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A Comparison of White-Collar Offenders and Non-White-Collar Offenders on the Psychological Variables of Personality, Criminal Thinking, and Psychopathy

ProQuest Dissertations and Theses, 2011
Dissertation
Author: Laurie Ragatz
Abstract:
The first purpose of this study was to replicate Walters and Geyer (2004) by examining how white-collar offenders differ from non-white-collar offenders on criminal thinking styles and lifestyle criminality. The second purpose was to examine the psychopathic characteristics of white-collar offenders in comparison to non-white-collar offenders. The third purpose was to explore the psychopathology of white-collar offenders compared to non-white-collar offenders. The study sample included 48 white-collar only offenders (offenders that only committed white-collar crime), 89 white-collar versatile offenders (offenders that have previously committed non-white-collar crime), and 89 non-white-collar offenders. Groups were matched on age and ethnicity. All participants completed the Psychological Inventory of Criminal Thinking Styles (PICTS), the Psychopathic Personality Inventory-Revised (PPI-R), and the Personality Assessment Inventory (PAI). The Lifestyle Criminality Screening Form (LCSF) was completed using participants' Presentence Investigation Reports (PSIs). Results demonstrated white-collar only offenders had lower scores on the PICTS Sentimentality scale and LCSF. Additionally, white-collar offenders scored higher on PPI-R subscales (i.e., Social Potency and Machiavellian Egocentricity) and PAI scales (i.e., Alcohol Problems and Anxiety-Related Disorders). Non-white-collar offenders had higher scores on the PAI Drug Problems scale. Logistic regression findings demonstrated PAI Drug and Alcohol Problem scales distinguished white-collar versatile and non-white-collar offenders. White-collar only offenders were differentiated from non-white-collar offenders by the PAI Anxiety-Related Disorders scale, PAI Drug Problems scale, PAI Alcohol Problems scale, and PPI-R total score. The logistic regression model was not significant for distinguishing white-collar only and white-collar versatile offenders. Research findings have implications for treatment practices with white-collar offenders.

Table of Contents Introduction…………………………………………………………………………………….....1 Prevalence of White-Collar Crime………………………………………...……………...1 Definitions of White-Collar Crime………………………………………………………..2 Demographic Variables...…………………………………………………………………4 Psychological Attributes of White-Collar Criminals……………………………………..7 Criminal Thinking Patterns………………………………………………………..…….10 Lifestyle Criminality Screening Form………………………………………………..…14 Psychopathy……………………………………………………………………………..15 Personality Assessment Inventory………………………………………………………22 Current Study……………………………………………………………………………26 Method…………………………………………………………………………………………..28 Participants………………………………………………………………………………28 Measures………………………………………………………………………………...31 Procedures……………………………………………………………………………….36 Design and Data Analysis……………………………………………………………….37 Results…………………………………………………………………………………………...38 Offender Group Comparisons for Demographic Variables……………….……………..38 Offender Group Comparisons for the LCSF and PICTS…………….…………………..38 Offender Group Comparisons for the PPI-R……………………………………….……41 Offender Group Comparisons for the PAI………………………………………………44 Using Psychological Variables to Predict White-Collar and Non-White-Collar Status...46 Discussion……………………………………………………………………………………….48

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Study Limitations…………………………………………………………………….…54 Implications and Future Directions………………………………………………….…61 References……………………………………………………………………………………...65 Footnotes…………………………………………………………………………………….…83 Tables……………………………………………………………………………………….….85 Figure………………………………………………………………………………………....103 Appendices……………………………………………………………………………….…...104 Curriculum Vitae………………………………………………………………………….…..114

