Information Visualization

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1 Infomation Visualization How Nw Gaphical EDA Tchniqus Can Rval Hiddn Rlationships Btwn th Ciminal Bhavio of dal Pisons and Thi Thinking Pattns David DsJadins, U.S. Buau of th Cnsus, and Dnis Golumbaski, ddi Mac (fomly with th U.S. dal Buau of Pisons) Kywods: SAS/Insight, SAS/JMP, Exploatoy Data Analysis (EDA), Data Pofil Plot, Ciminal Bhavio, Pison Rhabilitation Abstact: Th pupos of this pap is to show th sults of ou gaphical analysis of a dtaild attitud suvy conductd of a small goup of pison inmats in a mdium scuity fdal coctional facility. Th goal of ou sach was to gain insight into th possibl lationships btwn inmats thinking styls and ky ciminal pattns. Using nw, asy to us, gaphical Exploatoy Data Analysis (EDA) tchniqus, w w abl to find many factos woth futh invstigation. In this pap, w show that, fo this inmat population, th appa to b ky links btwn ciminal bhavio and ngativ attituds, choic of finds, and dysfunctional aly childhood xpincs. W hypothsiz that ths ciminal attituds/thought pattns, which also may possibly b linkd to a numb of ky ciminal habilitation factos, could b land/changd in intimat social goups. BACKGROUND NOTE: This pap focuss pimaily on th gaphical tchniqus usd to analyz ths data. This pap highlights how novic subjct matt spcialists can quickly and asily gain ky insights into thi data by utilizing th xtaodinay pow of ths asy-to-lan gaphical tools and tchniqus. This pap is an outgowth of an intoductoy gaduat lvl EDA statistics class taught by M. DsJadins and a tm pap wittn fo th class by Ms. Golumbaski. This is a hands-on class taching th SAS Copoation s point-and-click JMP softwa in addition to th application of that softwa to th data analysis task using EDA thoy. Intoduction: Dcads of sach into th causs of ciminal bhavio hav poducd a vast aay of thois, anging fom intuitiv asons such as gd, to mo obscu thois, such as dmon-possssion (Hagan and Sussman, 1988). As Hagan and Sussman (1988) not in thi viw of ciminology, xpts hav offd oth possibl factos as xplanations fo ciminal bhavio, such as conomic inquality, physical and mntal diffncs, and a lack of confomity to socital noms and valus. inally, som thoists suggst that ciminal bhavio is land in clos psonal lationships. Ths thois a known as social pocss thois, and point to th family as on of th pimay vhicls fo laning ciminal bhavio. In this pap, w us gaphical tchniqus of xploatoy data analysis to xamin social pocss thois and thi ol in ciminality. Bginning with th wok of Suthland (1947), ov 5 dcads of sach has invstigatd th ol of social laning in ciminal bhavio. Suthland poposd that ciminal bhavio is land within intimat social goups. And, h suggstd that not only a th bhavios land, but also, that th thoughts, attituds, and motivations associatd with ciminal bhavio a acquid in th sam mann. Th family is on xampl of an intimat social goup. As Goman and Whit (1995) not, sach has shown that pants and guadians influnc thi childn s bhavio, which in tun influncs th childn s choics of finds and oth social contacts. Thfo, dlinquncy and lationships with anti-social ps a pat of a dvlopmntal pocss that bgins in aly childhood (Goman & Whit, 1995). In analysis of data gathd fom th National Youth Suvy, (Wa & Staffod, 1991) suggst that th bhavio of ps has a gat influnc on adolscnts than do p attituds. Similaly, in a longitudinal study of sval hundd juvnils in th Nthlands, Jung (1988) found that dlinquncy of ps sms to b a sult, as wll as a caus, of dlinqunt bhavio. In pat, this suggsts that social laning plays a ol in xplaining th dlinqunt bhavio of juvnils (Jung, 1988). As w discussd abov, bfo th wok of Yochlson and Samnow (1976), it was thoizd that th causs of cim w pimaily aly motional tauma o socioconomic dpivation. Yochlson and Samnow futd ths thois by poviding vidnc basd on ov 17 yas of sach conductd on patints at St. Elizabth s dal mntal institution in Washington, DC. Yochlson gathd vidnc fom intviws with 255 psychiatic patints who w ciminals, and also intviwd thi clos finds and family mmbs. Th sachs discovd that th w som idntical thought pattns among ciminals of all ducational, social, and conomic backgounds. Yochlson and Samnow catd a list of th ciminal thought pattns that thy uncovd in thi sach, which includs pattns in xpssing fa, ang, and ag, fo xampl. Samnow (1991) discussd th ffct of pison lif on ciminals anti-social thoughts and bhavio. H suggsts that ciminals xhibit th sam bhavio