Adolescent criminal acts committed and substance use with a voluntary sample recruited from post-secondary institutions.
The current study focuses on exploring the relationships between various patterns of criminal acts committed, drug use, alcohol consumption, and adult personality traits in a sample with a low probability of life course persistent criminal behaviors. A retrospective questionnaire and the NEO-FFI (Five Factor Inventory) were administered to a volunteer sample (N=36). Three different frequencies of criminal acts committed (no-crimes, moderate-crimes, and substantial-crimes) were analysed based on adult personality traits, adolescent drug use, and adolescent alcohol use. The results of a MANOVA and discriminant analysis support the likelihood of groupings based on criminal acts committed being significantly influenced by drug use and alcohol use. Furthermore, agreeableness personality traits were significantly lower in the moderate-crimes group compared to the no-crimes group.
Keywords: Delinquency, substance use, adolescents, criminal behaviors, life trajectories, personality
A majority of youth participate in typical criminal acts at some point throughout the period of adolescence (DeLisi & Piquero, 2011; Moffitt, 2006). Unfortunately for researchers, the typical criminal acts committed by adolescents are largely underreported and not accounted for in official documentation (Israel & Wicks-Nelson, 2009: Krohn & Thornberry, 2000). Researchers mainly focus on adolescents recruited with a probable or actual enduring criminal involvement due to the high cost their behaviors impose on various societal services including judicial, policing, and treatment facilities (Hammersley, 2011; DeLisi & Piquero, 2011; Moffitt, 2006). Few studies have explored the relationship between different rates of self-reported typical adolescent crimes committed (Loeber, Stouthamer-Loeber, Masten, & Wei, 2004; Moffitt, 2006) in conjunction with various risk factors known to be associated to criminal behaviors in high risk populations. This study focused on recruiting a sample with a low probability of life course persistent criminal behaviors to analyse the differences between (Capaldi & Wiesner, 2003; Coontz & Sevigny, 2007) adolescent self-reported crimes committed, adolescent substance use, and adult personality traits. The main objective of the study was to better inform professionals working with low risk adolescent populations of the possible motivating factors behind their participation in criminal acts during adolescence (Moffitt, 2006).
For the purposes of this project a criminal act was defined as a self-reported typical crime committed during adolescence. In general, the objective of criminological studies employing self-reported methodologies of data collection is to capture data related to non-official documentation (Krohn & Thornberry, 2000). As a majority of criminal acts committed are not reported (DeLisi & Piquero, 2011), this non-official data collection is important to understanding the underlying dynamics of criminal behaviors (Krohn & Thornberry, 2000). Criminal acts typically committed by adolescents are related to theft, property damage, break and enter, alcohol consumption, and drug use (Bennell, Brown, Forth, Nunes, Pozzulo, & Serin, 2011; Israel & Wicks-Nelson, 2009; Dolgin & Rice, 2008).
The probability of an individual participating in a criminal act is influenced by various risk factors. Risk factors are defined as unique biological and environmental dynamics influencing the probability of an individual committing a criminal act (Andrews & Bonta 2010; Brochu, 2006; Brochu, Brunelle, & Cosineau, 2000). The risk factors known to influence the probability of committing a criminal act include: social influences of criminality, substance misuse, antisocial personality and cognitions, low levels of belongingness to family, low gratification in employment, limited educational attainment, and limited involvement in non-criminal activities (Andrews & Bonta, 2010; Andrews, Bonta, & Wormith, 2006). Subsequently, the risk factors of interest to the authors include adolescent drug use, adolescent alcohol use, and adult personality traits.
Adolescent substance use was a factor of interest due to its apparent entanglement with adolescent criminal behaviors. Generally, life course patterns related to crimes committed (Bennell, et al., 2011; Brochu, 2006; Jaffee & D'Zurilla, 2009; Moffitt, 2006) and substance use (Collins & Thakkar, 2006: Hammersley, 2011) begin during the period of adolescence, followed by a peak in frequency and severity during adolescence with a majority declining upon adulthood.
The common definition of the term drug includes various illegal and legal substances. For the purposes of this study, the term illicit drug refers to illegal substances. Illegal substances include hallucinogens, stimulants, and opiates (Collins & Thakkar, 2006). Furthermore, the term alcohol use or consumption refers to illegal underage alcohol consumption. The use of these substances can manifest in various patterns. For the purposes of this study, the patterns of use will be known as experimental use, recreational use, and frequent use (Collins & Thakkar, 2006; Milich, et al., 2000). Specifically, experimental users report trying substances a few times, while recreational users report intermittent use over a considerable period of time. Possible substance misuse can be indicated by patterns of frequent use over a considerable period of time (Milich, et al., 2000). The substances commonly used among adolescents are marijuana and alcohol (Brochu, Brunelle, & Cosineau, 2005; Dolgin & Rice, 2008; Israel & Wicks-Nelson, 2009).
Complex interactions are expected when analysing the criminal act of possessing an illegal substance and how it influences the possibility of committing other criminal acts (Andrews & Bonta, 2010; Brochu, et al., 2000). According to Goldstein (1985), The Drugs/Violence Nexus: A Tripartite Conceptual Framework describes three types of crimes related to the misuse or addiction to drugs and/or alcohol. The three types of crimes are: psychopharmacological crimes, compulsive economic crimes, and systematic crimes (Goldstein, 1985). In the current study, the focus will be on psychopharmacological crimes and compulsive economic crimes.
According to Goldstein (1985),psychopharmacological crimes are committed while the individual is intoxicated. Furthermore, the violent crimes related to intoxication have an increased likelihood of occurrence when the individual is influenced by mental illness, personality disorders, or an environment supporting violent acts (Brochu, 2006). According to Brochu (2006), psychopharmacological crimes most commonly occur when intoxicated on stimulants, amphetamines, sedatives, or alcohol. Compulsive economic crimes are related to the need to acquire drugs, most predominantly cocaine or heroin (Goldstein, 1985). According to Brochu (2006), physical and psychological dependency on cocaine or heroin manifests in a compulsion to use many times a day in order to avoid detoxification symptoms. The compulsive economic crimes committed are for lucrative purposes, such as theft or prostitution (Goldstein, 1985). For some individuals experiencing compulsive criminal involvement, the crime is a symptom of their drug misuse or addiction (Brochu, 2006).