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A Comparison of White-Collar Offenders and Non-White-Collar Offenders on the Psychological Variables of Personality, Criminal Thinking, and Psychopathy Bernie Madoff deceived investors out of nearly $65 billion in an elaborate ponzi scheme (CBC News, 2009). The Enron scandal, led by chief executive Kenneth Lay, cost stockholders $31.8 billion (BBC News, 2006). In fact, it is estimated that the costs of white-collar crime in the U.S. may reach as much as $1 trillion annually (Friedrichs, 2007; Schlegel, 2000). Of course, this estimate overlooks the psychological impact these crimes can have on their victims. Research has shown victims of white-collar crime are at an increased risk for both depression and anxiety (Sharp, Shreve-Neiger, Fremouw, Kane, & Hutton, 2004). Prevalence of WhiteCollar Crime White-collar crime prevalence data has been frequently gathered from various government organizations, media channels, and journals. This method of data collection is problematic because different coding methods and definitions are utilized across sources (Friedrichs, 2007). The 2007 white-collar crime data from the Federal Judiciary of the U.S. Courts showed there were 994 forgery, 10,678 fraud, and 565 embezzlement cases. Fraud offenses were broken down into 18 categories. The most prominent fraud convictions included conspiracies to defraud the U.S. (n = 2,195), identification or information fraud (n = 1,951), false statements (n = 811), mail fraud (n = 717), tax fraud (n = 615), wire or television fraud (n = 577), and health care fraud (n = 316). Embezzlement offenses were subdivided into the following categories: bank (n = 202), postal service (n = 173), financial institutions (n = 23), and other (n = 167) (National White-Collar Crime Center, 2008). These statistics substantially underestimate the prevalence of white-collar crime because they only included criminals who were prosecuted and convicted in federal courts. These statistics do not take into account white-collar crimes

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which are prosecuted in state criminal courts, civil courts, or at an administrative level. Furthermore, these statistics also do not include white-collar criminals who evade conviction. Definitions of WhiteCollar Crime Edwin H. Sutherland first defined white-collar crime as “crime committed by a person of respectability and high social status in the course of his occupation” (Sutherland, 1949, p. 9). Since Sutherland’s initial white-collar crime definition, debate regarding whether white-collar crime is best defined by offender characteristics (e.g., socioeconomic status, job position), offense characteristics (e.g., context, legal statute, victim type, nature of harm), or a combination of offender and offense characteristics has flourished (Friedrichs, 2007). Moreover, several terms for different subtypes of white-collar crime (i.e., elite deviance, occupational crime, and corporate crime) have been developed, which has led to even more confusion about the definition (Friedrichs, 2007). Clinard and Quinney (1973) asserted that the term white-collar crime should be replaced by the terms occupational crime and corporate crime. They defined occupational crime as “offenses committed by individuals for themselves in the course of their occupations and the offenses of employees against their employers” (p. 188). Corporate crime was described as “offenses committed by corporate officials for their corporation and the offenses of the corporation itself” (p. 188). Edelhertz (1970) advocated for a definition of white-collar crime which did not restrict such offenses to the occupational domain. Specifically, he stated white- collar crime was “an illegal act or series of illegal acts committed by nonphysical means and by concealment or guile to obtain money or property to avoid the payment or loss of money or property or to obtain business or personal advantage” (p. 3). The Federal Bureau of Investigation also excludes occupation context from their definition of white-collar crime, which

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they define as “those illegal acts which are characterized by deceit, concealment, or violation of trust and which are not dependent upon the application of threat of physical force or violence. Individuals and organizations commit these acts to obtain money, property, or services; to avoid the payment or loss of money or services; or to secure personal or business advantage” (United States Department of Justice, 1989, p. 3). When studying white-collar crime, scholars (Benson & Moore, 1992; Daly, 1989; Langton & Piquero, 2007; Poortinga, Lemmen, & Jibson, 2006; Walters & Geyer, 2004; Weisburd, Chayet, & Waring, 1990; Wheeler, Weisburd, Waring, & Bode, 1988) have predominately relied on the definition or an adaptation of the definition set forth by Wheeler, Weisburd, and Bode (1982), which stated that white-collar crimes are “economic offenses committed through the use of some combination of fraud, deception, or collusion” (p. 642). The definition has been then further qualified by requiring that the offender’s offense be one of eight types: bank embezzlement (taking company funds, which were meant for other purposes, and using them for their own personal gain), tax fraud (deceiving the government in effort to avoid paying or decrease the amount of taxes one pays), postal fraud (using a government-regulated means of communication, such as the mail, to deceive others), credit fraud (attempting to secure or securing loans with a dishonest application), false claims and statements (defrauding a government agency in order to receive undeserving funds), bribery (influencing a public officer by giving or promising to give him or her something in return), securities fraud (providing investors with untrue stock information meant to impact their purchasing practices and other illegal stock market actions), or antitrust violations (attempting to regulate or fix the prices of different merchandise and services) (Wheeler et al., 1982).

