Prevalence and correlates of academic dishonesty: towards a sustainable university

sustainability Article Prevalence and Correlates of Academic Dishonesty: Towards a Sustainable University Dámaris Cuadrado *, Jesús F. Salgado and Silvia Moscoso Faculty of Labor Relations, Campus Vida, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain; [email protected] (J.F.S.); [email protected] (S.M.) *Correspondence: [email protected]; Tel.: +34-881-815-147 Received: 11 September 2019; Accepted: 28 October 2019; Published: 31 October 2019   Abstract: Academic dishonesty (AD) is a problem that affects all higher education institutions. It hurts their reputation, undermines integrity programs, and sidelines sustainability efforts. To understand its negative impact, the empirical study of AD is a critical issue. Up to now, the majority of research on AD has taken place in the North American context. The current research analyzes the prevalence of AD in a non-American university and, focusing on individual differences, examines some of its causes and consequences. The results prove that: (1) AD is a problem that occurs frequently among students; (2) three dimensions of the big five personality model correlate with AD. These are conscientiousness ( ρ = − 0.49, p<0.01), extraversion ( ρ =0.39, p<0.01), and agreeableness ( ρ=−0.14 ,p<0.01); (3) AD is associated to students’ GPA (Grade Point Average) ( ρ = − 0.34, p<0.01 ) and contextual performance ( ρ = − 0.50, p<0.01); (4) personality accounts for 30% of AD variance (R=0.55, p<0.01 ); and (5) AD and some of the studied individual differences explain 38% and 41% of GPA and contextual performance variance (R=0.62, p<0.01 and R=0.64, p<0.01). Implications for research and practice are discussed. Keywords: academic sustainability; academic dishonesty; personality; big five; general mental ability; academic performance 1. Introduction As it becomes increasingly impossible to ignore scientists’ dire warnings about the environmental cost of maintaining the statu quo, many types of organizations, from multinational industries to non-profit institutions, have been progressively investing resources and plotting ways to achieve a more sustainable level of impact. Unsurprisingly, for the past few decades, universities have also begun to implement programs that include sustainability as a key focus. Whether this involves searching for a more efficient use of energy, disposing of waste properly, going more digital, teaching proper recycling methods, or promoting other “green” endeavors, academic institutions play a central role in creating a more sustainable future. Indeed, most of the research concerning sustainability has focused on the control of effects caused by organizations on the physical environment [ 1 ]. However, as Sammalisto and Lindhvist [ 2 ] point out, sustainability is a broad construct that incorporates not only an environmental dimension but also a social and ethical one. Therefore, it is important to emphasize ethical as well as ecological factors when visualizing what sustainability means. This broader conception is the basis of what is now known as the psychology of sustainability (see [3]). The purpose of higher education institutions is at least twofold: (a) To transfer technical knowledge and professional skills to a new generation, in other words, to help students obtain the best possible academic outcomes (i.e., excellent grades); and (b) to reinforce students’ values, principles, and moral development. With regard to the first purpose, a great deal of studies have shown that academic outcomes can be fundamentally predicted by individual differences variables, among them, cognitive Sustainability 2019,11, 6062; doi:10.3390/su11216062 www.mdpi.com/journal/sustainability Sustainability 2019,11, 6062 2 of 20 ability and personality. Recent meta-analyses showed that general mental ability (GMA) correlates with academic outcomes (i.e., grade point average). Some examples are the meta-analyses by Kuncel, Hezlett, and Ones [ 4 ]; Kuncel, Ones, and Sackett [ 5 ]; Poropat [ 6 ]; Postlethwaite [ 7 ]; Salgado [ 8 ]; and Strenze [ 9 ]. Personality variables included in the big five model of personality, particularly conscientiousness, were also shown to be good determinants of academic outcomes (see for instance [6,10–12]). With regard to the second purpose, universities are required to promote a climate where transparency, responsibility, and ethical decision-making are central goals. They must be committed to the creation of a framework that shapes students’ morals and conduct, so that they remain responsible and honest citizens after graduation. This set of academic procedures and ethical standards is often referred as the integrity system (IS). The development of an effective IS that is accepted and respected by instructors, students, and the academic community overall is a major step towards building socially sustainable universities. Academic institutions are intended to influence the development of students and help them become more ethical individuals who, ideally, will act in an honest and socially sustainable manner outside of school. Reinforcing these values and confronting dishonest conduct at the level of higher education is needed in order to stop unethical behaviors in the student’s subsequent occupational life. These efforts could help to lower negative behaviors, such as absenteeism, high-turnover, corruption, bribery, and other counterproductive work behaviors (see [ 13 ]). They could also promote positive values, such as inclusiveness, fairness, equality, and ethical economic, social, and environmental decision-making. Unfortunately, many ISs are often threatened by the occurrence of non-ethical behaviors performed by students. These activities, frequently referred to as counterproductive academic behaviors or academic dishonesty (AD), directly damage institutional efforts in the social and ethical spheres. Hence, policies implemented by universities with the aim of being more sustainable must necessarily incorporate two key factors: Academic integrity as a goal to reach and academic dishonesty as a phenomenon to fight. AD refers to an extensive range of behaviors that includes cheating on examinations, absenteeism, property damage, inappropriate use of resources, unpermitted