and thought pattns in pison as thy did in th f community. H suggsts that it is not pison which futh coupts ciminals, but that pison only povids thm with oppotunitis to fog nw tis with oth ciminals who suppot thi anti-social thinking and bhavio. Walts & Whit (1989) also asst that ciminal bhavio xists bcaus of th thinking styls of ciminals. Thy agud that although a ciminal s nvionmnt plays a ol, th nvionmntal factos a not pdictiv of ciminal bhavio. Building on th wok of Yochlson and Samnow (1976), Walts (1995) dvlopd th Psychological Invntoy of Ciminal Thinking Styls (PICTS). Rsach has confimd th validity of th PICTS fo us with mal inmats (Walts, 1995a, 1995b, 1996) and among a goup of fmal inmats (Walts, Elliott, and Miscoll, 1998). SURVEY DATA: Th psnt study uss th PICTS (Walts, 1995) to assss th thinking styls of a goup of 100 mal pisons paticipating in a habilitation pogam in a mdium scuity fdal pison. Eight-fiv inmats fom th pogam voluntd to paticipat. Th PICTS is an 80-itm slfpot qustionnai mad up of 2 validity scals, Confusion and Dfnsivnss, and 8 thinking styls:

2 Infomation Visualization (1) mollification - pointing out how littl contol on has ov thi actions to avoid sponsibility (2) cutoff - ability to liminat dtnts in on s lif (3) ntitlmnt - ganting onslf pmission to ngag in ngativ bhavios (4) pow ointation - compnsating fo wak psonal contol by xcising contol ov on s nvionmnt (5) sntimntality - dny o minimiz ham by pfoming good dds to appa kind and gnous (6) supoptimism - disgad of th long-tm consquncs of dug abus and an attitud of invulnability (7) cognitiv indolnc - lazy thinking that lads a pson to tak shot-cuts and cat sious futu poblms (8) discontinuity - difficulty in following though on commitmnts Each scal is compisd of ight itms. Th spondnts indicatd th numb which bst psntd thi lvl of agmnt with ach of th itms. Th lvls w: 1 = disag, 2 = unctain, 3 = ag, and 4 = stongly ag, xcpt fo fou of th itms on th confusion scal and fou itm on th discontinuity scal, which a scod in th vs diction: 4 = disag, 3 = unctain, 2 = ag, and 1 = stongly ag. Th scos on ach of th itms a thn totald within ach of th scals. A low sco on a scal indicats that th inmat dos not us a paticula thinking styl vy much, and a high sco indicats that th inmat uss th thinking styl mo fquntly. As pat of a dmogaphic qustionnai dvlopd by (Inns, 1997) which was also administd to inmats in this study, subjcts w askd to spond to itms about thi backgound (.g. ag, ac, offns, histoy of dug abus, and so on.) Additionally, thy w askd about thi attituds concning situations such as: what boths thm most about bing in pison, how do thy dal with thi poblms, and, what would thy do if thy could gt out of pison ight now. Of paticula intst to th psnt study a th lationships that th subjcts had bfo nting pison, ith with finds, family, and oths with whom thy associatd. This pap will focus on sponss gland fom th following: Th subjcts w askd to cicl ith tu o fals to th following statmnts which w will f h to as th amily Sco. (A.) As a kid, I was oftn punishd by my fath/stpfath o my Mom s boyfind in a way that mad m fl ashamd and humiliatd. (B.) If I stayd away fom hom without tlling anyon wh I was, thy would woy about m. (C.) I can mmb tims as a kid whn I was lft with buiss o hut fom bing bat. (D.) Gowing up, I livd in a lot of diffnt placs and with a lot of diffnt popl. (E.) At an aly ag, I statd staying away fom hom as much as I could bcaus I hatd bing th. (.) I always flt that my pants lovd m and would suppot m. (G.) I nv knw whn I was a kid what I would gt bat fo and what I would gt away with. (H.) I land to fl hat as a kid fom th way I was tatd at hom. Subjcts civd 1 point p itm fo indicating tu on itms A, C, D, E, G, o H, and 1 point p itm fo indicating fals on itms B o. If subjcts indicatd fals on itms A, C, D, E, G, o H, o tu on itms B o, thy civd 0 points fo ach of thos sponss. Th w totald, and low scos on th amily Sco indicat mo favoabl family situations, whil high scos indicat unfavoabl family situations. Also of intst to us fo this paticula study a sval oth qustions posd to th inmat subjcts. Thinking about all th popl you knw, not counting lativs, in th ya bfo you cam to pison, how many would you say Had a ciminal cod o w involvd in cim? How many did not hav a ciminal cod o w not involvd in cims? Thinking about only you clos finds, not counting lativs, duing th ya bfo you cam to pison, how many would you say Had a ciminal cod o w involvd in cim? How many did not