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Demographic Variables Despite the definition used to describe white-collar crime, scholars have recognized that white-collar offenders are unique from non-white collar offenders (e.g., drug dealing, theft) on several demographic variables. Wheeler et al. (1988) conducted a descriptive study of the demographic characteristics of non-white-collar offenders (n = 210, 31.4% women), white-collar offenders (n = 1,342, 14.5% women), and a U.S. community sample. White-collar crime was defined using Wheeler et al.’s (1982) criteria. The data was gathered from the presentence investigation (PSI) reports of offenders convicted in U.S. federal criminal courts in seven districts between the years of 1976 and 1978. The community sample data was collected from several different federal sources (i.e., Federal Judicial Center, United States Bureau of the Census). Non-white collar offenders were convicted of forgery or postal fraud. Findings showed white-collar offenders were more likely to be male (white-collar: 85.5% vs. non-white-collar: 68.6% vs. community: 48.6%), Caucasian (white-collar: 81.7% vs. non-white-collar: 34.3% vs. community: 76.8%), older age (white-collar: 40.0 vs. non-white-collar: 30.0 vs. community: 30.0), graduate from high school (white-collar: 79.3% vs. non-white-collar: 45.5% vs. community: 69.0%), graduate from college (white-collar: 27.1% vs. non-white-collar: 3.9% vs. community: 19.0%) and less likely to be unemployed (white-collar: 5.7% vs. non-white-collar: 56.7% vs. community: 5.9%) when compared to the non-white-collar offender and community samples. Findings also demonstrated that the costs of white-collar crime were more extensive, with white-collar criminals more likely to have 100 or more victims (white-collar: 17.7% vs. non- white-collar: 1.9%), to have caused damage to an organization (white-collar: 88.3% vs. non- white-collar: 28.9%) and to steal amounts greater than $100,000 (white-collar: 29.7% vs. non-

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white-collar: 2.1%) when compared to non-white-collar offenders. White-collar offenders were also more likely to have five or more codefendants (white-collar: 35.7% vs. non-white- collar:18.9%) and to have been perpetrating the crime for longer than a year (white-collar: 50.9% vs. non-white-collar: 7.0%) compared to non-white-collar offenders. Benson and Moore (1992) utilized the PSI reports of male and female federal white- collar (n = 2,462) and non-white-collar (n = 1,986) offenders convicted in eight federal districts (different federal districts than the districts examined in the Wheeler et al. [1988] study) between the years 1973 and1978. White-collar crimes included bank embezzlement, bribery, income tax violations, false claims and statements, and mail fraud. Non-white collar offenders were found guilty of drug crimes, postal forgery, or bank robbery. Overall, descriptive findings showed white-collar offenders were less likely to have an arrest history (embezzlement [18.4%], bribery [23.6%], income tax fraud [42.1%], false claims [49.0%], and mail fraud [65.9%]) than non- white-collar offenders (bank robbery [88.4%], postal forgery [82.6%] and drug crimes [72.2%]). The crimes each offender had previously committed were categorized into violent, property, white-collar, or minor offenses. A comparison of the criminal histories of white-collar and non- white-collar offenders demonstrated white-collar offenders were more likely to only have an arrest history of white-collar crime, while non-white-collar criminals showed a criminal history that included all four crime categories. White-collar offenders were also less likely to have previously used drugs (5.5% vs. 48.5%, respectively), to have used alcohol excessively (4.2% vs. 8.3%, respectively), and to have demonstrated impaired academic performance (24.6% vs. 53.5%, respectively) than non-white-collar offenders. A more recent study (Poortinga et al., 2006) of the demographic characteristics of white- collar offenders utilized a sample of male and female white-collar and non-white-collar criminals