collaboration, plagiarism, or fabrication. Among other negative effects, AD can bring about an unmerited assessment of students, interfere in the students’ ethical development, undermine the morale of faculty and academically honest peers, and weaken the reputation of academic institutions [ 14 ]. Furthermore, AD is not restricted to a concrete geographical area. Although a large part of the research on this topic has taken place in North America, some studies have proven that AD occurs on campuses all around the world (see for instance [ 15 – 18 ]). Given this alarming situation, the empirical study of AD is a necessary step in order to develop effective tools against its occurrence and help universities foster sustainability and academic integrity. The current study aims to: (1) Determine the prevalence rates of AD among a sample of university students in Spain; (2) examine the extent to which students’ personality and general mental ability (GMA) explain AD; and (3) analyze the incidence of AD and previous individual differences on students’ grade point average (GPA) and contextual performance (CP). The novelty of the present study lies in: (1) The analysis of the cited relationships using a sample of students enrolled in a Western European university. As mentioned, to date the vast majority of AD research has been performed in the North American context. Research examining the relationships tested in the current research is limited, especially in the Spanish context. Hence, we expect to contribute to the AD literature from a European point of view; (2) the study of relationships between AD and the big five dimensions using a quasi-ipsative forced-choice inventory. Empirical results produced so far in the AD literature have been obtained by using single stimulus personality inventories. However, it is well-known that single stimulus personality questionnaires are affected by faking. Despite the relevance of this issue, only one study [ 19 ] has addressed this question. Hence, it is our purpose to determine whether forced-choice quasi-ipsative inventories are a good option to predict AD; (3) the examination of the relationship between AD and contextual performance. To the best of our knowledge, the link between these two variables has never been tested; and (4) the development and testing of Sustainability 2019,11, 6062 3 of 20 a structural model of the relationships among personality, general mental ability (GMA), academic dishonesty, grade point average (GPA), and contextual performance (CP), since most of the research on AD has limited the analyses to the correlational level. To achieve the objectives of this research, we performed descriptive statistics for AD, correlational analyses, and structural equation models that will be described later. In the next sections, we will review the empirical evidence produced so far on the mentioned relationships and state the research hypotheses. 1.1. Personality and Academic Dishonesty (AD) In an attempt to contribute to the nomological network of the psychological basis of AD, we focused on the study of the big five model of personality. Despite the earlier pessimism about the importance of this personality model on the prediction of AD (see [ 20 ]), literature reviews published on this topic point out the relevance of some of the five dimensions. The meta-analysis carried out by Cuadrado, Salgado, and Moscoso [ 21 ] showed that conscientiousness and agreeableness are negatively related to AD ( ρ = − 0.24 and ρ = − 0.14, respectively). Both dimensions also generalize their predictive validity. Giluk and Postlethwaite [ 22 ] reached similar results ( ρ = − 0.22 for conscientiousness and ρ=−0.14 for agreeableness). Cred é , Roch, and Kieszczynka [ 23 ] found a negative effect size between conscientiousness and academic absenteeism ( ρ = − 0.24). Once more, conscientiousness generalized its predictive validity. These results were expected since students scoring high in conscientiousness are responsible, organized, dutiful, and work oriented. Agreeable people are characterized by traits, such as trust, altruism, and rejection of harmful behaviors. These attributes might explain why conscientious and agreeable students are less prone to behave in a dishonest manner in the academic domain. Moreover, recent meta-analytic research carried out in the educational and organizational domains showed that the type of personality questionnaire was a relevant moderator of the capacity of the big five personality factors to predict academic and occupational outcomes [ 12 , 24 – 27 ]. The bulk of the research was conducted using single stimulus (SS) inventories (i.e., NEO-PI-R) and no studies were done using forced-choice (FC) quasi-ipsative personality inventories. However, it is well established that: (1) FC quasi-ipsative inventories show larger validity for predicting GPA and job performance than SS inventories [ 26 ]; (2) SS inventories are affected by faking and social desirable response biases (i.e., [ 8 , 28 , 29 ]); and (3) FC personality inventories are robust against faking [ 27 , 30 , 31 ]. To the best of our knowledge, only one study [19] has partially examined the relationship between the big five and AD using a FC quasi-ipsative inventory. Therefore, additional and more complete studies using this type of measure should address this relationship. By using a FC quasi-ipsative inventory we can both estimate the link between the big five dimensions and AD and examine whether FC quasi-ipsative inventories show smaller, equal, or larger correlations that SS personality inventories. This is a relevant novelty of the current study. Therefore, based on the revised literature, we hypothesize that: Hypothesis 1. Conscientiousness correlates negatively with AD; and Hypothesis 2. Agreeableness correlates negatively with AD. 1.2. General Mental Ability (GMA) and Academic Dishonesty (AD) GMA is another important psychological variable that might be relevant in addressing the phenomenon of AD. Research on the relationship between GMA and AD suggests a negative correlation between the variables. As Dilchert, Ones, David, and Rostow [ 32 ] state, individuals scoring lower in GMA are less likely to evaluate the repercussion of their actions. Consequently, they might be less reluctant to engage in deviant