“Why Is This So Hard?” A Review of Detection of Malingered ADHD in College Students

Abstract

Objective: The purpose of this article is to review studies that have investigated malingered ADHD in college students and offer recommendations for assessing effort in clinical evaluations of ADHD in college students. Method: This article identified and summarized nineteen peer-reviewed, empirical studies published between 2002 and 2011 that investigated malingered ADHD in college students. Results: Few of the measures examined proved useful for detecting malingered ADHD. Most self-report questionnaires were not sensitive to malingering. While there is some variability in the usefulness of neuropsychological test failure, profiles between malingerers and individuals with ADHD are too similar to confidently detect malingered ADHD. Failure of three or more symptom validity tests proved most useful at detecting malingered ADHD. Conclusion: This review suggests that there is substantial need for measures designed specifically for detecting malingered ADHD simulators are able to produce plausible profiles on most tools used to diagnose ADHD.

Keywords

ADHD adult ADHD college students effort malingering

Estimates of the prevalence of ADHD in college students range from 2% to 8% depending on the criteria used (DuPaul, Weyandt, O’Dell, & Varejao, 2009). The National Comorbidity Survey Replication estimated the prevalence of ADHD to be 4.4% (Kessler et al., 2006). Heiligenstein, Conyers, Berns, and Smith (1998) reported that 4% of the 448 college students surveyed endorsed symptoms consistent with a diagnosis of ADHD. Of these 4%, 56% endorsed inattentive symptoms whereas 22% endorsed symptoms consistent with the hyperactive-impulsive and combined types, respectively. A survey of 770 college students revealed that 7% of students endorsed significant symptoms (defined by 1.5 SD above the mean) on the ADHD Rating Scale (ARS). In the same study, 8.7% endorsed significant childhood symptoms of ADHD on the Wender Utah Rating Scale (WURS; Ward, Wender, & Reimherr, 1993) and 2.5% endorsed significant symptoms on both measures (Weyandt, Linterman, & Rice, 1995); however, only 0.5% of students had scores that were greater than two standard deviations above the mean on both scales. DuPaul and colleagues (2001) reported that 2.9% of American men endorsed criteria for ADHD with a majority (2%) endorsing symptoms of hyperactivity-impulsivity. In addition, 3.9% of American women endorsed clinically significant symptoms of ADHD with a majority (2.3%) endorsing the hyperactivity-impulsivity subtype of ADHD. When collateral report of retrospective symptoms was combined with students’ self-reported current symptoms, the prevalence of ADHD was less than 1% (Lee, Oakland, Jackson, & Glutting, 2008).

Clinical correlates of ADHD may be similar for children and adults, namely, adults with ADHD are impulsive, inattentive, and restless (Faraone et al., 2000). However, other authors have found that young adults with ADHD show different symptom patterns that may be difficult to measure before the age of 7 years. These symptoms include executive functioning deficits such as poor planning, forgetfulness, problems involving delay of gratification, self-control problems, and difficulty in dividing and focusing one’s attention (Wasserstein, 2005). Researchers have not agreed on a neuropsychological profile that is characteristic of ADHD (Wasserstein, 2005). Some authors have reported that individuals who complained of ADHD symptoms did not perform differently on neuropsychological measures compared with individuals who made no such complaints (Riccio et al., 2005; Rosselli et al., 2000; Weyandt et al., 1995; Weyandt, Mitzlaff, & Thomas, 2002; Weyandt, Rice, Linterman, Mitzlaff, & Emert, 1998). Other authors suggest that self-reported executive functioning deficits play a larger role in impairments in occupational functioning than does performance on neuropsychological measures (Barkley & Murphy, 2006). However, there is some evidence that adults with ADHD perform significantly poorer on measures of executive functioning that require response inhibition (Rapport, VanVoorhis, Tzelepis, & Friedman, 2001) and also show impairments in vigilance, selective attention, divided attention, and cognitive flexibility (Tucha et al., 2008). In addition, impairments on neuropsychological tests of attention, behavioral inhibition (Hervey, Epstein, & Curry, 2004), working memory (Hervey et al., 2004; Marchetta, Hurks, Jolles, & Krabbendam, 2008), and set shifting (Marchetta et al., 2008) have been found in some individuals with ADHD. A meta-analysis of neuropsychological performance in adults with ADHD found moderate effect sizes for impaired performance in domains of complex attention and verbal memory in adults with ADHD (Schoechlin & Engel, 2005).

Adult ADHD is thought to cause substantial impairments in economic, academic, social, and occupational functioning (DuPaul et al., 2009; Faraone et al., 2000; Murphy & Barkley, 1996a), including meeting deadlines, completing tasks, planning ahead, and poor sense of time (Riccio et al., 2005). There is some evidence to suggest that inattentive symptoms related to ADHD may negatively impact college students’ study skills, academic adjustment, and grade point averages (GPA; Norwalk, Norvilitis, & MacLean, 2009). In their review of the literature, Weyandt and DuPaul (2008) found that ADHD results in poorer academic outcomes and increased psychological difficulties for college students. Blase and colleagues (2009) noted that college students with self-reported ADHD symptoms endorse significantly more depressive symptoms, emotional instability, and substance abuse. Other studies reported higher incidences of anxiety, depression, antisocial personality disorder, and substance abuse in college students with ADHD (Biederman et al., 1993; Murphy & Barkley, 1996a). Richards, Rosen, and Ramirez (1999) reported that individuals with ADHD diagnoses and individuals with self-reported ADHD symptoms but without diagnoses scored higher than a control group on every subscale of the Symptom Checklist–90 (SCL-90) with the exception of the Paranoid Ideation subscale.

While there is myriad evidence of the impairments caused by ADHD, which illustrates the importance of diagnosis and treatment of the disorder, not everyone with self-reported attention problems has ADHD. Diller (2010), who suggested that this subgroup may not actually “have” ADHD, offered one alternative explanation for impairments noted in adults with late onset of symptoms. He believes that individuals who experience a late onset of symptoms may belong to a cohort of underperforming individuals who are not motivated or academically prepared for higher education standards. Diller suggests that their symptoms do not generalize to every aspect of their lives and tend to be context specific, arising when goals are not easily met.

Recent literature has begun to acknowledge that individuals have a number of incentives for feigning ADHD deficits, and the base rate of probable malingering in a sample of individuals completing psychoeducational evaluations has been estimated to be 10.2% when external incentives are present (Pella, Hill, Shelton, Elliott, & Gouvier, 2012). Incentives for malingering ADHD include prescriptions for stimulant medications that enhance performance but are commonly used recreationally and are a valuable commodity in the illicit marketplace (Advokat, Guidry, & Martino, 2010; McCabe, Teter, & Boyd, 2006; Rabiner, Anastopoulos, Costello, McCabe, & Swartzwelder, 2009; Teter, McCabe, Cranford, Boyd, & Guthrie, 2005; Weyandt et al., 2009; B. P. White, Becker-Blease, & Grace-Bishop, 2006). In addition, students with ADHD are typically eligible for services such as academic accommodations that may further improve performance in school. The U.S. government has passed several laws (i.e., the Individuals With Disabilities Act of 1975, Section 504 of the Rehabilitation Act of 1973, and the Americans With Disabilities Act of 1990) that enable individuals with disabilities to receive accommodations (Latham & Latham, 1996). Depending on the area of impairment, usually demonstrated in a neuropsychological evaluation, students may receive extended time, quiet testing environments, note takers, or even alternative courses (Sullivan, May, & Galbally, 2007).