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in a psychiatric facility between the years of 1991 and 2002. All data were gathered from the court evaluations. White-collar offenders were charged with embezzlement (n = 70, 52.9% women) and non-white-collar offenders (n = 73, 39.7% women) were charged with a non-violent theft offense (i.e., retail fraud, stealing from a person, bank robbery without a weapon, vehicle theft). No significant differences were found between white-collar offenders and non-white- collar offenders on age (39.2 vs. 36.7, respectively), marital status, or gender. Study findings demonstrated white-collar offenders were more likely to have been employed (85.7% vs. 51.8%, respectively), have a higher level of education (12.9 years vs. 10.7 years, respectively), be Caucasian (80.9% vs. 60.3%, respectively), and to have been in management (21.9% vs. 0.0%, respectively) compared to non-white-collar offenders. White-collar offenders were also significantly less likely to have contact with police as a juvenile (12.8% vs. 37.2%, respectively), and were less likely to have an adult conviction (41.8% vs. 76.6%, respectively) than non-white-collar criminals. Non-white-collar offenders were significantly more likely to meet diagnostic criteria for substance abuse or dependence (90.9% vs. 64.4%, respectively) and less likely to meet diagnostic criteria for depressive disorders (13.0% vs. 32.3%, respectively) than white-collar offenders. Also, the average monetary damages caused by white-collar offenders were significantly higher than those caused by non- white-collar offenders ($35,792 vs. $246, respectively). This study concluded by showing the variables most predictive of white-collar criminality were not having a substance abuse disorder, being Caucasian, and having a higher education level. In sum, it appears that white-collar offenders are distinct from non-white-collar offenders on several demographic variables (e.g., ethnicity, age, education level, and criminal history).

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Psychological Attributes of WhiteCollar Criminals Research exploring the psychological characteristics of white-collar criminals is limited. Moreover, these limited studies have only compared the personality characteristics of white- collar offenders to those of non-criminal white-collar professionals. For instance, Collins and Schmidt (1993) used a self-report survey design to assess the personality traits of federal white- collar criminals (n = 329, 21.6% women) and non-criminal white-collar employees (n = 320, 53.8% women). White-collar criminals were convicted of the following crimes: antitrust violations, counterfeiting, embezzlement, forgery, fraud, interstate transportation of stolen vehicles, misuse of public money, money laundering, bribery, and racketeer influence in corrupt organizations. Personality was measured with the California Psychological Inventory (CPI; Gough, 1987), the General Biodata Questionnaire (GBQ; Owens, 1976), and the Employment Inventory (measure of work-related traits; Paajanen, 1988). White-collar offenders were significantly higher in anxiety, involvement in extracurricular activities, and social extraversion. In comparison, non-criminal white-collar professionals were significantly elevated in socialization, responsibility, tolerance, and performance. One study (Kolz, 1999) examined the personality traits of individuals admitting to employee theft. The study sample included 218 (69.3% women) individuals employed at a women’s clothing store chain. Nineteen percent of the sample endorsed taking part in workplace theft. All participants completed a demographic questionnaire and the NEO Five-Factor Inventory (NEO-FFI; Costa & McCrae, 1992). Study findings showed low conscientiousness and low agreeableness predicted admitting to employee theft. Utilizing a semi-structured interview procedure, Alalehto (2003) had 128 business professionals report on the behavior and personality traits of a colleague in the construction,

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music, or engineering business. Specifically, the researcher instructed participants to describe the illegal behavior of their friend or coworker if they had “close knowledge of whether or not the person committed the economic crime, regardless of whether that person was convicted of it” (p. 343). Participants who did not know of a colleague partaking in illegal activities at work were instead asked to describe a co-worker who did not participate in illegal acts at work. A total of 55 criminal white-collar offenders and 69 non-criminal white-collar professionals were described. The interview manual consisted of questions assessing six personality traits (i.e., extroversion, agreeableness, conceitedness, neuroticism, intellectualism, negative valency). Example interview questions included “Is he dutiful or does he take each day as it comes, rather thoughtlessly, and so forth?” and “Would he rather be liked by others in all that he does or is he not bothered much by this?” (p. 353). After a participant described his colleague’s attitude in response to a specific interview question, the descriptions were categorized into one of the six traits. A computer program was then used to assess the different combination of personality traits that were common among white-collar offenders and professionals. Descriptive data showed a greater number of white-collar offenders were described as extroverted (e.g., outgoing, controlling, calculating), less agreeable, and neurotic. The non-criminal white-collar professionals were more agreeable and conceited (e.g., diligent, frugal, refined). Blickle, Schlegel, Fassbender, and Klein (2006) explored the differences in personality between 76 (7.9% women) incarcerated white-collar offenders and 150 (37.3% women) business managers. The white-collar offenders (46.8 years) were older than the managers sample (44.1 years). White-collar offenders reported having had a mean annual income of $93,472 previous to their current incarceration. Individuals in the management sample reported a mean annual income of $148,326. All respondents completed self-report measures assessing social