According to Andrews and Bonta (2010), criminal behaviors are commonly explored by using a measure of personality traits. The term personality trait refers to a measure of persistent core traits demonstrated by individuals throughout a life span (Aske, Hale, Klimstra, Meeus, & Raaijmakers, 2010; Eysenck, 1964; Livesley, 2001). According to the literature (Andrews & Bonta, 2010; Brochu, 2006; Davis, Millon, & Millon, 1997; Trull, Waudby, & Sher, 2004; Varescon, 2005), antisocial personality disorder is the most influential factor directly related to criminality. Antisocial personality disorder can manifest as a lack of morals, ethics, and following societal norms with highly manipulative tendencies and behavioral difficulties (APA, 2000).
Antisocial personality traits have been consistently measured by specific domains of the Five Factor Model (FFM) (Joliffe, 2013; Caspi, Loeber, Lynam, Moffitt, Raine, & StouthamerLoeber, 2004; Jones, Lynam, & Miller, 2011). In the current study, a measure of the FFM is used to determine personality characteristics. Specifically, the five factors measuring personality tendencies are: Neuroticism, Extroversion, Openness to Experience, Agreeableness, and Conscientiousness. The literature (Andrews & Bonta, 2010; Caspi, et al., 2004; De Bruyn, Janssens, & van Dam, 2005; Joliffe, 2013; Jones, et al., 2011) reports that adolescent and adult males with antisocial personality disorder are consistently identified by low agreeableness, low conscientiousness, and to a lesser extent high neuroticism. According to Bijttebier, Claes, Lilinfeld, DeFruyt, Decuyper, and Roose (2012), low agreeableness indicates general psychopathology and low contentiousness represents impulsive behaviors in male populations.
Low agreeableness can also be an indicator of externalizing disorders (Akse, et al., 2010; Costa & McRae, 2010; Malouff, Schutte, & Thomsteinsson, 2005). According to Aytaclar, Kirisci, Mezzich, Reynolds, and Tarter (2009), females and males have the same difficulties with externalizing disorder traits during the period of mid-adolescence. Specifically, males and females at the period of mid-adolescence have difficulty with self-regulation of emotions, impulsivity, and neurological executive functions. Aytaclar et al. (2009), found the externalizing traits previously specified influence youth to choose association with delinquent peers which in turn influences drug use.
In this study, personality traits are measured in order to determine if Moffitt's antisocial personality model describes the participants. Moffitt's (1993; 2006) theory of antisocial behaviors describe the duration and intensity of delinquent behaviors: life-course persistent, adolescent-limited, and abstainers. Patterns of life-course persistent antisocial behaviors begin during childhood because of neuropsychological deficits and are influenced by high risk environments to continue during adolescence and adulthood. Furthermore, the most researched of the three groups is the life-course persistent group (Andrews & Bonta, 2010; Laub & Sampson, 2003; Moffitt, 2006) as they are costly to community and government resources (Hammersely, 2011; Moffitt, 2006).
The group with adolescent-limited antisocial behaviors display those behaviors during the period of adolescence only due to a 'maturity gap' (Barnes, Beaver, & Piquero, 2011; Becker, Nargiso, Simon, Spirito, Prinstein, Uhl, & Wolff, 2012; Breznia & Piquero, 2001; DeLisi & Piquero, 2011; Moffitt, 2006). The maturity gap is defined as an adolescent having gone through puberty and therefore recognized as an adult, however are not socially accepted by society as an adult (Barnes et al., 2011; Becker, et ah, 2012; Breznia & Piquero, 2001; DeLisi & Piquero, 2011; Moffitt, 2006). Adolescents commonly engage in typical criminal behaviors to achieve a sense of autonomy from their parents and to reinforce group membership with delinquent peers (Becker, et al., 2012; Breznia & Piquero, 2001; Moffitt, 2006). Moreover, adolescent-limited individuals tend to engage in non-violent adult behaviors considered to be illegal during the period of adolescence (ie; alcohol consumption) (Barnes et al., 2011; DeLisi & Piquero, 2011; Moffitt, 1993). Since the majority of adolescents are classified into the adolescent-limited group, it is considered a typical part of their development to participate in non-violent crimes (Barnes et al., 2011).
The research (Barnes et al., 2011; DeLisi & Piquero, 2011; Johnson & Manard, 2011; Moffitt, 1993) on abstainers postulates they are not involved significantly with delinquent peers and therefore do not adopt their criminal behaviors. Due to being a minority compared to adolescents whom participate in typical criminal behaviors, abstainers were postulated to be somewhat abnormal (Moffitt, 1993; Moffitt, 2006).
In the current study, the authors expect the no-crimes group to represent Moffitt's (2006) abstainers group and for the moderate (1-2 crimes committed) and substantial-crimes (3 or more crimes committed) groups to represent Moffitt's adolescent-limited group. The principal objective of the present research is to study the links between alcohol use, illicit drug use, and criminal acts committed during adolescence and adult personality traits in a sample with a low probability of life course persistent criminal behaviors. Specifically, this study seeks to identify the motivating factors influencing adolescents with a low risk of criminality to participate in criminal acts through interpretation of the results in light of the theories of Moffitt, Goldstein, and the personality traits described in the FFM previously described.
HI: The participants in the no-crimes group will have significantly lower rates of neuroticism, alcohol consumption, and illicit drug use as well as higher rates of conscientiousness and agreeableness compared to the participants in the moderate-crimes and substantial-crimes groups.
H2: The participants in the moderate-crimes group will have significantly lower rates of neuroticism, alcohol consumption, and illicit drug use as well as significantly higher rates of contentiousness and agreeableness compared to the participants in the substantial-crimes group.
Phase 1 participants (N= 49; Males= 27, Females= 22) were recruited with the stipulation of being 18 or older (M= 31.69, SD=1.69). The voluntary participants were asked to review and complete the created questionnaire described below. The revisions were used to ameliorate the comprehension of the questions and the answers were analysed for determining internal consistency.
The voluntary participants in phase 2 (N= 36; Males= 18, Females= 18) were mostly recruited at two post-secondary institutions. An exclusion criteria was stipulated that participants under 25 years of age (M= 33.33, SD= 8.16) would not be accepted. The age stipulation was based on research denoting common life trajectories of substance use and criminal acts diminishing upon adulthood for a majority of individuals (Bennell, et al., 2011; Brochu, 2006; Collins & Thakkar, 2006; Jaffee & D'Zurilla, 2009). Furthermore, the voluntary sample was comprised of university or college students, professors, staff, and non-post-secondary affiliated members of the community. The reader is referred to Appendix A for detailed demographic information of the participants by group.