conduct. GMA has also proven to be an excellent predictor of academic performance (see [ 4 , 7 , 33 , 34 ]). This could be the reason why more intelligent students do not have the need to engage in prohibited behaviors in order to achieve good academic results. Meta-analyses by Cuadrado, et al., [ 21 ] and Paulhus and Dubois [ 35 ] confirmed this negative relationship ( ρ = − 0.19 and Sustainability 2019,11, 6062 4 of 20 r= − 0.26, respectively). Using academic absenteeism as the criterion variable, Cred é et al., [ 23 ] found an effect size of ρ=−0.11. On the basis of the previous evidence, we posit the following hypothesis: Hypothesis 3. GMA correlates negatively with AD. 1.3. Academic Dishonesty (AD) and Grade Point Average (GPA) Theoretical rationale suggests a negative relationship between AD and GPA. In this respect, it seems reasonable to believe that students who skip class, cheat on exams, or get unpermitted collaboration on assignments also get lower grades as they are not acquiring the corresponding knowledge. Consequently, the GPA of dishonest students will be affected. Meta-analyses on this subject have confirmed a negative link between the constructs. Whitley [ 36 ] found inverse relationships despite the type of academic performance measure used (r= − 0.16 for GPA and r= − 0.30 for specific tasks). Results by Cuadrado [ 37 ] ranged from ρ = − 0.62 for examination grades to ρ = − 0.35 for a mixed category of different indicators of academic performance. The result for GPA was ρ = − 0.39. In all the cases, validity generalization occurred. Cred é et al., [ 23 ] also reported a negative effect size between academic absenteeism and GPA ( ρ = − 0.41). The magnitude increased to ρ = − 0.44 when the predictor variable was course grades. These results could be relevant since GPA is one of the most important variables in the academic field. GPA reflects students’ knowledge acquisition, ability, effort, and persistence. Additionally, it is an important criterion in social, educational, and organizational settings because many administrative decisions that affect students’ career (i.e., admission to certain universities, admission to master’s degrees, admission to PhD programs), economic decisions (i.e., obtainment of financial aid), and organizational decisions (i.e., inclusion in personnel selection procedures and, eventually, being hired) are based on students’ GPA (see [ 38 – 40 ]). Accordingly, in the current study, we expect that: Hypothesis 4. AD correlates negatively with GPA. 1.4. Academic Dishonesty (AD) and Contextual Performance (CP) Contextual performance (CP) involves behaviors, such as helping others with their tasks, giving support to the institution, encouraging others to comply with rules, showing initiative, or volunteering in responsibilities that go beyond those formally required [ 41 ]. From an ethical perspective, the promotion of these practices can help enhance sustainability in academic institutions. The relevance of this construct lies in the nature of the cited behaviors since they are, in any case, desirable in the educational domain and subsequent occupational life. CP has been frequently assessed and studied in organizational research (i.e., [ 42 , 43 ]). However, the assessment of this variable in the educational domain is very rare (see, for instance, [ 44 ]). In addition, the relationship between dishonest behaviors (counterproductive work behaviors) and contextual performance has largely been examined in organizational contexts (see [ 43 , 45 ]), showing a negative association between the constructs (see [ 46 , 47 ]). Nevertheless, the link between AD and CP has not yet been examined and, as posited before, it is an additional novelty of this study. Based on the negative relationships found in the organizational domain, our last hypothesis is: Hypothesis 5. AD correlates negatively with CP. 1.5. An Integrative Model of Correlates of Academic Dishonesty The hypotheses posited in the previous sections state the expected results at the correlational level. However, the bivariate relationships among personality, GMA, AD, GPA, and CP described above do Sustainability 2019,11, 6062 5 of 20 not suggest or imply direct and indirect effects among the variables. In addition, bivariate correlations do not consider the potential interactions among these variables. For this purpose, a structural model should be tested. With this aim, we applied structural equation modeling (SEM) techniques to the correlation matrix of the set of variables. To the best of our knowledge, previous research has not examined the full and complex relationships among the variables studied in this investigation. The development of the structural model was based on the empirical evidence found in the literature reviewed in the precedent sections. Thus, as posited by hypotheses 1, 2, and 3, conscientiousness, agreeableness, and GMA correlates negatively with AD. As personality and GMA temporality precedes AD, and it is obvious that AD does not modify (increases or decreases) conscientiousness, agreeableness, and GMA, these three individual differences variables can be hypothesized as antecedents of AD in a structural model. Next, as posited in hypotheses 4 and 5, AD correlates negatively with GPA and CP. We can extend our rationale by hypothesizing that AD is an antecedent variable of GPA and CP. For example, cheating and plagiarism are behaviors committed before obtaining the exam outcomes that produce GPA. In a similar vein, we can argue that AD (i.e., cheating and plagiarism) is a previous conduct to CP. Additionally, prior research has found that conscientiousness and GMA are predictors (antecedents) of GPA, so they can show both direct and indirect (through AD) effects on GPA. Similarly, conscientiousness, agreeableness, and GMA have been linked to CP. Consequently, they can also show both direct and indirect effects on CP. Taking these arguments together, we will test two models of the mediating role of AD, one for GPA as the dependent variable and another for CP as the dependent variable. The first model considers the two big five dimensions and GMA as direct predictors of AD and indirect predictors of GPA through AD. The second model replicates the latter but considers CP instead of GPA as the dependent variable. By