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desirability, hedonism, narcissism, and conscientiousness. Self-control was measured via an assessment in which individuals read four separate scenarios where cheating another individual was possible. If respondents chose to cheat, they were considered low in self-control. Self- control was also measured using the Retrospective Behavioral Self-Control scale (RBS; Marcus, 2003). A logistic regression showed higher hedonism, narcissism, conscientiousness, and lower levels of behavioral self-control predicted white-collar criminality. In a recent study, Listwan, Piquero, and Van Voorhis (2010) investigated whether white- collar criminals (n = 64) with specific personality styles were more likely to recidivate. The white-collar sample was primarily Caucasian (68.8%), married (59.4%), had children (82.5%), had a high school degree (29.7%), employed full-time (53.2%), and had a prior criminal record (78.1%). The mean age of respondents was 38.75. All white-collar criminals were male and convicted of at least one of the following crimes: bank crimes (e.g., bank fraud, bank fund theft) or fraud crimes (e.g., bribery, embezzlement, mail fraud, wire fraud, RICO violation, FDA violation, extortion). Data for this study was obtained at two different intervals. Between the years of 1986 and 1988, all participants completed the Jesness Inventory (Jesness, 1996). The Jesness Inventory was utilized to measure four personality styles (i.e., aggressive, neurotic, dependent, and situational). Ten years later, archival records (i.e., National Crime Information Center records, incarceration records) were reviewed to determine if the offender had recidivated (i.e., arrested). Listwan and colleagues then conducted a regression to examine the extent that personality predicted future arrest. Results demonstrated that white-collar offenders that had high scores on the neurotic personality dimension were significantly more likely to recidivate when compared to all the other personality types.

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Criminal Thinking Patterns Walters (1990; 2006a; 2006c; 2010) suggests three variables (i.e., conditions, cognitions, and choices) interact to initiate and maintain the criminal lifestyle. Conditions include both external environmental and internal personal factors which have an impact a person’s behavior. Conditions represent the alternatives available to a person in any given situation. An individual then makes a choice from the accessible options. Next, an individual evaluates the outcome of his or her selection. When an individual receives an unfavorable consequence because of his or her choice, he or she does not make that same selection in the future. However, when an individual receives a desired consequence, he or she subsequently makes that same selection in the future. What then follows is the development of a system of cognitions (i.e., criminal thinking patterns) an individual uses to substantiate his or her antisocial choices and acts. Yochelson and Samenow (1976) originally assumed there to be 52 criminal thinking errors which contributed to the criminal lifestyle. Walters (2006a, 2010) believes eight criminal thinking styles maintain the criminal lifestyle. Interventions which target criminal thinking assume that changing underlying thinking patterns ultimately lead to changes in behavior. Criminal thinking or attitudes conducive to a criminal lifestyle have been linked to several behavioral outcomes such as treatment completion (Staton-Tindall et al., 2007), treatment effects (Walters, 1995; 2003a; Walters, Trgovac, Rychlec, Di Fazio, & Olson, 2002), recidivism (Palmer & Hollin, 2004a; Walters, 1997; 2005; Walters & Elliot, 1999), risk for sexually offending (Walters, Deming, & Elliot, 2009), and participation in disciplinary acts in prison (Walters, 1996; 2007; Walters & Geyer, 2005; Walters & Mandell, 2007). In fact, one study with male federal inmates demonstrated that criminal thinking contributed to the prediction of three different types of disciplinary outcomes (i.e., severe, aggressive, total), above what was

