a conditional reasoning measure for aggression

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2005 8: 69Organizational Research MethodsBrian C. Frost, Sara M. Russell, Chris J. Sablynski, Terence R. Mitchell and Larry J. Williams

Lawrence R. James, Michael D. McIntyre, Charles A. Glisson, Phillip D. Green, Timothy W. Patton, James M. LeBreton,A Conditional Reasoning Measure for Aggression

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10.1177/1094428104272182ORGANIZATIONAL RESEARCH METHODSJames et al. / CONDITIONAL REASONING

A Conditional Reasoning Measure for Aggression

LAWRENCE R. JAMESMICHAEL D. MCINTYRECHARLES A. GLISSONPHILLIP D. GREENTIMOTHY W. PATTONJAMES M. LEBRETONBRIAN C. FROSTSARA M. RUSSELLUniversity of Tennessee

CHRIS J. SABLYNSKITERENCE R. MITCHELLUniversity of Washington

LARRY J. WILLIAMSVirginia Commonwealth University

This article describes a new approach for assessing cognitive precursors to ag-gression. Referred to as the Conditional Reasoning Measurement System, thisprocedure focuses on how people solve what on the surface appear to be tradi-tional inductive reasoning problems. The true intent of the problems is to deter-mine if solutions based on implicit biases (i.e., biases that operate below the sur-face of consciousness) are logically attractive to a respondent. The authors focuson the types of implicit biases that underlie aggressive individuals’attempts to jus-tify aggressive behavior. People who consistently select solutions based on thesetypes of biases are scored as being potentially aggressive because they arecognitively prepared to rationalize aggression. Empirical tests of the conditionalreasoning system are interpreted in terms of Ozer’s criteria for ideal personalityinstruments. Noteworthy findings are that the system has acceptable psychometricproperties and an average, uncorrected empirical validity of 0.44 againstbehavioral indicators of aggression (based on 11 studies).

Keywords: aggression; conditional reasoning; personality; measurement

Organizational Research Methods, Vol. 8 No. 1, January 2005 69-99DOI: 10.1177/1094428104272182© 2005 Sage Publications

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Efforts to develop and validate a new conditional reasoning test are described. Initialevaluations of the conditional reasoning approach to measurement were based on atest for achievement motivation (James, 1998). Research on academic samples indi-cated that this test had acceptable psychometric properties, including significantvalidities as high as 0.52 against academic achievement. The purpose of this article isto describe the subsequent development and validation of a conditional reasoning testfor aggression. This instrument was proposed originally, and pilot tested, in the 1998article. Overviews of the psychological foundation and content of the conditional rea-soning test for aggression are presented below. These treatments are followed by asynopsis of a series of psychometric studies.

The presentation is organized in terms of Ozer’s (1999, p. 671) principles for “whatmakes a personality assessment method or instrument ‘good.’” Ozer condensed anearlier treatment by Meehl (1972), which applied basic criteria for sound psychologi-cal test development to personality, to propose three principles (characteristics, crite-ria) for the “ideal” personality instrument (a fourth principle is not considered herebecause it applies to established measures). These principles are (1) the content of theinstrument should relate rationally to a psychological theory, (2) the item characteris-tics, scale characteristics, and factor structure of the instrument should be consistentwith the psychological theory, and (3) the instrument should possess demonstrablyhigh validities for the most theoretically relevant inferences. Although one would notexpect a work in progress such as conditional reasoning to have reached the lofty goalof “ideal,” it is informative to ascertain how advances made to this point stack upagainst exacting standards.

Principle 1: The measure’s “content” is fully rational within a psychological theory and isappropriate to specific, defined assessment circumstances.

This principle asks for a “theory of the instrument” (Ozer, 1999, p. 672). One needsto specify the content of the instrument and how this content relates rationally to a psy-chological theory. In the first of two sections, we specify the general content that is thesubject of measurement in the conditional reasoning system for aggression and howthis content relates rationally to psychological theory. The specific measurementinstrument is described in Section 2.

