Faking the Personality Profile: Easier Said Than Done. Richard L. Griffith, Mitchell H. Peterson, Joshua Quist & Ashley Benda

Transcription

1 1 Running head: FAKING PERSONALITY PROFILES Faking the Personality Profile: Easier Said Than Done Richard L. Griffith, Mitchell H. Peterson, Joshua Quist & Ashley Benda Florida Institute of Technology Amanda L. Evans PreVisor Address correspondence concerning this paper to: Richard L. Griffith College of Psychology and Liberal Arts Florida Institute of Technology 150 W. University Blvd. Melbourne, FL Paper presented in R. L. Griffith and M. H. Peterson (Chairs), Complex Problems, Simple Solutions: Contemporary Research in Applicant Faking Behavior. Symposium conducted at the 23 rd Annual Conference for the Society for Industrial and Organizational Psychology: San Francisco, CA.

2 2 Abstract While concern over the occurrence of applicant faking has resulted in a considerable body of literature devoted to developing methods for deterring and identifying fakers, no solution has provided a fail-safe guard against its occurrence. The current study attempted to determine the extent to which the use of a profile-based personality assessment scored in a non-compensatory manner could reduce the occurrence of faking. Our findings suggest that applicants were unsuccessful in their attempts at faking an entire personality profile. Implications for research and practice are also discussed.

3 3 Faking the Personality Profile: Easier Said Than Done The faking of personality-based selection measures has been a point of interest for practitioners and researchers almost as long as personality measures have existed (Zickar & Gibby, 2006). When motivated, applicants may convey an image of themselves that inflates desirable traits and exhibits attributes of an ideal employee (Rosse, Stecher, Miller, & Levin, 1998). Currently there is consensus that individuals can fake (Viswesvaran & Ones, 1999), and evidence that some applicants do fake in employment settings (Donovan, Dwight, & Hurtz, 2003; Griffith, Chmielowski, & Yoshita, 2007). Research has suggested that practitioners are aware of applicant faking and are interested in efforts to reduce it (Goffin & Christiansen, 2003; Rees & Metcalfe, 2003). However, methods to reduce faking such as warnings (Dwight & Donovan, 2003) and forcedchoice measures (Heggestad, Morrison, Reeve, & McCloy, 2006) have met with mixed results. In addition, scales used to detect faking (e.g. social desirability or unlikely virtues scales) have been heavily criticized, and may not adequately identify applicant faking (Burns & Christiansen, 2006). While new methods proposed to detect faking show promise (e.g., Kuncel & Borneman, 2007), additional research on faking reduction is warranted. Our goal for the current study, therefore, was to examine applicant faking behavior in order to develop a method of faking reduction that uses the fakers response patterns against them. While rarely discussed, research has suggested that not all fakers are successful in their attempts to game the system. In fact, between 10 and 20% of applicants attempting to manipulate their personality score actually reduce their chances of being selected when a single measure of personality is administered (Isaacson, Frei, Quist, & Griffith, 2007;

4 4 Hogan, 2005, personal communication). Given these base rates of maladaptive faking on transparent single construct measures, we suspected that applicants would have difficulty successfully faking across several constructs measured in a selection profile. Our results supported this suspicion. Combined with a targeted non-compensatory scoring scheme, the use of a multifaceted measure resulted in a drastic reduction in the number of successful fakers. In much the way a martial artist uses the momentum of the attacker against them, the proposed method to reduce the prevalence of faking behavior uses the act of faking against the applicant. In this paper, we will first present literature discussing the conceptual nature of applicant faking, the prevalence of this behavior in selection settings, and the consequences of faking for the organization. Next, we will discuss characteristics of the personality scale and the applicant that can lead to successful, or unsuccessful faking. We will then propose a set of research hypotheses, and discuss the methods we used to test these hypotheses. We will present our results and finally, discuss these findings and the implications for further research and personnel selection practice. Applicant Faking Behavior Faking is typically defined as an applicant s deliberate alteration of responses on a measure of personality, under motivated conditions, in order to present a more favorable impression to a prospective employer. Employers have expressed concerns regarding applicant faking (Rees & Metcalfe, 2003), and research suggests these concerns may have substance. Recent work in the area has suggested that applicant faking raises concern over selection system fairness (Morgeson et al., 2007) and reduces criterionrelated validity (Komar, Brown, Komar, & Robie, 2008). In addition, recent research