External incentives create an atmosphere that increases the possibility that some individuals may malinger deficits. Effort must be considered in addition to observed deficits during testing before diagnosing ADHD. This article seeks to review the current literature on malingered ADHD in adults, as there has been a growing body of literature in this area over the past few years. A literature search was conducted using PsycINFO, PsycArticles, Web of Science, Medline, and ERIC databases using the following search terms: ADHD + malingering. The search yielded 20 articles. An additional search including the terms ADHD + Effort + Adult yielded 41 articles; however, only two articles that had not previously been identified met inclusion criteria. Inclusion criteria for this article include the following: (a) the article must report original, empirical research; (b) participants must include college students; (c) the article must examine malingered ADHD; (d) the article must be written in the English language; and (e) the article must be published in a peer-reviewed journal. Seventeen articles met inclusion criteria. Reference sections from these articles were examined to identify additional articles that met inclusion criteria, and two additional articles were identified. A total of 19 articles were included in this study.

Malingering on Self-Report ADHD Questionnaires

Simulation studies

Quinn (2003) conducted one of the first experiments that examined simulated ADHD on self-report questionnaires in a sample of undergraduate college students. She compared 16 students with formal ADHD diagnoses, 23 college controls, and 23 college students asked to feign ADHD symptoms. College students asked to feign ADHD were successfully able to fake self-reported childhood symptoms of ADHD on the ADHD Behavior Checklist–Retrospective (Murphy & Barkley, 1996b). In the same study, college students asked to malinger ADHD on the ADHD Behavior Checklist–Current (Murphy & Barkley, 1996b) performed similar to the ADHD group, and both of these groups obtained higher scores than the control group. Fisher and Watkins (2008) compared responses of college students asked to feign on two self-report questionnaires, the ADHD Behavior Checklist and the College ADHD Response Evaluation (CARE; Glutting, Sheslow, & Adams, 2002). The students were coached using Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR; American Psychological Association, 2000) diagnostic criteria. The authors found that 93% of college students with no diagnostic history of ADHD were successfully able to feign self-reported ADHD symptoms on the CARE and 77% were successfully able to simulate ADHD on the ADHD Behavior Checklist. The authors determined that both scales were susceptible to malingered ADHD and neither scale was significantly better at reducing false positives for ADHD diagnosis compared with the other.

Young and Gross (2011) compared a clinical ADHD group with ADHD simulators. The ADHD simulators were asked to read a set of instructions and criteria for ADHD. The ADHD simulators endorsed significantly more symptoms of current inattention and hyperactivity and childhood inattention and hyperactivity compared with college controls on the ADHD Current and Childhood Symptoms Scales–Self-Report Forms (Barkley & Murphy, 2006). The ADHD simulators and clinical ADHD group did not differ in number of symptoms endorsed indicating that the ADHD Current and Childhood Symptoms Scales–Self-Report Forms are easily faked by malingerers.

Jachimowicz and Geiselman (2004) investigated the ability of 80 ADHD simulators to feign symptoms on the WURS (Ward et al., 1993), the Conners’ Adult ADHD Rating Scale (CAARS; Conners, Erhardt, & Sparrow, 1999), the Brown Adult ADHD Scale (BAAS; T. E. Brown, 1996), and the ARS (DuPaul, Power, Anastopoulos, & Reid, 1998). Participants read a copy of DSM-IV-TR diagnostic criteria for ADHD. No participants in this study had been previously diagnosed with ADHD, yet all four rating scales were easily faked by college students, specifically, 65% on the WURS, 75% on the ARS, 90% on the CAARS, and 95% on the BAAS. The WURS proved somewhat harder to fake as fewer students obtained elevated scores that would be consistent with a diagnosis of ADHD compared with the CAARS and BAAS. The ARS was harder to fake compared with the BAAS but not the CAARS. Further analysis of the subscales of these measures indicated that hyperactivity accounted for 47% and 83% of the variance in positive diagnoses for the ARS and the CAARS, respectively, indicating that symptoms of hyperactivity were significant factors in determination of diagnoses for these two scales. The authors speculate that college students may associate hyperactivity with ADHD to a greater degree than inattention and that these profiles reflect those perceptions. Unfortunately, these authors did not assess strategies used, so it is difficult to test that hypothesis.

Similarly, Sollman, Ranseen, and Berry (2010) compared recruited college students and assigned them to either an ADHD simulator group (n = 30), a control group (n = 14), or a clinical ADHD group based on the formal diagnosis (n = 29). The ADHD simulators were provided with instructions and pseudowebpages containing information on adult ADHD. They were given 5 min to read the information before being asked to complete self-report and cognitive measures. Self-report questionnaires included the Attention Rating Scale: Current and Childhood Symptom Checklists (Barkley & Murphy, 2006; Murphy & Barkley, 1996) and the CAARS, Self-Rating Scale, Long (CAARS-S:L; Conners et al., 1999). No statistically significant differences were found between ADHD simulators and the clinical ADHD group for the CAARS-S:L or the ARS Current or Childhood scales. Interestingly, the Inconsistency Index of the CAARS-S:L did not differentiate simulated ADHD from clinical ADHD.

Harrison, Edwards, and Parker (2007) compared CAARS scores of 35 college students with no known impairments and 35 college students asked to feign ADHD symptoms to archival data of 72 college students diagnosed with ADHD. The ADHD simulators were provided with diagnostic criteria from the DSM-IV-TR. The authors found that individuals asked to simulate ADHD on the CAARS were successfully able to feign self-reported ADHD symptoms. Individuals asked to fake ADHD obtained significantly higher scores on most subscales of the CAARS compared with the archival data of students with ADHD and the control group. However, most of the elevated scores obtained by ADHD simulators fell within a believable range.

Harp, Jasinski, Shandera-Ochsner, Mason, and Berry (2011) recently provided additional evidence for ease of feigning ADHD symptoms on self-report questionnaires. The authors asked a sample of individuals who had been formally diagnosed with ADHD to either respond honestly (clinical ADHD) or exaggerate symptoms (ADHD exaggerators). They also used a sample of college students without psychiatric diagnoses and asked them to either respond honestly (control) or feign ADHD (ADHD simulators). The ADHD simulators and ADHD exaggerators were provided with information about ADHD in the form of pseudo-web pages and were given a short period to prepare. The authors found that individuals asked to exaggerate symptoms endorsed significantly more symptoms compared with college controls but responded similarly to the honest clinical ADHD group on all scales of the CAARS-S:L. The ADHD simulators also obtained scores that were statistically similar to the honest clinical ADHD group on most scales of the CAARS-S:L; however, they did endorse significantly more symptoms on the Hyperactivity-Restlessness and Self-Concept scales. These elevations were in the believable range, meaning that ADHD simulators successfully feigned symptoms of ADHD on the CAARS-S:L. In a similar study, Jasinski and colleagues (2011) recruited 103 college students and asked them either to serve as college controls (no diagnosis and respond honestly; n = 28), ADHD simulators (no diagnosis but fake ADHD; n = 22), clinical controls (ADHD but respond honestly; n = 20), or ADHD exaggerators (ADHD but exaggerate symptoms; n = 18). Simulators and exaggerators were provided with scenarios and information from the Internet. The authors reported that ADHD malingerers, clinical ADHD group, and the ADHD exaggerators all scored significantly higher than the college control group on the CAARS-S:L Inattentive, Hyperactive/Impulsive, and Total indices. The ADHD malingerers, clinical ADHD group, and the ADHD exaggerators were not statistically significant from each other on any CAARS-S:L indices.

