Cheating The Researcher: A Study Of The Relation Between Personality Measures And Self-Reported Cheating – Statistical Data Included
Maleah F. Thorpe
Students from a moderate-sized state university and a private liberal arts college volunteered to complete a questionnaire that surveyed rates of various forms of academic dishonesty and measured three personality characteristics, self-esteem, locus of control, and social disability. The data confirm previous observations that some forms of cheating (e.g., copying homework) occur at greater rates than others (e.g., cheating on exams). The data also challenge previous observations concerning sex differences and differences in the rates of cheating between larger and smaller institutions. There were also significant negative correlations between the Crowne-Marlow (1960) measure of social desirability and rates of cheating.
Academic dishonesty, or cheating, is a ubiquitous phenomenon in higher education. Bowers (1964) conducted one of the first and most comprehensive studies of cheating behavior among college students. Although his work defies quick summarization, one can draw several generalizations. First, academic dishonesty of all kinds occurs within all institutions of higher education. Students copy each other’s homework, plagiarize, sneak crib notes into exams or copy from others, and lie to their faculty about the cause of missed deadlines.
A second generalization is that students with lower overall academic standing are more likely to cheat. Furthermore, students who cheat are motivated more by high grades and less by the acquisition of knowledge. Bowers (1964) also reported the relation between several demographic variables and cheating. Specifically, men are more likely to cheat than women, and cheating is more pervasive in larger institutions than smaller institutions.
During the past 30 years numerous researchers have replicated and expanded upon Bowers’s findings (see Davis, Grover, Becker, & McGregor, 1992; and Newstead, Franklyn-Stokes, & Armstead, 1996 for brief reviews). For example, McCabe and Bowers (1994) and Spiller and Crown (1995) reported that contemporary rates of cheating are equivalent to rates reported 30 years earlier. Similarly, several researchers (Anderson & Obenshain, 1994; Baldwin, Daugherty, Rowley, & Schwartz, 1996; Dans, 1996) reported evidence of academic dishonesty in medical schools. These results replicate the results reported for undergraduate institutions, namely, men are more likely to cheat than women and students with a history of cheating are likely to continue to cheat.
Other researchers have examined rates of cheating in different countries. Newstead et al. (1996) examined cheating behaviors among students at English colleges and universities. Their results demonstrated that the rates and patterns of cheating behavior replicate those found among American students. Davis, Noble, Zak, and Dreyer (1994) compared the levels of cheating between American and Australian students, finding that American students report more cheating than do their Australian counterparts. Davis et al. also found that Australian students are motivated more by learning than by obtaining high grades. By contrast, American students indicated a greater motivation for obtaining high grades. In similar research, Waugh, Godfrey, Evans, and Craig (1995) compared Australian student’s attitudes toward cheating to the attitudes of students from other countries. They concluded that Australian students are less tolerant of cheating because of a “cultural emphasis on fair play combined with personal achievement” (p. 73).
Although the existence of cheating is well documented, its specific causes have not been extensively studied until recently. During the past decade, several researchers have begun to examine the relation between personality and attributional variables that may help predict cheating behavior. For example, several researchers examined the relation between the Type A personality profile and cheating (e.g., Huss, et al., 1993; Perry, Kane, Bernesser, & Spicker, 1990). One of the defining characteristics of the Type A personality profile is competitive striving for achievement (Friedmand & Rosenman, 1977). As such, the Type A profile may be associated with high motivation to earn high grades and possible Cheating. Consistent with this expectation, Davis, Pierce, Yandell, Arnow, and Loree (1995) demonstrated that students characterized by a Type A personality have a higher motivation to learn and earn high grades than Type B students. In addition, Type A students are more likely to cheat on a task designed in which they did not have direct control. There was, how, ever, no correlation between Type A personality and reported levels of cheating.
The lack of a correlation between Type A personality and cheating may represent several conditions. First, students who cheat may deny their transgressions thus obscuring the true relation between personality and behavior. Second, this personality scale may not measure aspects of personality that portend the proclivity for cheating. Academic dishonesty is, most likely, a multidetermined behavior that includes environmental conditions, such as opportunity; as well as a host of personality and motivational factors. Finally, current measures of the Type A personality may lack the construct validity needed to detect the covariance among variables (e.g., Matthews, 1982). Therefore, greater attention should be given to examining a broader range of personality factors that will help explain this complex phenomenon.