5 5 suggests that applicants engaging in faking behavior may be prone to engaging in other deceptive behaviors once they are on the job. Peterson, Griffith, O Connell, and Isaacson (2008) reported a significant positive correlation (r =.27) between individual applicant faking levels in a selection setting and counterproductive work behavior. Thus, applicant faking behavior can potentially impact important organizational outcomes and reduce the effectiveness of personality measures used in a selection setting. Given the concerns of researchers and practitioners, methods to deter and detect faking behavior, such as warnings, forced-choice response formats, and social desirability scales have been proposed. However, these methods have met with mixed success. The subsequent section will provide a brief review of previous faking detection methods and deterrents. Existing Methods to Reduce the Effects of Faking Behavior Dwight and Donovan (2003) conducted a meta-analysis that examined the degree to which warnings reduced the amount of observed faking behavior. The authors reported an average d effect size of.23 between warned and non-warned subjects, suggesting that overall warnings can mitigate the amount of faking. However, the study highlighted wide variability in the effect of warnings on participant responding. Only 5 of the 11 studies reported a positive effect size, indicating that for 6 studies warnings had little effect, or potentially resulted in more faking. A study by Heggestad et al. (2006) examined the effectiveness of the forcedchoice (FC) response format in reducing the prevalence of applicant faking. Overall, faking was reduced when FC formats were employed. Mean-level differences between honest and faked FC measure scores were smaller than those between honest and faked

6 6 single-stimulus measure scores. However a substantial number of applicants were able to fake successfully on the FC measure, and the magnitude of the d effect sizes for the amount of faking (ranging from d =.07 for Agreeableness, to d = 1.20 for Conscientiousness) is still cause for concern. In addition, the ipsative properties of FC measures appear to complicate our measurement efforts. Heggestad et al. (2006) stated that the FC measures provided no better estimation of normative trait standing under conditions of faking than single-stimulus measures. Social desirability scales have been employed to detect faking for almost as long as personality measures have been used for organizational decision making processes (Zickar & Gibby, 2006). In fact, these measures form the basis for a line of research that has examined faking as a potential moderator of the criterion-related validity of personality measures (e.g., Ones, Viswesvaran, & Reiss, 1996). In general, these studies have suggested that faking is little more than a nuisance variable, and that, the reservation of industrial-organizational psychologists about using personality measures for personnel selection because of the potential of social desirability is unfounded (Ones et al., 1996; p. 671). The soundness of these findings rests on the assumption that scores on measures of SD adequately reflect observed faking behavior. This relationship is often assumed, but has remained largely untested in applicant samples. In a recent simulation examining the effects of SD corrections on validity and hiring decisions (Schmitt & Oswald, 2006) the researchers stated, Our analyses implicitly assume that the faking measure is a perfectly construct-valid measure (pg. 616). Recently, researchers have called the use of SD measures as proxies for faking into question (Burns

7 7 & Christiansen, 2006), and have provided empirical evidence that SD and faking are unrelated (Peterson et al., 2008). Overall, researchers have been thwarted in their attempts to effectively deter or detect applicant faking behavior. This failure has not resulted from a lack of effort and research emphasis. Over 900 theses and dissertations have examined applicant faking in the last 10 years (Zickar & Gibby, 2006). However, applicant faking remains a concealed and complex phenomenon. The Prevalence of Faking Until recently, the prevalence of faking behavior was unknown. Most studies examining applicant faking employed directed faking manipulations (e.g., McFarland & Ryan, 2000; Mueller-Hanson et al., 2003) or measures of SD as a proxy variable (e.g. Ones et al., 1996; Rosse et al., 1998). While directed faking studies provide information regarding an individual s ability to fake, they tell us little about the base rate of faking in applicant samples. Given the questions surrounding measures of SD, base rates of faking calculated in studies using these measures should also be taken with a grain of salt. One of the earliest studies to provide an estimate of the prevalence of faking behavior relied on applicants self-reports. Using the randomized response technique, Donovan, Dwight, and Hurtz (2003) examined the self-reported frequency of faking behavior in applicant settings. The study suggested that roughly 30% of applicants engage in faking behavior. The authors note the importance of this finding by stating that, contrary to the perspectives of some researchers (e.g., Hogan, 1991; Ones et al., 1996; Ones et al., 1993), a concern over applicant faking is justified, and the prevalence of