Booksh, Pella, Singh, and Gouvier (2010) compared scores on two self-report questionnaires of 110 college students assigned to a control group or an ADHD simulator group with archival data from 56 college students diagnosed with ADHD. The WURS was administered to assess self-reported childhood symptoms of ADHD. The ADHD simulators endorsed significantly more symptoms on the WURS compared with the control group, but WURS scores were not significantly different compared with the clinical ADHD group. The authors also administered the Attention Deficit Scales for Adults (ADSA; Triolo & Murphy, 1996) to assess current self-reported ADHD symptoms and found that ADHD simulators endorsed more symptoms, though not statistically significant, than did the control group and the clinical ADHD group. However, in this study, the clinical ADHD group endorsed fewer symptoms on the ADSA than expected, and the ADHD simulators scored in a believable range, successfully feigning ADHD symptoms.

Tucha, Sontag, Walitza, and Lange (2009) randomly assigned 78 college students to either a control group, a naïve simulator group, or a coached simulator group and compared their responses on the Brown Attention Deficit Disorder Scale (BADDS; T. E. Brown, 1996) with 12 adults who met formal diagnostic criteria for ADHD. Overall, the students assigned to the coached simulator group scored higher on all variables compared with the control group; however, the elevated scores did not reach statistical significance. Students asked to simulate ADHD without coaching scored similarly on all BADDS variables compared with adults in the clinical ADHD group except that naïve ADHD simulators scored higher on the subscale that measures problems in sustaining attention and concentration.

Clinical data

Suhr, Hammers, Dobbins-Buckland, Zimak, and Hughes (2008) divided archival data into three groups: a noncredible group (failed at least one effort index; n = 26), an ADHD group (formal diagnosis; n = 15), and a psychological symptom group, (no ADHD but current psychological condition; n = 24). Similarly, there were no significant differences on WURS scores between the noncredible group and the ADHD group, but both groups obtained significantly higher scores than the psychological symptom group. They reported that individuals in the noncredible group did not differ significantly from the clinical ADHD group on any of the CAARS subtests. The noncredible group only differed from the psychological symptom group on the Hyperactive-Impulsive Symptoms subscale. Suhr, Buelow, and Riddle (2011) developed an Infrequency Index for the Conners’ Adult Attention Deficit/Hyperactivity Rating Scale (CII). In the development stage, they examined responses of 71 individuals who had been diagnosed with ADHD, 147 college students who denied having ADHD but were experiencing significant symptoms of depression, and 955 college students with no history or ADHD or current symptoms of psychopathology on the CAARS. The authors identified 12 items that were endorsed “pretty much, often” to “very much, very frequently” by fewer than 10% of total study participants. They then summed the scores of these 12 items to create a scale and found that a cutoff score of 20 or more achieved specificity greater than 90%. Next, the authors validated the CII in college students who had failed the Word Memory Test (WMT; n = 29), individuals with ADHD diagnoses (n = 19), individuals with psychopathology (n = 43), and a control group (n = 33). They reported that the CII demonstrated acceptable sensitivity and excellent specificity for failure of WMT. While further validation is necessary, it appears that the CII is a promising embedded validity scale for detection of feigned ADHD on the CAARS.

Marshall et al. (2010) divided archival patient data into clinical ADHD, noncredible ADHD, no ADHD honest responders, and noncredible no ADHD. Noncredible performance was based on Slick criteria (Slick, Sherman, & Iverson, 1999). They found that the no ADHD honest responders obtained significantly lower scores compared with the other three groups, who did not differ significantly on scores of most Barkley ADHD Self-Report subscales. In addition, Barkley has recently identified nine symptoms that he determined are most effective in identifying ADHD (Barkley, Murphy, & Fischer, 2008). The noncredible ADHD group endorsed significantly more symptoms of inattention behaviors compared with all other groups; however, no other statistical differences were found between noncredible ADHD, clinical ADHD, and noncredible no ADHD groups suggesting that the symptoms are easily faked. They then looked at responses on the Barkley Adult ADHD Self-Report Forms (BAASRF; Barkley & Murphy, 2006) and compared patients’ responses on three items (“talks excessively,” “feel restless,” and “am easily distracted”) with pyschometrists’ ratings of similar dimensions. Self-report on the BAASRF was deemed invalid if patient’s ratings were extremely discrepant compared with psychometrist ratings. Marshall et al. also examined the performance on the Clinical Assessment of Attention Deficit-Adult (CAT-A) Infrequency Scale (Bracken & Boatwright, 2005). Overall, they found that cutoffs for these embedded measures did not offer an acceptable balance of sensitivity and specificity for detecting malingered ADHD.

Harrison and Edwards (2010) examined archival data of Canadian college students who completed the WMT and divided them into two groups: one group failed at least one effort indicators of the WMT whereas the other group passed all indices of the WMT. They found no significant differences on the CAARS subscales for individuals who failed WMT compared with individuals who gave credible effort. However, a trend (p = .06) was found indicating that individuals who failed the WMT endorsed more symptoms of hyperactivity/restlessness compared with those who passed the WMT. This finding is consistent with other studies who found that individuals feigning ADHD tended to endorse more symptoms of hyperactivity and restlessness on the CAARS (Suhr et al., 2008).

Conclusion

It is evident that self-reported symptoms of ADHD are easily simulated by college students without ADHD. A number of self-report questionnaires have been investigated thus far, and no questionnaire has proved sufficiently robust against false positives. Some of the questionnaires have only been investigated in a single study; however, the WURS, CAARS, ARS, and ADHD Behavior Checklist have been examined in multiple studies and have proven inadequate at detecting feigned ADHD symptoms. It does appear that individuals feigning ADHD tend to endorse more symptoms of hyperactivity/restlessness compared with clinical ADHD groups, but these differences do not appear to be clinically useful for detecting feigned ADHD (Harp et al., 2011; Harrison & Edwards, 2010; Jachimowicz & Geiselman, 2004; Suhr et al., 2008; Young & Gross, 2011).