The study was approved by the ethics committee at Universite de Moncton. Posters soliciting participants were placed at post-secondary institutions. The administration of the questionnaires took place in similar environments at the respective institutions. A procedure was established for the administration of the questionnaires. The protocol began with informed consent. Then, the NEO-FF1-3 was completed by the participant followed by the retrospective questionnaire pertaining to adolescence. All questionnaires were completed in a paper pencil format at the respective institutions. Furthermore, the NEO-FFI-3 was scored using the NEO Software System (Costa & McRae, 2010).
In order to achieve the objectives of this study, two phases were completed. The first phase entailed the validation of a questionnaire created to gather retrospective data relevant to alcohol use, drug use, and crimes committed during adolescence. The second phase gathered data to test the hypotheses previously postulated, using the retrospective questionnaire and a measure of the Five Factor Model.
The Five Factor Model (FFM) is one of the principal theories of personality (Bijttebier, et al., 2012; Joliffe, 2013; Jones, Lynam, & Miller, 2011). Although various tests for measuring the five domains are available, the authors used an English version of the self- report NEO-Five Factor Inventory-3 (NEO-FFI-3) (internal validity^ 0.68 to 0.86; external validity= 0.60 to 0.69; reliability= 0.75 to 0.83) (Costa & McRae, 2010; McDermut & Zimmerman, 2008). These levels of validity and reliability are all in the acceptable range (Fidell & Tabachnick, 2007). Respectively, these five domains are measured on likert scales ranging from 1 'very low' to 5 'very high' (Costa & McRae, 2010). The NEO-FFI-3 gathered data related to five personality domains: Neuroticism (M= 2.97, SD= 1.03), Conscientiousness (M= 3.28, SD= 1.06), Agreeableness (M= 3.11, SD= 0.88), Openness to Experience (M= 3.36, SD= 1.07), and Extroversion (M= 3.50, SD= 0.88).
Accordingly, high levels of Neuroticism reflect personality tendencies of impulsivity and poor self-regulation. Lower levels of Neuroticism are recognized in traits of calmness and appropriate reactions in stressful situations (Costa & McRae, 2010). High scores in the domain labelled Conscientiousness represent tendencies for good self-control, striving for goals, punctuality, and determinedness. However, low levels tend to represent unorganized, spontaneous, procrastination, and a preference for pleasure seeking behaviors (Costa & McRae, 2010). High levels of Agreeableness reflect characteristics of altruism, cooperation, and empathy. Contradictory to the high traits, those low on Agreeableness tend to be antagonistic, egocentric, skeptical, and competitive. Low agreeableness is associated to narcissistic, antisocial, externalizing disorders, and paranoid personality disorder (Costa & McRae, 2010). High levels of Openness to Experience signify creativity, self awareness, questioning authoritative figures, and the ability to envision new ways of approaching ethical dilemmas, morals, and political ideas. However, low scores represent more conservative, narrow scope, and muted emotions (Costa & McRae, 2010). Finally, those with high levels of Extroversion enjoy being social, participate in sensation seeking behaviors, and have positivistic attitudes. Consequently, low levels of Extroversion imply a preference for isolation and independence (Costa & McRae, 2010).
The authors' created a retrospective questionnaire to gather data regarding drug use, alcohol consumption, and crimes committed named the 'Retrospective Adolescent Substance Use and Delinquency Questionnaire' (Appendix A). This questionnaire was developed based on relevant literature and on adapted versions of the Drug Abuse Screening Test: Short version (DAST; internal consistency= 0.92; DAST questions used: 1, 3, 5, 6, 7, 8, 11,15,17 & 18) and the Self-Administered Alcoholism Screening Test: Short version (SAAST; excellent validity: sensitivity= 92% and specificity= 96%; SAAST questions used: 2, 4b, 8, 11, 18, 25, 27 & 31) (Corcoran & Fischer, 2007; Hunsley & Mash, 2008; Morse & Swenson, 1975; Skinner, 1983). These levels of validity are considered acceptable (Corcoran & Fischer, 2007). Due to the changes in tense and development of a new instrument, statistical analysis for validity regarding internal consistency was computed on the data collected (N=49, internal consistency= 0.88 to 0.95). Unfortunately, many biases are inherent with self-reported data collection including over and under estimation of criminal participation and drug or alcohol use. Following the recommendations of Krohn & Thomberry (2000) to maximise the accuracy of self-reported data collection, the Total Crimes, Drug use, and Alcohol Consumption tests were self-administered, crimes committed were presented with various options, and clarification questions were included regarding the context of the criminal act. The clarification questions regarding the context of the crimes were also gathered in order to better understand the dynamics behind the criminal acts committed (Lemer, 1996).
The Criminality (M=1.42, SD=1.16) variable was created based on self-reports regarding the types of crimes committed in the crimes committed section of the Retrospective questionnaire (Appendix B). The Criminality variable represents the frequency of types of illegal acts committed during adolescence. The crimes included in the variable are: graffiti/defacing property, property damage, driving while intoxicated: alcohol, driving while intoxicated: illegal drugs, theft, offering sexual acts for compensation, selling of illegal drugs, possession of illegal drugs, accepting sexual acts for payment, violent acts, and others. The greater the frequency of criminal acts reported implies an increased involvement in criminal behaviors. In order to classify the participants into three groups a best fit approach was used to optimize equal group sizes. The raw data in the Criminality variable was transformed to belong to one of three groups (0=1; 1=2, 2=2; 3=3,4=3, 5=3, 6=3, 7=3, 8=3, 9=3, 10=3, 11=3, 12=3). The three groups formed based on frequency of crimes committed are: no-crimes (n=ll), moderate-crimes (n=13), and substantial-crimes (n=12). According to Fidell & Tabachnick (2007), the power related to group sizes must surpass the number of predictor variables in the analysis. Therefore, the power is small in each group, but adequate for analysis.
In order to improve comprehension of the inherent complexities involved in the analysis of substance use, it is split into two factors: drug use and alcohol consumption (Becker, et al., 2012). The Drug Use variable includes measures of the different types of drugs tried, continued, or recreationally used by the participant. The Drug Use and Alcohol Consumption variables gathered data related to age of onset and the possible existence of an addiction. For example, a positive response to the question "Did you receive treatment for drug or alcohol use?" would indicate a greater possibility of an addiction. Furthermore, from the Drug Use and Alcohol Consumption section of the Retrospective questionnaire the Drug Use variable (M= 10.44, SD=9.49) was formed based on questions: 3., 4., 6., 8., 11., 12., 13.1., 14., 15.1., 16.1., 17.1., 18.1., 19.1., 20.1., 24., and 25. (Appendix A). The Alcohol Consumption variable (M=4.72, SD=2A\) was formed based on questions: 1.,8., 10., 13.11., 14., 15.11., 16.11., 17.11., 18.11., 19.11., 20.11., 21., 22., and 23. (Appendix A). The questions were scaled as 1 'yes', 2 'no'. The variables were respectively transformed into counts of all answers that were 1 (yes). However, Question 9. 'If you had wanted to could you have stopped taking...I. drugs; II. alcohol' was inverted to reflect the ability to not stop as contributing towards higher rates of substance misuse (1 'yes'=2 'no', 2 'no'=1 'yes'). The higher the count equates to greater drug use or alcohol consumption, respectively, during adolescence.