hav a ciminal cod and w not involvd in cim? On an avag day in th ya bfo you cam into pison, which of th things blow would you say you did fo at last an hou o two almost vy day? (1) Doing nothing in paticula o waiting fo somthing to happn (2) Woking o going to school (including homwok) (3) Rlaxing (watching TV, listning to music, o ading) (4) Excising o paticipating in tam spots (5) amily sponsibilitis (shopping, claning, o cooking) (6) amily activitis (going placs o spnding tim togth) (7) Rligious activitis (including svics and pay o mditation) (8) Hobbis (fishing, woodwoking, o at) (9) Bing activ in an oganizd goup (union, chuch, o svic clubs) (10) Dating o going to clubs (11) Hanging out with finds (12) Woking on o taking ca of you ca (13) Dinking and/o using dugs (14) Planning o doing cims USING GRAPHS TO TAKE A IRST LOOK AT THE DATA: Gaphical EDA tchniqus povid analysts any numb of ways to quickly and asily spot quality poblms within thi data sts. Data diting is, of cous, an ssntial fist stp in th data analysis task. Asid fom a numb of ky statistical limitations, th data that w hav analyzd fo this pap flcts many common poting os. o instanc: (a) On pison potd an ag of 4, anoth an ag of 10;

3 Infomation Visualization (b) igus w commonly oundd by spondnts to incmnts of 5 and 10 fo som masus; (c) As sn fo pison # 41 (blow), with 10 pio convictions and only 2 potd asts, som data just dosn t mak sns. 35, 256Ã %\Ã $55(676 $55(676 As was discussd abov, this data st is composd of a vy xtnsiv/vy ich st of qustions. As such, it opns th doo fo us to xplo many possibl caus-and-ffct lationships btwn ths vaiabls. Howv, th a sval ky limitations: (1) A small numb of subjcts (85 pisons) paticipatd in this suvy. (2) Considabl ounding was found. (Rounding is giving a ough stimat ath than an xact valu fo a vaiabl fo xampl, pio asts.) (3) Th attituds may not b gnalizabl to th total inmat population. Th paticipants in this suvy w volunts, and thy w in a habilitation pogam in which thy dcidd to paticipat. Thfo, this goup of inmats may b mo amnabl to civing tatmnt, and may actually hav btt attituds and mo socially accptabl thinking styls than pisons in th gnal inmat population who a not paticipating in this spcial pogam. (4) Th sults a not gnalizabl to th gnal population. (On could agu that pisons actually wind up in jail bcaus of ctain combinations of good and bad attituds -- whas, w ally nd to undstand how ths sam thinking styls and attituds tanslat to th actual bhavio of andomly slctd individuals w would mt vyday on th stt.) (5) This is slf-potd data, takn of pisons in a contolld nvionmnt -- a classic cas of a captiv audinc. Ths individuals a, no doubt, highly motivatd to say and do all th ight things to gain thi fdom. (6) In addition, simpl good statistical pactic quis us to sampl on th gnal population. uth, bcaus this is a suvy of inmats paticipating in a spcific habilitation pogam, data gading th ovall inmat population is not availabl. GRAPHIC METHODOLOGY -- TOOLS AND TECHNIQUES -- AN EDA PLAN O ATTACK In th EDA Statistics class that M. DsJadins tachs, h mphasizs an EDA Plan of Attack. In ssnc, this is lik a wa wh a battlfild command should utiliz all of his/h soucs (.g., infanty, plans, tanks) to thi bst possibl ffct. As such, ach gaphic fom (.g., boxplot, scattplot, data pofil plot) has its own paticula stngths and waknsss -- and ths nd to b usd in statgic conjunction with on anoth to achiv th bst ffct. In addition, just as th a battlfild statgis, ths gaphic tools nd to b combind with a st of vy powful intactiv tchniqus to bing ths gaphs to lif to foc th data to val its hiddn nuancs. Th pupos of ths intactiv tchniqus is to gain a al undstanding of th data -- to not simply poduc a singl statistic such as a colation cofficint. Using ths EDA tools, in minuts, th analyst can poduc litally hundds of gaphical psntations of a data st. Ths tchniqus bing ths data to lif and a fa supio to just a fw summay statistics and th fw dad gaphs shown in this pap. As discussd abov, th a a wid numb of vaiabls to chos fom --and th natu of som of th sponss by ths pisons is ath ough. As such, a good fist stp in ou analysis is to look fo gnal pattns in th data by bushing acoss sts of simpl ba chats of ths data. Using this tchniqu, in sconds, w can click on any of th bas in any of dozns of diffnt vaiabls and s if th a any gnal pattns vidnt in th data. H w hav clickd on 2 fo ac (Blacks/Afican Amicans). By looking at th shadd potions in all th oth bas, w can s that, gnally, thy a among th lowst in ag, hav th last numb of asts, and som of th lowst valus in th family sco suvy (indicating th most favoabl family situations). Of cous, w a not limitd to 6 vaiabls --in ou actual analysis, w would viw ov a dozn vaiabls at a tim. 2 RACE AGE ARRESTS PRI ORS MO_REE AMSCORE