testing these two models we might establish the directionality of the results found at the correlational level and, consequently, contribute to the knowledge of AD antecedents and the AD pervasive effect on these two important performance dimensions: Task performance (GPA) and contextual performance (CP). The two models will be tested using structural equation modeling (SEM) because this type of analysis allows an estimation of the magnitude of the direct and indirect effects and also provides information on the relative predictive weight of every single predictor variable (see [48]). Figures 1and 2illustrate the structure of the relationships for these two models. Note: C =conscientiousness, A =agreeableness; GMA =general mental ability; AD =academic dishonesty; GPA =grade point average. Figure 1. Relationships among the variables of Model 1. Sustainability 2019,11, 6062 6 of 20 Sustainability 2019, 11, x FOR PEER REVIEW 6 of 21 Figure 2. Relationships among the variables of Model 2. Note: C = conscientiousness, A = agreeableness; GMA = general mental ability; AD = academic dishonesty; CP = contextual performance. 2. Materials and Methods 2.1. Procedure and Participants The sample was composed of 388 students enrolled in the University of Santiago de Compostela (Spain). The mean age was 21.3 years old (SD = 3.4) and women accounted for 67% of the sample. In total, 89% were undergraduates, 7% were enrolled in a master’s program, and 4% were PhD students. The study was published by posting advertisements in several university buildings calling for the voluntary participation of students. We also asked the participants to provide a copy of their academic records in which the grades gained in the taken courses and the GPA were reported. Groups ranging from 10 to 40 subjects were organized to do the tests. Students attended voluntarily, provided their informed consent, and received 10 euros as compensation for their participation. After an average of a 10-week period, a second phase of the research was carried out to calculate the test re-test reliability of the measures administrated at time 1. Altogether, 138 students participated in the research at time 2. 2.2. Measures 2.2.1. Personality (Big Five) The big five personality dimensions were measured using the Quasi-Ipsative Personality Inventory (QI5F/Tri) developed by Salgado [49]. This inventory consists of a quasi-ipsative forced- Note: C =conscientiousness, A =agreeableness; GMA =general mental ability; AD =academic dishonesty; CP =contextual performance. Figure 2. Relationships among the variables of Model 2. 2. Materials and Methods 2.1. Procedure and Participants The sample was composed of 388 students enrolled in the University of Santiago de Compostela (Spain). The mean age was 21.3 years old (SD =3.4) and women accounted for 67% of the sample. In total, 89% were undergraduates, 7% were enrolled in a master’s program, and 4% were PhD students. The study was published by posting advertisements in several university buildings calling for the voluntary participation of students. We also asked the participants to provide a copy of their academic records in which the grades gained in the taken courses and the GPA were reported. Groups ranging from 10 to 40 subjects were organized to do the tests. Students attended voluntarily, provided their informed consent, and received 10 euros as compensation for their participation. After an average of a 10-week period, a second phase of the research was carried out to calculate the test re-test reliability of the measures administrated at time 1. Altogether, 138 students participated in the research at time 2. 2.2. Measures 2.2.1. Personality (Big Five) The big five personality dimensions were measured using the Quasi-Ipsative Personality Inventory (QI5F/Tri) developed by Salgado [ 49 ]. This inventory consists of a quasi-ipsative forced-choice measure composed of 140 triads (28 per dimension). Each triad comprises three items that assess different personality dimensions. Subjects were required to indicate which of the three statements describes them the best and which describes them the worst. The internal consistency coefficients for the current sample were, respectively, 0.67, 0.75, 0.84, 0.69, and 0.78 for emotional stability, extraversion, openness to experience, agreeableness, and conscientiousness (N=343). Test re-test reliability coefficients ranged from r=0.67 for emotional stability to r=0.90 for openness to experience (N=133). Range restriction coefficients were U=1.22 (u=0.82) for every dimension. 2.2.2. General Mental Ability (GMA) General mental ability (GMA) was measured using a Spanish adaptation of the Wonderlic Personnel Test [ 50 ]. The test consists of 50 items, including word comparisons, sentence parallelisms, numerical comparisons, numerical series, analyses of geometric figures, and logic and math questions. The level of difficulty increases as the test progresses. Subjects must correctly answer the largest number of questions in 12 minutes. The Cronbach’s alpha coefficient for the current sample was α =0.78 (N=386) and the stability coefficient was r=0.83 (N=138). In the manual, the reliability Sustainability 2019,11, 6062 7 of 20 coefficients obtained for the different versions of the test ranged from 0.82 to 0.94. The range restriction coefficient in this sample was U=1.11 (u=0.90). 