The purpose of the present study was to achieve several goals. First, we were interested in the degree to which self-reports of cheating are affected by response bias. To date, the majority of researchers studying cheating have depended on students completing anonymous surveys of cheating behaviors. Although this technique has been useful, few researchers have examined personality factors that may affect the veracity of these reports. Given the general negative connotation of cheating, many students may be motivated to deny cheating even in an anonymous form. If true, cheating surveys may actually under represent the rate of academic dishonesty among university students.
A second goal of our research was to examine the relation between two common personality characteristics, locus of control and self-esteem, and cheating. We hoped to determine the degree to which cheating behaviors can be understood by examining the student’s perception of self. Locus of control is the attribution concerning whether individuals believe they control their destiny or are controlled by external forces (Leftcourt, 1991; Rotter, 1966). We anticipated that students who believe they have little control over their lives may be more likely to cheat to compensate for the lack of control. Self-esteem is a general measure of the degree to which the individual sees him or herself as a good person (Blascovich & Tomaka, 1991). We examined the relation between self-esteem and cheating to determine whether students of higher or lower self esteem are more likely to cheat.
Previously, Antion and Michael (1983) found no relation between locus of control and self reports of cheating. The lack of effect may be due to several factors. First, their sample size was small (e.g., N = 148). Although cheating may be ever-present, low rates of the behavior require larger samples in order to ensure sufficient statistical power to detect a relation between personality and cheating. Antion and Michael also used a non-standard locus of control scale that did not reflect more recent theoretical development in locus of control theory or measurement technology.
Finally, we compared the rates of various cheating behaviors among men and women at both a small private liberal arts college and a moderate-sized state school with an open enrollment policy. We also compared the rates of different forms of cheating (e.g., plagiarism vs cheating on an exam). Although cheating is a pervasive phenomenon, some forms of cheating are more prevalent than others. For example, Newstead et al. (1996) demonstrated that plagiarism occurs at a much greater frequency than cheating during exams. Previous research has also demonstrated that men are more likely to cheat than women, and that the rate of cheating is greater at larger institutions.
A total of 310 students completed a questionnaire surveying rates of academic dishonesty and a series of questions measuring various personality dimensions. Of the whole sample, 138 (57 men, 81 women) were enrolled in one of five sections of an introductory psychology course taught at a small (enrollment of 1,000) private liberal arts college located in the eastern midwest. Some, but not all students, received extra credit for participation in the study. The average age of these students was 20.4 (SD = 4.14). The other 172 participants (48 men, 124 women) were enrolled in introductory psychology courses taught at a moderate sized (enrollment of 5,000) regional state college in the midwest. All students at the larger institution received extra course credit for their participation in the study. Their average age was 22.6 (SD=5.39). The difference in mean age between the two institutions is statistically significant, t(308)=4.04, p [is less than] .05; [w.sup.2]=0.047, but the effect size is small.
All respondents completed a survey consisting of two parts. The first part contained a questionnaire that recorded demographic information about the student (e.g., age, sex, class standing, work history, and participation in varsity sports) and his or her history of cheating behavior. Specifically, we asked whether or not students had a) cheated on tests, b) plagiarized material, c) submitted another student’s paper as their own, and d) copied another student’s homework. Students were asked the same questions about cheating while in high school and college. If students indicated that they had performed a type of cheating, they were then asked to indicate on a 7-point scale the relative frequency of the cheating (1 = once or twice, 7 = 13 or more times).
The second part of the questionnaire consisted of 67 questions derived from the Crowne-Marlowe Social Desirability Scale (Crowne & Marlowe, 1960), Levensen’s Locus of Control Scale, (1973, 1981) and Rosenberg’s Self-Esteem Scale (1965). To ensure consistency in response style, the response scale for all questions was a 6-point scale ranging from -3 (strongly disagree) to 3 (strongly agree). We randomly arranged the order of the questions.
Crowne-Marlowe. The Marlowe-Crowne Social Desirability Scale (CM: Crowne & Marlowe, 1960) consists of 18 the statements that describe desirable but rare behaviors (e.g., consistently good table manners) and 15 statements describing undesirable but common behaviors (e.g., seeking revenge). As a generality, the Crowne-Marlowe scale is an index of impression management and can be used to determine the degree to which test results are contaminated by the individual’s desire to present a favorable impression of his or her character. Paulus (1991) offered an alternative interpretation of the test, noting that more current research indicates that the instrument measures an individual’s need for approval or, more specifically, need to avoid disapproval. Higher scores represent greater need for approval.