4 Infomation Visualization JMP softwa givs us th capability of intactivly adding and subtacting componnts of ach of ths displays. In th gaphs blow, w hav slctd Rac typ #5 (whits) -- and again s no cla pattn in sntnc lngth o oth vaiabls. 5$&( )U HTXHQL HV Howv, w can now quickly xplo how ths pattns might chang fo substs of ths individuals (.g., whits who statd dinking bfo th ag of 15). It is a simpl matt to contol/click on th bas to liminat th vast majoity of ths inmats who potd that thy statd dinking und th ag of 18 (blow). H, a ky pattn is vidnt. Ths individuals all sco lowst in th family suvy and hav th lowst attitud sco fo ntitlmnt. This is a vy ffctiv mthodology fo daling with this typ of a data st. Accodingly, by using this vy powful intactiv tool, an analyst can quickly xplo any numb of data combinations and subst hypothss in m minuts. $*('5,1. $JHÃ RI ÃL QPDWH )$06&25( 6(17(1&( MORE ADVANCED GRAPHICAL METHODS: Bcaus ths data a so ough -- and bcaus w hav so many vaiabls to look at -- th fist data diting/analysis stps shown abov a highly commndd. Sinc litally hundds of possibl colations can b xplod in minuts, convntional statistical mthodology (such as stpwis gssion o colation cofficints) coms in a poo scond in compaison. o xampl, if w lookd at colation cofficints fo this data, w could find that sval intsting lationships xist o that th a gnal tnds in th data. Howv, outlis (17, 7/(0 4XDQW L O HV 0RPHQWV would confound ths data -- and w would also b missing impotant infomation that is not vald in a fw simpl statistics. To illustat this point, w can fist poduc Pason colation cofficints. o this dmonstation, w dcidd to look at possibl lationships btwn scos on th PICTS suvy masuing ciminal thinking styls, and th following factos: # of finds in cim, # of finds not in cim, # of acquaintancs in cim, and # of acquaintancs not in cim. W found that th w sval statistically significant lationships btwn th potd # of finds in cim and 4 of th scals on th PICTS suvy. A colation significant at th 0.01 lvl was found btwn th # of finds in cim and th ntitlmnt and supoptimism scals. Th was also a statistically significant lationship at th 0.05 lvl btwn th # of finds in cim and th scos on th pow ointation and mollification scals. Howv, w did not find any significant lationships xisting btwn th # of finds not in cim and th # of acquaintancs in o not in cim in spct to th vaious ciminal thinking styls masud by th PICTS. Also, in poducing futh Pason colation cofficints, w find that th was a statistically significant lationship btwn pisons scos on th family suvy and th following scals on th PICTS: discontinuity, ntitlmnt, pow ointation, and mollification. Ths w all significant at th 0.01 lvl. At this point, w hav found som intsting colations. W could stop h, and pot ou sults. But, using SAS JMP, w can asily xplo oth tnds in th data that a not vald in ths colation cofficints that w povidd. H, w can us anoth st of impotant EDA tools and tchniqus whn looking at possibl colations -- th us of symbols/colos in conjunction with a scattplot matix. Shown blow is a data pofil plot a scattplot matix in conjunction with box plots. In th scattplot matix, it quickly shows th possibl colations btwn th numb of pio convictions, th numb of months th pison was f sinc his last conviction, th numb of potd finds in cim, and th scos attaind on th st of family qustions. Highlightd with a * a th individuals who hav th highst numb of pio convictions. It is intsting to not that ths individuals gnally hav th lowst scos on th family suvy. Th al pow of ths EDA tools and tchniqus can b dmonstatd whn a lag numb of ths ky gaphic tools and intactiv tchniqus a usd in conjunction with on anoth. Box plots automatically isolat valus that a unusually high o low with spct to a nomal distibution of th data st. H, w s how th abov individuals also gnally hav unusually high sponss in th suvy masuing ciminal thinking styls. Again, by bushing on oth combinations, w can intactivly xplo how ths individuals might likwis hav th highst sntnc lvls, th lowst scos on th family suvy, and som of th lowst numb of finds in cim. It is almost a cim to put this much analytical pow into th hands of an analyst!