2.2.3. Grade Point Average (GPA) Academic performance was measured using the students’ grade point average (GPA) reported in the copy of the official transcripts that participants had to submit for the research. GPA is considered one of the best indicators of academic success because: (1) It represents technical knowledge, persistence, and motivation; (2) it is based on several evaluations (exams, assignments) made by different evaluators (teachers); and (3) it reflects the level of performance over a long period of time [ 51 ]. In the present study, with a range of possible values from 0 to 10, the average GPA was 6.95 (SD =0.86, N=359). The internal consistency coefficient was calculated using the estimations provided by seven primary studies (see Table 1). As can be seen, the mean value was α=0.87 (SD =0.06). Table 1. GPA reliability distribution. Study Nα [52] DeRosier and Lloyd (2011) 1255 0.88 [53] Hanks and Beier (2012) 60 0.78 [54] LeBlanc III (2005) 1617 0.88 [55] McNeal (2014) 12,101 0.88 [56] Salgado and Moscoso (2012) 410 0.87 [ 57 ] Schmitt, Oswald, Friede, Imus, and Merrit (2008) 903 0.85 [58] Thompson and Zamboanga (2004) 347 0.98 Σ=16,693 M=0.87 SD =0.06 2.2.4. Contextual Performance (CP) Contextual performance (CP) was evaluated using the Academic Contextual Performance Questionnaire (CDCE, Cuestionario de desempeño contextual en estudiantes) developed by Salgado [ 59 ]. This instrument consists of 30 items that assess the behaviors included in the taxonomy proposed by Borman et al., [ 41 ]: (1) Personal support, referring to actions, such as helping, cooperating, and motivating other students (12 items, e.g., I make suggestions to my classmates with the aim of improving their academic results); (2) organizational support, including behaviors, such as supporting and knowingly representing the university (9 items, e.g., Every time I have the opportunity, I show my explicit support to my university); and (3) conscientious initiative, which evaluates actions, such as persisting with extra effort or taking the initiative on academic opportunities (9 items, e.g., I attend courses even when they are not mandatory). Subjects had to indicate to what extent they performed the described behaviors using a Likert scale that ranged from 1 (never)to5(always). Both the Cronbach’s alpha and the test re-test coefficients were 0.85 (N=344 and N=134, respectively). 2.2.5. Academic Dishonesty (AD) Academic dishonesty (AD) was measured using the Questionnaire of Negative Academic Performance (CDAN, Cuestionario de desempeño acad é mico negativo) by Salgado [ 59 ]. This instrument is composed of 30 items classified in the following scales (6 items per scale): (1) Cheating on examinations (e.g., I have used crib-notes during examinations); (2) inappropriate use of resources (e.g., I have stolen objects, such as books or notebooks); (3) absenteeism (e.g., I have voluntarily failed to attend class); (4) breach of rules (e.g., I have used false excuses for my academic benefit); and (5) low effort (e.g., I have delivered poor quality works on purpose). Participants had to indicate the frequency with which they engaged in the described actions using a Likert scale ranging from 1 (never)to5(always). The internal consistency reliability was α =0.89 (N=379). It ranged from α =0.66 for breach of rules to α =0.84 for low-effort Sustainability 2019,11, 6062 8 of 20 behaviors. The test re-test coefficient was r=0.80 for the entire measure (N=138), and ranged from r=0.51 for breach of rules to r=0.81 for low effort. Ucoefficient was estimated to perform the correction for range restriction when AD was used to predict GPA and CP. We obtained a Uvalue of 1.19 (u=0.84) when the criterion was GPA, and 1.22 (u=0.82) when it was CP. 3. Results 3.1. Descriptive Statistics of AD Table 2shows AD descriptive statistics. With a distribution of values ranging between 30 and 150, the average result was 62.04 (SD =13.02). The scores for the specific scales could vary from 6 to 30. In the current sample, the behavior most frequently performed by the students was cheating on examinations (M=15.17, SD =4.29), followed by absenteeism (M=14.89, SD =3.97) and low effort ( M=14.22 ,SD =4.34). The lowest average score was for the inappropriate use of resources, with a mean of 7.85 (SD =2.28). Table 2. Descriptive statistics of academic dishonesty. M SD N Max. Min. Academic Dishonesty 62.04 13.02 379 123 30 Cheating 15.17 4.29 379 30 6 Inappropriate use of resources 7.85 2.28 379 21 6 Absenteeism 14.89 3.97 379 28 6 Breach of rules 9.90 3.08 379 28 6 Low effort 14.22 4.34 379 29 6 Table 3shows the frequency (in percentage) of the AD behaviors. The results showed that 77.9%, 76.5%, and 76.2% of the students reported to have engaged in absenteeism, cheating, and low-effort behaviors, respectively, at some point during their studies. However, the majority of the sample never inappropriately used academic resources (76.5%). Overall, the results confirm that AD is a widespread phenomenon among the evaluated sample. Table 3. Percentage of subjects engaging in AD behaviors at each level of frequency. Cheating Inappropriate Use of Resources Absenteeism Breach of Rules Low Effort Never 23.5 76.5 22.1 56.6 23.8 Hardly ever 25.5 17.2 26.1 26.5 31.3 Sometimes 32.3 5.5 37.2 13.3 32.6 Usually 12.1 0.5 10.8 2.7 8.7 Always 6.6 0.3 3.8 0.9 3.6 Note: N =379. 3.2. Analyses of Correlations Table 4presents the correlations among the variables. Observed correlations are shown below the diagonal and corrected correlations above the diagonal. All coefficients were corrected for lack of reliability. The relationships of AD with personality, GMA, and performance were corrected for lack of reliability in predictor and criterion variables but also for indirect range restriction in the predictor variables. There are two important reasons to conduct these corrections. First, as Schmidt and Hunter [ 60 ] state, the measurement error in X and Y underestimates the real magnitude of the effect size. Second, in order to carry out multiple regression analyses and structural equation Sustainability 2019,11, 6062 9 of 20 modeling, methodologists indicate that measurement error and range restriction produce the violation of the independence-of-errors assumption and, therefore, the estimation of parameters can be biased (see, for example, [ 48 , 60 – 62 ]). To obtain ρ values, we used a two-step process. First, we corrected the observed correlation by the attenuation effect of reliability of X and Y [ 48 , 60 ]. Once the last coefficient was obtained, it was corrected using the indirect rage restriction formula given by Schmidt and Hunter [60]. As reported in Table 4, sex negatively correlated with AD (r= − 0.22, p<0.01), so that men have a greater tendency than women to engage in AD. Age did not correlate with global AD. However, it was related to cheating behaviors (r=−0.12, p<0.05). With regard to personality dimensions, conscientiousness showed the larger correlation with AD ( ρ = − 0.49, p<0.01). The analyses also indicated that conscientiousness was significantly related to AD regardless of the type of negative behavior considered, with ρ values ranging from ρ = − 0.24 ( p<0.01 ) for breach of rules to ρ = − 0.54 (p<0.01) for low effort. These