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College students' general creativity as a predictor of cognitive risk tolerance.

INTRODUCTION

Creativity or "general creativity" is a preference for thinking in new ways with the ability to produce novel and appropriate work, which requires boldness and imagination (Charyton, 2008). Environmental factors, such as educational settings, can facilitate or hinder manifestations of creativity (Csikszentmihalyi, 1984). To be creative, students need to take risks; however, many educational settings discourage risk taking (Sternberg & Lubart, 1992). Education is vital in our global economy, in which the United States has taken the lead in higher education. To address today's national and global challenges, graduates need skills that promote creativity, risk tolerance and innovation (Adams et al., 2006). In this economic climate, the state of the industry depends on innovation and creativity (Deposito, 2009). For sustainability and global competitiveness, we need the next generation of human capital to consist of creative and innovative college graduates. Creativity has become a necessity for global competitiveness in our society.

The problems surrounding the magnitude of novelty for an idea to be deemed as genuinely creative are not easy to answer (Kaufmann, 2004); however, while creativity remains a complex topic, several theories have gained general support. First, all people possess creativity and creative problem solving abilities to some extent (Gupta, 1988; Weisberg, 1986, 2006). Second, while recognizing that many people have creative abilities, some individuals tend to be more creative than others (Gough, 1985; Sternberg, 2001). Third, creativity may be a manifestation of cognitive skills developed as a product of one's environment. Last, creativity can be explained by a combination of these themes, known as "the investment theory"--a willingness and ability to buy low (pursue an idea with potential) and sell high (move onto the next new and unpopular idea) (Sternberg & Lubart, 1996, 1999). According to the investment theory, creativity requires distinct but interrelated resources such as intellectual abilities, knowledge, styles of thinking, personality, motivation and environment (Sternberg, 2003). Furthermore, there is a strong need to move towards an empirically-driven development of new tools and environments for creativity (Burkhardt & Lubart, 2010).

CREATIVITY CONSTRUCTS

Several aspects of creativity and personal attributes are of particular relevance to cognitive risk tolerance. We detail these constructs in order to illustrate our purpose and hypotheses.

Cognitive risk tolerance. Cognitive risk tolerance is a construct that was first developed through observations of college students and how they prefer unconditional positive regard (Snelbecker, 1967). Through unconditional positive regard, students can learn to voice their opinion in the classroom. This may facilitate the higher order thinking needed for creativity.

The concept of cognitive risk tolerance derives from Snelbecker's (1967) early research on personality correlates of behavior and achievement. His interpretation of studies published in the professional literature concerning 'underachievement' and 'academic apathy' observed among many female college students in the 1950s and early 1960's was that these phenomena were due to worry about anticipated academic failure and/or social rejection (Feldman, 2003, p. 9).

Snelbecker (1967) found that students' perceptions of therapists were more favorable when students viewed them demonstrating empathic understanding, a high level of regard, and unconditionality of regard. Unconditional positive regard has been consistently shown to be conducive for creativity in the classroom (Runco, 2007).

Sensible, advantageous risk taking can be considered a form of cognitive risk tolerance. Cognitive risk tolerance is defined as an individual's ability to formulate and express one's ideas despite potential opposition, ridicule, or negative assessment in regard to reputation, integrity and honor (Charyton, 2005; Charyton, 2008). It is believed that a person who is more likely to demonstrate cognitive risk tolerance is more likely to demonstrate attributes of creativity (G.E. Snelbecker, personal communication, August, 27, 2003; Farley, 1991).

Cognitive risk tolerance may be needed when making decisions under uncertainty. Cognitive risk tolerance is similar to Sternberg's (2003) theory of intelligence and creativity in that cognitive risk tolerance includes critical, creative, and cognitive thinking processes that can be developed over time. Furthermore, like Sternberg's (2003) investment theory, cognitive risk tolerance is a decision or choice. Cognitive risk tolerance may be exhibited differently in various settings (such as home, school, or work) and may vary based on individual differences. Individuals may respond differently in various settings when being exposed to uncertainty.