Section 1: General Content andRelation to Psychological Theory

Conditional reasoning originated with the idea that systematic biases in what a peo-ple regard as rational analyses open a window into the operation of their implicit minds(James & Mazarolle, 2002). Recent thinking and research on defense mechanisms, theunconscious, and implicit biases in social cognition suggested a means to investigate

70 ORGANIZATIONAL RESEARCH METHODS

Authors’Note: The authors would like to thank the following individuals for their helpful suggestions and ad-vice: Jack M. Feldman, Robert J. House, Michael D. Mumford, Benjamin Schneider, and Ross R. Vickers.James M. LeBreton is now at Wayne State University. Chris J. Sablynski is now at California State Univer-sity, Sacramento. Reprints may be obtained from Lawrence R. James, who is now at the Georgia Institute ofTechnology, School of Psychology and College of Management, Atlanta, GA 30332.



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this idea (see Cramer, 2000; Fiske & Taylor, 1991). We describe our approach from theperspective of the model in Figure 1.

Figure 1 begins with two motives, the first of which is the motive to aggress. Astrong motive to aggress is defined as a desire to overcome opposition forcefully, tofight, to revenge an injury, to attack another with intent to injure or kill, and to opposeforcefully or punish another (Murray, 1938). Aggressive individuals are predisposedto respond to frustrating situations, varying between single events (being passed overfor promotion) and chronic conditions (e.g., a malingering coworker), with anger,accompanied by desires to inflict harm on the perceived source of the anger (i.e., thetarget; Baron & Richardson, 1994; Berkowitz, 1993). Included here are spontaneousverbal or physical attacks as well as the more long-term seeking of vengeance and ret-ribution via indirect or passive aggression. Data indicate that approximately 12% ofpeople from the general population have moderately strong to strong motives toaggress (James & McIntyre, 2000).

Aggressive people may or may not be aware that they possess a powerful desire toinflict harm. This is because motives have both explicit (conscious) and implicit(unconscious) components (McClelland, Koestner, & Weinberger, 1989; Murray,1938). Whereas some aggressive people are aware that they are intrinsically aggres-sive, the motive remains largely implicit and protected by defense mechanisms formost aggressive people (see Baumeister, Dale, & Sommer, 1998; Westen, 1998). Themotive to aggress remains largely implicit because it conflicts with the motive to hold afavorable view of the self; that is, to possess moderate to high sense of self-worth (seeBaumeister, Campbell, Krueger, & Vohs, 2003; Cramer, 1998, 2000). That is, peoplein general need to see themselves as possessing self-worth, which means that theywant to think of themselves as being moral, prosocial, stable, and capable of self-control (see Bersoff, 1999; Loewenstein, Weber, Hsee, & Welch, 2001). This is largelyan explicit motive, although it may possess some implicit components (e.g., uncon-scious direction toward use of downward comparisons with less fortunate others toenhance sense of efficacy or well-being).

The conflict within aggressive individuals between the motive to hold a favorableview of the self and the motive to aggress sets in motion unconscious defensive pro-cesses that make possible the expression of aggression while simultaneously protect-ing the sense of self-worth. These unconscious self-protective processes are known as“defense mechanisms” and consist of mental operations designed to keep painfulthoughts and emotions, such as that one is disposed to be hostile and inclined to harmothers, out of consciousness (see Cramer, 2000; Kilstrom, 1999).

The conditional reasoning system focuses on the degree to which conscious think-ing is influenced by the defense mechanism of “rationalization.” Rationalization isdefined as the use of ostensibly plausible reasons to justify behaviors that are unknow-ingly caused by unconscious (e.g., repressed), unacceptable, and/or unwanted motives(Baumeister, Smart, & Boden, 1996; Westen & Gabbard, 1999). In Figure 1, we haveseparated the overall rationalization defensive activity into the two right-most compo-nents in Figure 1. The first of these components consists of the key implicit cognitiveoperations involved in the process of rationalizing. The second or rightmost compo-nent displays the primary product of these processes, namely, the self-deceptivereasoning that we think of as a rationalization.