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already accounted for in the model by psychopathy (measured with the Psychopathy Checklist: Screening Version [Hart, Cox, & Hare, 1995]), age, and prior disciplinary acts (Walters & Mandell, 2007). Additionally, criminal thinking dimensions have been found to be moderately correlated with a self-report measure of antisocial personality (i.e., Antisocial Features scale of the Personality Assessment Inventory [PAI]) and to a much lesser extent with other dimensions of psychopathology (i.e., Somatic Complaints, Depression, Mania, Schizophrenia, Paranoia, and Anxiety scales of the PAI) (Morey, 1991; 2003; Walters & Geyer, 2005). Several self-report measures exist for assessing criminal thinking dimensions (e.g., Texas Christian University Criminal Thinking Scale [Knight, Simpson, & Morey, 2002], Criminal Sentiments scale [Andrews & Wormith, 1984]), with the Psychological Inventory of Criminal Thinking Styles (PICTS; Walters, 2006a; 2010; see Walters & Schlauch, 2008) having the most extensive empirical foundation. The PICTS contains 80-items, which load onto19 subscales and also a general criminal thinking scale. The 19 scales of the PICTS includes two validity indices, eight thinking style scales, four factor scales, two content scales, two composite scales, and a Fear-of-Change scale (Walters, 2006a; 2010). The PICTS General Criminal Thinking scale score can be computed by summing responses to the 64-items of the eight thinking style scales (see Walters & Schlauch, 2008). Criminal thinking patterns have been studied in research with male federal offenders (Walters, 1995), sex offenders (Hatch-Maillette, Scalora, Huss, & Baumgartner, 2001;Walters et al., 2009), white-collar offenders (Walters & Geyer, 2004), female federal offenders (Walters, Elliott, & Miscoll, 1998), female state offenders (Walters et al. 1998; Walters & Elliot, 1999), male English offenders (Palmer & Hollin, 2004b), male Irish probationers (Healey & O’Donnell, 2006), male Dutch prisoners (Bulten, Nijman, & van der Staak, 2009), and male and female

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college students (McCoy et al., 2006; Walters, Felix, & Reinoehl, 2009; Walters & McCoy, 2007). For instance, Hatch-Maillette et al. (2001) found child molesters demonstrated lower scores on the Cutoff (eliminating distress with drugs or by committing illegal acts) and Discontinuity (proclivity to be frequently distracted, which leads to neglect of personal goals) thinking style scales in comparison to a non-sex offender sample. A comparison of male federal offenders and female federal and state offenders on the eight thinking style scales showed female offenders (combined state and federal samples) had significantly higher scores on all eight scales than males (Walters et al., 1998). Walters and McCoy (2007) showed female offenders (sample included state and federal offenders) scored highest on seven of the eight thinking style scales (female offenders were significantly lower on Power Orientation [preferring to be in control of circumstances] compared to male students) when compared to male students, female students, and male federal offenders. Additionally, research suggests that male federal offenders (Walters, 1995) have demonstrated significantly lower levels of criminal thinking on the eight thinking style scales when compared to male English (Palmer & Hollin, 2004b) and Irish (Healy & O’Donnell, 2006) offenders. Only one previous study (Walters & Geyer, 2004) has investigated criminal thinking patterns unique to white-collar offenders. In this study, the definition of white-collar crime was adopted from Wheeler et al. (1982) and included offenders that committed the eight crimes (i.e., antitrust offenses, securities and exchange fraud, postal/wire fraud, false claims/statements, credit fraud, bank embezzlement, tax fraud, and bribery) specified by Wheeler et al. with the addition of two white-collar offenses (i.e., health care fraud and counterfeiting). The white- collar offenders were then divided into two separate groups. One group consisted of 34 male