Tolerance for decision making under uncertainty. Persons take risks when making judgments without knowing the actual outcome or consequences (Kahneman, 2003 a; Kahneman & Tversky, 1984). In the decision making process, variants of uncertainty differ based on degrees of confidence (Kahneman & Tversky, 1982). These cognitive biases can have implications for judged probabilities (Tversky & Kahneman, 1986). In particular, problem situations combine novelty and familiarity, which are necessary in order to reach appropriate solutions (Simon, 1986).

Nobel laureate Kahneman (2003b) discussed risk taking and intuitive judgments that led to his Nobel Prize in Economics with Tversky. Kahneman reflected on his comfort collaborating with Tversky, which was fostered by openness and humor; these are characteristics of creativity (Runco, 2007). Creative persons also exhibit risk taking (Farley, 1991; Sternberg, 1999; Sternberg & Lubart, 1996) and self-confidence (Amabile, 1994; Feist, 1999).

Unlike risky behavior, creative people may take sensible risks and produce ideas that are respected (Sternberg, 2003). Perceptions of risk and benefit influence the decision making process (Slovic, Finucane, Peters, & MacGregor, 2004; Slovic & Peters, 2006). Analytic tools, such as risk assessment and decision analysis, increase rationality of experimental thinking (Slovic & Peters, 2006). These are concepts that are consistent with cognitive risk tolerance.

Cognitive risk tolerance in comparison with tolerance for ambiguity. Cognitive risk taking may be consistent with tolerance for ambiguity. Plucker and Renzulli (1999) stated, "Tolerance for ambiguity is often mentioned as a personality characteristic of creative individuals" (p. 42). Although cognitive risk tolerance may be interrelated with personality-centered approaches in that it is also a cognitive orientation consistent with a tolerance for ambiguity and cognitive responses to uncertainty (Frenkel-Brunswik, 1949).

Cognitive risk tolerance is dissimilar to intolerance of uncertainty, worry and anxiety (Ladouceur, Talbot & Dugas, 1997) and rigidity in cognitive processes (Frenkel-Brunswik, 1948). Cognitive risk tolerance has been thought of as a positive psychology construct (Feldman, 2003) in that it is the antithesis of ambivalence within the individual and "black and white" solutions (Frenkel-Brunswik, 1949). Tegano (1990) suggested that tolerance for ambiguity may be a trait that is central to creativity and creative thinking; in her study, creativity (defined as intuition and perception), playfulness, and tolerance for ambiguity were interrelated moderately and positively.

Zenasni, Besancon, and Lubart (2008) also found tolerance for ambiguity and creativity were interrelated moderately and positively. Similarly, cognitive risk tolerance has been shown to be related with creativity moderately and positively (Charyton, 2005, 2008; Charyton & Snelbecker, 2007). However, more research is indicated to compare various college majors and other individual difference variables and to assess the degree of this potentially predictive relationship.

The few existing risk tolerance measures mainly assess financial investing (Grable & Lytton, 1999) and not risk tolerance across situations or settings (Jackson, Hourany, & Vidmar, 1972). Calculated risks may be a part of financial planning but are also important for decisions in other settings besides investment. There is a need to predict risk tolerance beyond stocks and bonds (Faro & Rottenstreich, 2005).

Contrarianism. Currently, the internationalization of markets leads toward economic uncertainty (Pyhrr, Roulac, & Borne, 1999). Contrarianism is a view that each investment is a risk-reward decision (Atwater & Malick, 2009). Doing the opposite of others during market extremes (unlike herding) is contrarian (Atwater & Malick, 2009). However, being a contrarian is not disagreeing for the sake of disagreeing. Instead, contrarianism is about trying to recognize when available money is in or out of the market. Homogeneity may cause herding while contrarians are more likely to go against the grain and act independently (Cipriani & Giordino, 2008). Fisher (1995) recommends that contrarianism works during extreme market turns. Currently, we have had extreme market turns globally. Cognitive risk tolerance may offer a construct and method for measuring a greater tolerance for leadership and uncertainty during turbulent economic times that are a part of our current global economy and global political era. The current economic times are affecting college graduates. Currently, fifty percent of undergraduates cannot find employment in the United States.

