Effects of Positive Impression Management on the Psychopathic Personality Inventory

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Law and Human Behavior [lahu] PP137-302022 May 9, 2001 11:54 Style file version Nov. 19th, 1999

Law and Human Behavior, Vol. 25, No. 3, 2001

Effects of Positive Impression Management on thePsychopathic Personality Inventory1

John F. Edens,2,3 Jacqueline K. Buffington,2 Tara L. Tomicic,2

and Brandon D. Riley2

The Psychopathic Personality Inventory (PPI; S. O.Lilienfeld & B. P. Andrews, 1996)is a self-report test that has shown considerable promise as a screening measure forpsychopathy. A current limitation of the PPI is that no data exist regarding the impactof response sets such as positive impression management. Although the PPI containsa validity scale (Unlikely Virtues) designed to identify response biases such as “fakinggood,” its utility has not yet been assessed. In this study a repeated measures analoguedesign was employed in which 186 respondents completed the PPI both under standardconditions and with specific instructions to create a favorable impression of themselves.In the “fake good” condition, participants were able to appear significantly less psy-chopathic, with those who obtained higher scores in the standard instruction conditionshowing the largest decreases in their PPI scores. Receiver Operating Characteristicanalyses indicated that, although the Unlikely Virtues scale significantly differentiatedbetween “fake good” and honest protocols (area under the curve= .73), a considerablenumber of misclassifications occurred. The clinical and forensic implications of thesefindings are discussed.

Psychopathy is a personality pattern characterized by superficial charm, egocentric-ity, a callous disregard for others, and “inadequately motivated antisocial behavior”(Cleckley, 1976). The assessment of this construct has considerable relevance to men-tal health professionals because psychopathy has been shown to predict various neg-ative outcomes (e.g., violence, institutional misbehavior, criminal recidivism) amongboth diverse offender samples, such as general population prison inmates (Hare,1991, 1996; Salekin, Rogers, & Sewell, 1996), sex offenders (Furr, 1993; Hemphill,Hare, & Wong, 1998), mentally disordered inmates (Heilbrun, Hart, Hare, Gustafson,

1A preliminary version of this study was presented at the 1999 annual convention of the AmericanPsychological Association, Boston, MA.

2Department of Psychology and Philosophy, Sam Houston State University, Huntsville, Texas.3To whom correspondence should be addressed at Department of Psychology and Philosophy, SamHouston State University, Huntsville, Texas, 77341-2447; e-mail: psy [email protected]

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0147-7307/01/0600-0235$19.50/1 C© 2001 American Psychology-Law Society/Division 41 of the American Psychology Association

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236 Edens, Buffington, Tomicic, and Riley

Nunez, & White, 1998; Rice, & Harris, 1995), and “youthful” offenders (Edens,Skeem, Cruise, & Cauffman, 2001), as well as among nonoffender samples, suchas psychiatric patients (Nolan, Volavka, Mohr, & Czobor, 1999; Silver, Mulvey, &Monahan, 1999) and college students (Forth, Brown, Hart, & Hare, 1996). The as-sessment of this syndrome has become even more germane in legal/judicial settingsin recent years because several states have adopted or revised statutes regarding thecivil commitment of “predatory” or “dangerous” sex offenders that focus on thisor related diagnoses (e.g., Antisocial Personality Disorder, Sociopathy) as part ofthe decision-making criteria. For example, Florida’s recently revised statute assertsthat a subset of sex offenders have “antisocial personality features” that make themlikely to engage in sexually violent behavior (Otto, Poythress, Borum, & Petrila,1999). Similarly, Texas recently has passed legislation that requires an assessment ofpsychopathy specifically as part of the civil commitment evaluation procedures forsex offenders (Civil Commitment of Sexually Violent Predators Act, 1999).

The vast majority of recent research linking psychopathy to negative outcomeshas employed the Psychopathy Checklist-Revised (PCL-R) (Hare, 1991), which ap-pears to be the most reliable and valid indicator of this personality pattern—at leastin forensic and correctional contexts. Although the content of the PCL-R was basedon Cleckley’s formulation of psychopathic personality features (Cleckley, 1976), italso taps criminal and antisocial behaviors more heavily than Cleckley’s original listof descriptors. This distinction between “core” personality features and antisocialbehavior patterns has been reflected in factor analyses of the PCL-R, which typicallyidentify one factor that contains items comprising the former (e.g., inflated sense ofself-worth, conning/manipulative, pathological lying) and a second factor compris-ing items reflecting the latter (e.g., irresponsibility, parasitic lifestyle, poor behavioralcontrols).

Although considered by many to be the “gold standard” for assessing this con-struct, there are several limitations of the PCL-R that prohibit its application in manysettings (correctional and noncorrectional) in which the assessment of psychopathymight prove useful. First, the PCL-R is labor intensive. It must be administered bytrained raters, using extensive institutional file and interview data that must be col-lected individually. This requirement effectively limits the widespread adoption ofthe PCL-R as a screening measure for identifying “high risk” inmates in most cor-rectional settings. Second, the PCL-R cannot be scored reliably without access toadequate file data; thereby precluding its use in settings in which such informationis minimal, unreliable, or unavailable (e.g., first time criminal offenders, preemploy-ment screenings for public safety occupations). Third, the PCL-R cannot be usedto assess changes occurring as a response to treatment, given that it primarily isscored on the basis of static, historical factors that will not change appreciably overtime (Hare, 1998). This limitation is of particular concern in those forensic contextsin which offenders periodically are reassessed following some period of manda-tory intervention, such as civilly committed “psychopathic” sex offenders who mustbe judged to be at lower risk for recidivism before being released. Obviously, anyostensible gains achieved during treatment will not be reflected in any significantdecrease in PCL-R scores. Given these limitations, it is clear that there are many set-tings (e.g., correctional classification settings, preemployment screenings, treatment

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Faking Good on the PPI 237

outcome evaluations) in which the PCL-R is inappropriate and alternative assess-ment methods would prove valuable.