8 8 faking is somewhat higher than what one might expect from past estimates (pp ). Two recent studies (Ellingson, Sackett, & Connelly, 2007; Griffith et al. 2007) used a within-subjects design to directly assess the magnitude and frequency of applicant faking behavior in applicant samples. Ellingson et al. (2007) used a within-subjects design to examine response distortion, operationalized as score change across personnel selection and personal development contexts. The authors identified individuals (from an archival dataset) who had completed the CPI on two occasions, across four specific testretest conditions (i.e., development development, development selection, selection development, selection selection). The authors argued that while observed fluctuations in personality scores across contexts could be due to a variety of factors, faking should only be observed in the development selection or selection development conditions). As such, the authors adjusted faking estimates in order to allow for the potential for potential true personality change over time and potential change due to developmental feedback. In the end, this procedure resulted in a negligible effect size for faking across contexts (d =.075). Thus the authors concluded that little faking occurs, and that it does not result in changes to the rank-ordering of applicants beyond what would be expected due to the unreliability of the measure over time. In contrast to the findings of Ellingson et al. (2007), a study by Griffith et al. (2007) used a within-subjects design to examine faking in a sample of applicants to a temporary employment agency. Conscientiousness scores were collected from job applicants, who were then asked to complete the same assessment one month later in a research context. Griffith et al. reported significant mean-level score differences between

9 9 applicant and honest responses, in addition to observing changes in simulated individual hiring decisions across the applicant and research contexts. Thus, in one study faking mattered little, while in the other, its occurrence substantially changed hiring decisions. A closer examination of the differences in these two studies reconciles the apparent contradictory findings and reveals information that would be useful in developing a fake-resistant measure of personality. The first notable difference in the studies lies in the level of analysis. Both studies look at the standardized differences between applicant and non-motivated response conditions at a group level. Ellingson et al. report a standardized difference of.26 between the development and selection settings. However, methodological problems with the data set, namely unstandardized periods between administrations (ranging from 12 days to 7 years), necessitated corrections for personality change over time that reduced the estimated d effect size to.075. Griffith et al. (2007), using a constrained and standardized re-test interval, reported a d effect size of.61. While this group index provides some information regarding the amount of applicant faking, it is deficient in that it addresses the issue at an inappropriate level of analysis. Faking is an individual behavior, and should be investigated at an individual level of analysis (Snell, Sydell, & Leuke, 1999). Griffith et al. analyzed data at this level and thus were able to provide estimates of the frequency of faking behavior. Their findings suggested that between 22% and 49% of applicants faked their responses (depending upon how the faking variable was operationalized), a finding that is congruent with estimates drawn from Donovan et al. s (2003) self-report study. Ellingson et al. did not analyze the within subjects data at the individual level of analysis, thus estimates of the prevalence of faking were not reported.

10 10 The second major difference between the studies is the nature of the personality measures used to collect the data. First, the scales differed in response format; Ellingson et al. (2007) utilized a measure with a true-false format, while Griffith et al. (2007) used a Likert scale instrument. It is possible that item format accounted for the differences in the magnitude of observed faking behavior, as has been evidenced in the FC literature (Heggestad et al., 2006). Second, and perhaps more importantly, the measures used in these studies differed in the number of constructs assessed. Ellingson et al. used a multifaceted measure comprised of 20 facets, while Griffith et al. employed a single construct measure assessing conscientiousness. The average effect size for faking across scales may mask differences in both the relative fakabilty of each scale, as well as an individual s ability to fake effectively across all subscales. Thus, the index of faking in the Ellingson et al. study reflects successful faking across all 20 subscales, which should be considerably more difficult than a single scale. In fact closer examination of the results of Ellingson et al. reveal that considerable amounts of faking occurred at the individual subscale level, in some cases exceeding the effect size reported in Griffith et al. (2007). Most studies that have examined faking assume (at least implicitly) that the faker will be successful in their attempt to elevate their score. However, previous research has demonstrated that not all applicants are successful at faking, even on relatively transparent single-construct measures (Isaacson et al., 2007). Using a within-subjects design to directly assess applicant faking, Isaacson et al. reported that 9 percent of applicants significantly reduced their score on a measure of conscientiousness when they attempted to fake. While we are unable to assess the frequency of maladaptive faking in