Returning to the first component of rationalization, the process of rationalizing typ-ically involves inductive forms of reasoning. That is, people rationalize by generating




inferences that frame (i.e., interpret) their behavior, identify causes of (i.e., explain)their behavior, and forecast consequences of their behavior. What makes rationaliza-tion unique is that the true but unconscious intent of reasoning is to enhance the ratio-nal appeal of behaviors that express aggressive individuals’desire to inflict harm. Thisintent is accomplished via the use of “justification mechanisms,” which are self-protective biases that implicitly shape reasoning so as to enhance the rational appeal ofbehaving aggressively (James, 1998). Basically, justification mechanisms are the cog-nitive tools that work below the surface of consciousness to produce rationalizations.James and colleagues (James, 1998; James & Mazerolle, 2002; James & McIntyre,2000) searched professional and lay literatures to identify a seminal set of implicitbiases that served as justification mechanisms for aggression. This search uncoveredthe six justification mechanisms (JMs) summarized in Table 1.

The last component of Figure 1 illustrates the conscious output of justificationmechanisms, namely the self-protective reasoning that constitutes the expressed ratio-nalizations for aggression. Rationalizing behaviors that harm others allows aggressivepeople to foster the unrealistic self-concept that they can behave aggressively andremain moral, prosocial, stable, and capable of self-control. Rationalizing aggressionis accordingly an act of self-deception that (a) is intended to conceal from awarenessthe true but unacceptable cause of aggression, namely, a strong motive to inflict harm,while (b) sparing the aggressive person the anxiety and guilt of self-perceptions thatshe or he is a hostile, malicious, or malevolent person (see James & Mazerolle, 2002).We should note that the model in Figure 1 is typically operational both when anaggressive person is building an ostensibly rational case to aggress (with the provisothat some acts of aggression are spontaneous) as well as after aggressive behavior hasbeen enacted (see Loewenstein et al., 2001; Winter, John, Stewart, Klohnen, &Duncan, 1998).

Rationalizations based on JMs for aggression are often not convincing or logicallypersuasive to nonaggressive individuals. There is little or no incentive for non-aggressive people to possess JMs to rationalize aggression because these individualsare not antagonistic, combative, hostile, or obstructive (see Crick & Dodge, 1994;Huesmann, 1988). Indeed, rationalizations for aggression based on the JMs are likelyto be viewed by nonaggressive individuals as improbable, unlikely, implausible, far-fetched, and excessive (Gay, 1993; James & Mazerolle, 2002). On the other hand, rea-soning based on the JMs can be subtle and exploit uncertainties in fact and evidence.The result is that reasoning, especially solitary instances of reasoning, based on JMstypically cannot be rejected outright on logical grounds. This creates a condition ofuncertainty. Even here, however, nonaggressive individuals will tend to be skeptical ordoubtful of reasoning based on JMs (James, 1998).

What is more logically persuasive to nonaggressive individuals is framing andanalyses of work and other social situations that are consistent with their proclivities tobe civil, polite, friendly, congenial, cooperative, and peaceful, that is, to engage inprosocial or socially adaptive behaviors (James & Mazerolle, 2002). (Prosocial isused here in the same sense that it is employed in personality to refer to dynamics thatmove “people closer together” [Buss & Finn, 1987, p. 435] and that act as contrasts toaggression [Wright & Mischel, 1987]). It is not defined exclusively in terms of extrarole or organizational citizenship behaviors, although these are included.) Becauseprosocial behaviors promote a harmonious and peaceful work environment, as

James et al. / CONDITIONAL REASONING 73




Table 1Justification Mechanisms for Aggression

1. Hostile attribution bias’s core is an implicit assumption that (like oneself) people tend to bemotivated by a desire to harm others (Anderson, 1994; Tedeschi & Nesler, 1993; Toch,1993). This latent bias is instrumental in shaping conscious attempts to explain why othersbehave as they do. Such explanations show a strong predilection to attribute behavior tomalevolent purpose and harmful intent (cf. Crick & Dodge, 1994; Dodge & Coie, 1987). Evenbenign or friendly acts may be credited to hidden, hostile agendas designed to inflict harm.The attributions of hostile intent are central to the aggressive person’s attempts to rational-ize his or her own hostile behaviors as acts of self-defense intended to ward off physical orverbal attack.