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white-collar offenders (with no criminal history or only a history of committing white-collar offenses) and the other white-collar offender group consisted of 23 male criminally versatile white-collar offenders (had a criminal history of offenses that were not white-collar crimes). A comparison group of 66 male non-white-collar criminals (primarily convicted of drug, theft, or firearm violation offenses) was also utilized. Individuals in all three groups completed the PICTS (Walters, 2006a; 2010) and the Social Identity as a Criminal Scale (SIC; Cameron, 1999). The authors chose to focus only on the factor scales of the PICTS (Problem Avoidance, Interpersonal Hostility, Self- Assertion/Deception, and Denial of Harm) when comparing the three groups. In addition, the researchers looked at the three subscales (In-Group Ties, Centrality, and In-Group Affect) of the SIC (measure of degree one identifies with other offenders). The In-Group Ties subscale measured the extent an individual has corresponded with other criminals. The Centrality subscale assessed whether an individual believed identity in a group was necessary. The In-Group Affect subscale assessed an individual’s viewpoint of offenders. Lastly, the authors looked at differences between groups on a modified version (arrest items were eliminated) of the Lifestyle Criminality Screening Form-Revised (LCSF-R; Walters, 1998; Walters, White, & Denney, 1991). The LCSF-R was utilized to measure four interpersonal subtypes important to criminality (irresponsibility, self-indulgence, interpersonal intrusiveness, and social rule-breaking) and was completed via file review by one of the study authors. Several noteworthy differences were found between the three groups on demographic variables. The white-collar crime only group was significantly older (50.1) than the white-collar criminally versatile (43.6) and non-white-collar (41.6) offender groups. Also, education differences were found, with the white-collar crime only individuals (16.0) having more

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education than white-collar criminally versatile (14.1) and non-white-collar offenders (12.4). Lastly, both white-collar offender groups were primarily Caucasian compared to non-white- collar offenders. An ANOVA comparing the three groups found white-collar only offenders to be significantly lower on the PICTS Self-Assertion/Deception subscale (tendency to justify or give reasons for taking part in criminal behavior) and also the SIC In-Group Ties subscale. Additionally, the criminally versatile white-collar offender group had a significantly higher score on the SIC Centrality subscale when contrasted with the other groups. The non-white-collar group was found to score significantly highest on the LCSF-R measure, followed by the criminally versatile white-collar offender group, and lastly the white-collar crime only group. When controlling for the influence of demographic differences (i.e., education, age, sentence length, ethnicity, marital status) between groups, the only significant findings that remained was on the SIC In-Group Ties subscale and the LCSF-R total score. Interestingly, findings from Walters and Geyer’s (2004) study suggest that the PICTS did not distinguish well between white-collar offenders and non-white-collar offenders. However, these results could be an artifact of just focusing on the PICTS factor scales. Future research should examine whether differences exist on the eight thinking style scales or the general criminal thinking scale score. Lifestyle Criminality Screening Form The LCSF (Walters et al., 1991), a measure of behaviors associated with criminality, consists of four subscales: Irresponsibility, Self-Indulgence, Interpersonal Intrusiveness, and Social Rule Breaking. The LCSF is a short risk appraisal measure which is completed in ten minutes by reviewing information available in client files. Much of the instrument’s data can be collected from the PSI.

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Walters (2006b) conducted a meta-analysis of 22 studies utilizing either a risk appraisal instrument (completed with existing records) or self-report measure to predict recidivism or institutional misconduct. Findings demonstrated that each assessment type (risk appraisal vs. self-report) contributed unique variance to the prediction of recidivism or institutional misconduct (Walters, 2006b). Furthermore, Walters and Geyer (2004) found white-collar only offenders could be reliably distinguished from white-collar criminally versatile offenders and a non-white-collar offender group by a self-report measure (SIC In-Group Ties subscale) and risk- appraisal measure (LCSF). This demonstrates the importance of utilizing both risk appraisal and self-report measures with forensic samples to answer psycho-legal questions. LCSF total scores have been found to be predictive of substance misuse (Walters, 1995), unemployment (Walters & McDonough, 1998), violation of parole or probation terms (Walters, Revella, & Baltrusaitis, 1990), reconviction (Kroner & Mills, 2001; Walters et al., 1990; Walters & Chlumsky, 1993), and disciplinary infractions (Walters, 2005; 2007). The LCSF has also been found to significantly predict criminal justice outcomes, beyond what can be predicted by a diagnosis of antisocial personality disorder (Walters & Chlumsky, 1993) and demographic variables (e.g., age, gender, and race) (Walters et al., 1990; Walters & Chlumsky, 1993; Walters & McDonough, 1998). The Psychopathy Checklist-Revised (PCL-R; Hare, 1991; 2003b) is a risk appraisal instrument with considerable research demonstrating its usefulness in predicting criminal justice outcomes. Research shows that the LCSF is just as capable of predicting recidivism and institutional misconduct as the PCL-R (Walters, 2003b). Psychopathy Cleckley (1941/1988), in his renowned book The Mask of Sanity, described the 16 traits he believed exemplified the psychopath. Some of the traits described by Cleckley were

