

Another limitation of this study is that the K-DOCS questionnaire does not measure creativity directly, it only measures the perceptions of creativity of given individual.

Thus, it relies on the mechanism of the self-fulfilling prophecy, even though the measure has been positively correlated with other measures measuring creativity directly. Moreover, the questionnaire is also subjective; it had to be assumed that honest responses were provided from participants. Therefore, for example, real creative tasks could not be given for each domain of creativity, however, the administration procedure would than take too long and therefore the sample size would most likely shrink even more as it would be harder to find willing participants.

Another possible limitation is the cross-sectional design of this research, which only looks at the variable at a single point in time. Even though non-verbal general intelligence should be quite stable, creativity, as measured by K-DOCS, can be developed, as well as academic performance. Therefore, more telling results about associations and even causality would, if the domains of creativity, intelligence as well as academic performance scores, would be taken following a given sample of population from primary school throughout their entire studies. Another limitation concerns the domains of creativity, as measured by K-DOCS since there is no previous research on these five specific domains of creativity in association with academic performance, the hypothesis had to be only theoretically established, in which the research’s own subjectivity might have played a role.

5. Conclusions

Both independent variables, creativity and non-verbal intelligence, are traits that are valued in a person, as they give one a more fruitful life, and have been shown to be

useful in different areas of human experience (Csikszentmihalyi, 1996 & Gottfredson,

1997& Bilton, 2007). Both creativity and intelligence are the traits through which we humans make progress and find new directions. For example, they are both important for successful career and intelligence has even been indicated to be important trait for better general life quality (Bilton, 2007) (Gottfredson, 1997). Therefore, they should be valued and developed throughout society, as they are both influenced by the permissiveness of environments (Gottfredson, 1997) (Csikszentmihalyi, 1996).

Moreover, academic performance is the behavior that is performed throughout studies and the outcomes of this behavior and is a stepping stone on individual paths through their life success, as well as telling of future career behavior.

Previous findings indicate that general non-verbal intelligence is associated positively with academic performance, which means that the higher the intelligence, the better the academic performance. Regarding creativity, previous findings have not been as consistent as with intelligence, however, they mostly indicate a positive correlation.

Unfortunately, the results in this thesis did not confirm the previous findings. Since intelligence was found to not to be significantly correlated with academic performance and from the five domains of creativity studied (Self/ Everyday, Scholarly,

Performance, Mechanistic/ Scientific and Artistic creativity) only scholarly creativity was found to have a small positive correlation with academic performance.

Firstly, this implies that since both of these variables (general non-verbal intelligence and five domains of creativity) are important for many other aspects of life, it is important to have more rigorous research in these two areas, especially when it comes to creativity. Another implication is that scholarly creativity since it positively

contributes to academic performance at undergraduate university level, should be increased in university students. This means that different interventions should be designed to augment scholarly creativity, this would consequently lead to better individual academic performance.

Moreover, from the results of this thesis it may also be inferred that perhaps the school systems in the Slovak and Czech Republics (since most participants- 47 % were from these two republics, who share a similar school system) are environments in which students are able to develop and use their creativity and intelligence. The developmental theories state that permissive, tolerant and less controlling environments, are conducive to creativity, whereas one that has the possibility of autonomous though is conducive to the development of intelligence (Kozbelt et al., 2010). Since this kind of research has not been done in these two Republics before, there is a possibility that the environment for these traits in schooling is not part of the culture in these countries and should be changed and researched.

Therefore, a study that would measure creativity and intelligence at different levels of schooling in Czech and Slovak republics; from primary school, up to doctorate students should be considered in order to determine if the school system influences creativity and intelligence positively or negatively. Consequently, if this large-scale study would not have significant correlations between theses variables, it would point out an error in the schooling system, which does not sustain qualities that are essential for progress of our society; creativity, and intelligence. Thus, it would be important to consider developmental theories of both creativity and intelligence and apply those in

reconstruction of the school system in order to induce potentially more creative and intelligent individuals.

6. References

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Appendix A

I. Consent Form

Please note that this survey only concerns the following parties: Undergraduate students in Czech or Slovak Republic. Please do not proceed if you do not meet these criteria.

Dear Participant,

Thank you for taking part in this survey. The survey examines creativity in relation to CGPA. It should not take you more than 10 minutes to complete. The survey is completely anonymous and it does not include any information that could identify you. Your participation is voluntary and it will be used for research purposes only.