One alternative assessment approach that potentially obviates the limitations ofthe PCL-R noted above is a self-report format. A valid self-report measure of psy-chopathy would offer several advantages, in that it would allow for group screeningof large numbers of examinees, eliminate the reliance on institutional file data, andprovide a more dynamic measure of personality functioning than the PCL-R thatwould be more sensitive to possible changes in personality over time or followingintervention. This would be useful particularly in the assessment of “predatory sexoffenders,” who in some states must document changes in the “mental condition”presumed to be the cause of their sexual offending prior to being released from civilcommitment. Researchers have not been optimistic about assessing psychopathy viaself-report methods, however, for various reasons, including (a) the putative lack ofinsight of persons with psychopathic personality traits, and their concomitant inabil-ity to describe themselves accurately; (b) the association between psychopathy anddeceptiveness/manipulation; and (c) the documented inability of most self-reportmeasures to correlate highly either with the PCL-R or each other (e.g., Cooney,Kadden, & Litt, 1990; Edens, Hart, Johnson, Johnson, & Olver, 2000; Hare, 1985;Hart, Forth, & Hare, 1991).

Despite these concerns, several recently developed self-report measures haveshown some promise in assessing this personality construct. For example, thePsychopathic Personality Inventory (PPI; Lilienfeld & Andrews, 1996) is a relativelynew instrument designed to assess the core personality features of this disorder innon-offender samples. The PPI was constructed through “. . . the delineation of fo-cal constructs deemed relevant to psychopathy . . . informed by a comprehensivereview of the theoretical and empirical literature” (Lilienfeld & Andrews, 1996,p. 492). Items were generated to reflect 24 focal constructs (see Table 1 in Lilienfeld& Andrews, 1996, p. 493) that had been posited to reflect psychopathic personalitytraits by various experts in the field (e.g., Albert, Brigante, & Chase, 1959; Cleckley,1976; Gray & Hutchinson, 1964; Hare, 1991). In an effort to construct a more “pure”personality scale, items reflecting criminal or antisocial behavior were not includedin the item pool. Following several iterations of item writing, data collection, andfactor analysis, a final item pool of items was delineated that contained eight factoranalytically derived subscales that tap personality dimensions historically associatedwith this syndrome (see Measures section).

Among nonoffender samples, the PPI has been shown to correlate moderatelyto highly with various self-report, peer-report, and interview-based measures of psy-chopathy (e.g., Psychopathy Checklist: Screening Version [PCL:SV]; Hart, Cox, &Hare, 1995) and to correlate moderately with self report, peer-report, and interview-based measures of relevant Cluster B (e.g., antisocial, narcissistic, histrionic) person-ality disorder symptomatology (Hamburger, Lilienfeld, & Hogben, 1996; Lilienfeld& Andrews, 1996; Lilienfeld et al., 1998). Significant correlations with other theo-retically relevant constructs also have been found, including deceptiveness, aggres-sion, constraint (negative), and fearlessness (Edens, Buffington, & Tomicic, 2000;Lilienfeld & Andrews, 1996).

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Although developed for use with and validated on nonoffender samples, thePPI recently has been employed in several correctional settings and shows somepromise as a measure of psychopathic features among offender populations. Amongjail and prison inmates, the PPI has been shown to correlate moderately with mea-sures of aggression, empathy (negatively), and other Axis II Cluster B personal-ity traits and to correlate modestly with criminal history variables (e.g., number ofprior arrests, history of juvenile delinquency, verbally aggressive and violent disci-plinary infractions; Edens, Poythress, & Lilienfeld, 1998; Edens, Poythress, & Watkins,in press; Sandoval, Hancock, Poythress, Edens, & Lilienfeld, 2000). Preliminary re-search with a sample of “youthful offender” prison inmates (Poythress, Edens, &Lilienfeld, 1998) indicated that the PPI correlated moderately highly (r = .54) withthe PCL-R, and an optimally derived cutting score correctly classified 86% of the50 participants as psychopathic/nonpsychopathic (i.e., PCL-R scores≥ 30 indicatingpsychopathy). Similar results were obtained by Kruh et al. (2000) in a sample of notguilty by reason of insanity (NGRI) acquittees who were administered the PCL:SV(r = .62).

In terms of the predictive validity of the PPI in comparison to the PCL-R, officialprison disciplinary infraction data for the participants in the Poythress et al. (1998)study were reported in a subsequent study (Edens, Poythress, & Lilienfeld, 1999).Both measures showed modest (but significant) zero-order correlations with verballyaggressive/violent infractions. Neither demonstrated incremental validity over theother—that is, neither accounted for unique variance in the outcome measure. Kruhet al. (2000), however, found that the PPI accounted for unique variance beyond thatof the PCL:SV in the prediction of self-reported violent acts among their sample ofNGRI acquittees.

As noted earlier, the PPI was designed to tap personality features consistent withclassic descriptions of psychopathy (similar to Factor I of the PCL-R) rather thansimply criminal and antisocial behavior patterns. Nonetheless, the PPI also tends tocorrelate moderately with the chronically unstable lifestyle features of psychopa-thy associated with Factor II of the PCL measures. The PPI/Factor II correlationsreported by Poythress et al. (1998) and Kruh et al. (2000) were .40 and .65, respec-tively, suggesting that the PPI may not be a “pure” measure of personality. This isnot entirely surprising, given that the two factors of the PCL-R are moderately (∼.5)intercorrelated (Hare, 1991).

Although preliminary, these findings suggest collectively that the PPI might pro-vide useful information for screening or classification purposes (or both) in forensicsettings, in which identification of inmates who are at high risk for violence or otherdisciplinary infractions is a prominent concern (Clements, 1996), or in nonforensicsettings where extensive file information typically is unavailable, such as preem-ployment screenings. The PPI may be especially useful when screening applicantsfor occupations (e.g., police, airline pilots) in which psychopathic features such asirresponsibility, impulsivity, or grandiosity might be particularly undesirable.