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superficial charm, high intelligence, self-centeredness, lack of nervousness, impaired judgment, not having goals, undependable, propensity to commit antisocial acts, dishonesty, lack of regret for actions, and impaired emotional capabilities. The book is largely a constellation of case studies which illustrate that psychopathy can be seen across professions (i.e., scientist, physician, and businessman) and social classes. Furthermore, Babiak (2007) asserted that many characteristics of the psychopath may be favorable in the business or corporate domain. For instance, self-centeredness might be recognized as having “Self-confidence” or a lack of specified goals might be deemed “Visioning” (Babiak, 2007). Recently, Babiak, Neumann, and Hare (2010) explored the relation between psychopathy and various work performance dimensions in a sample of 203 (77.8% male, 91.1% Caucasian) corporate professionals. Psychopathy scores (as measured by the PCL-R) were found to be positively correlated with being a successful communicator across several modalities (e.g., writing, presenting), producing and following through with new proposals, and having critical thinking skills. Psychopathy scores were negatively correlated with effectively getting along with other employees and managing employees appropriately so that they work successfully together. Hare (1991; 2003), relying on the work of Cleckley, developed the gold standard for the assessment of psychopathy: the PCL-R. This instrument requires extensive training to implement, and is based on extensive file-review information (often taking two hours or longer) along with a supplemental interview with the individual being assessed (Hare, 1991; 2003). Factor analytic research with the PCL-R has provided the most support for a two-factor model. Factor 1 has been said to be most representative of the interpersonal and affective features of psychopathy, with items such as superficial charm, shallow affect, remorselessness, and grandiosity loading on this factor. Factor 2 is described as primarily composed of the behavioral

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Abstract: The first purpose of this study was to replicate Walters and Geyer (2004) by examining how white-collar offenders differ from non-white-collar offenders on criminal thinking styles and lifestyle criminality. The second purpose was to examine the psychopathic characteristics of white-collar offenders in comparison to non-white-collar offenders. The third purpose was to explore the psychopathology of white-collar offenders compared to non-white-collar offenders. The study sample included 48 white-collar only offenders (offenders that only committed white-collar crime), 89 white-collar versatile offenders (offenders that have previously committed non-white-collar crime), and 89 non-white-collar offenders. Groups were matched on age and ethnicity. All participants completed the Psychological Inventory of Criminal Thinking Styles (PICTS), the Psychopathic Personality Inventory-Revised (PPI-R), and the Personality Assessment Inventory (PAI). The Lifestyle Criminality Screening Form (LCSF) was completed using participants' Presentence Investigation Reports (PSIs). Results demonstrated white-collar only offenders had lower scores on the PICTS Sentimentality scale and LCSF. Additionally, white-collar offenders scored higher on PPI-R subscales (i.e., Social Potency and Machiavellian Egocentricity) and PAI scales (i.e., Alcohol Problems and Anxiety-Related Disorders). Non-white-collar offenders had higher scores on the PAI Drug Problems scale. Logistic regression findings demonstrated PAI Drug and Alcohol Problem scales distinguished white-collar versatile and non-white-collar offenders. White-collar only offenders were differentiated from non-white-collar offenders by the PAI Anxiety-Related Disorders scale, PAI Drug Problems scale, PAI Alcohol Problems scale, and PPI-R total score. The logistic regression model was not significant for distinguishing white-collar only and white-collar versatile offenders. Research findings have implications for treatment practices with white-collar offenders.