The validity of the retrospective questionnaire included computations of Cronbach's alpha and confirmatory factor analysis (N= 49). Due to the sample size, high factor loadings are needed in order to be considered robust (Fidell & Tabachnick, 2007). Cronbach's alpha and exploratory factor analysis using principal component analysis with a varimax rotation was performed through SPSS. The alpha coefficients revealed a high to moderate level (0.88 to 0.95) of internal consistency for each subtest in the questionnaire (Table 1.). It is important to note the mathematical calculations used to determine the alpha coefficients are a function of the number of test items and inter-item correlation (Field, 2005). Therefore, the smaller the number of test items or greater inter-item correlations equal to a higher coefficient.
The internal consistency demonstrates the extent of reliability in the questionnaire to measure a latent construct (Field, 2005). Principal component analysis with varimax rotation was performed on each subtest to determine the inherent factors (Table 2.). The results of the principal component analysis are to be interpreted with caution due to the lack of a robust sample size. According to Fidell and Tabachnick (2007), a robust sample size is 100 cases, however 50 plus can be acceptable. Factor loadings are considered to be interpretable when loadings are 0.32 with an overlap of 10% and excellent loadings are in excess of 0.71 and overlap 50% (Fidell & Tabachnick, 2007). The loadings vary substantially within each subtest ranging approximately from interpretable to very good.
The Alcohol Consumption subtest demonstrates a very good level of certainty with one factor measured (0.62-0.89 with 69.72% overlap). Furthermore, the Drug Use subtest reveals one rotated factor in the reasonable range (0.45-0.92 with 43.21% overlap). This factor is interpreted to represent the level of overall drug misuse. The second rotated factor (0.48-0.93 with 12.94% overlap) in the Drug Use subtest meets the criteria for interpretation. It is interpreted to represent a deeper level of drug use as it loads questions related to chemical or hard drugs as compared to soft drugs (marijuana and hashish). One acceptable factor and one interpretable rotated factor were found in the Criminality subtest (0.42-0.98 with 27.35% overlap and 0.43-0.88 with 19.19%, respectively). The difference between the factors was understood to represent a split between delinquent acts and criminality. Delinquent acts are considered to be: vandalism, selling soft drugs, theft, possession of illegal drugs, and driving while intoxicated. The criminality factor represents a deeper level of criminal acts: arrests, convictions, violent acts, accepting sexual acts for payment, theft, and selling drugs. Additional factor loadings in the Drug Use and the Criminality subtests lacked high factor loadings for interpretation. These questions were pertinent to clarification of the crimes committed and drug use. They were subsequently removed from the final version of the questionnaire and referred to verify the context of the crimes committed and drug use.
The data analysis was executed on SPSS statistical software (version 18.0). A preliminary analysis of the raw data was conducted for data entry errors, missing values, normality (skewness and kurtosis), univariate outliers, multivariate outliers, linearity, collinearity, multicollinearity, and homogeneity. Preliminary analysis was computed on grouped and ungrouped data when feasible (Fidell & Tabachnick, 2007).
Based on the research questions postulated, a MANOVA and a discriminant analysis were conducted on the data set (Fidell & Tabachnick, 2007). The MANOVA was used due to multiple levels in the dependent variable and the need to ensure that the three groups are not significantly different based on demographic data. When groups are significantly different they are incomparable statistically. The purpose of choosing discriminant analysis was to determine group membership based on the interaction of the variables involved to inform intervention strategies.
The Box's M using the crimes total variable and the demographic data of gender [M= 5.992, F (2, 33) = 0.584, p > 0.05] and age [F (2, 33) = 1.705, p > 0.05] reveals non-significance. Therefore, equivalence of groups or homogeneity is assumed. The Drug Use and the Alcohol Consumption variables revealed concern due to a moderate correlation (r = 0.64, p < 0.01). According to Fidell and Tabachnick (2007), correlations or covariates greater than r = 0.70 are of concern in statistical analyses. Therefore, the moderate correlation is not of concern. It may appear a univariate outlier is influencing the drug misuse variable results in the substantial-crimes group, although preliminary analysis revealed all of the cases had standardised z-scores within the required [+ or -] 3.29 limit (Fidell & Tabachnick, 2007).
A multivariate analysis of variance was computed using the Criminality (independent) variable with three levels and seven dependent variables: Drug Use, Alcohol Consumption, Neuroticism, Conscientiousness, Agreeableness, Openness to Experience, and Extroversion.
Wilks' Lambda revealed the combined dependent variables were significantly influenced by the total frequency of crimes committed [F (2, 33) = 4.02, p < 0.001] with a medium effect size ([[eta].sup.2.sub.partial] 0-51). The individual variances explained by the dependent variables (Table 3.) demonstrated significance using Bonferroni's correction. Bonferroni's correction is a method of correcting for Type 1 error and is computed by dividing 0.05 by the number of dependent variables in the analysis [0.05/7 (dv) = 0.007], Bonferroni's correction was applied to the significant findings: Alcohol Use [F(2,33)=12.35, p < 0.001, [[eta].sup.2.sub.partial] = 0.43], Drugs Use [F(2,33)=18.35, p < 0.001, [[eta].sup.2.sub.partial] = 0.53], and Agreeableness [F(2,33)= 9.07, p < 0.01, [[eta].sup.2.sub.partial] =0.36]. The non-significant variables are: Extroversion [F(2,33)= 0.03, p > 0.05, [[eta].sup.2.sub.partial] = 0.002], Openness to Experience [F(2,33)= 0.25, p > 0.05, [[eta].sup.2.sub.partial] = 0.02], and Contentiousness [F(2,33)= 0.93, p > 0 05, [[eta].sup.2.sub.partial] = 0.05 ].
The MANOVA revealed significant differences regarding the Agreeableness variable between the no-crimes (M= 3.82, SD= 0.15,p < 0.05) and moderate-crimes (M= 2.54, SD= 0.87,; p < 0.05) groups (Table 4.). The moderate-crimes group was significantly lower in agreeableness compared to the no-crimes group. There were no significant differences concerning the Contentiousness, Neuroticism, Openness to Experience, or Extroversion variables among the groups.