The CAARS and CAT-A are two of the only self-report ADHD questionnaires that contained validity indices; however, they have proven insensitive to malingered ADHD. Marshall et al. (2010) examined an effort measure by comparing individual’s responses to psychometrists’ ratings, but they reported inadequate sensitivity or specificity for malingered ADHD. Suhr, Buelow, and Riddle (2011) proposed a second validity index for the CAARS, the Infrequency Index (CII). In its development and validation study, the CII demonstrated promise in detecting malingered ADHD, but future studies are needed to validate this index. Overall, college students have proved able to simulate not only current symptoms of ADHD but childhood symptoms as well, making it impossible to determine whether an individual is faking or exaggerating symptoms based on self-report alone.

Malingering on Broad, Objective Personality Inventories

Simulation studies

In addition to self-reported ADHD symptoms, Young and Gross (2011) administered the Minnesota Multiphasic Personality Inventory, Second Edition (MMPI-2; Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989). They found that the ADHD simulators scored significantly higher than the clinical ADHD group and the control group on multiple MMPI-2 validity scales including, the Infrequency (F; Butcher et al., 1989), Infrequency Psychopathology Scale (Fp; Arbisi & Ben-Porath, 1995), Backward Infrequency Scale (Fb; Butcher et al., 1989), Response Bias Scale (RBS; Gervais, Ben-Porath, Wygant, & Green, 2007), Fake Bad Scale (FBS; Lees-Haley, English, & Glenn, 1991), and the Henry–Heilbronner Index (HHI; Henry, Heilbronner, Mittenberg, & Enders, 2006). The ADHD simulators also obtained significantly higher scores on the FBS scale compared with controls, but there were no significant differences between ADHD simulators and the clinical ADHD group on the FBS. A cutoff score of ≥5 on the Fp scale demonstrated the best combination of sensitivity (.59) and specificity (.94). A cutoff score of ≥9 on the HHI yielded a sensitivity of .469 and a specificity of .887. The recommended cutoff scores for FBS and RBS did not produce adequate sensitivity. The authors concluded that these findings may suggest that individuals attempting to feign ADHD endorse more symptoms related to externalizing complaints while the RBS and FBS capture cognitive and somatic symptoms, respectively.

More recently, Harp and his colleagues examined the utility of the newest revision of the MMPI, the MMPI-2-Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008), in detecting malingered ADHD. The MMPI-2-RF has numerous validity scales including, F-r (infrequent responses), Fp-r (infrequent psychopathology responses), Fs (infrequent somatic responses), and FBS-r (symptom validity). They reported that, in general, individuals asked to feign ADHD produced clinical profiles that were similar to honest responders. Students asked to feign ADHD obtained statistically significant differences compared with the honest clinical ADHD group on the RC1 subscale. When the MMPI-2-RF validity scales were examined, the normal individuals asked to simulate ADHD obtained significantly higher scores compared with the honest clinical ADHD group on the F-r subscale. There were no significant differences on any clinical scales between ADHD participants who were asked to respond honestly and participants with ADHD who were asked to exaggerate symptoms. The cutoff scores published for validity scales of the MMPI-2-RF demonstrated poor sensitivity to feigned ADHD symptoms. Alternative cutoff scores demonstrated better sensitivity and for feigned ADHD (F-r ≥ 70: .409; Fp-r ≥ 70: .636; Fs ≥ 91: .364). Overall, the Fp-r demonstrated the most promise for the detection of malingered ADHD, but further validation of the proposed cutoff scores are needed.

Clinical data

Sullivan, May, and Galbally (2007) examined archival clinical data and compared individuals evaluated for ADHD, Learning Disabilities (LD), or comorbid ADHD and LD. They divided the patients in each group into those who passed the WMT and individuals who failed at least one validity index of the WMT (Green, 2003; Green, Allen, & Astner; 1996). Individuals in this study completed the Personality Assessment Inventory (PAI; Morey, 1991). The authors examined three indices designed to detect malingered symptoms: Negative Impression Management scale, the Malingering Index (Morey, 1993, 1996), and Rogers Discriminant Function (Rogers, Sewell, Morey, & Ustad, 1996). Surprisingly, few individuals obtained scores above the cutoffs for the validity scales. No patients obtained significant elevations on the negative impression management scale, one patient scored above 70T on the Rogers discriminant function, and two patients scored below the cutoff on the Malingering Index. The authors concluded that the lack of sensitivity to malingered ADHD may be a result of specific malingering strategies as opposed to a general indiscriminate response style.

Conclusion

Three studies to date have examined the use of validity indices embedded in broad, objective personality inventories for detecting simulated ADHD. Studies of the MMPI-2 (Young & Gross, 2011) and MMPI-2-RF (Harp et al., 2011) suggest that the measures of infrequently endorsed items related to psychopathology, Fp and Fp-r demonstrate the most promise in detecting simulated ADHD. However, these findings did not generalize to the embedded validity indices of the PAI (Sullivan et al., 2007). It appears that embedded indices of the MMPI-2 and MMPI-2-RF demonstrate more promise than do self-report ADHD questionnaires, but future studies are needed for further validation.

Malingering on Neuropsychological Tests

Simulation studies

Leark, Dixon, Hoffman, and Huynh (2002) asked college students with no history of psychopathology or ADHD to simulate ADHD on the Test of Variables of Attention (TOVA; Greenberg, Kindschi, Dupuy, & Corman, 1996), a computer-based Continuous Performance Test (CPT). The authors reported that ADHD simulators performed significantly worse than controls on the raw omission and commission error scores and response time for quarters two, four, half-two, and the total. In addition, ADHD simulators performed significantly worse than controls on scores of variability and d’ across all quarters, halves, and the total score. They concluded that clinicians should consider responses bias when patients obtained excessively elevated scores on the TOVA.

Quinn (2003) administered the Integrated Visual and Auditory Continuous Performance Test (IVA CPT) to analog ADHD malingerers, college controls, and a clinical ADHD group. She found that ADHD simulators scored significantly lower than the clinical ADHD group and controls on 81% of the IVA CPT subscales. Analog malingerers scored significantly worse than the ADHD group on the following subscales: Full Attention Quotient, Auditory Attention Quotient, Visual Attention Quotient, Full Response Control, Auditory Response Control, Visual Response Control, Auditory Consistency, Visual Consistency, Auditory Focus, Visual Focus, Auditory Prudence, Visual Prudence, Auditory Vigilance, Visual Vigilance, Auditory Comprehension, and Visual Comprehension. Based on the observation that ADHD simulators had difficulty faking ADHD on the IVA CPT, Quinn proposed an impairment index derived from attention quotients and response control scores. She determined cutoff scores by finding the midpoint between the lower bound confidence interval for the mean clinical ADHD score and the upper bound confidence interval for the mean ADHD simulator score. She reported that using the Auditory Response Control <74 + Auditory Attention Quotient <44 demonstrated a sensitivity of .94 and a specificity of .91 suggesting that an algorithm of the IVA CPT may be useful in detecting the simulated ADHD.