Despite the initial positive findings regarding the validity of the PPI, each ofthe studies described earlier involved confidential or anonymous participation inresearch in which participants were assured that there would be no consequences(positive or negative) related to how they responded to the protocols. Therefore,

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respondents may have been more candid and honest than typically would be ex-pected in an actual forensic evaluation or preemployment screening in which min-imization of psychopathic tendencies might be expected. Whether responses to thePPI would be influenced by an attempt to create a favorable impression has yet to beestablished. For example, it is possible that offenders may realize that appearing lesspsychopathic (e.g., less grandiose, manipulative, or callous) may be to their benefit, tothe extent that they may expect that being identified as a “psychopath” might resultin more restrictive confinement, decreased likelihood of receiving early release, orcivil commitment as a “psychopathic sexual predator.”

Despite these concerns, there are reasons to believe that the PPI may be less sus-ceptible to response distortion, particularly positive impression management, thanother self-report inventories. First, the PPI contains a validity measure, the UnlikelyVirtues scale, that comprises items specifically developed to identify response biasessuch as positive impression management. Second, measures of social desirability wereadministered along with the PPI during several of the developmental studies, andindividual items that correlated highly with this response style either were deletedor rewritten accordingly. As a result, earlier studies (Lilienfeld & Andrews, 1996)indicated that the PPI was correlated only modestly (between−.2 and−.3) with theMarlowe–Crowne Social Desirability Scale (Crowne & Marlowe, 1960).

Although these results suggest that the PPI is not saturated with item contentthat correlates with the general construct of social desirability when completed in aconfidential research context, it should not be presumed that the PPI will be immuneto impression management strategies when the “stakes” are higher, such as whenadministered in a more evaluative or adversarial context. Research examining theseissues clearly is needed before the PPI could be adopted for clinical use. Only onestudy to date has examined the impact of response sets on the PPI (Edens, Buffington,et al., 2000). However, this research only addressed the impact of attempting to ma-linger mental illness, and the generally positive results of that research do not informour understanding of the effects of “faking good” on the PPI. Research examiningthe effect of response sets on other psychological tests such as the MMPI/MMPI-2suggests that the identification of fake good response sets may be more difficult thanthe identification of “faking bad” or malingering (Baer, Wetter, & Berry, 1992; Baer,Wetter, Nichols, Greene, & Berry, 1995; Nicholson et al., 1997).

This study represents a preliminary attempt to assess the effects of engaging inpositive impression management on the PPI. Two general questions were addressed.First, are respondents able to appear less psychopathic when instructed to “fakegood” on the PPI? More specifically, can those individuals who have elevated scoreson the PPI (when answering honestly) achieve a significant decrease in those scoreswhen engaging in positive impression management? Second, can participants en-gaging in positive impression management be differentiated reliably from honestrespondents based on their performance on the Unlikely Virtues validity scale ofthe PPI? To examine these questions, a repeated measures analogue design was em-ployed, in which participants (a) completed the PPI under standard conditions (i.e.,instructed to respond honestly) and (b) completed the PPI with specific instructionsto create a very favorable impression of themselves in response to a hypotheticallegal or job applicant scenario (discussed later). Although there are conceptual and

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methodological limitations regarding the use of analogue designs to examine the ef-fects of positive impression management on clinical measures, Rogers (1997, p. 410)has argued that such procedures are useful in the preliminary stages of instrumentvalidation, particularly when combined with a repeated measures design.

METHOD

Sample

The sample consisted of 186 participants (135 women, 51 men) recruited froma psychology department human subjects pool at a large southwestern universityin the United States. Participants were primarily young (M = 20.69; SD= 4.18),single (87%), and Caucasian (70%), with relatively smaller percentages of AfricanAmerican (16%) and Hispanic (12%) participants (2% reporting “Other”).

Measures

Psychopathic Personality Inventory (PPI)

The PPI (Lilienfeld & Andrews, 1996) is a 187-item self-report measureoriginally designed to assess the core personality features of psychopathy amongnoncriminal populations. Respondents rate themselves on each item, using a scalefrom 1 to 4 (i.e., False, Somewhat False, Somewhat True, True). A total score can bederived for the PPI, as well as eight factor analytically derived subscales that measurepersonality dimensions associated with psychopathy:

• Machiavellian Egocentricity consists of 30 items (e.g., “I always look out formy own interests before worrying about those of the other guy” [True]) andassesses narcissistic and ruthless attitudes in interpersonal functioning;• Social Potency consists of 24 items (e.g., “Even when others are upset with

me, I can usually win them over with my charm” [True]) and assesses one’sperceived ability to influence and manipulate others;• Coldheartedness consists of 21 items (e.g., “I have had ‘crushes’ on people that

were so intense that they were painful” [False]) and measures a propensitytoward callousness, guiltlessness, and an absence of sentimentality;• Carefree Nonplanfulness consists of 20 items (e.g., “I often make the same

errors in judgment over and over again” [True]) and assesses an attitude ofindifference in planning one’s actions;• Fearlessness consists of 19 items (e.g., “Making a parachute jump would really

frighten me” [False]) and assesses absence of anticipatory anxiety concerningharm and a willingness to participate in risky activities;• Blame Externalization consists of 18 items (e.g., “I usually feel that people

give me the credit I deserve” [False]) and assesses a tendency to blame othersfor one’s problems and to rationalize one’s misbehavior;• Impulsive Nonconformity consists of 17 items (e.g., “I sometimes question

authority figures ‘just for the hell of it’ ” [True]) and measures a reckless lackof concern regarding social mores;

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• Stress Immunity consists of 11 items (e.g., “I can remain calm in situationsthat would make many other people panic” [True]) and assesses an absenceof marked reactions to anxiety-provoking events.

Adequate levels of reliability (i.e., internal consistency, test-retest) have been re-ported in the initial validation studies for the PPI as well as in subsequent reports.Internal consistency (Cronbach’s alpha) in the present sample for the total scorewas .94 in the standard instruction (i.e., “honest”) condition. A growing body ofresearch, detailed earlier, supports the construct validity of this instrument in bothoffender and nonoffender samples (Edens, Poythress et al., 1998, 1999; Hamburgeret al., 1996; Kruh et al., 2000; Lilienfeld et al., 1998; Poythress et al., 1998; Sandovalet al., 2000).