2. Potency bias is grounded in the implicit assumption that interactions with others are conteststo establish dominance versus submissiveness (Anderson, 1994; Gay, 1993; Millon, 1990).This bias unconsciously shapes framing; the actions of others pass through a perceptualprism primed to distinguish (a) strength, assertiveness, dominance, daring, fearlessness,and bravery from (b) weakness, impotence, submissiveness, timidity, compliance, and cow-ardice (James & Mazerolle, 2002). Such framing promotes reasoning that the use of aggres-sion to dominate others demonstrates strength, bravery, control, and fearlessness. Not act-ing aggressively is associated with weakness, fear, cowardice, and impotence. An aggres-sive person may thus rationalize aggression by reasoning (a) that aggression is an act ofstrength or bravery that gains respect from others and (b) that to show weakness is to invitepowerful others to take advantage of you.

3. Retribution bias centers on an implicit assumption that exacting retribution is of greater con-sequence than preserving or maintaining a relationship. This bias surfaces as a proclivity tofavor retaliation as a more rational behavior than reconciliation (cf. Bradbury & Fincham,1990; Dodge, 1986; Laursen & Collins, 1994). For example, aggression is seen as justifiableif it is intended to restore respect or to exact restitution for a perceived wrong. Retaliation isthus assumed to be more reasonable than forgiveness, vindication appears more reason-able than reconciliation, and obtaining revenge appears more reasonable than maintaininga relationship. This bias often underlies justifications for aggression engendered bywounded pride, challenged self-esteem, and perceived disrespect (cf. Baumeister, Smart, &Boden, 1996).

4. Victimization by powerful others bias has as a nucleus an implicit assumption that the power-ful will inflict harm on the less powerful (Averill, 1993; Finnegan, 1997; Toch, 1993). This as-sumption underlies a conscious proclivity to see oneself as the victim of inequity, exploita-tion, injustice, and oppression by those who are more powerful in one’s life (e.g., parents,teachers, supervisors, employing organizations, or institutions such as the Internal RevenueService). Framing of events, hypotheses about cause and effect, and confirmatory searchesfor evidence both engender and reinforce inferences that people are being victimized bypowerful others. This reasoning furnishes the foundation for justifying acts of aggression aswarranted corrections of inequities or legitimate strikes against oppression.

5. Derogation of target bias consists of an unconscious tendency to characterize those onewishes to make (or has made) targets of aggression as evil, immoral, or untrustworthy (cf.Wright & Mischel, 1987). To infer or associate such traits with a target makes the targetmore deserving of aggression.

6. Social discounting bias has at heart an implicit assumption that social customs restrict freewill and the opportunity to satisfy needs. Reasoning shaped by this latent bias reflectsdisdain for traditional ideals and conventional beliefs (cf. Finnegan, 1997; Loeber &Stouthamer-Loeber, 1998; Millon, 1990). For example, attempts to identify the most logicallyplausible causes of social events typically lean toward the cynical and critical. Reasoningwill further evidence a lack of sensitivity, empathy, and concern for social customs, often ac-companied by the absence of rational prohibitions against behaving in socially unorthodoxways. Socially deviant behavior intended to harm others is rationalized by inferring that it al-lows one to attain freedom of expression, release from the shackles of social customs, andliberation from confining social relationships.



opposed to the hostile, disharmonious work environments often engendered byaggressiveness, they are the normative and expected (i.e., role-prescribed) standardsfor appropriate conduct in work and most other social situations.