Harrison et al. (2007) also compared the responses of students asked to simulate ADHD with controls and ADHD individuals on the Woodcock–Johnson Psychoeducational Battery (Woodcock, McGrew, & Mather, 2001) and found that individuals faking ADHD obtained lower scores than controls or persons who met criteria for ADHD on the reading fluency, visual matching, and processing speed subtests. ADHD simulators scored significantly lower on the decision speed subtest compared with the ADHD group; however, the ADHD simulators’ scores did not differ significantly compared with controls. These authors used discriminant function analyses to examine the usefulness of lower Woodcock–Johnson scores in combination with elevated scores on the CAARS in identifying malingered ADHD, but found that diagnostic accuracy of such a combination was only modestly useful, producing approximately a 25% error rate.

Booksh et al. (2010) compared the control, clinical ADHD, and ADHD simulator groups’ scores on a battery of neuropsychological tests. For the Conners’ Continuous Performance Test (C-CPT; Conners, 1995), they examined the mean total score and the sum of clinically elevated scales. They also used the Trail Making Test Parts A and B (Reitan, 1955); Wechsler Adult Intelligence Scale, Third Edition (WAIS-III; Wechsler, 1997a); Processing Speed Index and mean of Digit Span; and Letter Number Sequencing scaled scores of the WAIS-III. The ADHD simulators performed significantly lower on all tests compared with the control participants. There were statistically significant differences between ADHD simulators’ and the clinical ADHD groups’ scores for the Trail Making Test Part A and both variables from the CPT. However, the ADHD simulators and the clinical ADHD group performed similarly on the WAIS-III variables and the Trails Part B. It is noteworthy that ADHD simulators were successfully able to feign deficits on three commonly used measures of attention and concentration. Also, of note, the findings of this study are not consistent with Suhr et al. (2008) who reported that the WAIS-III Working Memory Index and Trails Part B were significantly lower in individuals with noncredible performance but Trails Part A was not.

Sollman et al. (2010) administered a brief battery of neuropsychological tests. The authors reported no significant differences between the clinical ADHD and control groups on the Wechsler Memory Scale–Third Edition, Word Lists subtest (WMS-III-WL; Wechsler, 1997b); the Stroop Color–Word Test (Golden, 1978; Golden & Freshwater, 2002); or the Nelson–Denney Word Reading Test, Reading Speed subtest (Brown, Fishco, & Hanna, 1993). The ADHD simulators did obtain significantly lower scores on the Stroop Color and Word subtests compared with the clinical ADHD group. However, no significant differences were found between ADHD simulators and the clinical ADHD group on the Reading Speed subtest of the Nelson–Denny Word Reading Test or the WMS-III-WL. The authors reported that the C-CPT-II for Windows (Conners & MHS Staff, 2004) was not sensitive to ADHD as the clinical ADHD group and the control group obtained similar scores. The ADHD simulator group obtained clinically significant elevations on the C-CPT; however, the authors concluded that the C-CPT was successfully faked by most ADHD simulators as no extreme elevations were noted and profiles were believable for individuals with ADHD.

Clinical data

In Sullivan et al. (2007), 10 of the 21 cases presenting for an ADHD evaluation failed one or more WMT indices. Failure of the WMT was correlated with cognitive measures including WAIS-III Full Scale IQ (FSIQ) scores. Post hoc analyses revealed that individuals who passed all WMT effort indices had higher Performance IQ scores compared with those who failed any WMT effort indices. No significant differences were found for Verbal IQ scores. Scores on the California Verbal Leaning Test, Second Edition, were significantly correlated with the WMT effort measures. Similarly, scores on the WMT were correlated with the responses on the CAARS-S:L. Overall, the authors concluded that the failure of the WMT during psychoeducational evaluations of ADHD is related to poorer scores on cognitive measures and self-reported ADHD symptoms.

In Suhr et al. (2008), the noncredible group’s scores did not differ significantly from the clinical ADHD group or the psychological symptom group on the following tests: WAIS-III Processing Speed Index, Trail Making Test Part A, Controlled Oral Word Association Test, or the Stroop Color–Word Test. The noncredible group did obtain significantly lower scores compared with the clinical ADHD group and the psychological symptom group on the Auditory Verbal Learning Test, immediate recall, delayed recall, and recognition subtests as well as the Working Memory Index of the WAIS-III, Trails B, and the Stroop Color–Word Interference Score. This study shows that individuals who did not put forth credible effort on a symptom validity test (SVT), the WMT, performed significantly worse on some neuropsychological measures, highlighting the importance of effort in assessment of ADHD. Suhr, Sullivan, and Rodriguez (2011) examined clinical data from a noncredible group (individuals who failed two or more WMT indices), a clinical ADHD group, and a psychological symptom group. They reported that the noncredible group performed significantly worse than did the psychological symptom group on most CPT variables except perseverations and hit reaction time change over blocks. However, the noncredible group performed similar to the clinical ADHD group on all CPT variables except reaction time variability and reaction time change over interstimulus intervals.

Marshall et al. (2010) examined archival data and identified a subset of individuals whose performance was deemed noncredible based on the effort test failure. They compared the noncredible group with patients determined to have put forth adequate effort (credible group). The noncredible group demonstrated slowed processing speed scores on the Symbol Search and Digit Symbol subtests of the WAIS-III and the Delis–Kaplan Executive Function System Color–Word Interference Test (DKEFS-CWIT; Delis, Kaplan, & Kramer, 2001) combined naming and reading score. Although the noncredible group scored significantly lower than the credible group on these measures, none of these measures demonstrated adequate sensitivity to malingered ADHD when psychometric properties were examined. Also, the noncredible patient group performed similar to the clinical ADHD group on other processing speed measures including the Neuropsychological Assessment Battery-Numbers and Letters Test part A (NAB-NLA; White & Stern, 2003) or the C-CPT (Conners, 2000) reaction time. This study demonstrated that individuals in a clinical setting who meet Slick et al. (1999) criteria for malingering are difficult to detect based solely on performance during neuropsychological testing. The results of this study are not consistent with other studies that reported that individuals in a clinical sample that failed the WMT obtained similar scores on the WAIS-III Processing Speed Index compared with a clinical ADHD sample that put forth good effort (Booksh et al., 2010; Suhr et al., 2008).

Harrison and Edwards (2010) examined patient data from 144 Canadian college students assessed for problems related to learning. They identified individuals who exhibited noncredible performance by failure of one or more indices of either the WMT or the Medical Symptom Validity Test (MSVT; Green, 2004). The authors found that individuals who failed the WMT obtained lower scores on the WAIS-III, particularly the Performance IQ scores. This finding is consistent with Sullivan et al. (2007), who found significant correlations between WMT failure WAIS-III FSIQ and Performance IQ. In this study, however, WMT delayed recall and consistency scores were also significantly correlated with Wechsler Intelligence Scale for Children–III (WISC-III) verbal IQ scores. Statistically significant lower scores were found for individuals who failed the WMT compared with those who passed the WMT for the following WAIS-III subtests: WAIS-III FSIQ, Performance IQ, Verbal IQ, Verbal Comprehension, and Perceptual Organization. There were no statistically significant differences found for the WAIS-III Working Memory or Processing Speed Indices. The Woodcock–Johnson Psychoeducational Battery was administered, but only the Decision Making Speed Subtest yielded statistically significant differences. On the WMS, the individuals who failed the WMT scored significantly lower on the Logical Memory and Family Pictures Immediate and Delayed Recall subtests. There were no statistically significant differences found between groups on the Nelson–Denny Reading Vocabulary or Reading Comprehension subtests. It should be noted, however, that the individuals who failed the WMT obtained mean scores on most indices and tests that were within one standard deviation below the mean, making their performance believable.