5 Infomation Visualization PRI ORS MO_REE RI ENDCR CONCLUSIONS: AMSCORE D E E N S I V Th a any numb of significant limitations to a fomal statistical analysis of ths data. Howv, in spit of ths limitations (lik th small sampl siz, and how a numb of ths sponss simply do not mak logical sns), w can still gain impotant infomation gading possibl links to inmats attituds/styls of thinking to latd vaiabls by using innovativ gaphical EDA tchniqus. Using ths mthods, w hav assssd ky aspcts in th data, and hav found a numb of gnal lationships among ths factos. o instanc, w viwd with th data pofil plot th gnal lationship btwn th numb of pio asts, and poo thinking styls as masud by th PICTS suvy. Ths would b difficult to obsv using mo convntional statistical analysis tchniqus. This study lnds suppot to th hypothsis that thinking styls of ciminals a land in intimat social goups, in paticula, fom th family and fom finds involvd in cim. This sach dmonstats th possibility that offnds with lss xtm ciminal thinking styls (thos who w not th outlis shown abov) may b mo influncd by th ngativ bhavios of family and finds in cim. On th oth hand, thos offnds with xtmly ngativ ciminal thinking pattns may b lss influncd by th actions and attituds of finds and family mmbs. BROADER IMPLICATIONS/APPLICATIONS: Ths nw, vy powful EDA tchniqus can b quickly and asily land by Subjct Matt Spcialists -- vn thos with limitd xptis in statistical analysis. uth, if this asily land, 3 d gnation, point-and-click softwa is taught in conjunction with a pactical, hands-on, EDA cous, makabl bakthoughs in th analysis and undstanding of data can b achivd. By assssing th ciminal thinking styls, attituds, and lationships with a much boad ciminal population, th findings of ou study can b validatd/usd to assss options fo habilitation and offnds futu isk fo committing nw cims. It is possibl that som offnds, with lss xtm ciminal thought pattns may b mo amnabl to chang, and may lan mo socially accptabl and lss ciminal appoachs to lif though clos lationships such as thos gaind though habilitation pogams. It is possibl thy hav land thi ciminal bhavio and thinking pattns in th sam mann though clos associations with oths. Howv, thos with mo xtm ciminal thinking styls and appoachs to lif may nd to b assssd fo oth '( 02&8(1 32 6( 68 &2 ', possibl avnus towad aching habilitation, as w dmonstatd that thi associations with oths and thi family backgounds hav not povidd an xplanation fo thi ciminal lif styls. As Walts and Whit (1989) suggst, it may b that a ciminal s nvionmnt (g. lationships and so on) plays a ol in thi ciminal bhavio, but that ths xtnal factos a not th total xplanation fo ciminality. And, as Yochlson & Samnow (1976) suggst, th stong cas may b mad fo thinking styls as th ky facto in xplaining ciminal bhavio. Not: Th viwpoints psntd h a th opinions of th authos, and a not ncssaily thos of th dal Buau of Pisons, ddi Mac, o th U.S. Buau of th Cnsus. To nsu anonymity of th paticipants, data which could spcifically idntify individual paticipants was not includd in this analysis. Th authos gatfully acknowldg that th sach pojct potd on in this pap was concptualizd by dal Buau of Pisons staff. Th suvy data was collctd by Buau staff as wll, and all inmats who paticipatd did so on a voluntay basis. Autho Infomation: David DsJadins, SRD, Rm. 3211A-4, U.S. Buau