In the academic setting, cognitive risk tolerance has also been found to be related to positive psychology constructs such as academic hardiness and resilience (Feldman, 2003). Previous research has indicated creativity (creative personality and creative temperament) were moderately and positively related to cognitive risk tolerance (Charyton, 2005, 2008; Charyton & Snelbecker, 2007). However, further research is necessary to establish the predictors of cognitive risk tolerance as well as to identify similarities and differences in various college majors.

PERSONAL ATTRIBUTES

Personal attributes may explain higher levels of individual creativity (Eysenck, 1994; Gough, 1985). In creativity research, students in art (Gough, 1995) and psychology (Gough, 1979, 1995) have demonstrated higher creativity. Researchers in physical sciences and engineering (Gough, 1964, 1995) have also demonstrated more creativity. Less creative fields included military academy students (Gough & Bradley, 1996) and police officer applicants (Gough, 1995). Personality similarities and differences are prevalent in artistic and scientific fields (Feist, 1998, 1999). "Creative people are able to approach solutions in novel and original ways and are not likely to be functionally fixated as less creative people" (Feist, 1998, p. 301). Kaufmann (2003) suggested that "scientific ambition" maximizes creative accomplishments (p. 238). Creative scientists demonstrated traits that were more aesthetically oriented, ambitious, confident, deviant, dominant, expressive, flexible, intelligent, and open to experiences than noncreative peers. Creative artists demonstrated traits that were more aggressive, cold, egocentric, impulsive, antisocial, creative, and tough minded than non-artists (Feist, 1998).

Agarwal and Kumari (1982) suggested further research is necessary to examine gender similarities and differences for both creativity and risk tolerance. This is especially true since historically women have been underrepresented in research (Simonton, 2002). In the 1950s and early 1960s literature suggested that underachieving female students were more sensitive to negative social evaluation and avoided taking academic risks (Feldman, 2003). Karakowsky and Elangovan (2001) found distinct gender differences in risky decision making and risk tolerance. Males tended to be more tolerant of risk and were apt to take greater risks. Dewett (2006) also reported significantly higher risk taking in males, yet differences were inconsistent across cultures. Chauvin, Hermand, and Mullet (2007) reported gender differences in risk perception; however, no differences were found when creativity was considered. Previous creativity studies also suggest no gender differences (Agarwal & Kumari, 1982; Charyton, 2005, 2008; Charyton & Snelbecker, 2007; Kaufman, Baer, & Gentile, 2004; Matud, Rodrigues & Grande, 2007). Baer and Kaufman (2008) identified a need to investigate gender differences in creativity. Their extensive review found 35 studies with no differences, 9 studies where females scored higher in creativity and 30 studies with mixed findings.

Furthermore, few risk tolerance studies have addressed age and ethnicity. Previous studies suggest older adults had less cognitive flexibility toward risks (Hallahan, Faff, & McKenzie, 2004; Wallach & Kogan, 1961) and that ethnic minorities had less risk tolerance (Sung & Hanna, 1996; Yao, Gutter, & Hanna, 2005). Feldman (2003) found that mixed ethnic groups had marginally lower levels of cognitive risk tolerance than African Americans or Caucasians.

PURPOSE AND HYPOTHESIS

The goal of this study was to further examine the degree of the relationship between cognitive risk tolerance and general creativity. We also wanted to determine predictors of cognitive risk tolerance by investigating the relationship of the independent variables (demographics, creative personality and creative temperament) on the dependent variable (cognitive risk tolerance) without controlling for variables.

Based on existing creativity research, our hypothesis was that some college majors, such as engineering or art, may be more likely to exhibit cognitive risk tolerance than other majors (Charyton & Snelbecker, 2009). Previous creativity research (Gough 1964, 1979, 1995) suggested that there may be significantly greater differences in art, psychology, engineering, or the physical sciences.