Conclusion

Overall, the data suggests that ADHD simulators are able to produce cognitive profiles on neuropsychological test batteries that are relatively similar to individuals with ADHD. The most promising neuropsychological tests appear to be CPTs (Booksh et al., 2010; Leark et al., 2002; Quinn, 2003) and the Stroop test (Sollman et al., 2010; Suhr et al., 2008). It should be noted, however, of the few studies that have examined the same measures, data are mixed. For example, Suhr et al. (2008) stated that a group of patients who failed the WMT performed significantly worse on the WAIS-III Working Memory Index and Trails B but performed similarly on Trails A; however, Booksh et al. (2010) reported that ADHD simulators performed significantly worse on Trails A but similarly on Trails B and the sum of Letter Number Sequencing and Digit Span subtests of the WAIS-III. Marshall et al. (2010) found significant differences on the WAIS-III Processing Speed Index whereas other authors have not found such differences (Booksh et al., 2010; Suhr et al., 2008); Harrison and Edwards found no significant differences for WAIS-III Processing Speed or Working Memory Indices. Finally, despite several studies reporting some significant differences on CPTs, Sollman et al. (2010) reported that the C-CPT was not useful at discriminating individuals with ADHD from controls. These differences may reflect the nature of the samples used or small sample sizes, but future research is needed to clarify these findings.

The most definitive conclusion that can be drawn based on this literature is that neuropsychological test data alone are insufficient to detect poor effort during psychoeducational evaluations. Studies that use data from credible and noncredible patients highlight the necessity of effort assessment during ADHD evaluations (Marshall et al., 2010; Suhr et al., 2008; Sullivan et al., 2007). It may be best to collect data from multiple sources for determination of ADHD (Booksh et al., 2010).

Malingering on SVTs

Simulation studies

Frazier, Frazier, Busch, Kerwood, and Demaree (2008) assigned 89 college students to three groups: control, ADHD simulators, and reading disorder simulators. The simulators were given instructions adapted from the Quinn (2003) study and were given several minutes to strategize their approach to faking. The authors reported that the ADHD simulators obtained significantly lower scores on the Digit Symbol subtest of the WAIS-III and the Reading subtest from the Wide Range Achievement Test, Third Edition (WRAT-3; Wilkinson, 1993). The authors found significant differences between the ADHD simulator group and the control group for the Rey Fifteen-Item Test (Rey, 1964), Victoria Symptom Validity Test (VSVT; Slick, Hopp, Strauss, & Thompson, 1997), and the Validity Indicator Profile (VIP; Frederick, 2003) with the exception of the verbal slope. For the VIP, a cutoff score of 75 yielded .789 sensitivity and .964 specificity. A cutoff score of 12 for the Rey Fifteen-Item Test demonstrated sensitivity of .71 and specificity of .931. The best discrimination between ADHD simulators and the control group was found for the VSVT hard items and the VIP nonverbal slope. A cutoff score of 19 for the VSVT hard items yielded a sensitivity of .80 and a specificity of 1.00. Based on these findings, it appears that SVTs may be useful in psychoeducational assessments of ADHD.

In addition to self-reported symptoms and neuropsychological tests, Sollman et al. (2010) also investigated the utility of several effort indices for detecting feigned ADHD. They reported robust effect sizes for all effort measures used: Miller Forensic Assessment of Symptoms Test (M-FAST; Miller, 2001), Digit Memory Test (DMT; Hiscock & Hiscock, 1989), Letter Memory Test, Card Version (LMT; Inman et al., 1998; Schipper, Berry, Coen, & Clark, 2008), and Green’s Nonverbal–Medical Symptom Validity Test (NV-MSVT; Green, 2006) for distinguishing ADHD simulators from the clinical ADHD group. The Test of Memory Malingering (TOMM; Tombaugh, 1996) demonstrated the most promise for diagnostic purposes with moderate sensitivity (.867) and specificity (.828) for Trial 1. All other SVTs demonstrated excellent specificity but less sensitivity, so the authors then summed the number of tests that were failed. They found that when three or more SVTs were failed, 100% positive predictive power was achieved.

Jasinski et al. (2011) administered the DMT, LMT, TOMM, b Test error score, and NV-MSVT to college controls, ADHD simulators, clinical controls, and ADHD exaggerators. The malingering group scored significantly worse on all measures compared with the clinical ADHD group. Similarly, the ADHD exaggerator group scored significantly lower compared with the clinical ADHD group on most measures except for the b Test and the NV-MSVT. All SVTs administered were more sensitive to the malingered ADHD group compared with the ADHD exaggerator group. The authors reported that when results of SVT failure were combined, positive predictive power increased. Specifically, when two or more tests were failed, positive predictive power was 1.00.

Clinical data

Suhr et al. (2008) compared scores on neuropsychological and effort measures for a noncredible patient group, a credible ADHD group, and a psychological symptom group. Individuals who failed the WMT failed few other effort indices including: the Exaggeration Index of an expanded Auditory Verbal Learning Test (EIAVLTX; n = 2; Barrash, Suhr, & Manzel, 2004); the WAIS-III Digit Span score of less than 5 (n = 5; Iverson & Franzen, 1994), a Working Memory Index score of less than 70 (n = 1; Etherton, Bianchini, Ciota, Heinly, & Greve, 2006), the Auditory Verbal Learning Test recognition score of less than 10 (n = 3; Boone, Lu, & Wen, 2005), the Vocabulary–Digit Span score greater than or equal to 2 (n = 8; Greve, Bianchini, Mathias, Houston, & Crouch, 2003), and the Inconsistency score of the CAARS (n = 3). Overall, most embedded validity indices investigated the demonstrated unacceptably poor sensitivity to feigned ADHD.

In one of the largest studies to date, Marshall and colleagues (2010) examined archival data of 268 patients from a neuropsychological practice, more than half of whom were college students (n = 151). Of the college students evaluated, 17% were determined to have given suspect effort. An individual’s performance was considered noncredible if either two separate SVT measures were failed or one SVT and unusually impaired performance on one cognitive test was noted or one SVT or unusually impaired performance of one cognitive test in combination with invalid completion of a behavioral rating scale. Marshall et al. used the following effort measures: Dot Counting Test (Boone, Lu, & Herzberg, 2002a), Sentence Repetition Test (Strauss, Sherman, & Spreen, 2006) score of less than 10 (Schroeder & Marshall, 2010), California Verbal Learning Test, Second Edition (Delis, Kramer, Kaplan, & Ober, 2000), Forced Choice Recognition Test (Root, Robbins, Chang, & Van Gorp, 2006), b Test (Boone, Lu, & Herzberg, 2002b; E-score, commission errors, total time, d error, omission errors), Reliable Digit Span score of less than 6 (Babikian et al., 2006), and the WMT (immediate recall, delayed recall, pass/fail, and consistency). Also, they examined variables from the C-CPT (omission and commission T-scores) and the TOVA (Omission Errors, Commission Errors, and Reaction Time Variability) that were considered indicative of suspect effort. Most measures produced excellent specificity (>90%) with the exception of the Conners’ Omission T-score cutoff <20. However, few measures demonstrated acceptable sensitivity: the WMT immediate recall, consistency, and pass/fail cutoffs of 82.5% yielded sensitivities of 63.64%, respectively, followed by the TOVA Omission Errors >25 (62.86%), the TOVA response time variability >180 (54.29%). There was little overlap in shared variance for most measures indicating that the SVTs are independent measures of effort and supporting the use of multiple SVTs. In addition, probability of malingering increased significantly when three or more SVTs were failed.