Nonaggressive individuals tend to internalize the underlying system values (i.e.,prosocial ideologies and rationales) that support their socially adaptive behaviors(James & Mazarolle, 2002). In contemporary terms, we would say that their reasoningabout what constitutes rational behavior in work situations is often automatically (i.e.,implicitly, Schneider & Shiffrin, 1977) shaped by the socially adaptive system valuesthat most of them have internalized. Consequently, nonaggressive individuals areprone to make inferences about the rationality of behavior that reflect prosocial ideolo-gies and rationales. The ensuing reasoning appears to them to be more plausible, bal-anced, probable, sound, and sensible than rationalizations for aggression. For exam-ple, in contrast to aggressive individuals’ proclivities to reason in terms of JMs, wemight expect most nonaggressive individuals

• to see greater probabilities for amicable or benign intent (as opposed to hostile intent) asthe default option in seeking explanations for the actions of others;

• to reason from the perspective that people can operate in society without being exploited,demeaned, or made victims of injustices and inequities (as opposed to selectively focus-ing on being victimized by powerful others); and

• to analyze social events from the perspective that society benefits from respect for tradi-tional ideals and conventional beliefs (e.g., cooperation) as well as from acceptance of so-cial customs (as opposed to reasoning grounded in rejection of conventional beliefs andsocial ideals).

Generally speaking, prosocial behavior does not need to be protected by JMs or ra-tionalizations. Not only are these behaviors manifestations of internalized socialnorms, but they also serve nonaggressive individuals’motives to hold favorable viewsof themselves (e.g., to see themselves as moral and responsible). At a deeper level, it ispossible that the shaping of nonaggressive reasoning by prosocial ideologies could en-gender defensive reasoning, such as when prosocial proclivities result in a failure torecognize a dangerous situation and someone is injured. However, we do not need togo into such depth. All that is required for our purposes is that nonaggressive individu-als be prone to conclude that reasoning emanating from prosocial ideologies andrationales is more convincing than reasoning based on JMs for aggression.

Section 2: Measuring the Effects of JMs on Reasoning

A system was desired that measured the extent to which JMs for aggression areinstrumental in shaping a person’s reasoning. The major stumbling block to develop-ing such a system was that the biases operate unconsciously. (This is a problem gener-ally encountered in constructing measures of defense mechanisms.) By virtue of beingimplicit and thus hidden from introspection, JMs cannot be assessed by the popularmethod of self-report (Nisbett & Wilson, 1977; Winter et al., 1998). Rather, to mea-sure JMs, “indirect measurements are theoretically essential” (Greenwald & Banaji,1995, p. 5). Indirect measures “are identifiable chiefly by their lack of the defining fea-ture of direct measures, that is, by their not alerting the subject to the identity of the[variable] being measured” (Greenwald & Banaji, 1995, p. 8).




Unfortunately, development of efficient indirect measures has proven difficult inpsychology. Greenwald and Banaji (1995) based the following conclusion on a reviewof the then-current state of indirect measurement:

Research on latency decomposition, projective tests, and miscellaneous other proce-dures indicate indirect measurement of individual differences in implicit social cog-nition is possible. At the same time, such measurement has not yet been achieved inthe efficient form needed to make research investigation of individual differences inimplicit social cognition a routine undertaking. (p. 20)

James and Mazerolle (2002) suggested that this conclusion remains valid.The system of indirect measurement proposed by James (1998) assesses whether

reasoning engendered by JMs for aggression is judged by respondents to be logical orillogical. This assessment is based on a new type of inductive reasoning problem.These problems are analogous to traditional inductive reasoning tasks in that respon-dents are asked to determine which general conclusion follows most reasonably froma set of premises. More specifically, respondents make judgments about which evi-dence is credible, which arguments are valid, which assumptions are tenable, and, ulti-mately, which one of several conclusions is most likely to be true. Two illustrativeproblems are presented in Table 2.

Our experience is that respondents generally believe that their critical intellectualskills guide their attempts to identify a logically correct conclusion for each problem.But the true demands of the inductive reasoning tasks are only partially intellectual(and this demand is quite modest). The bulk of the demand appears intellectual but infact is produced by a tacit requirement to judge whether reasoning based on a JM ismore or less reasonable than reasoning based on more temperate, prosocial ideologiesand rationales.


Table 2Illustrative Conditional Reasoning Problems

1. American cars have gotten better in the past 15 years. American carmakers started to buildbetter cars when they began to lose business to the Japanese. Many American buyersthought that foreign cars were better made.Which of the following is the most logical conclusion based on the above?a. America was the world’s largest producer of airplanes 15 years ago.b. Swedish carmakers lost business in America 15 years ago.c. The Japanese knew more than Americans about building good cars 15 years ago.d. American carmakers built cars to wear out 15 years ago so they could make a lot of

money selling parts.