We also investigated demographic factors by underrepresented groups in regard to gender, age and ethnicity since these variables may also contribute or detract from higher levels of cognitive risk tolerance. We wanted to determine which demographic characteristics and general creativity attributes predict higher cognitive risk tolerance.

Since cognitive risk tolerance has been shown to be related academic performance (Feldman, 2003), we also wanted to determine if higher levels of creativity (creative personality and creative temperament) would be associated with higher levels of cognitive risk tolerance in various college majors. Creative attributes may be predictive of tolerance (diversity and cognitive risk tolerance) that can be demonstrated in higher education. Slovic (2001) suggested that linear models are remarkably successful in predicting judgments of performance and risk. Therefore, a linear model on significant predictive factors in college students may be useful to assess how general creativity could lead toward higher cognitive risk tolerance that would be expressed in higher education.

METHOD

Participants

After Institutional Review Board (IRB) approval was obtained for the research, data were collected from 1164 college students (676 males, 487 females), who volunteered to participate, from various college majors in engineering, psychology, music and English courses from a large Midwestern university in the US. The sample consisted of 81% Caucasian and 19% non-Caucasian students (6% African American, 7% Asian American, 1% biracial, 1% multiracial, 3% other) with an average age of 20 years.

Procedure and Instruments

Students were invited to volunteer to participate in our study from engineering, psychology, music and engineering courses. They were administered a demographic questionnaire, the Creative Personality Scale (CPS), the Creative Temperament Scale (CT), and the Cognitive Risk Tolerance Scale (CRT), in a fixed order, after consenting to participate in the research study.

A Demographic Questionnaire was administered requesting information such as age, gender, ethnicity, class, college major and class rank.

The Creativity Personality Scale (CPS) of the Adjective Checklist (ACL) (Gough, 1979) was administered to assess creativity attributes. According to Gough (1979), aesthetic dispositions are related to creative potential. This instrument was designed as an appraisal of the self. The CPS measures personality attributes such as unconventionality and individualism (Gough, 1979). Gough developed the CPS to address the challenges of measuring creativity in comparison with creative processes and products. "To develop a criterion for the 70 interviewed participants, the interviewers' checks on five adjectives and placement of five Q-sort items were summed. The five adjectives were imaginative, insightful, intelligent, original and resourceful" (Gough, 1979, p. 1400). Ratings of creativity were converted into standard scores. Thirty items were selected to be included in the CPS. In this sorting protocol, 1 point was given to each of the 18 positive items was checked, and 1 point was deducted for each of the 12 negative items selected. This test was selected because it is highly regarded, reliable and widely used as a general creativity test (Oldham & Cummings, 1996; Plucker & Renzulli, 1999; Runco, 2007; Sternberg, 1999).

The Creative Temperament Scale (CT) (Gough, 1992) was adapted from the California Psychological Inventory (CPI), which was designed to assess personality characteristics and predict what people will say and do in specific contexts. Gough (1992) suggested that this measure is capable of forecasting creative attainment in various domains. Any domain requires a specific set of skills, yet this measure assesses general personality qualities that exist across disciplines. A split-halves reliability of .73 was found. Items were found to correlate with observers' rating of creativity (.44). Gough (1992) stated "the CT scale correlated with .33 with the overall composite for creativity" [and] "the overall rating of creativity for this sample correlated .47 with the scores of the CT" (p. 245). The Creative Temperament Scale is one of the special purpose scales of the CPI.