Please circle the appropriate answer:

I have read and understood the research and want to participate in the study.

Yes No

I am over 18 years of age:

Yes No

Signature ______Date ______

If you wish to know the results of the study write your email here: ______

Age:

GPA:

CGPA:

Nationality:

Major:

Gender:

Consent Form for Beta III

Please note that this survey only concerns the following parties: Undergraduate students in Czech or Slovak Republic. Please do not proceed if you do not meet these criteria.

Dear Participant,

Thank you for taking part in this test. The test examines general intelligence. It should not take you more than 17 minutes to complete. The assessment is completely anonymous and it does not include any information that could identify you. Your participation is voluntary and it will be used for research purposes only.

Please circle the appropriate answer:

I have read and understood the research and want to participate in the study.

Yes No

I am over 18 years of age:

Yes No

Signature ______Date ______

If you wish to know the results of the study write your email here: ______

Appendix B

II. Kaufman Domains of Creativity Scale (K-DOCS)

Instructions:

Compared to people of approximately your age and life experience, how creative would you rate yourself for each of the following acts? For acts that you have not specifically done, estimate your creative potential based on your performance on similar tasks.

Example:

Much Less Less Creative Neither More More Creative Much More Creative Nor Less Creative 1 2 Creative 4 5 3 a. Singing in the bathroom __2__

1. Finding something fun to do newspaper, newsletter, or when I have no money _____ magazine _____ 2. Helping other people cope with 13. Writing a letter to the editor a difficult situation ______3. Teaching someone how to do 14. Researching a topic using many something _____ different types of sources that 4. Maintaining a good balance may not be readily apparent between my work and my _____ personal life _____ 15. Debating a controversial topic 5. how to make from my own perspective _____ myself happy _____ 16. Responding to an issue in a 6. Being able to work through my context-appropriate way _____ personal problems in a healthy 17. Gathering the best possible way _____ assortment of articles or papers 7. Thinking of new ways to help to support a specific point of people _____ view _____ 8. Choosing the best solution to a 18. Arguing a side in a debate that I problem _____ do not personally agree with 9. Planning a trip or event with _____ friends that meets everyone’s 19. Analyzing the themes in a good needs _____ book _____ 10. Mediating a dispute or argument 20. Figuring out how to integrate between two friends _____ critiques and suggestions while 11. Getting people to feel relaxed revising a work _____ and at ease _____ 21. Being able to offer constructive 12. Writing a nonfiction article for a feedback based on my own

of a paper _____ pottery _____ 22. Coming up with a new way to 48. Appreciating a beautiful think about an old debate _____ painting _____ 23. Writing a poem _____ 49. Coming up with my own 24. Making up lyrics to a funny interpretation of a classic work song _____ of art _____ 25. Making up rhymes _____ 50. Enjoying an art museum ____ 26. Composing an original song _____ 27. Learning how to play a musical instrument _____ 28. Shooting a fun video to air on YouTube _____ 29. Singing in harmony _____ 30. Spontaneously creating lyrics to a rap song _____ 31. Playing music in public _____ 32. Acting in a play _____ 33. Carving something out of wood or similar material _____ 34. Figuring out how to fix a frozen or buggy computer _____ 35. Writing a computer program _____ 36. Solving math puzzles_____ 37. Taking apart machines and figuring out how they work _____ 38. Building something mechanical (like a robot) _____ 39. Helping to carry out or design a scientific experiment_____ 40. Solving an algebraic or geometric proof _____ 41. Constructing something out of metal, stone, or similar material _____ 42. Drawing a picture of something I’ve never actually seen (like an alien) _____ 43. Sketching a person or object _____ 44. Doodling/drawing random or geometric designs _____ 45. Making a scrapbook page out of my photographs _____ 46. Taking a well-composed photograph using an interest- ing angle or approach _____ 47. Making a sculpture or piece of

Appendix C

Table I. Descriptive statistics of age N Valid 51 Missing 0 Mean 22.20 Median 22.00 Mode 20a Std. Deviation 2.829 Minimum 18 Maximum 36 a. Multiple modes exist. The smallest value is shown

Table II.