The PPI contains three validity scales designed to assess various response sets.Items that comprise these scales are embedded throughout the measure. The DeviantResponding scale contains ten items in which the bizarre content, although seem-ingly psychopathological, does not relate to any known type of mental disorder (e.g.,“I look down at the ground whenever I hear an airplane flying above my head”).The Inconsistency scale comprises 40 item pairs that are highly intercorrelated andassesses the extent to which participants are providing consistent responses to itemsthat tend to be endorsed in the same direction by most respondents.

The Unlikely Virtues scale, which is the validity scale of primary interest in thisstudy, was adapted from the Multidimensional Personality Questionnaire (Tellegen,1978/1982). The content for this scale originally was derived by writing two items(one keyed true and one keyed false) that each reflected one of the “seven deadlysins.” This scale presumably assesses the extent to which participants are willingto admit to common trivial human frailties and the extent to which they claim toengage in admirable but highly unlikely forms of behavior (e.g., “I have alwaysbeen completely fair to others”). Although designed to serve as an index of socialdesirability, no published studies have examined empirically its utility as a measureof positive impression management. However, in the present study, the UnlikelyVirtues scale correlated significantly (r = .73, p < .01) with the Marlowe–CrowneSocial Desirability Scale (SDS; discussed later).

Marlowe–Crowne Social Desirability Scale (SDS)

The SDS (Crowne & Marlowe, 1960) is a 33-item self-report measure of the ex-tent to which individuals endorse (a) socially sanctioned and approved but improb-able types of behaviors and (b) socially undesirable but common behaviors. Personswho obtain high scores on this scale appear motivated to avoid the disapproval ofothers and are unwilling to admit to minor faults or inadequacies that most personswould readily endorse. Extensive data supporting the psychometric properties of theSDS have been reported (see Paulhus, 1991, for a review of this literature). Internalconsistency (Cronbach’s alpha) in the present sample was .83 in the honest condition.The SDS was included to serve as an index of the extent to which participants wereengaging in positive impression management by endorsing items consistent with asocially desirable response set when participating in the simulation condition, as well

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as to serve as a measure of the extent to which they engaged in a socially desirableresponse style when answering honestly.

Post Tests

Despite the widespread use of analogue designs in research on response sets(Rogers, 1997), relatively few studies examine systematically whether participantsare attentive to and adhere to the instructional sets they are provided. When such“manipulation checks” have been used (e.g., Edens, Otto, & Dwyer, 1998, 1999) ithas been found that large percentages of examinees drawn from university humansubjects pools fail to comprehend the experimental instructions they receive. Moregenerally, it has been reported that large percentages of respondents (29–60%) ac-knowledge that they respond randomly to at least part of personality tests (Berryet al., 1992). Given these concerns, participants in this study completed posttestquestionnaires after each administration (i.e., honest and fake good conditions) thatassessed their understanding of and compliance with the experimental instructions,as well as their motivation to participate in the study. More specifically, these posttestscontained multiple-choice questions wherein the respondents were asked to iden-tify the instructions they received from among four response options (i.e., “respondhonestly,” “create a favorable impression,” and two distracters). They also containeda 5-point Likert scale asking them to rate their level of motivation (1 = not at all,5= extremely) regarding their participation in the study. The fake-condition posttestalso included a multiple-choice question in which respondents were asked to identifythe specific role they were instructed to assume (discussed later).

Procedure

Participants were voluntarily enrolled in the study and received course credit forattendance. Data were collected primarily in groups of 8–15. Students first completedinformed consent measures, followed by a brief demographic questionnaire. For theresearch protocol, a repeated-measures simulation design was employed whereinparticipants were administered the PPI and SDS under both standard instructionalsets (i.e., respond honestly) and with instructions to create a favorable impression ofthemselves. To control for possible order of administration effects, the standard andfake instructional sets were counterbalanced.

Respondents completed the measures under one of three experimental “fakegood” instructional sets. They were instructed to assume the role of someone whowas either (1) applying for employment as a police officer; (2) applying for a positionas an airline pilot; or (3) being screened as part of a presentence investigation afterbeing convicted of a crime. These specific roles were chosen because they representsituations in which (a) examiners historically have been concerned about the po-tential effects of positive impression management and (b) the detection of traits orbehaviors associated with psychopathy (e.g., irresponsibility, impulsivity, grandiosity,aggression) might influence the outcome of the assessment or screening process (see,generally, Borum & Stock, 1993; Butcher & Han, 1995; Gustafson & Ritzer, 1995;Lanyon, 1993; Lanyon, Dannenbaum, Wolf, & Brown, 1989; O’Mahony & Murphy,

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1991; Riggs & Greenberg, 1999; Rosse, Stecher, Miller, & Levin, 1998). Instructionsfor the “criminal” condition are reproduced here:

We would like you to complete the following questionnaire in order to create a specificimpression. We would like you to assume the role of someone who is trying to create avery favorable impression of him- or herself. It might be helpful to pretend that you arebeing evaluated by a psychologist after being convicted of a crime and you want to makesure that you “put your best foot forward,” so that the psychologist doesn’t see you in anegative manner or think you will be a “difficult” prison inmate. Assume that how you fillout the test may influence the type of facility you spend your sentence in (for example,going to maximum security versus a work release program). However, the questionnairewas designed to detect people who don’t respond honestly to it, so it is important to presentyourself in a believable manner. That is, we want you to create a favorable impression whilealso not getting identified as being dishonest on the test.

Instructions for the remaining two conditions are provided in the appendix. Instruc-tions were provided both orally and in written format. Following completion of theresearch instruments in both conditions, participants completed the posttests assess-ing their comprehension of the instructional sets.

A total of 252 respondents attended the data collection sessions. Of these, 8were excluded because they failed to provide complete data on the measures. Anadditional 58 participants incorrectly answered one of the three posttest multiple-choice items assessing their understanding of the experimental instructions (e.g.,indicating that they had been instructed to assume the role of a convicted criminalwhen in fact they had been assigned to the police recruit condition). To minimize thepossible confounding effects of careless responding or inattention to the instructionalsets provided, these cases were deleted and the results presented here are based onthe remaining 186 (76%) participants.4 Although this may be an overly conservativeprocedure—because participants who did not report accurately what specific rolethey were to assume (e.g., pilot applicant versus police recruit) still may have engagedin positive impression management when completing the measures—we believe suchan approach is warranted in analogue studies, given the potential threat to internalvalidity that inattentive or careless responding may pose (Paulhus, Bruce, & Trapnell,1995). For example, Edens, Otto, et al. (1999) found that respondents who providedincorrect posttest information also reported that they were less motivated to followthe instructions they had received in the experimental condition.