The Cognitive Risk Tolerance Scale (CRT) (Snelbecker, McConolgue, & Teitlebaum, 2001) consists of 35 self report items that assess an individual's ability to formulate and express their ideas. The CRT was developed to assess the ability to formulate and express one's ideas despite potential opposition in educational, work, family and social settings. Responses to items are on a Likert Scale ranging from 0 (Very Strongly Disagree) to 9 (Very Strongly Agree). Higher scores indicate higher levels of cognitive risk tolerance. In settings, individuals face choices of whether to remain with the crowd or to challenge the status quo and express a different, their own, opinion. The CRT was developed as an extension of an earlier risk tolerance model developed by Snelbecker and colleagues (Roszkowski, Snelbecker, & Leimberg, 1989; Snelbecker, Roszkowski, & Cutler, 1990). During a pilot study (N=78) data indicate moderately high reliability with a Cronbach's alpha coefficient of .76. According to J. Feldman (personal communication, May 27, 2003; Feldman, 2003), reliability of the revised measure was improved with a Cronbach's alpha coefficient of .78. This instrument showed modest correlations with other strength-based variables and was correlated with academic hardiness in her dissertation (Feldman, 2003). Charyton and Snelbecker (2007) found the CRT measure was moderately correlated with the CPS (r = .36, p < .01) and CT (r = .34, p < .01), which were moderately related to each other (r = .35, p < .01). See Appendix A for examples of CRT questions in different settings.

RESULTS

First, demographics are reported, followed by Cronbach's alpha on the CRT and CPS and a Kuder-Richardson on the CT and a regression analysis. Demographics of college students are provided in Table 1. Students were [M.sub.age] = 20.18 SD = 3.71 years. In this study, the CRT yielded a Cronbach's alpha coefficient of .82 while the CPS yielded a Cronbach's alpha of .71 and the CT yielded a Kuder-Richardson of .65. Correlations among the six independent variables (see Table 2) with the dependent variable (cognitive risk tolerance) were generated using the model building data set (N = 1164) are presented in Table 3. Correlation coefficients of the independent variables with the dependent variable ranged from -.38 to .42. Examination of the correlation matrix indicated that creative personality, creative temperament, college major and age were significantly correlated with the dependent variable (p < .01). The correlations of the remaining independent variables with the dependent variable were found to be non-significant (p > .01). An alpha level of .01 was determined to be set due to the large sample size in order to minimize false positives.

A multiple linear regression analysis was performed on 1023 college students. The composite cognitive risk tolerance score was regressed on the variables (age, gender, ethnicity, college major, creative personality and creative temperament). For the college major, seven dummy variables (engineering, education, medical/health professions, psychology/social sciences, music, business and arts/architecture) were used into the analysis. Undecided was omitted from the analysis due to the need to omit one category in this regression analysis as a control. The overall fit of the model was found to be statistically significant ([R.sup.2] = .280; [F.sub.(12,1023)] = 33.13,p < .001). Significant cognitive risk tolerance characteristics included creative personality, creative temperament, age, ethnicity, social sciences (psychology, history and political science) and engineering college majors.

Examination of the residual plots indicated no systemic variation of the error terms with the level of values of any of the continuous independent variables and the assumption of normality of the error term distribution was not violated. The problem of multicollinearity was not present. While entering data, it was suspected that there were some outliers. To investigate these outlying cases, post-analysis diagnostic statistics (leverage value, studentized deleted residual, DFFITS value) were generated for each observation. Using a criterion value of greater than 2(p/n) = .020 (where p is the number of independent variables and n is the sample size) as a guide for identifying outlying observations with regard to the independent variables, the values of leverage point identified 62 observations as outlying with regard to the independent variable. Using the criterion value of greater than 2 as a guide for identifying outlying observations with regard to the dependent variable, the absolute value of studentized deleted residuals identified 64 observations as outlying with regard to the dependent variable. Using the criterion value of greater than 2[(p/n).sup.1/2] = .198 as a guide for identifying influential observations, absolute values of DFFITS identified 101 cases as influential. Comparison of the Cook's D for each of these 101 cases was far below the 50th percentile ([F.sub.(12, 1206, .50)] = .95), indicating that the influence of these cases was not severe enough to employ remedial measures.

Table 3 provides the results of the final analysis for data set (N = 1164) based on the same model building variables. As indicated, creative personality (B = 2.57; t = 11.84, p < .0001), creative temperament (B = 1.28; t = 7.87,p < .0001), social science majors (psychology, history and political science) (B = 12.30 t = 3.47, p < .01), engineering majors (B = 5.99 t = 1.99,p < .05), age (B = 1.20; t = 5.93,p < .001), and ethnicity (B = 4.14; t = 2.20,p < .05) were statistically significant.