Moreover, the MANOVA revealed significant differences between the no-crimes and moderate-crimes groups compared to the substantial-crimes group regarding Drug Use (M= 2.73, SD= 3.71,p < 0.05) and Alcohol Use (M= 2.91, SD= 1.86, p < 0.05) compared to the substantial-crimes group (M= 19.42, SD= 9.04, p < 0.05). Furthermore, the moderate-crimes group were significantly lower regarding Drug Use (M= 8.69, SD= 6.17, p < 0.05) and Alcohol Use (M= 4.38, SD- 1.71, p < 0.05) in comparison to the substantial-crimes group (Alcohol Consumption: M=6.75, SD= 2.05, p < 0.05; Drug Use: M= 19.42, SD= 9.04, p < 0.05). No significant differences were found between the no-crimes and moderate-crimes groups concerning the Alcohol Consumption and Drug Use variables.
According to Fidell & Tabachnick (2007), when a MANOVA reveals significance based on the mean differences, then discriminant analysis can be effectuated in order to verify how the variables predict classification to groups. In the discriminant analysis the grouping variable is the Criminality variable and the predictor variables are Drug Use, Alcohol Consumption, Neuroticism, Conscientiousness, Agreeableness, Openness to Experience, and Extroversion.
A direct discriminant analysis using classification weighted on group sizes was effectuated using the previously mentioned predictor variables. These variables were the predictors of group membership into groups based on the total frequency of crimes committed during adolescence: the no-crimes group (n = 11), the moderate-crimes group (n = 13), and the substantial-crimes group (n = 12) (Table 5.).
The overall results of the discriminant analysis reveal one significant function through the Wilks' Lambda calculations. This function was significant [[chi square](14)= 42.85, p < 0.001] with a moderate correlation coefficient (r = 0.80). Specifically, the results indicate support for Alcohol Consumption (structure coefficient = 0.634) and Drug Use (structure coefficient= 0.771) significantly classifying cases into their respective groups based on the frequency of types of crimes committed. The Agreeableness variable was expected to significantly classify groups, since the MANOVA indicated a significant relationship between the no crimes and moderate crime groups.
However, due to the low structure coefficient (-0.278), agreeableness minimally predicts group membership between the three groups.
The main objective of the present study was to analyze the differences among varying levels of typical adolescent crimes committed (no-crimes, moderate-crimes, and substantial-crimes), adolescent drug use, adolescent alcohol consumption, and adult personality traits in a sample with a low probability of life course persistent criminal behaviors. The hypotheses in this study were partially supported by the findings.
The results regarding the first hypothesis revealed the participants in the moderate-crimes group had significantly lower rates of agreeableness compared to the participants in the no-crimes group. Furthermore, the no-crimes group had significantly lower rates of alcohol consumption and drug use compared to the substantial-crimes group.
The personality differences between the no-crimes and moderate-crimes groups may explain the differences in criminality between the two groups. The no-crimes group were postulated to be somewhat abnormal in comparison to their delinquent peers (Moffitt, 1993; 2006). According to Barnes et al. (2011), DeLisi and Piquero (2011), and Johnson and Manard (2011), adolescents which abstain from committing crimes are not significantly involved with delinquent peers and therefore do not adopt their delinquent behaviors. The lack of criminality in the no-crimes group may be due to their higher levels of agreeableness compared to their delinquent peers. These personality differences are postulated to be reflected in the no-crimes group not developing delinquent peer affiliations during adolescence. In this study, the participants in the no-crimes group self-reported not feeling a sense of belongingness to their delinquent peers. Furthermore, the lack of identifying with their delinquent peers may also explain the significantly lower rates of alcohol and drug use compared to the substantial-crimes group (Barnes et al., 2011; DeLisi & Piquero, 2011; Johnson & Manard, 2011).
The participants in the moderate-crimes group were significantly lower in agreeableness compared to the no-crimes group, but not significantly lower in contentiousness. These findings could be an indicator of continued adolescent antisocial personality traits into adulthood, although low contentiousness was not found (Aske, et al., 2010; Bailley, Lutz & Ross, 2004; Caspi, et al., 2004; Joliffe, 2013; Le Corff & Toupin, 2009; Malouff et al., 2005). The author's would like to explain the possible lack of a significant difference in contentiousness being related to frontal lobe development, which is lacking in most adolescents and not fully present until adulthood (Littlefield, Sher, & Wood, 2009; Gaultney & Peach, 2013). The probability of low contentiousness being present during adolescence and the participants in the moderate-crimes group having low levels of agreeableness would probably fit Moffitt's adolescent-limited theory. However, the study's findings regarding low agreeableness could be better explained by other personality traits or psychopathological symptoms related to various externalizing disorders (Akse, et al., 2010; Costa & McRae, 2010; Malouff et al., 2005). According to Aytaclar, et al. (2009), externalizing disorder traits such as difficulty with self-regulation of emotions, impulsivity, and neurological executive functions are common during the period of mid-adolescence. Aytaclar, et al. (2009), found the externalizing traits previously specified influence youth to choose association with delinquent peers which in turn influences drug use. Therefore the moderate-crimes group motivation to participate in crime was probably their need to reinforce group membership with delinquent peers (Aytaclar, et al., 2009; Becker, et al., 2012; Brezina & Piquero, 2001; Moffitt, 2006). This motivation may have come from neurobehavioral sources such as externalizing disorders (Aytaclar, et al., 2009) or to fulfill a need to develop autonomy from family and self-identify as an adult through solidifying acceptance from delinquent peers (Moffitt, 2006). A majority of the moderate-crimes group participants in this study reported feelings of belongingness to their delinquent peers, which supports the author's interpretation regarding Mofifitt's antisocial-limited personality trajectory and the findings previously explained regarding the influence of externalizing disorders on peer group choice (Aytaclar, et al., 2009).
The moderate-crimes group in this study reported using significantly less alcohol and drugs than the substantial-crimes group. According to Collins and Thakkar (2006) and Milich, et al. (2000), the moderate-crimes group are considered to be a mix of experimental and recreational substance users. In this study, the moderate-crimes group committed typical adolescent criminal acts (Bennell, et al., 2011; Barnes et al., 2011; DeLisi & Piquero, 2011) including: theft, driving intoxicated on alcohol, possession of illegal drugs, and driving intoxicated. Furthermore, a majority of the moderate-crimes group in this study reported feelings of belongingness to their delinquent peers. The typical adolescent crimes and the belongingness to delinquent peers could offer support to Moffitt's (1993; 2006) theory of adolescent-limited offenders. However, the current findings demonstrated a significantly low level of agreeableness which could reflect drug use motivated by externalizing disorder tendencies rather than antisocial personality tendencies (Akse, et al., 2010; Costa & McRae, 2010; Malouff et al., 2005). A combination of identifying with delinquent peer acceptance and psychopathologies are probably the motivating factors for the crimes committed in the moderate-crimes group.