Descriptive Statistics of CGPA N Valid 51

Missing 0 Mean 3,1143 Median 3,1000 Mode 3,00a Std. Deviation ,46443 Minimum 2,04 Maximum 4,00 a. Multiple modes exist. The smallest value is shown

Table III. Descriptive Statistics of Cumulative Grade Point Avarage Std. N Minimum Maximum Mean Deviation Variance CGPA 51 2,04 4,00 3,1143 ,46443 ,216

Table IV. Descriptive Statistics of Beta III IQ N Valid 51 Missing 0 Mean 100,53 Median 100,00 Mode 91 Std. Deviation 14,873 Variance 221,214 Minimum 73 Maximum 141

Table V Descriptives Statistics of the five Domains of Creativity

Scholarl Self_Every y Performan Artistic day_Creati Creativit ce_Creati Mechanical_Scie Creativ vity y vity ntificCreativity ity N Valid 49 49 49 49 49 Missing 2 2 2 2 2 Mean 39,06 35,69 27,31 22,98 30,61 Median 40,00 35,00 27,00 23,00 30,00 Mode 41a 31a 25 13 29 Std. Deviation 4,351 6,138 8,277 8,179 6,689 Variance 18,934 37,675 68,509 66,895 44,742 Range 23 25 36 30 29 Minimum 24 26 10 9 16 Maximum 47 51 46 39 45 a. Multiple modes exist. The smallest value is shown

Table VI Tests of Normality Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. CGPA .074 49 .200* .984 49 .746 IQ .099 49 .200* .973 49 .318 Self_Everyday_Creativi .116 49 .097 .954 49 .056 ty Scholarly_Creativity .113 49 .158 .948 49 .031 Performance_Creativity .107 49 .200* .975 49 .386 Mechanical_Scientific_ .116 49 .094 .958 49 .078 Creativity Artistic_Creativity .089 49 .200* .981 49 .609 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction

Table VII

Descriptives CGPA Statistic Std. Error Skewness -.063 .340 Kurtosis -.256 .668 IQ Skewness .527 .340 Kurtosis .127 .668 Self_Everyday_Creativity Skewness -.706 .340 Kurtosis 1.644 .668 Scholarly_Creativity Skewness .699 .340 Kurtosis -.139 .668 Performance_Creativity Skewness .317 .340 Kurtosis -.524 .668 Mechanical_Scientific_Cre ativity Skewness .235 .340 Kurtosis -1.039 .668 Artistic_Creativity Skewness .210 .340 Kurtosis -.261 .668

Table VIII

Model Summaryb

Adjusted R Std. Error of Model R R Square Square the Estimate 1 .387a .150 .028 .44987

Table IX Model Summaryb Continued Change Statistics R Square Durbin- Change F Change df1 df2 Sig. F Change Watson .150 1.235 6 42 .308 2.409

Predictors: (Constant), Artistic_Creativity, IQ, Mechanical_Scientific_Creativity, Self_Everyday_Creativity, Performance_Creativity, Scholarly_Creativitya

Dependent Variable: CGPAb

Table IX ANOVAa Sum of Model Squares df Mean Square F Sig. 1 Regression 1.499 6 .250 1.235 .308b Residual 8.500 42 .202 Total 9.999 48 a. Dependent Variable: CGPA b. Predictors: (Constant), Artistic_Creativity, IQ, Mechanical_Scientific_Creativity, Self_Everyday_Creativity, Performance_Creativity, Scholarly_Creativity

Table X Standardiz ed Unstandardized Coefficient Coefficients s Model B Std. Error Beta t Sig. 1 (Constant) 2.924 .729 4.014 .000 IQ .003 .004 .087 .589 .559 Self_Everyday_Cr -.016 .018 -.152 -.899 .374 eativity Scholarly_Creativit .030 .013 .403 2.274 .028 y Performance_Creat -.015 .009 -.264 -1.585 .120 ivity Mechanical_Scient .001 .010 .013 .073 .942 ific_Creativity Artistic_Creativity -.004 .011 -.055 -.350 .728

Table XI Collinearity Statistics Tolerance VIF .921 1.086 .705 1.418 .645 1.549 .729 1.372 .673 1.487 .829 1.207

Figure I.

Figure II.

Figure III.

Figure IV.

Figure V

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Figure VII

Figure IX

Figure X

Figure XI

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Figure XIII

Figure XIV

Figure XV

Figure XVI

Figure XVII

Figure XVIII