DISCUSSION

This study further supports that general creativity and cognitive risk tolerance are interrelated constructs (Charyton, 2005, 2008; Charyton & Snelbecker, 2007). General creativity, as measured by creative personality and creative temperament, predicted higher cognitive risk tolerance. We also found that social science and engineering majors, older students, and ethnic minorities (non-Caucasians) tended to demonstrate higher cognitive risk tolerance. There were no significant gender differences in cognitive risk tolerance.

While creativity and cognitive risk tolerance overlap, they are still separate constructs, as evidenced by their moderate relationship. Cognitive risk tolerance may be expressed in educational, occupational, technological, and leadership (family, social and societal) settings, see Appendix A. An individual's perceptions about decision making and risk tolerance may influence their expression of ideas in society. Like contrarianism, a person may need to go against the grain to express their ideas (Cipriani & Giordino, 2008). Furthermore, cognitive processes may influence behavior and willingness to take leadership and stand up for opinions despite opposition. Studies on creative temperament have also indicated that creative individuals prefer autonomy and independence (Batey & Furnham, 2006).

These findings support our hypothesis that some majors exhibit greater cognitive risk tolerance. Social science majors (psychology, history and political science) often study abstract constructs that require greater tolerance for ambiguity. For example some physicists would clearly stay away from psychology due to its ambiguous nature (R. Perry, personal communication, February 8, 2011). Engineering students take cognitive risks while testing data to make informed design decisions (Schlosser, Parke & Merrill, 2008). These risks are calculated since lives may be at stake when developing designs. Previous research has shown that higher levels of education were strong predictors for financial risk taking (Finke & Huston, 2003). Students and graduates may be taking calculated risks when expressing their ideas in educational and occupational settings, respectively. These individual differences may influence decision making, tolerance for ambiguity and risk tolerance.

We speculate that as people age and develop, they may have less fear about vocalizing different opinions within various settings (Deakin, Aitken, Robbins, & Sahakian, 2004). Our findings suggest a greater tolerance for decision making and self expression as one ages, which differs from previous studies (Finucane, Mertz, Slovic & Schmidt, 2005; Hallahan, Faff, & McKenzie, 2004; Wallach & Kogan, 1961; Yook & Everett, 2003). Previous studies found males were more likely to engage in risk taking and risky decision making than females (Dewett, 2006; Karakowsky & Elangovan, 2001). However, our study is similar to creativity studies (Baer & Kaufman, 2008; Charyton, Basham, & Elliott, 2008; Charyton, 2008; Charyton, 2005; Charyton & Snelbecker, 2007; Kaufman, Baer, & Gentile, 2004; Matud, Rodriguez, & Grande, 2007) that identified no gender differences. We also found that nonCaucasians demonstrated higher cognitive risk tolerance, in contrast to previous research (Sung & Hanna, 1996; Yao, Gutter, & Hanna, 2005). We speculate that experiences of being a minority may influence the ability to express opinions that differ from the majority.

Limitations

Limitations include unequal sample sizes in college major. Previous studies did not find that male and female engineering and psychology students differed in cognitive risk tolerance (Charyton, Jagacinski, & Merrill, 2008). Further research should include more students from business, music, the arts, and architecture, as well as examining academic and creative achievement in college. Future studies will address the relationship between cognitive risk tolerance with openness to experience, tolerance for ambiguity and various aspects of creativity including creative performance and divergent thinking in addition to self report measures. Longitudinal research would also contribute toward increasing our understanding creativity and cognitive risk tolerance.

Implications

Florida (2005) suggested that the three Ts of tolerance, talent, and technology are relevant to creativity and global competitiveness. Since creative personality and creative temperament predict cognitive risk tolerance, it is plausible that students demonstrate creativity through risk tolerance.