Finally, the substantial-crimes group was significantly higher in rates of alcohol consumption and drug use compared to the no-crimes and the moderate-crimes groups. However, no personality differences were found between the substantial-crimes group and the moderate or no-crimes groups. In this study, the substantial-crimes group are considered frequent users of drugs and alcohol, which could have lead to substance dependency for some of the participants (Milich, et al., 2000). In the current study, the substantial-crimes group committed crimes not typical of adolescents (Bennell, et ah, 2011; Barnes et ah, 2011; DeLisi & Piquero, 2011) including: accepting sexual acts as payment and selling drugs. Goldstein's (1985) theory described a psychopharmacological substance users being motivated to increase the severity of criminal acts committed due to an elevated need for drug use. The authors propose the criminal acts committed related to substance use by those in the substantial-crimes group fit Goldstein's (1985) psychopharmacological model. Furthermore, the substantial-crimes group reported a majority of violent or aggressive acts were committed while intoxicated on alcohol. According to the relevant literature (Bennett, Farrington & Flolloway, 2008; Brochu, 2006; Brochu et ah, 2005; Coontz & Sevigny, 2007; Goldstein, 1985), a majority of violent crimes are committed while intoxicated on alcohol or other substances. The findings in this study indicate alcohol intoxication during property damage and violent crimes in the substantial-crimes group. The authors expected an antisocial personality profile from the substantial-crimes group, however no significant personality differences were found. The results of this study revealed the substantial-crimes group participated in selling drugs which demonstrates an acceptance of criminal behaviors not noted in the other groups. The author's postulate mental illness, social environment, neglectful or authoritative parenting, or experiencing traumatic events as possible precursors beyond the scope of this study to explain the extreme levels of drug use and alcohol consumption in the substantial-crimes group compared to the no-crimes and moderate-crimes groups.
In conclusion, since a majority of adolescents commit typical criminal acts (DeLisi & Piquero, 2011; Moffitt, 2006), a criminal act in and of its self does not equal to a delinquent adolescent (Bogt, Boniel-Nissim, Kolobov, Kuntsche, Harel-Fisch, & Walsh, 2013). In fact, there were no differences in personality traits between the highest level of criminal acts committed and the no criminal acts committed groups. The no-crimes and substantial-crimes groups did however differ according to drug use and alcohol consumption. Therefore, the criminal acts reported in the substantial-crimes groups appear to be motivated by problems with substance use. Furthermore, the substance use is not motivated by personality related influences in the substantial-crimes group.
In addition to the conclusions above, there were no differences found in the no-crimes and moderate crimes group regarding drug use and alcohol consumption, however significant personality differences were found. The low levels of agreeableness found in the moderate crimes group appear to be the motivation for their participation in criminal acts. The low agreeableness was interpreted to fuel a desire to affiliate with delinquent peers. The desire to affiliate with delinquent peers was interpreted to be rooted in neurobehavioral deficits (Aytaclar, et al., 2009) or a need for autonomy and to self-identify as an adult (Moffitt, 2006). The higher rates of agreeableness found in the no-crimes is probably motivated by the personality differences found when comparing the no-crimes group to their delinquent peers.
Clinical applications of this study indicate professionals working with youth at low risk for life enduring criminal behaviors should evaluate the motivating factor behind the criminal behavior. Possible motivating factors for adolescents with a low probability of life long criminal behaviors include delinquent peer affiliations, underlying externalizing disorder traits, drug or alcohol addiction, and other psychopathologies. An analysis of the adolescent's biological development, personality traits, and environment should be considered in formulating an opinion on the motivating factors behind committing criminal acts.
This study raised questions regarding youth with a low risk for life enduring patterns of criminal behaviors. What are the interactions between biological and environmental factors which influence low risk adolescents to cease their participation in typical criminal behaviors? Do adolescent typical criminal behaviors and drug use in low risk samples affect their adulthood? Future research projects should capture longitudinal data regarding the frequencies of crimes committed, rates of alcohol consumption, rates of drug use, and personality traits at adolescence and at adulthood to further validate this study's findings. Due to the time and economic limitations, a longitudinal design was not feasible in this study.
There are limits to this study, as with most research projects. These results may not be replicable with other samples. The small sample size used in the validation of the retrospective questionnaire could have influenced the reliability of these results. Moreover, the retrospective self-reports of substance use, alcohol use, and criminal behaviors may not be representative of reality due to the biases incumbent with the retrieval of past memories. Informed consent may have triggered demand characteristics or social desirability as the participants were aware of the information being gathered related to delinquency, drug use, and alcohol use. Further analyses using self-reported criminal acts are needed to confirm the patterns observed.
Correspondence concerning this article should be addressed to: Said Bergheul, Ph.D., Universite du Quebec en Abitibi Temiscamingue, 445 Boulevard de L'umiversite, Rouyn-Norduda, Quebec, Canada J9X5E4; Telephone: (819) 762-0971
Tessa Collette, Sarah Pakzad, Ph.D, Universite de Moncton, New-Brunswick, Canada
Said Bergheul, Ph.D.
Universite du Quebec en Abitibi-Temiscamingue, Quebec, Canada
American Psychiatric Association (2000).American Psychiatric Association (2000). Diagnostic and Statistical Manual of Mental Disorders (4th ed., Text Revision). Washington, DC: American Psychiatric Association.
Andrews, D.A., & Bonta, J. (2010). The Psychology of Criminal Conduct (5th ed.). New Providence, NJ: Anderson Publishing.
Andrews, D.A., Bonta, J., & Wormith, J.S. (2006). The Recent Past and Near Future of Risk and/or Need Assessment. Crime and Delinquency, 52(1), 7-27.
Aske, J., Hale III, W.W., Klimstra, T. A., Meeus, W.H. J., & Raaijmakers, Q. A.W. (2010). Longitudinal Associations Between Personality Traits and Problem Behavior Symptoms in Adolescence. Journal of Research in Personality, 44, 273-284.
Aytaclar, S., Kirisci, L., Mezzich, A.C., Reynolds, M., & Tarter, R. E. (2009). Prospective Study of the Association Between Neurobehavior Disinhibition and Peer Environment on Illegal Drug Use in Girls and Boys. The American Journal of Drug and Alcohol Abuse, 35, 145-150.