RESULTS

Preliminary Analyses

Given our use of an analogue design and our elimination of a significant num-ber of protocols from respondents who failed the posttest questionnaire, several

4The percentage of cases removed was relatively consistent (range = 20–26%) across the three exper-imental conditions, χ2(186) = .79, p = .67. Although this 24% rejection rate for the entire sample issomewhat high, similar results (17–34%) have been obtained when manipulation checks have been usedto ascertain protocol validity in other simulation studies (e.g., Edens, Buffington et al., 2000; Edens, Ottoet al., 1998, 1999; Robertson & Milner, 1985).

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analyses were conducted in order to assess whether those 58 eliminated protocolsdiffered in some meaningful way from the remaining 186. In terms of demographiccharacteristics, there were no significant gender, age, or race/ethnicity differencesbetween those 58 participants who were excluded from the study versus those 186who were included (p values for all comparisons > .10). However, similar to theresults of Edens, Otto, et al. (1999), those who were eliminated scored significantlylower on the 5-point Likert rating of motivation than did those who answered all theposttest questions correctly, t(242) = 2.67, p < .01. Furthermore, those who failedthe posttest also obtained higher scores on the PPI Inconsistency scale in both thehonest, t(242) = 1.96, p = .05, and simulation, t(242) = 4.11, p < .001, conditions,suggesting a lack of consistent item endorsement. A similar finding was obtained forthe Deviant Responding scale in the simulation condition, t(242) = 3.53, p = .001,with those who failed the posttest obtaining higher scores on this validity indicator.This difference only approached significance in the honest condition, t(242) = 1.56,p= .12. However, these results suggest collectively that the posttest procedures wereuseful in identifying respondents whose protocols appear to be suspect.

Given our use of a nonoffender sample, preliminary analyses also were con-ducted in order to examine the extent to which participants were endorsing psycho-pathic personality features when responding in the “honest” condition. Obviously,it would be difficult to determine whether such features could be minimized in the“fake good” condition if they were not sufficiently evident in the honest condition.Raw scores on the PPI hypothetically can range from 163 to 652. The mean scoreon the PPI in the honest condition was 360 (SD = 45) and ranged from 240 to 496.This mean score generally is consistent with previous studies using college studentsamples (e.g., Edens, Buffington, et al., 2000; Lilienfeld & Andrews, 1996) and issomewhat lower than what has been obtained in correctional samples (which wouldbe expected given the presumed higher rate of psychopathy in such settings). Forexample, Poythress et al. (1998) obtained a mean score of 392 (SD = 46) in a sam-ple of 50 youthful offender prison inmates. Although the mean score is somewhatlower, 23% of the present sample obtained a score beyond the mean of the Poythresset al. prison sample, suggesting that a significant percentage of the present samplewas endorsing considerable psychopathic symptomatology.5

Effects of Positive Impression Management on the PPI

To examine the effects of participants’ attempting to engage in positive im-pression management, a paired-samples t test was performed for the entire samplecomparing the PPI total scores across the two instructional conditions (honest andfake good). Results of this analysis indicated a significant decrease in PPI scores fromthe honest (M = 360, SD = 45) to the fake good (M = 342, SD = 40) conditions,

5This is not meant to imply that 23% of the participants are “psychopaths.” It simply is meant to illustratethe overlapping nature of the distributions and the degree of psychopathic personality features endorsedby a significant number of the college students in comparison to a criminal sample that would be expectedto be relatively more psychopathic.

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Table 1. Descriptive Statistics for the Psychopathic Personality Inventoryacross the Honest and Positive Impression Management Conditions

Conditiona

Fake type Honest Fake good t∗ d

Pilot applicant (n = 62) 356.37 (38.70) 343.04 (32.77) 3.48 .37Police recruit (n = 65) 371.72 (43.46) 355.29 (36.86) 3.24 .41Criminal (n = 59) 350.73 (51.20) 327.42 (45.21) 3.23 .48

aValues represent mean (SD).∗All p values < .005.

t(185) = 5.56, p < .001, d = .41.6 These analyses were then repeated for each of thethree simulation conditions (i.e., Police Recruit, Pilot Applicant, Criminal). Resultsof these analyses are summarized in Table 1. As expected, a significant effect wasobtained for each comparison. Participants produced lower scores on the PPI totalscore in each of the three positive impression management conditions. Follow-upanalyses were performed in order to determine if there were significant differencesacross the three simulation conditions in terms of the magnitude of change for thePPI across the honest and simulation conditions. These were conducted by first com-puting a “difference” score by subtracting the simulation condition PPI score fromthe honest condition PPI score. This variable was then used as a dependent measurein an ANOVA comparing the three simulation conditions. There was no main effectfor the type of simulation instruction, F(2, 183) = .83, p = .43. This suggests thatthe magnitude of the “suppression effect” seen in the simulation condition was notsignificantly different across the three experimental instructional sets.

To examine whether persons who endorsed more psychopathic personality fea-tures were better able to suppress their scores in the simulation condition, the samplewas split into a “high psychopathy” group (those whose honest-condition PPI scorewas above the sample mean) and a “low psychopathy” group (those PPI scores belowthe mean). Paired-samples t tests comparing the honest and fake good PPI scoreswere then performed on these two subsamples. Results of this analysis for the highpsychopathy group indicated a significant decrease in PPI scores from the honest(M = 396, SD = 29) to the fake good (M = 358, SD = 39) conditions, t(90) = 8.40,p < .001, d = 1.12. The change in scores for the low psychopathy group, M = 325(SD = 28) versus M = 328 (SD = 35), was not significant, t(94) = .71 p = .48, d =.07. Table 2 presents the results of these repeated-measures analyses for the threeseparate instructional conditions. As can be seen, participants who were more psy-chopathic had much larger decreases in their PPI scores when faking good than did

6Dunlap, Cortina, Vaslow, and Burke (1996) have demonstrated that the effect size will be overestimatedif t is converted directly to d without accounting for the correlation between the pre- and post-measures.The correct equation for estimating d from t is (Dunlap et al., 1996, p. 171):

d = tc

√[2(1− r)

n

]where tc is a t for repeated measures designs, r is the correlation between measures, and n is the samplesize.