results supported hypothesis 1. Extraversion showed the second largest correlation with AD, with a positive result of ρ =0.39 ( p<0.01 ). Furthermore, extraversion showed a positive and significant relationship with every type of dishonest behavior, ranging from ρ =0.23 (p<0.01) for inappropriate use of resources and low effort behaviors to ρ =0.38 (p<0.01) for absenteeism. Agreeableness also correlated significantly with AD ( ρ = − 0.14, p<0.05) which supported hypothesis 2, but the correlation was not significant for all the AD facets. Emotional stability and openness to experience did not correlate significantly with AD, although emotional stability significantly correlated with absenteeism and inappropriate use of resources ( ρ = 0.20, p<0.01 and ρ =0.24, p<0.01, respectively) and openness to experience with absenteeism ( ρ = 0.15, p<0.05), cheating (ρ=−0.15, p<0.05), and inappropriate use of resources (ρ=0.14, p<0.05). Concerning the relationship between GMA and AD, the corrected correlation was not statistically significant. Therefore, hypothesis 3 was not supported. The result for the relationship between AD and GPA showed a ρ value of − 0.34 (p<0.01), supporting, therefore, hypothesis 4. The correlations of GPA with the AD facets of cheating, absenteeism, low effort, and breach of rules were significant and negative, ranging from ρ = − 0.15 (p<0.01) for cheating to ρ = − 0.50 (p<0.01) for low-effort behaviors. Finally, the corrected correlation between AD and CP was ρ = − 0.50 (p<0.01), which confirmed hypothesis 5. AD also correlated with the AD facets, with ρ -values ranging from ρ=−0.15 (p<0.01) for cheating to ρ=−0.55 (p<0.01) for low-effort behaviors. Sustainability 2019,11, 6062 16 of 20 American context. Second, on the publication of new studies that analyze additional variables that might account for the remaining variance of AD, GPA and CP. We also state three recommendations for practitioners and the academic administration overall. First, to use quasi-ipsative forced-choice personality inventories in the prediction of AD, GPA, and CP in order to improve academic decision-making. Second, to focus on the importance of CP as a relevant criterion closely related to ethical and moral sustainability, and to promote policies that reinforce these behaviors. Third, to consider the control of AD as a main target of sustainability due to the fact that: (a) It is a large-scale phenomenon that causes serious consequences; (b) most students have engaged in AD at some point during their academic career; and (c) AD is a proven antecedent of students’ GPA and CP. 4.2. Study Limitations Similar to many empirical studies, the present one also has some limitations that must be noticed. First, the research was not anonymous and, therefore, there is a possibility that some of the results might be biased due to socially desirable responses, especially those concerning AD. Although our prevalence estimations can be described as high, it is possible that real rates of AD might be even higher. Second, for a better comparison between the validity of SS and FC quasi-ipsative inventories, two questionnaires should be used. However, time limitations precluded the use of two personality inventories since this would require an excessively large testing session. 5. Conclusions In summary, the present research has contributed to a better knowledge of the prevalence, causes, and consequences of AD. As the world faces countless ethical and social crises, the study of AD in academic institutions becomes as important as demanding a higher level of integrity in any other type of organization. Universities are asked to make institutional efforts that keep sustainability on track. The results found in the current study can be useful in accomplishing this goal. We encourage future researchers to replicate this research and to continue examining other potential causes and consequences of AD. Author Contributions: All authors listed have made substantial, direct, and intellectual contribution to this research. Conceptualization, J.F.S.; Formal analysis, D.C., J.F.S., and S.M.; Funding acquisition, J.F.S. and S.M.; Investigation, D.C., J.F.S., and S.M.; Writing—original draft, D.C., J.F.S., and S.M. Funding: This research was funded by Ministry of Economy and Competitiveness, grant number PSI2017-87603-P to Jesús F. Salgado and Silvia Moscoso. Acknowledgments: We acknowledge the support given by Alexandra Mart í nez and Inmaculada Otero during the data collecting and coding procedures. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. References 1. Pfeffer, J. Building sustainable organizations: The human factor. Acad. Manag. Perspect. 2010 ,24, 34–45. [CrossRef] 2. Sammalisto, K.; Lindhqvist, T. Integration of sustainability in higher education: A study with international perspectives. Innov. High. Educ. 2008,32, 221–233. [CrossRef] 3. Di Fabio, A. The psychology of sustainability and sustainable development for well-being in organizations. Front. Psychol. 2017,8, 1534. [CrossRef] [PubMed] 4. Kuncel, N.R.; Hezlett, S.A.; Ones, D.S. A comprehensive meta-analysis of the predictive validity of the graduate record examinations: Implications for graduate student selection and performance. Psychol. Bull. 2001,127, 162–181. [CrossRef] [PubMed] Sustainability 2019,11, 6062 17 of 20 5. Kuncel, N.R.; Ones, D.S.; Sackett, P.R. Individual differences as predictors of work, educational, and broad life outcomes. Personal. Individ. Differ. 2010,49, 331–336. [CrossRef] 6. Poropat, A.E. A meta-analysis of the five-factor model of personality and academic performance. Psychol. Bull. 2009,135, 322–338. [CrossRef] 7. Postlethwaite, B.E. Fluid Ability, Crystallized Ability, and Performance across Multiple Domains: A Meta-Analysis. Ph.D. Thesis, University of Iowa, Iowa, IO, USA, 2011. 8. Salgado, J.F. A theoretical model of psychometric effects of faking on assessment procedures: Empirical findings and implications for personality at work. Int. J. Sel. Assess. 2016,24, 209–228. [CrossRef] 9. Strenze, T. Intelligence and socioeconomic success: A meta-analytic review of longitudinal research. Intelligence 2007,35, 401–426. [CrossRef] 10. O’Connor, M.C.; Paunonen, S.V. Big Five personality predictors of post-secondary academic performance. Personal. Individ. Differ. 