Harrison, Rosenblum, and Currie (2010) reviewed data from 141 Canadian college students who were assessed for leaning problems. They classified a subgroup of individuals (n = 17) as malingering based on Slick et al. (1999) criteria including failure of the WMT and failing an additional SVT. They investigated the utility of WAIS-III embedded validity indices including the Digit Span (≤5; Iverson & Tulsky, 2003), Vocabulary–Digit Span (cutoff ≥ 5; Iverson & Tulsky, 2003), and Reliable Digit Span scores (cutoff ≤ 6). Overall, the Digit Span score ≤5 and Reliable Digit Span score ≤6 yielded excellent specificity but poor sensitivity, .071 and .357, respectively. The Vocabulary–Digit Span produced an unacceptable balance between sensitivity (.142) and specificity (.724). When Receiver Operating Characteristic curves were examined, no measures produced strong classification accuracy; however, because the Digit Span and Reliable Digit Span scores incorrectly identified very few honest participants, failure of one or both of these subtests could indicate poor effort during psychoeducational evaluations.

Conclusion

It appears that the most promising means of detecting malingered ADHD are SVTs that were originally designed to detect malingered cognitive symptoms. Most measures demonstrated excellent specificity, but few of the measures examined produced adequate sensitivity. However, combining SVTs may drastically increase the ability to identify malingerers (Jasinski et al., 2011).

The WMT is the most widely investigated SVT to date and demonstrated some utility in detecting malingered ADHD (Booksh et al., 2010; Marshall et al., 2010). It has been used to classify individuals as having poor effort in a number of studies (Harrison & Edwards, 2010; Harrison et al., 2010; Marshall et al., 2010; Suhr et al., 2008). Harrison et al. (2010) suggested that very low Digit Span scores or a combination of Digit Span and Reliable Digit Span scores that fall below the cutoff may be useful in detecting malingered ADHD; however, in their study, both tests produced less than optimal sensitivity. The ability to diagnose simulated ADHD increases when three or more SVTs are failed (Marshall et al., 2010; Sollman et al., 2010).

Strategies Used to Fake ADHD

Quinn (2003) examined strategies used to simulate ADHD by asking what strategies were used for recording responses. All participants reported that they used multiple strategies. The majority (61%) reported that they used a strategy of general inattention, while 43% and 17% reported that they used strategies of ignoring visual and auditory stimuli, respectively. Also, 57% intentionally made errors of commission, 35% reported that they made errors of omission, and 9% reported that they responded randomly. Behavioral strategies used included double-clicking the mouse to indicate hyperactivity (35%), general fidgeting (13%), and slowed responding at the end of the test compared with the beginning (9%). This study suggests that college students asked to simulate ADHD chose to feign symptoms of inattention more often than symptoms of hyperactivity.

Harrison et al. (2007) examined strategies used by ADHD simulators. The most common strategies used included slowly completing tasks (31%); trying to act like an acquaintance with ADHD (29%); “zoning out” or attending to distracting noises (26%); choosing incorrect answers, particularly on harder items (23%), and skipping items (23%); and responding quickly and carelessly while completing tasks (20%). Other strategies used by ADHD simulators include being inattentive to verbal instructions or disobeying instructions (14%), selecting items on the CAARS that matched DSM-IV criteria (11%), unfocusing their eyes or only focusing on the middle of the page (9%), reading questions over (6%), beginning tasks before being told to begin (3%), and letting focus wane at the ends of tasks (3%). Frazier and colleagues (2008) presented students with a questionnaire in which they were asked to respond dichotomously, yes/no, to various strategies. In all, 90% of ADHD simulators reported that they tried to show difficulty paying attention and attempted to appear less intelligent. Other items that were endorsed by a majority of ADHD simulators include trying to miss difficult items (87%), responding inconsistently (87%) and slowly (77%), and pretending to have trouble remembering things (74%). Additional strategies endorsed include demonstrating difficulty reading or reading more slowly (71%), trying to miss easy items (71%), acting confused (68%) or nervous (55%), and acting as though they had difficulty organizing information (45%) or understanding the tests (42%). These findings are generally consistent with Quinn (2003), who reported that college students asked to feign ADHD choose strategies related to symptoms of inattention more often than symptoms of hyperactivity.

Marshall et al. (2010) examined data from patients who responded in either a credible or a noncredible manner. They performed ANCOVAs, covarying FSIQ, to determine whether individuals who met Slick criteria for noncredible performance differed from the credible group in responses to neuropsychological tests to examine whether individuals feigning symptoms of ADHD employed specific strategies. They reported that individuals who exhibited noncredible performance attempted to feign specifically on tests that assess attention and processing speed. More interestingly, the manner in which individuals chose to distort their responses was associated with failure of particular measures. Individuals that failed two SVTs were strategic in only feigning on measures of memory and sustained attention while those suspected of feigning on rating scales tended to feign only on measures of sustained attention and those whose ratings of symptoms differed from psychometrist ratings did not appear to attempt to distort their responses on any cognitive tests. Marshall and colleagues reported that individuals employed specific strategies for malingering symptoms, so embedded validity indices may not be sensitive to strategies employed.

Conclusion

Most college students asked to simulate symptoms of ADHD on self-report measures or neuropsychological tests chose to respond in a manner that they believed reflected symptoms of inattention. When Marshall et al. (2010) examined patient data from neuropsychological batteries, they found that patients who met Slick et al. (1999) for malingering were strategic in the manner that they chose to respond. Even more so, it appears that patients chose several different strategies and that each strategy on effort measures had concomitant feigned neuropsychological deficits. This study offers unique insight into strategies that patients use to simulate ADHD symptoms. Future research should consider similar methodologies in an attempt to validate these findings.

Discussion

Malingered ADHD in college students is a topic that has received a growing interest in the past few years. This review article identified 19 articles that empirically investigated malingered ADHD. Most articles compared college students asked to simulate ADHD with a clinical sample of individuals that had formally been diagnosed with ADHD and a control group (Booksh et al., 2010; Fisher & Watkins, 2008; Frazier et al., 2008; Harp et al., 2011; Harrison et al., 2007; Jachimowicz & Geiselman, 2004; Jasinski et al., 2011; Leark et al., 2002; Quinn, 2003; Sollman et al., 2010; Tucha et al., 2009; Young & Gross, 2011). Several studies identified patient data that were not credible based on effort test failure (Harrison & Edwards, 2010; Suhr et al., 2008; Suhr et al., 2011, Suhr et al., 2011; Sullivan et al., 2007) or Slick et al. (1999) criteria (Harrison Rosenblum, & Currie, 2010; Marshall et al., 2010).