11 11 the Ellingson et al. study, several negative d effect sizes were reported. This suggests that for several constructs faking may have reduced scores on the subscale. One key feature of most faking studies that have examined methods to reduce faking is that they employ single construct measures of personality. Commonly cited meta-analyses (e.g., Viswesvaran & Ones, 1999) also report fakability estimates separately for each sub-scale, thereby offering estimates of faking that are bound to single construct measures. While applicants may be successful in faking these single construct measures it may be considerably more difficult to fake an entire profile. Many personality based selection measures are based on profiles established through the use of a detailed job analysis, and contain several narrow bandwidth constructs. In order to successfully fake across all the included constructs, applicants would need to be able to understand the requirements of the job, and which traits may be necessary for effective job performance (Vasilopoulos & Cucina, 2006). Christiansen, Burns, and Montgomery (2005) suggest that applicants rely on implicit job theories to help them identify job-relevant traits and the relationships among them. Previous research has demonstrated that not all applicants are successful at faking, even on relatively transparent single construct measures (Isaacson et al., 2007). It is then likely that even fewer applicants would be successful at faking across several constructs, all of which may have complex relationships with job performance (positive, negative, and curvilinear). This percentage of fakers may further decrease if the individual scales comprising the profile are scored in a non-compensatory manner. Therefore, we hypothesized that the percentage of applicants who could successfully fake a profilebased personality measure scored in a non-compensatory fashion would be negligible.

12 12 Our reference to compensatory vs. non-compensatory scoring requires a brief definition. In a compensatory scoring methodology, an applicant s standing on each assessed characteristic is averaged/summed in order to arrive at a total score (Guion, 1998). Conversely, when non-compensatory scoring is used, if an applicant fails to meet a specified cutoff on any one characteristic, he or she is removed from further consideration. In the present study, non-compensatory refers to a situation in which an applicant is removed from consideration if any one of his or her personality trait scores falls outside of a job-analysis based score range for that trait. We tested this hypothesis in a sample of 21, 250 applicants for a sales position at an international office equipment company. Participants completed the Craft Personality Questionnaire (CPQ) (Craft & Waldo, 2006), which is an online 75-item assessment of eight personality traits. Once the applicants completed the items in a way that best described themselves, they were instructed to complete the CPQ in a fashion that would leave the most favorable impression on a potential employer. Instructional set faking studies have been criticized in the literature because they do not accurately reflect the prevalence of faking in applicant samples (Hough et al., 1990). However, in the current study we were interested in the participant s ability to fake (not their choice to fake in an applicant sample). Given this goal, the use of instructional sets is an appropriate strategy (Mesmer-Magnus & Viswesvaran, 2006). Method Participants Participants included 21, 250 applicants for a sales position at an international office equipment company. Applicants originated from locations throughout North

13 13 America, including the United States and Canada. The sample consisted of 6,380 (30%) females and 14,870 (70%) males. For the current study analysis, the sample was randomly split into two equal N data sets. We used the first sample to test the fakeresistant scoring scheme, and replicated the scheme on the second data set. Measures Craft Personality Questionnaire (CPQ). Personality was measured using the CPQ (Craft, 2006), which is a 75-item assessment of eight personality traits: Goalorientation, Need for Control, Social Confidence, Social Drive, Detail-Orientation, Good Impression, Need to Nurture, and Skepticism. When completing the CPQ, participants respond to each item twice, once providing a most favorable response and once providing their most honest response. Therefore, the measure consists of 150 responses. For the purposes of our study, we were only interested applicant responses to the items asking for their most favorable response. Each item is presented in a dichotomous forced-choice format and instructions are provided to the participant beforehand that state, For your most favorable response (MFR), DO NOT DESCRIBE YOURSELF. Respond to each statement as you think your employer would want you to respond to leave the most favorable impression. For your most honest response (MHR), please describe yourself honestly, frankly, and even selfcritically without trying to favorably impress your employer. Applicants respond to the MHR item first then answer the corresponding MFR item. The CPQ is scored as a profile in which ideal ranges of scores are derived via personality-based job analysis. For some constructs, high values are considered desirable, while for others low levels of the construct lead to high job performance. The

14 14 ideal intervals ranged from 8 points to 30 points (on a scale of one hundred) with an average interval of For example, the trait of Goal Orientation has an ideal range of 8 points (92-100%). Candidates who fall within these ideal ranges for all eight traits are considered acceptable for the job. Procedure Applicant data in this study were collected via the Internet from January 2005 through July All respondents were applying for a job in an office equipment sales organization, and were requested to complete the CPQ and a measure of intelligence prior to a formal interview. To examine whether successful faking across multiple scales was possible, the CPQ was scored in a non-compensatory fashion in which participants were assigned one point if their faked score fell in the confidence interval surrounding the ideal score derived from the job analysis for the sales position. Participants with a total of 8 points were considered successful fakers, in that they would not have been removed from the applicant pool, and would have received a job offer based on their MFR score. We then conducted a chi-square analysis to determine if our scoring schemes significantly reduced the prevalence of faking from the most conservative estimate of faking at the individual level of analysis. We used the 22% estimate from Griffith et al. (2007) as a comparison point. Results Descriptive statistics for the CPQ can be found in Table 1. For the first sample the Chi Square analysis suggested that the use of multiple constructs and the noncompensatory scoring scheme were successful in reducing the percentage of fakers that