Bailley, S.E., Lutz, C.J., & Ross, S.R. (2004). Psychopathy and the Five Factor Model in a Non-Institutionalized Sample: A Domain and Facet Level Analysis. Journal of Psychopathology and Behavioral Assessment, 26, 213-223.
Barnes, J.C., Beaver, K.M., & Piquero, A.R. (2011). A Test of Hypotheses of Delinquency Abstention. Criminal Justice and Behavior, 38, 690-709.
Becker, S.J., Nargiso, J.E., Simon, V., Spirito, A., Prinstein, M.J., Uhl, K.M., & Wolff, J.C. (2012). Temporal Relationship Between Substance Use and Delinquent Behavior Among Young Psychiatrically Hospitalized Adolescents. Journal of Substance Abuse Treatment, 43, 251-259.
Bennell, C., Brown, S., Forth, A., Nunes, K., Pozzulo, J., & Serin, R. (2011). Psychology of Criminal Behavior: A Canadian Perspective. Toronto, ON: Pearson Education Canada.
Bennett, T., Farrington, D., & Holloway, K. (2008). The Statistical Association Between Drug Misuse and Crime: A Meta-analysis. Aggression and Violent Behavior, 13, 107-118.
Bijttebier, P., Claes, L., Lilienfeld, S.O., De Fruyt, F., Decuyper, M., & Roose, A. (2012). Psychopathic Traits in Adolescence and the Five Factor Model of Personality. Journal of Psychopathology and Behavior Assessment, 34, 84-93.
Bogt, T., Boniel-Nissim, M., Kolobov, T., Kuntsche, E., Harel-Fisch, Y., & Walsh, S.D. (2013). Exploring Substance Use Normalization Among Adolescents: A Multilevel Study in 35 Countries. Social Science & Medicine, 97, 143-151.
Breznia, T., & Piquero, A.R. (2001). Testing Moffitt's Account of Adolescent-Limited Delinquency. Criminology, 39, 353-370.
Brochu, S. (2006). Drogue et la criminalite: Une relation complexe (2nd ed.). Montreal, QC: Les Presses de l'Universite de Montreal.
Brochu, S., Brunelle, N., & Cosineau, M-M. (2000). Drug-Crime Relations Among Drug-Consuming Juvenile Delinquents: A Tripartite Model and More. Contemporary Drug Problems, 27, 835-866.
Brochu, S., Brunelle, N., & Cosineau, M-M. (2005). Juvenile Drug Use and Delinquency: Youths' Accounts of Their Trajectories. Substance Use & Misuse, 40, 721-734.
Capaldi, D.M., & Wiesner, M. (2003). Relations of Childhood and Adolescent Factors to Offending Trajectories of Young Men. Journal of Research in Crime and Delinquency, 40, 231-262.
Caspi, A., Loeber, R., Lynam, Moffitt, T.E., Raine, A., & Stouthamer-Loeber, M. (2004). Adolescent Psychopathy and the Big Five: Results from Two Samples. Journal of Abnormal Child Psychology, 33, 431-443.
Collins, C., & Thakkar, V. G. (2006). Psychological Disorders: Addiction. New York, NY: Chelsea House Publishers.
Coontz, P. D., & Sevigny, E. L. (2007). Pattern of Substance Involvement and Criminal Behavior. International Journal of Offender Therapy and Comparative Criminology, 52(4), 435-453.
Corcoran, K., & Fischer, J. (2007). Measures for Clinical Practice and Research: A Sourcebook (4th ed. Adult). New York, NY: Oxford University Press.
Costa, P.T., & McCrae, R.R. (2010). NEO Inventories: Professional Manual. Odessa, FL: Psychological Assessments Resources Inc.
Davis, R., Millon, C., & Million, T. (1997). MCMI-III: Millon Clinical Multiaxial Inventory-Ill Manual (2e ed.). Minneapolis, MN : National Computer Systems Inc.
De Bruyn, E.E.J., Janssens, J.M.A.M., & van Dam, C. (2005). PEN, Big Five, Juvenile Delinquency and Criminal Recidivism. Personality and Individual Differences, 39, 7-19.
DeLisi, M., & Piquero, A.R. (2011). New Frontiers in Criminal Research, 2000-2011: A state-of the-art review. Journal of Criminal Justice, 39, 289-301.
Dolgin, K.G., & Rice, F.P. (2008). The Adolescent: Development, Relationships, and Culture. New York, NY: Pearson Education Inc.
D'Zurilla, T. J., & Jaffee, W. B. (2009). Personality, Problem Solving, and Adolescent Substance Use. Behavior Therapy, 40, 93-101.
Eysenck, H.J. (1964). Crime and Personality. New York, NY: Houghton Mifflin Company.
Fidell, L. S., & Tabachnick, B. G. (2007). Using Multivariate Statistics, (5th ed.). New York, NY: Pearson.
Field, A. (2005). Discovering Statistics Using SPSS, (2nd ed.). Thousand Oaks, CA: Sage Publications Ltd.
Gaultney, J.F., & Peach, H.D. (2013). Sleep, Impulse Control, and Sensation-Seeking Predict Delinquent Behavior in Adolescents, Emerging Adults, and Adults. Journal of Adolescent Health, 53, 293-299.
Goldstein, P. J. (1985). The Drug/Violence Nexus: A Tripartite Conceptual Framework. Journal of Drug Issues, 39, 143-174.
Hammersley, R. (2011). Pathways Through Drugs and Crime: Desistance, Trauma, and Resilience. Journal of Criminal Justice, 39, 268-272.
Hser, Y.-I., Longshore, D., & Anglin, M.D. (2007). The Life Course Perspective on Drug Use: A Conceptual Framework for Understanding Drug Use Trajectories. Evaluation Review., 31, 515-547.
Hunsley, J., & Mash, E. J. (2008). A Guide to Assessments That Work. New York, NY: Oxford University Press.
Israel, A.C., & Wicks-Nelson, R. (2009). Abnormal Child and Adolescent Psychology. Upper Saddle River, NJ: Pearson Prentice Hall.
Johnson, M.C., & Menard, S. (2011). A Longitudinal Study of Delinquency Abstention: Differences Between Life-Course Abstainers and Offenders From Adolescents to Adulthood. Youth Violence and Juvenile Justice, 10, 278-291.
Joliffe, D. (2013). Exploring the Relationship Between the Five-Factor Model of Personality, Social Factors and Self-Reported Delinquency. Personality and Individual Differences, 55, 47-52.