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Table 2. PPI Scores for the High and Low Psychopathy Groups across the Honest andPositive Impression Management Conditions

Conditiona

Fake type Honest Fake good t d

High psychopathyPilot applicant (n = 25) 393.88 (27.94) 366.04 (29.60) 4.45∗ .97Police recruit (n = 40) 397.62 (32.59) 367.90 (36.30) 4.37∗ .86Criminal (n = 26) 395.92 (23.92) 334.15 (41.51) 6.69∗ 1.82

Low psychopathyPilot applicant (n = 37) 331.03 (19.49) 327.51 (24.91) .85 .16Police recruit (n = 25) 330.28 (20.11) 335.12 (28.22) .91 .19Criminal (n = 33) 315.12 (36.56) 322.12 (47.89) .98 .16

aValues represent mean (SD).∗p < .001.

those participants who scored below the mean in the honest condition across all threeconditions.

Paired-samples t tests also were conducted for the Unlikely Virtues validityscale and the SDS, again comparing the honest and fake good condition scores.Results of this analysis for the Unlikely Virtues scale indicated a significant in-crease in scores across the honest (M = 31, SD = 5.4) and fake good (M = 37,SD = 7.7) conditions, t(185) = 9.98, p < .001, d = .91. A similar increase was foundon the SDS, honest M = 15 (SD = 6.1) versus fake good M = 24 (SD = 7.0),t(185) = 15.34, p < .001, d = 1.39.7 These analyses also were performed sepa-rately for the three instructional conditions and are summarized in Table 3. Par-ticipants produced significantly elevated scores on both of these scales across thethree groups when attempting to create a favorable impression. The magnitude ofthese differences was quite large in all three conditions, based on the indices of effectsize.8

Classification Accuracy of the Unlikely Virtues Validity Scale

Although group mean scores on the Unlikely Virtues scale increased signifi-cantly when participants were attempting to create a favorable impression, a moreimportant question is whether individual participants could be classified accuratelybased on their scores on this scale. To examine this issue, Receiver Operating Char-acteristic (ROC) analyses were performed (see discussions by Centor, 1991; Hsiao,Bartko, & Potter, 1989). This procedure involves calculating the sensitivity and false

7These analyses were repeated for the “high” and “low” psychopathy groups described earlier. Resultsacross these two subgroups were consistent with results for the entire sample.

8“Difference” scores were computed for the Unlikely Virtues and SDS scales by subtracting the scoresacross the honest and fake good conditions, similar to the difference score that was created for the PPItotal score. These difference scores were then used as dependent measures in an ANOVA examiningwhether the magnitude of the changes differed across the Pilot, Police, and Criminal conditions. Resultsof these analyses were not significant for either measure, Fs(2, 183) = 1.66 (Unlikely Virtues), p = .19,and 2.81 (SDS), p= .06.

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Table 3. Descriptive Statistics for the Unlikely Virtues Validity Scale and the SDSacross the Honest and Positive Impression Management Conditions

Conditiona

Fake type Honest Fake good t∗ d

Unlikely VirtuesPilot applicant (n = 62) 30.39 (5.72) 37.12 (8.09) 6.25 .95Police Recruit (n = 65) 31.62 (5.03) 39.00 (7.74) 6.47 1.13Criminal (n = 59) 31.08 (5.53) 35.11 (6.63) 4.56 .66

SDSb

Pilot Applicant (n = 62) 14.26 (5.65) 23.34 (7.75) 9.11 1.32Police Recruit (n = 65) 15.54 (6.12) 25.97 (6.47) 9.58 1.66Criminal (n = 59) 15.05 (6.51) 22.83 (6.53) 7.90 1.19

aValues represent mean (SD).bSDS = Social Desirability Scale.∗All p values < .001.

positive rates associated with all possible cutoff scores on a test, plotting these values,and connecting them to form a curve. Two characteristics of the curve are signifi-cant in terms of interpreting the results: elevation and shape. Elevation typically isdefined by the area under the ROC curve (AUC), which reflects the test’s diagnosticefficiency across its entire range of scores. In this context, AUC is interpretable asthe probability that a randomly selected fake good protocol will have a higher scoreon the Unlikely Virtues scale than a randomly selected protocol from the honestcondition. An AUC of .5 indicates chance-level predictive accuracy, greater than.5 indicates above-chance accuracy, and less than .5 indicates below-chance accu-racy. Regarding the shape of the curve, a prominent “elbow” jutting out close tothe top left corner of the chart (i.e., false positive rate = 0, sensitivity = 1) indicatesthe existence of a cutoff score that maximizes overall diagnostic accuracy. Similarly,curves that touch the top or left side of the chart indicate the existence of cutoffsthat may be useful for screening purposes. All ROC analyses were performed usingSPSS for Windows, release 9.0.0. AUCs were calculated using the nonparametricmethod.

The ROC curve for discriminating honest versus faked protocols, using the Un-likely Virtues scale is presented in Fig. 1. A “line of no information” also is provided,running at 45◦ from the bottom left corner to the top right corner of the chart. ROCcurves above and below the line of no information indicate above-chance and below-chance diagnostic efficiency, respectively. AUC was .73 (SE = .03), p < .01, with a95% confidence interval (CI) of .68–.78. The shape of the curve was quasi-linear,with no “elbow” indicating a cutoff that maximized diagnostic efficiency. A cut scoreof 35 or greater on Unlikely Virtues optimized overall predictive accuracy (over-all hit rate = 68%; sensitivity = 61%; specificity = 75%). ROC curves also werecomputed separately for the three simulation conditions, with relatively consistentAUCs being obtained (Pilot Applicant= .74, SE= .04; Police Recruit= .78, SE= .04;Criminal = .68, SE = .05).