15 15 have been observed in single construct studies (X 2 = , 1df, p <.001). The prevalence of successful faking for a single construct was as high as 66% (Table 2). However, of the 10,625 participants who attempted to fake the full profile, not a single candidate successfully matched the profile developed from the personality based job analysis (Table 3). Results from the second sample were similar. Chi-Square results (X 2 = , 1df, p <.001) suggested that there was a statistically significant difference between the expected faking rate of 22% and the observed rate of successful faking. Successful faking rates for individual constructs can be found in Table 4. Once again, no candidates were successful faking all 8 traits in the CPQ profile. Successful faking rates for each trait for the second sample can be found in Table 5. Discussion While most research supports the use of personality measures as effective components of selection batteries, the issue of applicant faking behavior has caused some practitioners and researchers to question the use of these measures. Until recently, little was known about the prevalence of faking behavior. However, in the last decade some light has been shed on the issue. It is our opinion that most researchers and practitioners generally accept that faking does occur in applicant samples (Reese & Metcalf, 2003; Robie, Brown, & Beaty, 2007), however the extent to which this behavior is present is still debated. While some recent studies suggest that little faking occurs (Ellingson et al., 2007), others report significant percentages of applicants engage in the behavior (Griffith et al. 2007). The current study examined differences in these two studies in an attempt to find a mechanism to develop fake resistant measures.

16 16 While there were several differences, we chose to focus on the difference in the number of constructs assessed, and the bandwidth of measurement. When a broad global construct was used faking was substantial, and did have negative consequences on hiring decisions (i.e., Griffith et al., 2007). However when the measurement instrument contained multiple, narrowly defined constructs we saw a reduction in the average amount of faking. We hypothesized that applicants would be unsuccessful at navigating many constructs, and would eventually fake in the wrong direction. We also thought that by coupling this maladaptive faking with a non-compensatory scoring scheme successful faking would be reduced to negligible amounts. Our data suggest that is the case. When we tested our hypotheses in a directed faking study using the CPQ, which contains eight traits, not a single respondent in a sample of 10, 000 applicants could successfully fake all eight. Our results were replicated in a large independent sample. A narrow multi-construct approach to personality measurement offers an elegant solution to the potential problem of applicant faking behavior. Instead of warning applicants not to fake, which undoubtedly will be ignored (at least by some applicants), the faker is left to undermine their own strategy of self-presentation. Instead of trying to catch fakers with poor proxy measures, we simply let the applicant fake their way out of consideration. Other proposed methods (Kuncel & Borneman, 2007) have used the response patterns of fakers in an effort to catch those who are attempting to elevate their scores. They examined the effects of faking at the item level to find idiosyncratic responses when applicants engaged in faking behavior. These items were then aggregated to form a scale that was unrelated to the trait being measured, but should have proven helpful in

17 17 detecting response distortion. The authors calibrated the scores on the measure to reduce or eliminate false positives. However, this calibration reduced the overall effectiveness of the scale. While false positives were less than 1%, fakers were correctly identified between 20 and 37% of the time. The proposed approach improves on this method in several key aspects. First, there is no effort to detect faking, which is a complex concealed behavior. The use of multifaceted measures simply capitalizes on the fact that some fakers guess wrong in their attempt to elevate their score. In that regard we are only handing them more rope to hang themselves with. Secondly, we have a zero false positive rate. No honest applicants are removed from consideration because they have inadvertently been flagged for faking. Applicants who do not score in the ideal range would not likely be the best performers, regardless of whether they are faking or not. In a way, the issue of faking becomes irrelevant; it is simply a matter of whether the applicant scores in the desired range, regardless of the response motivation. While the proposed method seems to be effective in reducing levels of applicant faking behavior, it does potentially pose other problems. Non-compensatory scoring often requires larger applicant pools to find enough individuals that fit the job profile. Depending on the desired selection ratio chosen by the organization, this method may result in additional recruiting and testing costs. In addition, the level of homogeneity likely to arise from the use of non-compensatory scoring may not be healthy for the organization. This level of homogeneity is exactly the situation that William Whyte warned us about in The Organization Man (Whyte, 1956), one of the original sources that provided coaching on faking personality measures.