2007,43, 971–990. [CrossRef] 11. Richardson, M.; Abraham, C.; Bond, R. Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychol. Bull. 2012,138, 353–387. [CrossRef] 12. Salgado, J.F.; T á uriz, G. The Five-Factor Model, forced-choice personality inventories and performance: A comprehensive meta-analysis of academic and occupational validity studies. Eur. J. Work Organ. Psychol. 2014,23, 3–30. [CrossRef] 13. Dilchert, S. Counterproductive sustainability behaviors and their relationship to personality traits. Int. J. Sel. Assess. 2018,26, 49–56. [CrossRef] 14. Whitley, B.E., Jr.; Keith-Spiegel, P. Academic Dishonesty: An Educator’s Guide; Lawrence Erlbaum Associates, Inc.: Mahwah, NJ, USA, 2002. 15. Brimble, M.; Stevenson-Clarke, P. Perceptions of the Prevalence and Seriousness of Academic Dishonesty in Australian Universities. Aust. Educ. Res. 2005,32, 19–44. [CrossRef] 16. Ison, D.C. The influence of the Internet on plagiarism among doctoral dissertations: An empirical study. J. Acad. Ethics 2015,13, 151–166. [CrossRef] 17. Teixeira, A.A.; Rocha, M.F. Academic cheating in Austria, Portugal, Romania and Spain: A comparative analysis. Res. Comp. Int. Educ. 2006,1, 198–209. [CrossRef] 18. Teixeira, A.A.; Rocha, M.F. Cheating by economics and business undergraduate students: An exploratory international assessment. High. Educ. 2010,59, 663–701. [CrossRef] 19. Hendy, N.T. Forced-choice personality measures and academic dishonesty: A comparative study. J. Acad. Ethics 2017,15, 293–306. [CrossRef] 20. Williams, K.M.; Nathanson, C.; Paulhus, D.L. Identifying and profiling scholastic cheaters: Their personality, cognitive ability, and motivation. J. Exp. Psyhol. Appl. 2010,16, 293–307. [CrossRef] 21. Cuadrado, D.; Salgado, J.F.; Moscoso, S. Personality, Intelligence, and Academic Dishonesty: A Meta-analysis. J. Personal. Soc. Psychol. 2019. Under review. 22. Giluk, T.L.; Postlethwaite, B.E. Big Five Personality and Academic Dishonesty: A Meta-analytic Review. Personal. Individ. Differ. 2015,72, 59–67. [CrossRef] 23. Cred é , M.; Roch, S.G.; Kieszczynka, U.M. Class Attendance in College: A Meta-Analytic Review of the Relationship of Class Attendance with Grades and Student Characteristics. Rev. Educ. Res. 2010 ,80, 272–295. [CrossRef] 24. Fisher, P.A.; Robie, C.; Christiansen, N.D.; Speer, A.B.; Schneider, L. Criterion-related validity of forced-choice personality measures: A cautionary note regarding Thurstonian IRT versus classical test theory scoring. Personal. Assess. Dec. 2019,5. [CrossRef] 25. Salgado, J.F. Moderator effects of job complexity on the validity of forced-choice personality inventories for predicting job performance. J. Work Organ. Psychol. 2017,33, 229–238. [CrossRef] 26. Salgado, J.F.; Anderson, N.; Táuriz, G. The validity of ipsative and quasi-ipsative forced-choice personality inventories for different occupational groups: A comprehensive meta-analysis. J. Occup. Organ. Psychol. 2015,88, 797–834. [CrossRef] 27. Salgado, J.F.; Lado, M. Faking resistance of a quasi-ipsative forced-choice personality inventory without algebraic dependence. J. Work Organ. Psychol. 2018,34, 213–216. [CrossRef] 28. Viswesvaran, C.; Ones, D.S. Meta-analyses of fakability estimates: Implications for personality measurement. Educ. Psychol. Meas. 1999,59, 197–210. [CrossRef] Sustainability 2019,11, 6062 18 of 20 29. Birkeland, S.A.; Manson, T.M.; Kisamore, J.L.; Brannick, M.T.; Smith, M.A. A meta-analytic investigation of job applicant faking on personality measures. Int. J. Sel. Assess. 2006,14, 317–335. [CrossRef] 30. Cao, M.; Drasgow, F. Does forcing reduce faking? A meta-analytic review of forced-choice personality measures in high-stakes situations. J. Appl. Psychol. 2019. [CrossRef] 31. Mart í nez, A. Empirical Evaluation of a Theoretical Model of the Faking Effects on Occupational Personality Measures. Ph.D. Thesis, University of Santiago de Compostela, Santiago de Compostela, Spain, July 2019. 32. Dilchert, S.; Ones, D.S.; Davis, R.D.; Rostow, C.D. Cognitive Ability Predicts Objectively Measured Counterproductive Work Behaviors. J. Appl. Psychol. 2007,92, 616–627. [CrossRef] 33. Kuncel, N.R.; Hezlett, S.A.; Ones, D.S. Academic performance, career potential, creativity, and job performance: Can one construct predict them all? J. Personal. Soc. Psychol. 2004,86, 148–161. [CrossRef] 34. Salgado, J.F.; Moscoso, S. A New Look at Validity of the GATB Tests of General Mental Ability for Five Academic and Organizational Criteria. Front. Psychol. 2019, in press. 35. Paulhus, D.L.; Dubois, P.J. The link between cognitive ability and scholastic cheating: A meta-analysis. Rev. Gen. Psychol. 2015,19, 183–190. [CrossRef] 36. Whitley, B.E., Jr. Factors associated with cheating among college students: A review. Res. High. Educ. 1998 , 39, 235–274. [CrossRef] 37. Cuadrado, D. Deshonestidad Acad é mica, Desempeño y Diferencias Individuales. Ph.D. Thesis, University of Santiago de Compostela, Santiago de Compostela, Spain, 2018. 38. Briihl, D.S. Life after College: Psychology Students’ Perceptions of Salary, Business Hiring Criteria, and Graduate Admission Criteria. N. Am. J. Psychol. 2001,3, 321–330. 39. Reilly, R.R.; Warech, M.A. The Validity and Fairness of Alternatives to Cognitive Tests. In Policy Issues in Employment Testing; Wing, L.C., Gifford, B.R., Eds.; Kluwer: Boston, MA, USA, 1994; pp. 131–224. 40. Rynes, S.L.; Orlitzky, M.O.; Bretz, R.D. Experienced hiring versus college recruiting: Practices and emerging trends. Personal. Psychol. 1997,50, 309–339. [CrossRef] 41. Borman, W.C.; Penner, L.A.; Allen, T.D.; Motowidlo, S.J. Personality Predictors of Citizenship Performance. Int. J. Sel. Assess. 2001,9, 52–69. [CrossRef] 42. Hoffman, B.J.; Blair, C.A.; Meriac, J.P.; Woehr, D.J. Expanding the criterion domain? A quantitative review of the OCB literature. J. Appl. Psychol. 2007,92, 555–566. [CrossRef] 43. Harari, M.B.; Reaves, A.C.; Viswesvaran, C. Creative and innovative performance: A meta-analysis of the relationship with task, citizenship, and counterproductive job performance dimensions. Eur. J. Work Organ. Psychol. 2016,25, 495–511. [CrossRef] 44. Gore, J.S.; Kiefner, A.E.; Combs, K.M. Personality Traits that Predict Academic Citizenship Behavior. J. Appl. Soc. Psychol. 