Jones, S.E., Lynam, D.R., & Miller, J.D. (2011). Personality, Antisocial Behavior, and Aggression: A Meta-Analytic Review. Journal of Criminal Justice, 39, 329-337.
Krohn, M. D., & Thornberry, T. P. (2000). The Self-Report Method for Measuring Delinquency and Crime. Criminal Justice, 4, 33-83.
Laub, J.H., & Sampson, R.J. (2003). Shared, Beginnings, Divergent Lives: Delinquent Boys to Age 70. Cambridge, MA: Harvard University Press.
Le Corff, Y., & Toupin, J. (2009). Comparing Persistent Juvenile Delinquents and Normative Peers With the Five-Factor Model of Personality. Journal of Research in Personality, 43, 1105-1108.
Lerner, R.M. (1996). Relative Plasticity, Integration, Temporality, and Diversity in Human Development: A Developmental Contextual Perspective About Theory, Process, and Method. Developmental Psychology, 32, 781-786.
Littlefield, A.K., Sher, K.J., & Wood, P.K. (2009). Is "Maturing out" of Problematic Alcohol Involvement Related to Personality Change? Journal of Abnormal Psychology, 118, 360-374.
Livesley, W.J. (2001). Handbook of Personality Disorders. New York, NY: Guilford Press.
Loeber, R., Stouthamer-Loeber, M., Masten, A.S., & Wei, E. (2004). Desistance From Persistent Serious Delinquency in the Transition to Adulthood. Developmental and Psychopathology,!6, 897-918.
Malouff, J.M., Schutte, N.S., & Thorsteinsson, E.B. (2005). The Relationship Between the Five Factor Model of Personality and Symptoms of Clinical Disorders: A Meta-Analysis Journal of Psychopathology and Behavioral Assessment, 27, 101-114.
McDermut, W., & Zimmerman, M. (2008). Chapter 30: Personality Disorders, Personality Traits, and Defense Mechanisms Measures. Found in: Rush, A.J., First, M.B., & Blacker, D. (2008). Handbook of Clinical Measures, (2nd ed.). Washington, D.C.: American Psychiatric Publishing. 687-729.
Milich, R., Lynam, D., Zimmerman, R., Logan, T. K., Martin, C., Leukefeld, C., et al. (2000). Differences in Young Adult Psychopathology Among Drug Abstainers, Experimenters, and Frequent Users. Journal of Substance Abuse, 11, 69-88.
Moffitt, T.E. (1993). Adolescence-Limited and Life-Course Persistent Antisocial Behavior: A Developmental Taxonomy. Psychological Review, 100(A), 674-701.
Moffitt, T.E. (2006). Life-Course Persistent Versus Adolescent-Limited Antisocial Behavior: Research Review. Developmental Psychopathology (2nd ed., Risk, Disorder, and Adaptation). Cicchetti, D., & Cohen, D.J. (eds.). New York, NY: Wiley.
Morse, R.M., & Swenson, W.M. (1985). The Use of a Self-Administered Alcoholism Screening Test (SAAST) in a Medical Center. Mayo Clinic Proc., 50, 204-208. Found in Corcoran, K. & Fischer, J. (2007). Measures for Clinical Practice and Research: A Sourcebook (4th ed. Adult). NY: Oxford University Press.
Skinner, H.A. (1983). The Drug Abuse Screening Test. Journal of Addictive Behaviors, 7, 363-371. Found in Corcoran, K. & Fischer, J. (2007). Measures for Clinical Practice and Research: A Sourcebook (4th ed. Adult). NY: Oxford University Press
Trull, T.J., Waudby, C.J., & Sher, K.J. (2004). Alcohol, Tabacco, and Drug Use Disorders and Personality Disorder Symptoms. Experimental and Clinical Psychopharmacology, 12(1), 65-75.
Varescon, I. (2005). Psychopathologie des conduites addictives: Alcoolisme et toxicomanie. Paris, FR: Editions Belin.
TABLE 1 Internal Consistency of the Retrospective Questionnaire: Cronbach's Alpha Subtest Alpha coefficients N # of items Alcohol Consumption 0.92 49 9 Drug Use 0.95 49 48 Criminality 0.88 49 34 TABLE 2 Internal Consistency of the Retrospective Questionnaire: Principal Component Analysis Factor Loadings % of Unrotated Rotated Subtests Factor Eigenvalues variance (varimax) Alcohol 1 6.28 69.72% 0.62-0.89 (1) Consumption Drug Use 1 20.31 43.21% 0.42-0.92 2 6.08 12.94% 0.48-0.93 Criminality 1 9.57 27.35% 0.41-0.98 2 6.72 19.19% 0.43-0.88 (1) Unrotated due to a single factor being extracted. TABLE 3 Multivariate analysis of variance results: substance use and personality traits. Variable Mean SD F p Alcohol Consumption 4.72 2.41 12.35 0.000 Drug Use 10.44 9.49 18.34 0.000 Neuroticism 2.97 1.03 0.65 0.53 Conscientiousness 3.28 1.06 0.93 0.41 Agreeableness 3.11 0.89 9.07 0.001 Openness to Experience 3.36 1.07 0.25 0.78 Extroversion 3.50 0.88 0.03 0.98 TABLE 4 Group comparisons highlighting the variables found to be significant in the multivariate analysis of variance. Group Post Hoc: Variable Group Mean SD Comparison Tukeys Drug Use 1 (a) 2.73 3.71 1-2 -5.97 2 (b) 8.69 6.17 2-3 -10.72 * 3 (c) 19.42 9.04 1-3 -16.69 * Alcohol 1 (a) 2.91 1.87 1-2 -1.48 Consumption 2 (b) 4.38 1.71 2-3 -2.37 * 3 (c) 6.75 2.05 1-3 -3.84 * Agreeableness 1 (a) 3.82 0.75 1-2 1.28 * 2 (b) 2.54 0.88 2-3 -0.54 3 (c) 3.08 0.52 1-3 0.73 * Significant at the 0.05 level. (a) Group 1 (n=11) represents the no crimes committed group. (b) Group 2 (n=13) represents the moderate crimes committed group. (c) Group 3 (n=12) represents the substantial crimes committed group. TABLE 5 Frequency of criminal acts committed results of the discriminant analysis. Variables Structure Coefficients Drug Misuse .771 Alcohol Misuse .634 Agreeableness -.278 Conscientiousness -.129 Neuroticism .132 Openness to Experience -.055 Extroversion .014
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|Author:||Collette, Tessa; Pakzad, Sarah; Bergheul, Said|
|Publication:||Journal of Alcohol & Drug Education|
|Date:||Aug 1, 2015|
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