Locus of Control. Levensen’s (1973, 1981) Locus of Control (LOC) scale consists of three subscales of eight items each. The three scales include Locus of Control-Internal (LOC:I), Powerful Others (LOC:P), and Chance (LOC:C). The internal scale assesses the degree to which the person believes that he or she is in control of his or her life. The other scales are subdivisions of Rotter’s (1966) concept of external locus of control. The powerful others dimension indicates the degree to which individuals see their lives controlled by people in positions of authority (e.g., teachers and administrators). By contrast, the chance dimension reflects the belief that the events in one’s life can be neither predicted nor controlled. For each scale, higher scores represent a greater identification with the underlying construct. Leftcourt (1991) provided a favorable evaluation of psychometric properties of the instrument.
Self-Esteem. The Rosenberg (1965) Self-Esteem Scale (SE) consists of 10 statements describing positive (e.g., I have good qualities) or negative (e.g., I am a failure) attributions of self. Higher scores indicate greater levels of self esteem. Blascovich and Tomaka (1991) reported the scale has good reliability and validity indices and is recognized as a unidimensional measure of self-esteem.
The first author or the instructor distributed the questionnaire during a class. Instructions accompanying the questionnaire guaranteed anonymity and confidentiality. All students had the opportunity not to participate;none declined.
Table 1 presents the rates of different forms of cheating reported for high school and college work. The column labeled “combined” is the simple total of the types of cheating behaviors. Consequently, scores for this index can range from 0 (no cheating) to 4 (performance of all types of cheating). These data are in keeping with previous reports (e.g., Bower, 1964; Newstead et al., 1996). Namely, men tend to report higher levels of cheating than do women for most forms of cheating. Similarly, students at larger institutions tend to report higher levels cheating than students at smaller institutions, and students report cheating more in high school than in college. Finally, some forms of cheating are more prevalent than others. Specifically, plagiarism and copying homework are more common than cheating on tests or submitting another student’s paper.
Table 1 Proportion of Students Reporting Various Forms of Cheating While In High School and College
High School Cheating
n Paper Test
Men 57 19.6 90.9
Women 81 5.0 72.5
Total 138 11.0 80.0
Men 48 4.2 72.9
Women 124 4.0 53.2
Total 172 4.1 58.7
Men 57 8.9 12.3
Women 81 2.5 6.3
Total 138 5.1 8.8
Men 48 25.5 27.7
Women 124 12.2 16.1
Total 172 15.9 19.3
Grand Mean College 310 11.1 14.6
Men vs. Women z=2.18(*) z=1.64
Small vs. Large College z=-3.01 z=-2.60(*)
High School Cheating
Plagerize Homework Combined
Men 61.4 91.2 2.6
Women 44.3 79.5 2.0
Total 51.5 84.4 2.3
Men 37.5 63.0 1.8
Women 37.1 62.1 1.6
Total 37.2 62.4 1.7
Men 21.1 42.1 0.84
Women 12.3 38.3 0.54
Total 15.9 39.9 0.66
Men 21.3 37.5 1.13
Women 23.6 32.8 0.84
Total 22.9 34.1 0.92
Grand Mean College 19.8 36.7 0.80
Men vs. Women z=0.44 z=0.86 t(308)=2.17(*)
Small vs. Large College z=-1.54 z=1.05 t(308)=2.66(*)
Notes. z score represents standardized score for the difference between proportions. (*) p<.05
A detailed analysis of the data yields a more complex set of results. Regarding sex differences, there is a significant but small effect for the combined index, t(308)=2.17, p [is less than] .05; [w.sup.2]=0.012. When examining the individual forms of cheating using a difference between proportions test, the only significant difference for college cheating is submitting a paper written by another author, z=2.177, p [is less than] .05. These results suggest that the differences in reported cheating rates between men and women may represent a small effect size.
Regarding school size, there is a significant, but small effect size, difference for the combined index, t(300)=2.361, p [is less than] .05; w2=0.015. For the individual forms of cheating, significant results were found for cheating on tests, z = 2.600, p [is less than] .05, and for submitting a paper written be another author, z = 3.009, p [is less than] .05. Comparisons of the proportions among the types of cheating behavior indicated that copying homework was reported significantly more often than plagiarizing material. In turn, students reported plagiarizing material at a higher rate than submitting a paper written by another author. There were no significant differences between plagiarizing and cheating on a test.