18 18 If the selection ratio or homogeneity are concerns for the organization it would be possible to employ a less restrictive, empirically-driven non-compensatory scoring scheme in which the least fakable construct is chosen as the subscale to be scored in a non-compensatory fashion. An examination of the successful faking rate across scales (see Table 3) suggested that Detail Orientation had one of the lowest rates of successful faking (and one of the highest criterion-related validities). When asked to present themselves favorably, most applicants for the sales position indicated that high detail orientation would be a desirable trait for the job. However, the job analysis indicated that low levels of the trait were more likely to lead to successful job performance. Only 3 percent of applicants were correct when asked to fake this subscale. Using this scoring scheme, faking would not be eliminated, but drastically reduced. It is important to remember that this 3% represents the number of respondents able to fake in the right direction, but in applicant settings many applicants do not make an attempt to fake. Given current estimates of attempted faking behavior, we believe that this approach would reduce the number of successful fakers to roughly 1%. Limitations One of the limitations of the current study is the use of an instructed faking design. These designs have been heavily criticized for a lack of generalizability to the applicant setting (e.g. Hough et al. 1990). In instructed designs, all participants attempt to fake, while in applicant settings a limited number of applicants may actually choose to misrepresent themselves. Therefore, the amount of faking in instructed designs may be more drastic than what would be observed in selection contexts. We agree that these designs are limited in what they can reveal about actual applicant faking behavior.

19 19 However, the goal of the current design was to test the ability of respondents to successfully fake multiple traits on a personality profile. We were not interested in applicants propensity to fake in the current study. Given our study goals, the use of the instructional set design is appropriate (Mesmer-Magnus & Viswesvaran, 2006). Another possible limitation of the study is the confounded responses between the most honest responses and the most favorable responses. The study data were collected in an actual applicant setting. Therefore, applicants who attempted to fake their most honest responses may have tried to mask that behavior in their most favorable responses. In essence, some of the participants in this study may have double faked. This double faking may alter the nature of the most favorable responses observed in the current dataset. Future Research Based on the potential limitations inherent in obtaining directed faking scores in the applicant context, future research should investigate the use of a similar scoring procedure using a within-subjects design with applicant and honest responses. This would allow for a more detailed examination of differences between applicant and honest responses. Additionally, research using this type of a design would allow for a better understanding of whether applicants use more sophisticated faking attempts than those which are observed in directed faking studies. Finally, the use of this type of design would also allow for a more thorough examination of whether applicants are, in some cases, actually faking in the wrong direction.

20 20 Conclusions The results of the current study suggest that the occurrence of successful faking on an assessment that uses profile-based scoring in a non-compensatory fashion may be a rare occurrence. No applicants, when directed, were able to successfully fake all eight scales in the current assessment, and therefore be retained for employment. While the current study was exploratory in nature, more focused research aimed at examining scoring schemes that both reduce faking, and result in the hiring of effective employees is warranted. Instead of the often suggested advice of grabbing the bull by the horns, perhaps a more elegant solution will be more effective. Rather than overpowering the faker with punitive warnings and statistical corrections, perhaps we should take a lesson from the matador who simply waits until the bull has worn itself out.

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25 25 Table 1 Descriptive Statistics Measure CPQ-Favorable Scale Goal-orientation Need for Control Social Confidence Social Drive Detail-orientation Good Impression Need to Nurture Skepticism CPQ-Honest Scale Goal-orientation Need for Control Social Confidence Social Drive Detail-orientation Good Impression Need to Nurture Skepticism Study α Mean StdDev Table 2 Successful Faking on Each Trait CPQ Trait % Detail Orientation Good Impression Goal Orientation Need for Control Need for Nurturing Social Confidence Social Drive Skepticism

26 26 Table 3 Percentage of Successful Fakers Across the Profile for Sample 1 Correct Fake Score Frequency Percent Cumulative Percent Total Table 4 Successful Faking on Each Trait (Sample2) CPQ Trait % Detail Orientation Good Impression Goal Orientation Need for Control Need for Nurturing Social Confidence Social Drive Skepticism

27 27 Table 5 Percentage of Successful Fakers Across the Profile for Sample 2 Correct Fake Score Frequency Percent Cumulative Percent Total