Our findings suggest that individual differences impact cognitive risk tolerance. As a newly developed measure, these results further establish convergent validity. Cognitive risk tolerance is advantageous risk taking at times of uncertainty. Taking calculated risks may offer graduates leadership opportunities as they enter the workforce. Students need to learn these skills in the classroom. Cognitive risk tolerance could be used to assess skills for handling uncertainty in various settings. Future research is also needed outside of the university setting.

CONCLUSIONS

"Creativity is a valued commodity in every kind of human endeavor" (Gough, 1979, p. 1398). Yet, at the same time, creativity has traditionally been one of psychology's orphans (Sternberg, 2003). This pattern for creativity being neglected is evident in other fields also. At the same time of Guilford's presidential address to the American Psychological Association (APA) the importance of creativity research and the need for assessment (Guilford, 1950), Vannevar Bush's interests in engineering creativity led to establishment of the National Science Foundation (NSF) in the 1950's. Yet, "as interest in engineering design faded in most engineering schools, creativity was put on a back burner" (Ferguson, 1992, p. 57). The pace of innovation could be accelerated with a greater societal acceptance of creativity (Maugh, 1974).

Cognitive risk tolerance and creativity need to be fostered in higher education. Some college majors expressed creativity and cognitive risk tolerance differently. A variety of experiences may contribute toward self-expression. Higher education is the optimal setting to encourage students to effectively voice their cognitions despite potential rejection or embarrassment. Students will also learn to handle conditions of uncertainty in other settings. Innovation is a driving force in the United States. The United States is a leading and driving force in higher education. Through the promotion of risk tolerance and creativity, college students can gain higher order cognitive skills that contribute to innovation in our global society.

Correspondence concerning this article Christine Charyton, Department of Psychology Ohio State University, 1835 Neil Avenue, Columbus, OH 43210, USA. E-mail: charyton.1@osu.edu

Christine Charyton,

Ohio State University,

Glenn E. Snelbecker

USA Temple University, USA

John O. Elliott and Mohammed A. Rahman

Ohio State University, USA

Professor Glenn E. Snelbecker passed away during the final write up on this manuscript on January 24, 2010 at 78 years of age. He was working on the cognitive risk tolerance scale for over 20 years which received positive interest from the New York Times and other sources. He was also an honored guest in South Korea for his work in Learning Theory, Instructional Theory and Psychoeducational Design. His legacy will continue through the work of his previous students that he mentored, co-authored the cognitive risk tolerance scale, and published cognitive risk tolerance findings with him.

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APPENDIX A

Cognitive Risk Tolerance Scale (2)

This scale has been designed to assess your level of cognitive risk tolerance. Your identity will remain anonymous and all of your responses will be completely confidential. Please respond to the following items as accurately as possible by filling in the appropriate number in the column to the left of each item. Please refer to the key for a description of the corresponding number. Thank you for your participation. It is greatly appreciated.