The means and standard deviations for the five personality dimensions are presented in Table 2. The data for each test was subjected to a 2 (schools) x (2 (sex) analysis of variance. The resulting F-ratios and mean square errors (MSEs) are also listed in Table 2. There were no significant effects observed for SE or the LOC:I. There were, however, significant main effects for sex within the LOC:P and LOC:C scales. Overall, women appear less likely to perceive that powerful others or chance controls their lives. For the Crowne-Marlowe scale, there are significant main effects for the school and sex. These results indicate that students at the larger institution indicated a higher need for approval than students at the smaller institution. Similarly, women showed a greater need for approval than men.
Table 2 Mean, Standard Deviations, and F-Ratios for Self-Esteem, Locus of Control-Internal, Locus of Control; Powerful Others, Locus of Control; Chance, and Crowne-Marlowe Scales.
n M SD M SD
Men 57 16.26 7.39 10.75 5.33
Women 81 14.22 10.05 9.12 5.40
Total 138 15.10 9.07 9.80 5.41
Men 48 16.35 10.63 10.21 7.25
Women 124 15.05 10.11 11.00 5.15
Total 172 15.41 10.24 10.78 5.80
LOC:P LOC:C CM
M SD M SD M SD
Men -3.12 7.44 -0.95 7.06 -7.33 12.59
Women -6.10 7.16 -3.64 7.07 -1.19 20.40
Total -4.87 7.40 -2.53 7.17 -3.73 17.81
Men -1.85 7.81 -0.60 8.60 1.02 19.35
Women -4.82 7.70 -4.62 7.22 4.08 20.41
Total -3.99 7.82 -3.50 7.66 3.23 20.03
School F(1,306)=0.15 F(1,306)=9.55 F(1,306)=1.95
Sex F(1,306)=2.01 F(1,306)=0.38 F(1,306)=10.564(*)
School x Sex F(1,306)=0.1 F(1,306)=3.16 F(1,306)=0.001
MSE 94.842 31.547 56.745
School F(1,306)=0.13 F(1,306)=8.74(*)
Sex F(1,306)=14.412(*) F(1,306)=3.995(*)
School x Sex F(1,306)=0.097 F(1,306)=0.449
MSE 53.164 361.010
LOC:I=Locus of Control: Internal
LOC:P=Power of Control: Powerful Others
LOC:C=Locus of Control: Chance
MSE=Mean Square Error
Table 3 presents the correlations among the five personality dimensions. These data warrant careful consideration. Within the locus of control dimension, the correlation among the three subscales matches previously published convergent validity data (Leftcourt, 1991). However, the correlations between the locus of control scales and the Crowne-Marlowe scales are much higher than previous reports. There is also a moderate correlation between the SE and CM scales. Therefore, it would appear that the bias for social desirability has contaminated the other scales and any interpretation of the correlation between these personality measures and cheating should be made with caution.
Table 3 Correlations Among the Five Personality Measures
SE LOC:I LOC:P LOC:C CM
SE 1.000 .410(*) -.348(*) -.301 .397(*)
LOC:I 1.000 -.051 -.112 .174(*)
LOC:P 1.000 .491(*) -.292(*)
LOC:C 1.000 -.394(*)
Note. (*) p<.05, df=308
Table 4 presents the correlation between the cheating behaviors and the personality measures. Most notable in the table is the high correlation between the CM scale and all forms of cheating for high school. For all forms of cheating, there is a moderate and significant negative correlation between cheating and the CM scale. In other words, students with a greater need for approval report lower rates of cheating. This pattern of correlation is partially repeated for the college data. The fact that there are not significant correlations for cheating on tests and using other’s papers may be explained by the low frequency of these behaviors thereby restricting the range of scores and reducing the size of the correlation.
Table 4 Correlations Between the Five Personality Measures and the Cheating Behaviors
Behavior SE LOC:I LOC:P LOC:C CM
Paper -.014 -.023 .133(*) .237(*) -.166(*)
Test -.111 -.089 .053 .237(*) -.359(*)
Plagiarize -.087 -.08 -.094 .134(*) -.254(*)
Home Work -.150(*) -.086 .100 .216(*) -.328(*)
Combined -.148(*) -.106 .137(*) .307(*) -.424(*)
Paper -.042 -.098 .073 .024 .082
Test -.043 -.007 .105 .067 -.078
Plagerize -.126 -.103 .144(*) .169(*) -.190(*)
Home Work -.006 -.118 .065 .127(*) -.217(*)
Combined -.106 -.164(*) .161(*) .185(*) -.267(*)
Note. (*) p <.05 two-tailed
Table 5 lists the correlations between the reported frequency of admitted cheating and the personality profiles. These correlations replicate the previously reviewed pattern. Specifically, students with a higher need for approval report fewer episodes of cheating. Contrary to our original prediction, the locus of control does not appear to be a reliable correlate with cheating behavior.