In the studies presented, base rates of noncredible performance ranged from 8.3% to 47.6%. Sullivan et al. (2007) reported that 47.6% of students presenting for an ADHD evaluation, 15.4% of students presenting for assessment of comorbid ADHD and learning disorder, and 9.4% of students presenting only for the assessment of a learning disorder scored below one or more cutoff scores on the WMT. Suhr et al. (2008) reported that 31% of individuals who presented for a psychoeducational evaluation at a psychology clinic affiliated with a university failed one or more of the WMT indices. Harrison and Edwards (2010) found that 14.6% of Canadian college students failed one or more indices of either the WMT or MSVT. In this study, base rates of malingering did not differ by referral questions: comorbid LD and ADHD (12.5%), neurological injury (16.7%), LD (14.7%), or ADHD (18.2%). Two studies have used Slick et al. (1999) criteria for classification of malingering in patient samples. Marshall et al. (2010) reported that a total of 22% of individuals examined demonstrated suspect effort using Slick et al. criteria. A substantial subset of patients were college students and the base rate of malingering among the college students was 17% (Marshall et al., 2010). In addition, Harrison et al. (2010) reported that 8.3% of Canadian college students met Slick criteria for malingering. The difference in base rates of malingered neurocognitive dysfunction (MND) is likely a result of various methods of classifying malingerers. In addition, it appears that Canadian college students tend to produce slightly lower base rates of malingering compared with American college students.

The data presented suggest that college students are able to successfully simulate ADHD on most self-report questionnaires. This is particularly interesting because some authors suggest that self-reported symptoms are all that are need for diagnosis of ADHD. However, it is obvious that numerous false positives are found when only using self-report data. The CAARS had one validity index, the inconsistency scale, but it demonstrated poor sensitivity for detecting malingered ADHD. Suhr and colleagues (2011) proposed a new validity index for the CAARS, the Infrequency Index, that shows promise in detecting simulated ADHD; however, more research is needed to validate this measure in clinical samples. Other validity indices have been proposed for self-report measures, but they have failed to produce adequate diagnostic statistics (Marshall et al., 2010). While only three studies investigated the utility of three broad-symptom inventories, data are mixed about the usefulness of validity scales embedded in them. Sullivan and colleagues (2007) examined validity indices of the PAI and found little support for their use in detecting malingered ADHD. Young and Gross (2011) found that some of the embedded indices of the MMPI-2 may successfully detect the simulated ADHD, but Harp and his colleagues (2011) examined validity indices of the MMPI-2-RF and found less support. Of all the embedded indices in the MMPI-2/MMPI-2-RF, it appears that the scales measuring infrequently endorsed symptoms of psychopathology (Fp) show the most promise.

It is clear that individuals who obtain lower scores on symptom validity measures tend to score somewhat lower on neuropsychological tests though differences may not be statistically significant (Marshall et al., 2010; Suhr et al., 2008; Sullivan et al., 2007), and even when results are statistically different, profiles are too similar to be clinically useful. Data from studies on performance on specific neuropsychological subtests are mixed, but overall, it appears that it would be difficult to detect malingered ADHD with any degree of accuracy by using data from cognitive tests. The most promising cognitive tests to date are CPTs and the Stroop Color–Word Test, but further research is needed to examine clinical utility.

Of all the measures examined to date, effort tests demonstrate the most clinical usefulness for detecting malingered ADHD in college students. Data from Frazier et al. (2008) suggest that stand-alone SVTs demonstrate acceptable psychometric properties. Suhr et al. (2008) investigated the usefulness of multiple embedded effort indices but reported that few of them were failed by individuals who failed the WMT. However, in other studies, most effort indices exhibited adequate to excellent specificity, but did not demonstrate adequate sensitivity when used alone (Marshall et al., 2010; Sollman et al., 2010). The diagnostic accuracy of simulated ADHD increases substantially when more than one SVTs are failed (Jasinski et al., 2011). When clinical data were examined in psychoeducational setting where malingered ADHD is likely, most malingerers would be detected when three or more effort tests are failed (Marhsall et al., 2010; Sollman et al., 2010). One concern about the use of many SVTs in this population is that most of these measures are obviously easy and would likely be detected by sophisticated malingerers or even passed by college students with substantial incentive to be classified as ADHD. For this reason, SVTs should be designed specifically for the detection of malingered ADHD.

The vast majority of students who were queried about strategies used reported that they used multiple strategies, including attempting to portray inattention by intentionally missing items, slowing their processing speed, and ignoring stimuli (Frazier et al., 2008; Harrison et al., 2007; Quinn, 2003). It is interesting that most self-report questionnaires found that individuals simulating ADHD (Harp et al., 2011; Jachimowicz & Geiselman, 2004; Young & Gross, 2011) or demonstrating noncredible performance during clinical assessments (Harrison & Edwards, 2010; Suhr et al., 2008) tend to obtain elevations on the Restlessness/Hyperactivity subscales compared with Inattentive subscales though most ADHD simulators queried reported using strategies that were more consistent with symptoms of inattention. This difference may reflect an attempt to portray symptoms of hyperactivity on self-report questionnaires and display symptoms of inattention on cognitive tests. Also, the evidence suggests that, despite their attempts to distort responses, their data, for the most part, did not differ significantly from clinical groups. Possible explanations are that college students are poor at suppressing their responses on neuropsychological tests or that they are cautious when responding so as not to be detected. However, one alternative explanation may be that the clinical samples used were tainted by individuals who exaggerated symptoms and were not detected by traditional measures. At this time, it would be difficult to determine, even retrospectively, whether some of the individuals were malingering symptoms unless multiple effort measures had been used in each battery.

Conclusion

College students have substantial incentives to malinger ADHD, and studies suggest that the base rate of malingered ADHD is significant in psychoeducational evaluations. Alarmingly, few measures that are commonly used in the assessment of ADHD demonstrate adequate sensitivity to detect malingered ADHD meaning that there is likely a high rate of false-positive diagnoses. Most obviously, relying on college students’ self-report alone is not acceptable as it has been demonstrated repeatedly that college students are successfully able to simulate the symptoms of ADHD on self-report questionnaires. This review highlights the need, not only for multiple vectors of information but also for the use of SVTs in psychoeducational evaluations. Furthermore, research needs to focus on validating current indices that show promise as well as developing specific validity indices aimed at detecting ADHD.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Mandi Musso is a fourth year graduate student at Louisiana State University currently preparing to begin an internship at Alpert Medical School of Brown University this summer. Her dissertation research is related to malingered ADHD in college students.

William Drew Gouvier is a professor of psychology at Louisiana State University. He has completed his 30th year as a researcher, teacher, and clinician. He will soon and gladly step aside to allow the new generation to handle the reins for their turn in the driver’s seat.

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