Educational

0 1 2 3 4 5 6 7 8 9

7. Most students view complicated problems as challenges that can help them learn new ideas.

0 1 2 3 4 5 6 7 8 9

26. I prefer to work on challenging problems that have more than one possible "correct answer".

Occupational

0 1 2 3 4 5 6 7 8 9

8. I generally wait to hear what a boss or other authority figure says before expressing my views.

0 1 2 3 4 5 6 7 8 9

27. Most people fear being discredited in their job or profession. 4

Technological

0 1 2 3 4 5 6 7 8 9

4. I like using computers in new ways to get things done.

0 1 2 3 4 5 6 7 8 9

13. I'd rather use an old piece of equipment to avoid learning how to handle a newer version.

Leadership

Family

0 1 2 3 4 5 6 7 8 9

9. It is disrespectful to challenge your parents' beliefs.

0 1 2 3 4 5 6 7 8 9

25. It is important to develop my own views even when they challenge the beliefs of my parents.

Social

0 1 2 3 4 5 6 7 8 9

21. I enjoy discussions where other people challenge me to justify my views.

0 1 2 3 4 5 6 7 8 9

16. I prefer conversations that force me and other people to think about things in new ways.

Societal

0 1 2 3 4 5 6 7 8 9

14. Many of society's rules should be challenged.

0 1 2 3 4 5 6 7 8 9

32. Supporting unpopular opinions will hurt your credibility.

Decision Making

0 1 2 3 4 5 6 7 8 9

20. Decisions may be "good" even when my original beliefs turn out not to be correct.

0 1 2 3 4 5 6 7 8 9

22. Making some poor decisions is better than making no decisions

Risk Tolerance

0 1 2 3 4 5 6 7 8 9

33. Being truly successful includes having ideas that contradict what other people say.

0 1 2 3 4 5 6 7 8 9

10. Long-held beliefs are practically "sacred" and should not be challenged.

(1) An earlier version of this paper was presented at the 2008 American Psychological Association Convention in Boston, MA as a part of APA Division 10 programming..

(1) AUTHOR NOTE. Christine Charyton, Department of Psychology, Department of Neurology, Ohio State University, Glenn E. Snelbecker, Psychological Studies in Education, Temple University, John O. Elliott, Department of Neurology, Ohio State University and Mohammed A. Rahman, Office of Information Technology, Ohio State University.

(2) Copyright permission. From Cognitive risk tolerance survey. By G.E. Snelbecker, T. McConologue and J.M. Feldman, 2001, Unpublished manuscript. Copyright 1999, 2001 by M*A*T*C*H- Snelbecker McConlogue & Teitelbaum, Wyndmoor, PA. Reprinted with permission.

Table 1
Descriptive Characteristics of College Students

Variable                         n (%)

Gender                          676 (58)
  Male                          487 (42)
  Female
Ethnicity
  Caucasian                     931 (81)
  Non-Caucasian                 223 (19)
College Major
  Engineering                   469 (40)
  Education                     179 (15)
  Medical/Health Professions    139 (12)
  Psychology/Social Sciences    119 (10)
Music                            29 (3)
  Business                       88 (8)
  Arts/Architecture              59 (5)
  Undecided                      82 (7)

n =1164

Table 2
Correlation Matrix: Model Building Data

Predictors

Variable                     X1        X2        X3

Predictors
X1 Age                       --
X2 Gender                   .08 **     --
X3 Ethnicity                .07 *     .02        --
X4 College Major           -.12 **   -.38 **   -.04
X5 Creative Personality    -.04       .12 **   -.08 **
X6 Creative Temperament     .05       .06 *     .08 **

Dependent
Cognitive Risk Tolerance    .18 **    .08 *     .04

Predictors

Variable                     X4        X5        X6

Predictors
X1 Age
X2 Gender
X3 Ethnicity
X4 College Major             --
X5 Creative Personality    -.15 *      --
X6 Creative Temperament    -.02      .34 **

Dependent
Cognitive Risk Tolerance   -.12 **   .42 **    .35 **

Note: * indicates significance at 5 percent level,
** indicates significance at the 1 percent
Level Overall n = 1164

Table 3
Results of Final Linear Regression Analysis for Cognitive
Risk Tolerance in College Students

Variable                  b      SE    [beta]       t      p

Age                    1.20     .20     .161     5.93    .000
Gender                 2.84    1.66     .052     1.71    .088
Ethnicity              4.14    1.88     .059     2.20    .028
Major:
  Psychology/
  Social Sciences     12.30    3.54     .136     3.47    .001
  Engineering          5.99    3.01     .108     1.99    .047
  Music               -8.50    5.61    -.046    -1.52    .130
  Education            5.59    3.24     .075     1.72    .085
  Medical/Health       6.53    3.45     .076     1.90    .058
  Business             1.83    3.75     .018      .49    .625
  Arts/Architecture    6.06    4.27     .048     1.42    .156
CPS Total Score        2.57     .22     .344    11.84    .000
CT Total Score         1.28     .16     .227     7.87    .000

[R.sup.2] = .280; F(12, 1023) = 33.13, p < .001
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Author:Charyton, Christine; Snelbecker, Glenn E.; Elliott, John O.; Rahman, Mohammed A.
Publication:The International Journal of Creativity and Problem Solving
Date:Oct 1, 2013
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