Table 5 Correlations Between Personality Measures and Reported Frequency of Cheating
Behavior SE LOC:I LOC:P LOC:C CM
Test .020 .044 .129 .267(*) -.224(*)
Book .055 .021 .125 .174 -.230(*)
Paper -.123 -.238 .265 .000 -.295
Home Work -.065 -.029 .146(*) .253(*) -.335(*)
Test -.094 .116 .027 .154 -.268(*)
Book -.216 .031 .131 .064 -.45(*)
Paper -.121 .086 -.018 .208 .156
Home Work .014 .049 .044 .090 -.311(*)
Note. (*) p<.05 two-tallied.
The present results confirm previous findings regarding cheating behavior, challenge several generalizations, and create a vexing paradox. To begin, the data of the present study confirm previous results. Specifically, cheating of various forms is a prevalent behavior among college students. Furthermore, cheating does not seem to be a behavior students acquire in college as students reported cheating more while in high school than in college. The differences between the rates of cheating reported by men and women, and for students at larger and smaller institutions appears to replicate previous reports. Our research also demonstrated that the reported rates of cheating are related to the student’s need for approval.
Our findings confirm the data of Newstead et al. (1996) and Nuss (1988) who showed that different forms of cheating are viewed differently by students. Specifically, Newstead et al. and Nuss demonstrated that students perceive cheating during an exam to be a more egregious infraction than plagiarizing or copying homework assignments. Consequently, as we observed in our data, students reported higher rates of plagiarizing and copying homework than cheating on exams. Making the distinction among the types of cheating is important as it affects how one interprets the rate of cheating among students. In other words, cheating behavior cannot be viewed as a highly congruent set of behaviors, and the common practice of treating all cheating behaviors as a whole may ignore important interactions among variables. As an example, when we combined all cheating behavior into a single index, there was an unambiguous sex difference, men reported more cheating than women. When the individual forms of cheating were examined, however, the only difference that remained existed for plagiarizing in papers, which was the least frequently occurring behavior. Therefore, future research on cheating behavior should make a clear distinction among the types of cheating behavior examined to better understand the motivation and causes of academic dishonesty in general.
A related question arises from the differences between the two schools. We found that more students from the larger school reported cheating on exams than at the smaller institution. How much of the difference between the two institutions is clue to student variables versus numbers and types of opportunities to cheat? Smaller colleges may have less cheating because of the types of tests given. Specifically, faculty at smaller institutions may be more likely to use essay exams, which do not afford the same opportunities for cheating, than multiple choice exams. Similarly, larger class sizes, as typically found at larger institutions, may also afford more opportunities to cheat (Bowers, 1964). Note, as an example, that the rate of cheating on tests is much higher at the larger institution, but that the rates of copying homework and plagiarizing are equivalent.
The correlation between the CM scale and the rates of cheating creates an interesting paradox. One interpretation of the data follows from the assumption that the CM scale is an explicit measure of response bias. Therefore, one may conclude that the data concerning rates of cheating are contaminated by the bias to present one’s self as a virtuous person. An alternative interpretation presumes that the CM scale is a measure of personality. If the CM is a measure of the degree to which a person wishes to avoid disapproval, then one may interpret the current results to indicate that students who do not cheat do so to avoid the stigma of cheating. Unfortunately, the data from the present study do not afford a rational solution to this conundrum.
These data do reveal the need to consider the dimension measured by the CM scale for future research. For example, we had hoped to demonstrate a clear relation between cheating, self-esteem, and locus of control. However, the dominate nature of the CM dimension appeared to eclipse these relations. Therefore, future research examining the motivations and personalities of students who are prone to cheat need to examine the systematic variance attributed to the CM construct.
In summary, academic dishonesty is a complex behavior that defies simplistic explanation. Studying this behavior is complicated by the fact that the behavior is, by its nature, covert. Cheating too, like most behavior, is determined by a host of interrelated variables.
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A copy of our survey is available upon request.
We thank Martha Bleeker and Shelby Evans for distributing our survey at Emporia State University.
Portions of this research were conducted as a part of the first author’s thesis project and were presented at the annual meeting of the National Undergraduate Research Convention, Austin, TX.
Correspondence concerning the manuscript should be sent to David J. Pittenger, Department of Psychology, Marietta College, Marietta, OH, 45750; or at email@example.com.
MALEAH F. THORPE, DAVID J. PITTENGER, & BRENDA D. REED Marietta College
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