2012,42, 2433–2456. [CrossRef] 45. Carpenter, N.C.; Berry, C.M. Are counterproductive work behaviour and withdrawal empirically distinct? A meta-analytic investigation. J. Manag. 2017,43, 834–863. [CrossRef] 46. Dalal, R.S. A Meta-analysis of the Relationship between Organizational Citizenship Behavior and Counterproductive Work Behavior. J. Appl. Psychol. 2005,90, 1241–1255. [CrossRef] [PubMed] 47. Podsakoff, N.P.; Whiting, S.W.; Podsakoff, P.M.; Blume, B.D. Individual-and organizational-level consequences of organizational citizenship behaviors: A meta-analysis. J. Appl. Psychol. 2009 ,94, 122–141. [CrossRef] [PubMed] 48. MacKinnon, D.P. Introduction to Statistical Mediation Analysis; Lawrence Erlbaum Associates: Mahwah, NJ, USA, 2008. 49. Salgado, J.F.; University of Santiago de Compostela, Santiago de Compostela, Spain. Reliability, Construct and Criterion Validity of the Quasipsative Personality Inventory (QI5F/Tri). 2019. 50. Wonderlic Personnel Test. Wonderlic Personnel Test User’s Manual; Wonderlic: Libertyville, IL, USA, 1992. 51. Kuncel, N.R. The Prediction and Structure of Academic Performance. Ph.D. Thesis, University of Minnesota, Minneapolis, MN, USA, 2003. 52. DeRosier, M.E.; Lloyd, S.W. The Impact of Children’s Social Adjustment on Academic Outcomes. Read. Writ. Q. 2011,27, 25–47. [CrossRef] [PubMed] 53. Hanks, A.R.; Beier, M.E. Differential Prediction of Preparatory and Performance Self-Efficacy Judgments. Hum. Perform. 2012,25, 318–334. [CrossRef] Sustainability 2019,11, 6062 19 of 20 54. LeBlanc, P.H., III. The Relationship between Attendance and Grades in the College Classroom. In Proceedings of the 17th Annual Conference of the International Academy of Business Disciplines, Pittsburgh, PA, USA, 17 April 2005. 55. McNeal, R.B., Jr. Parent involvement, academic achievement and the role of student attitudes and behaviors as mediators. Univ. J. Educ. Res. 2014,2, 564–576. [CrossRef] 56. Salgado, J.F.; Moscoso, S.; University of Santiago de Compostela, Santiago de Compostela, Spain. Individual Differences, Negative Performance, and Academic Outcomes. 2012. 57. Schmitt, N.; Oswald, F.L.; Friede, A.; Imus, A.; Merritt, S. Perceived fit with an academic environment: Attitudinal and behavioral outcomes. J. Vocat. Behav. 2008,72, 317–335. [CrossRef] 58. Thompson, R.A.; Zamboanga, B.L. Academic aptitude and prior knowledge as predictors of student achievement in introduction to psychology. J. Educ. Psychol. 2004,96, 778–784. [CrossRef] 59. Salgado, J.F.; University of Santiago de Compostela, Santiago de Compostela, Spain. Escalas de Desempeño Académico: CDTE, CDCE y CDAN. 2010. 60. Schmidt, F.L.; Hunter, J.E. Methods of Meta-Analysis: Correcting Error and Bias in Research Findings; Sage: Thousand Oaks, CA, USA, 2015. 61. Hoyle, R.H.; Kenny, D.A. Sample Size, Reliability, and Tests of Statistical Mediation. In Statistical Strategies for Small Sample Research; Hoyle, R., Ed.; Sage: Thousand Oaks, CA, USA, 1999; pp. 195–222. 62. Muth é n, B.O.; Muth é n, L.K.; Asparouhov, T. Regression and Mediation Analysis Using Mplus; Muth é n & Muthén: Los Angeles, CA, USA, 2016. 63. Jöreskog, K.; Sörbom, D. LISREL 8.20 and PRELIS 2.20.; Scientific Software Inc.: Chicago, IL, USA, 1998. 64. Browne, M.W. Predictive Validity of a Linear Regression Equation. Br. J. Math. Stat. Psychol. 1975 ,28, 79–87. [CrossRef] 65. Watson, D.; Clark, L.A.; Chmielewski, M.; Kotov, R. The value of suppressor effects in explicating the construct validity of symptom measures. Psychol. Assess. 2013,25, 929–941. [CrossRef] 66. MacKinnon, D.P.; Lockwood, C.M.; Hoffman, J.M.; West, S.G.; Sheets, V. A comparison of methods to test mediation and other intervening variable effects. Psychol. Methods 2002,7, 83–104. [CrossRef] 67. Preacher, K.; Leonardelli, G. Calculation for the Sobel Test: An Interactive Calculation Tool for Mediation Tests. 2003. Available online: http://quantpsy.org/sobel/sobel.htm (accessed on 12 November 2017). 68. Tofighi, D.; MacKinnon, D.P. RMediation: An R package for mediation analysis confidence intervals. Behav. Res. Methods 2011,43, 692–700. [CrossRef] [PubMed] 69. Hurtz, G.M.; Donovan, J.J. Personality and job performance: The Big Five revisited. J. Appl. Psychol. 2000 ,85, 869–879. [CrossRef] [PubMed] 70. Organ, D.W.; Ryan, K. A meta-analytic review of attitudinal and dispositional predictors of organizational citizenship behavior. Personal. Psychol. 1995,48, 775–802. [CrossRef] 71. Nonis, S.; Swift, C.O. An examination of the relationship between academic dishonesty and workplace dishonesty: A multicampus investigation. J. Educ. Bus. 2001,77, 69–77. [CrossRef] 72. Sims, R.L. The relationship between academic dishonesty and unethical business practices. J. Educ. Bus. 1993,68, 207–211. [CrossRef] 73. McCabe, D.L. Cheating among college and university students: A North American perspective. Int. J. Educ. Integr. 2005,1, 1–11. [CrossRef] 74. Rettinger, D.A.; Jordan, A.E.; Peschiera, F. Evaluating the motivation of other students to cheat: A vignette experiment. Res. High. Educ. 2004,45, 873–890. [CrossRef] 75. Smyth, M.L.; Davis, J.R. Perceptions of dishonesty among two-year college students: Academic versus business situations. J. Bus. Ethics 2004,51, 63–73. [CrossRef] 76. Witherspoon, M.; Maldonado, N.; Lacey, C.H. Undergraduates and academic dishonesty. Int. J. Bus. Soc. Sci. 2012,3, 76–86. 77. Christiansen, N.D.; Burns, G.N.; Montgomery, G.E. Reconsidering Forced-Choice Item Formats for Applicant Personality Assessment. Hum. Perform. 2005,18, 267–307. [CrossRef] 78. Jackson, D.N.; Wroblewski, V.R.; Ashton, M.C. The impact of faking on employment tests: Does forced choice offer a solution? Hum. Perform. 2000,13, 371–388. [CrossRef] 79. McAbee, S.T.; Oswald, F.L. The criterion-related validity of personality measures for predicting GPA: A meta-analytic validity competition. Psychol. Assess. 2013,25, 532–544. [CrossRef] [PubMed] Sustainability 2019,11, 6062 20 of 20 80. Trapmann, S.; Hell, B.; Hirn, J.O.W.; Schuler, H. Meta-analysis of the relationship between the Big Five and academic success at university. J. Psychol. 2007,215, 132–151. [CrossRef] 81. Glass, G.V.; McGaw, B.; Smith, M.L. Meta-Analysis in Social Research; Sage: London, UK, 1981. 82. Ozer, D.J. Correlation and the coefficient of determination. Psychol. Bull. 1985,97, 307–315. [CrossRef] © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).