AUC also was computed for those in the “high psychopathy” condition de-fined earlier in order to determine if the Unlikely Virtues scale might perform

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Fig. 1. Receiver Operating Characteristic (ROC) curve indicating di-agnostic efficiency of the Unlikely Virtues scale for identifying positiveimpression management on the PPI.

better among those participants showing a large decrease in their PPI scores. Resultswere quite similar to the preceding analyses, however, with AUC only reaching .74(SE = .04) for the entire “high psychopathy” sample. AUCs across the threesimulation conditions were: Pilot Applicant = .69 (SE = .08); Police Recruit = .79(SE = .05); Criminal = .72 (SE = .07).

Supplementary Analyses

It could be argued that the previous analyses put the Unlikely Virtues scale toan unfair test because some respondents may have been engaging in a socially desir-able response set when completing the questionnaires in the honest condition. Thatis, their general response style may be to present themselves in an overly positivemanner in terms of denying or minimizing undesirable traits or behaviors and en-dorsing unlikely but socially desirable characteristics (see, e.g., Paulhus, 1991). If so,then attempting to discriminate between this response style and the simulated “fakegood” response set in which they were instructed to engage might have been exceed-ingly difficult. To address this concern, the classification analyses described earlierwere repeated after eliminating the protocols of those respondents who scored 1.5

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Fig. 2. Receiver Operating Characteristic (ROC) curve indicating diag-nostic efficiency of the SDS for identifying positive impression manage-ment on the PPI.

standard deviations above the mean on the SDS (i.e., scores greater than 24) in thehonest condition. Removing these 24 cases (i.e., 12 participants’ honest and fakegood protocols) from the analyses did not improve classification accuracy substan-tially, however, with AUC improving only to .75 (SE = .03).9

A final ROC analysis was performed using the SDS as the predictor variableto determine if it might serve as a better indicator of positive impression manage-ment. As can be seen in Fig. 2, a somewhat higher level of diagnostic efficiency wasobtained when analyzing data for the entire sample. AUC was .83 (SE = .02), witha 95% CI of .79–.87. The difference between the AUC values for the two tests wascompared using the procedure described by Hanley and McNeil (1983, formula 3),which corrects for comparisons of AUCs that are derived from the same sample. Acritical ratio z value of 4.66 was obtained, indicating that the AUC for the SDS scalewas significantly higher than that obtained for the Unlikely Virtues scale (p < .001).ROC analyses also were computed for the three simulation conditions, with similar

9We note that there is no categorical standard for differentiating between socially desirable and non-socially desirable responding on the SDS and that our use of 1.5 standard deviations above the samplemean as a cut off is a somewhat arbitrary definition. Similar results were obtained after eliminating allthose who scored more than one standard deviation above the mean (n = 31) on the SDS in the honestcondition as well.

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AUC values being obtained: Pilot Applicant = .81 (SE = .04); Police Recruit = .87(SE= .03); Criminal= .80 (SE= .04). Although performing significantly better thanthe Unlikely Virtues scale, it is worth noting that a substantial number of predictiveerrors continued to be made when using the SDS to identify positive impressionmanagement.

DISCUSSION

As noted earlier, there is a strong need for alternative methods of assessingpsychopathy, and a growing body of research supports the construct validity of thePPI as a measure of psychopathic personality features in research settings. However,the results of the present study suggest that, although the PPI appears to be use-ful for research purposes in which participants have no obvious need to engage inpositive impression management, caution is warranted if the results of the PPI wereto be interpreted in applied settings in which respondents might have some incen-tive to “fake good” when completing the test. Significantly lower mean PPI scoreswere obtained in the fake good condition in comparison to the honest condition—afinding that is similar to the results of other simulation studies that have examinedthe effects of faking good on other ostensible measures of psychopathy such as theMMPI-2 Psychopathic Deviate scale (Bagby et al., 1997). Furthermore, this effectwas particularly pronounced among those persons who obtained higher scores onthe PPI (i.e., endorsed more psychopathic personality traits) when answering it hon-estly. This finding also is consistent with prior simulation research (Edens, Buffington,et al., 2000) that found that individuals who were “faking bad” (i.e., simulating symp-toms of mental illness) on the PPI also were able to alter their scores significantly.Collectively, these results are concordant with most other research examining thesusceptibility of self-report inventories to response distortion (see Greene, 1997, fora review) and suggest that PPI scores are not immune to the effects of impressionmanagement despite the precautions taken during its development (e.g., elimina-tion or revision of items correlating with the SDS). The inclusion of three separate“fake good” simulation conditions and the relative consistency of the results acrosseach suggests that the suppression effect found in this study is not simply a functionof the specific role assumed by the respondents. Rather, participants were able toappear less psychopathic regardless of the hypothetical context to which they wereassigned. These results clearly call into question the validity of PPI scores in contextsin which there is reason to suspect that respondents may be attempting to minimizepsychopathic traits.

Given that PPI scores can be influenced by overt attempts at impression manage-ment, the utility of its validity scales to identify individuals engaging in such responsesets is of considerable importance. The limited amount of previous research has beensupportive of the utility of the PPI’s validity scales, in that the Deviant Respondingscale has been shown to identify effectively respondents who are attempting to “fakebad” (Edens, Buffington, et al., 2000). However, results from the present study forthe Unlikely Virtues validity scale—which was designed to identify socially desirable

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response sets—indicated that, although it was effective in identifying a large numberof respondents attempting to “fake good,” a considerable number of participantswere able to avoid detection.10 This was true even after excluding those examineeswho “naturally” engaged in a more socially desirable response set—that is, obtainedelevated scores on the SDS in the honest condition—who arguably should not be ex-pected to be classified as not faking good in the honest condition, given their sociallydesirable self-presentation. Eliminating these cases did not improve the classifica-tion accuracy of the Unlikely Virtues scale appreciably, however, suggesting thatclassification errors were not simply a function of misidentifying those respondentswho presented themselves in a socially desirable manner in both the honest andsimulation conditions.