of th Cnsus, Washington, DC david.l.dsjadins@ccmail.cnsus.gov Tl: M. DsJadins is an Opations Rsach Pojct Manag in th Statistical Rsach Division at th U.S. Buau of th Cnsus. H has taught numous classs in Exploatoy Data Analysis tchniqus at th Buau of th Cnsus. H is also an instucto at th Gaduat School of th U.S. Dpatmnt of Agicultu wh h tachs Exploatoy Data Analysis. Dnis Golumbaski ddi Mac PWP, Mailstop C Wstwood Cnt Div Vinna, VA Dnis_Golumbaski@fddimac.com Tl: Ms. Golumbaski is a Businss Analyst at ddi Mac (dal Hom Loan Motgag Copoation) pimaily involvd in SAS pogamming in th Non-Pfoming Loans Dpatmnt. Sh was fomly a Social Scinc Rsach Analyst at th U.S. dal Buau of Pisons fo 7 yas, and holds an MPA dg in Public Administation fom Pnn Stat Univsity. Rfncs: Clvland, William, 1993, Visualizing Data, Hobat Pss. DsJadins, David, 1997, Nw Gaphical Tchniqus fo th Analysis of Cnsus Data, Statistics Canada Confnc Symposium 97 Pocdings, Ottawa, Canada Goman, D.M. & Whit, H.R. (1995). You can choos you finds, but do thy choos you cim? Implications of diffntial association thois fo cim pvntion policy. In H.D. Balow (Ed.), Cim and public policy: Putting

6 Infomation Visualization thoy to wok, (pp ). Bould, CO: Wstviw Pss. Ganquist, L, 1997, Maco-Editing - Th Agggat Mthod Statistical Data Editing, UN Confnc of Euopan Statisticians Statistical Standads and Studis, Gnva (Switzland) tadmaks of th SAS Institut Inc. in th USA and oth countis. Hagan,.E. & Sussman, M.B. (1988). Dvianc and th family: Wh w hav bn and wh a w going? In.E. Hagan & M.B. Sussman (Eds.), Dvianc and th family, (pp. 1-22). Nw Yok: Hawoth Pss. Inns, C.A. & Jackson, K. (1997). Suvy of Inmats in th Valus Pogam at CI Gnvill, U.S. dal Buau of Pisons, Washington, DC, (1997). Jung, M. (1988). Social contol thoy vsus diffntial association: A tst on panl data. In J. Jung-Tas & R.L. Block (Eds.), Juvnil dlinquncy in th Nthlands, (pp ). Bkly, CA: Kugl Publications. Samnow, S.E. (1978). Ciminal psonality - Implications of a 16-ya study. om Institut of Contmpoay Coctions and th Bhavioal Scincs - Intagncy Wokshop - Pocdings, 13 th Annual, Houston, TX. Samnow, S.E. (1991). Ciminal psonality xists bfo pison. In S.L. Tipp (Ed.), Amica s pisons: Opposing viwpoints. (pp ). San Digo, CA: Gnhavn Pss. Suthland, E.H. (1947). Pincipls of ciminology (6 th d.). Philadlphia: J.B. Lippincott. Walts,G.D. (1995a). Th Psychological Invntoy of Ciminal Thinking Styls: Pat I. Rliability and pliminay validity. Ciminal Justic and Bhavio, 22, Walts, G.D. (1995b). Th Psychological Invntoy of Ciminal Thinking Styls: Pat II. Idntifying simulatd spons sts. Ciminal Justic and Bhavio, 22, Walts. G.D. (1996). Th Psychological Invntoy of Ciminal Thinking Styls: Pat III. Pdictiv validity. Intnational Jounal of Offnd Thapy and Compaativ Ciminology, 40, Walts, G.D., Elliott, W.N., & Miscoll, D. (1998). Us of th Psychological Invntoy of Ciminal Thinking Styls in a goup of fmal offnds. Ciminal Justic and Bhavio, 25, Walts, G.D. & Whit, T.W. (1989). Thinking ciminal: A cognitiv modl of lifstyl ciminality. Ciminal Justic Rsach Bulltin, 4, Wa, M. & Staffod, M. (1991). Influnc of dlinqunt ps: What thy think o what thy do? Ciminology, 29, Yochlson, S. & Samnow, S.E.(1976). Ciminal psonality, v.1, A pofil fo chang. Nothval, NJ: Jason Aonson, Inc. SAS, SAS/JMP, and SAS/INSIGHT a gistd