These results suggest that caution is warranted when relying on the UnlikelyVirtues validity scale to identify respondents who are engaging in positive impressionmanagement. One possible explanation for the number of false negatives is thatparticipants were warned prior to completing the questionnaires that the tests weredesigned to identify those individuals who were presenting themselves in an overlyfavorable manner. This may have prompted them to be more cautious in endorsingsocially desirable items than would be expected from a respondent who was not givensuch a notification. It is noteworthy, however, that even with these warnings the SDSperformed appreciably better than the Unlikely Virtues scale. In fact, the results ofthe ROC analysis for the SDS were comparable to several other validity indicators(AUCs ranging from .79 to .86) that have been used to identify defensive profileson the MMPI-2 (Nicholson et al., 1997). These results indicate that, if using the PPIin applied settings, it may be useful or necessary to use complementary indices ofpositive impression management such as the SDS in order to evaluate the validity ofthe obtained results.

Limitations of this research are worth noting. First, although there are someclear advantages to simulation studies (see, generally, Rogers, 1997), the use of ananalogue design of course raises some questions about the generalizability and exter-nal validity of the present results. For example, one common criticism is that the levelof motivation of participants may not be similar to that of respondents in real worldcircumstances. We completely agree that motivation may be considerably higher inthose settings in which performance might have a significant impact on an examinee’slife or livelihood and we would be quite cautious in making strong generalizationsto “real world” settings if our analogue results indicated that the PPI was relativelyimmune to the effects of response distortion. If anything, the finding that respon-dents were able to reduce their PPI scores significantly without being detected as“faking good” in a simulation study in which there were no tangible consequences

10We have avoided evaluative statements regarding what exactly constitutes “good” versus “bad” pre-dictive accuracy because such determinations are dependent on the context in which the predictionis being made, the base rate of the event/condition being predicted, and the consequences associatedwith both false positives and false negatives. For example, AUCs in the .70–.80 range are greetedwith considerable enthusiasm in the violence prediction literature (see, e.g., Quinsy, Harris, Rice,& Cormier, 1998), but probably would be less well received regarding the identification of varioustypes of medical disorders.

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(or rewards) raises serious concerns about the effects of positive impression man-agement in a setting in which the outcome is much more salient to the examinee(e.g., being civilly committed as a “psychopathic” sex offender, being rejected as ajob candidate). Aside from motivation, however, there may be other relevant factorsthat might affect examinees’ success at faking good, such as education level or intel-ligence. For example, individuals who are relatively less intelligent and less educatedmight be more easily detected when attempting to create an overly favorable impres-sion of themselves. Research examining the effects of these variables would increaseour confidence in the generalizability of our findings to more diverse populations ofexaminees.

Another line of research that would be informative (although not without itsown methodological limitations) would be studies that contrasted “naturally occur-ring” groups of individuals who would be expected to be attempting to create afavorable impression, such as prison inmates seeking parole, with control groups ofparticipants who are under no such demand characteristics, such as demographicallysimilar inmates with considerable time remaining on their sentences. Such studiesare exceedingly difficult to complete, however, because of the ethical constraintsagainst administering experimental measures or questionnaires to examinees as partof clinical/forensic evaluations. Moreover, the results of such research only wouldbe informative at the group (as opposed to individual) level. The absence of anyclear “faking good” metric among the potential parolees makes the determinationof who is engaging in positive impression management quite problematic, unless oneassumes that all examinees are faking, which is a highly questionable assumption.Similarly, the use of “known groups” designs has been argued as a potential improve-ment upon simulation studies when conducting research on response sets. Althoughsuch results may be informative in studies of malingered mental illness, in which itcould be argued that there is a clearer criterion being predicted, the identificationof known groups of examinees engaging in positive impression management seemsconsiderably more difficult.

A final limitation to the generalizability of our findings is our use of a non-correctional sample. Most of the research on psychopathy has focused on forensicor correctional populations, which is not surprising given its established associationwith criminality, violence, and recidivism. Although our sample limits the general-izability of our findings to offender samples, we do not believe this necessarily is alimitation in the study of psychopathy per se. The field’s extensive reliance on crim-inal populations to study psychopathy has been criticized (e.g., Edens, Buffington,et al., 2000; Lilienfeld, 1994; Widom, 1977) because it ostensibly includes only “unsuc-cessful” psychopaths and precludes the study of those individuals with psychopathicpersonality traits who successfully avoid major legal sanctions. The distribution ofscores on the PPI clearly suggests that psychopathic personality traits were evidentin the present sample, which suggests that our experimental approach provided auseful first test of the effects of faking good on this measure. Nevertheless, studies ofcriminal populations in which the base rate of psychopathy would be expected to behigher would extend our understanding of the effects of impression management onthe PPI and would make generalizations to correctional and forensic settings moredefensible.

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APPENDIX: “FAKE GOOD” INSTRUCTIONS

Job Applicant – Police Officer

Now we would like you to complete the following questionnaire in order tocreate a specific impression. We would like you to assume the role of someone who istrying to create a very favorable impression of him- or herself. It might be helpful topretend that you are applying for a job as a police officer and you are completing thisquestionnaire as part of the evaluation. You want to make sure that you “put yourbest foot forward,” so that the person in charge of hiring you doesn’t see you in anegative manner or think you are a poor job candidate. However, the questionnairewas designed to detect people who don’t respond honestly to it, so it is important topresent yourself in a believable manner. That is, we want you to create a favorableimpression of yourself while also not getting identified as being dishonest on the test.

Job Applicant – Airline Pilot

Now we would like you to complete the following questionnaire in order tocreate a specific impression. We would like you to assume the role of someone who istrying to create a very favorable impression of him- or herself. It might be helpful topretend that you are applying for a job as an airline pilot and you are completing thisquestionnaire as part of the evaluation. You want to make sure that you “put yourbest foot forward,” so that the person in charge of hiring you doesn’t see you in anegative manner or think you are a poor job candidate. However, the questionnairewas designed to detect people who don’t respond honestly to it, so it is important topresent yourself in a believable manner. That is, we want you to create a favorableimpression of yourself while also not getting identified as being dishonest on the test.

ACKNOWLEDGMENTS

This research was funded in part by a Sam Houston State University ResearchEnhancement Program grant awarded to the first author. We thank Teresa DuPontfor her help in collecting and entering the data.

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Documents

Effects of Positive Impression Management on the Psychopathic Personality Inventory