2. The old saying, “an eye for an eye,” means that if someone hurts you, then you should hurtthat person back. If you are hit, then you should hit back. If someone burns your house, thenyou should burn that person’s house.Which of the following is the biggest problem with the “eye for an eye” plan?a. It tells people to “turn the other cheek.”b. It offers no way to settle a conflict in a friendly manner.c. It can be used only at certain times of the year.d. People have to wait until they are attacked before they can strike.



The mapping of JMs versus prosocial ideologies and rationales into inductive rea-soning problems is made possible by building two solutions to each problem. One ofthese solutions offers a conclusion whose logical credibility is conditional on reason-ing being shaped by a JM or set of JMs. For example, consider Alternative d on Prob-lem 1 in Table 2. The perceived reasonableness of this alternative to a respondentincreases with increases in the strength of the respondent’s implicit propensity toassume that powerful others intentionally inflict harm on and exploit less powerfulothers. Alternative d was constructed by fashioning reasoning on the implicit assump-tions underlying the hostile attribution bias and the victimization by powerful othersbias (see Table 1). Selection of this alternative as the most reasonable solution to theproblem furnishes a single piece of indirect evidence that these two JMs areinstrumental in shaping a respondent’s reasoning.

We hasten to note that some respondents may select Alternative d for reasons thatdiffer from those above. For example, a respondent may have had negative experi-ences with an American automobile manufacturer that influences his or her reasoningon this problem. We do not, therefore, place undue weight on the responses to a singleproblem. What is important for measurement is whether a respondent consistentlyselects reasoning based on JMs across a set of problems that vary in terms of inductiveargument and subject matter.

Nonaggressive individuals are expected to acknowledge that Alternative d is logi-cally feasible but improbable. For example, these respondents may find this solutionlogically unlikely because it indicates that American carmakers held a deeply cynicalattitude toward customers, whereby profit by any means overrode more constructiveand practical concerns such as pride in product and attracting repeat customers. Toview this or related reasoning as improbable, extreme, and unlikely indicates that theJMs used to build the argument are not instrumental in shaping a respondent’sreasoning.

Alternative c was designed to appeal to nonaggressive individuals’ need for ananswer that, to them, is more plausible. This inference follows logically from the pre-mises but lacks the cynicism and enmity of Alternative d. It offers an option whose log-ical credibility is conditional on reasoning being shaped by prosocial ideologies andrationales. In this specific instance, the perceived reasonableness of the conclusion to arespondent increases with increases in the strength of a respondent’s propensity toattribute the behavior of powerful others to benign and nonharmful intent.

Aggressive respondents are, on average, expected to acknowledge the plausibilityof Alternative c. However, they should, with a significantly greater probability thannonaggressive individuals, determine that Alternative d shows greater insight into thetrue intentions of American automobile makers. Alternative d is referred to asthe “AG” alternative because it is designed to appeal to aggressive individuals. Alter-native c is designated the “NA” alternative because it is intended for nonaggressiverespondents.

To enhance the face validity of the task, and to protect the indirect nature of mea-surement, Alternatives c and d were embedded within a set of four inferences (conclu-sions). The other two conclusions (i.e., Alternatives a and b in Problem 1) are meant tobe clearly illogical and rejected by respondents (which is usually the case). Problemssuch as Problem 1 are referred to as “conditional reasoning problems” because howeach is solved is dependent on whether (or to what degree) JMs for aggression areinstrumental in shaping reasoning.




Problem 2 in Table 2 further illustrates the conditional reasoning procedure. Alter-natives a and c are clearly illogical. Alternative d is the AG alternative. It offers respon-dents the opportunity to determine that reasoning based on the retribution bias and thevictimization (by powerful others) bias is the logically preferable solution. The per-ceived reasonableness of this alternative increases with increases in the implicit pro-clivity to favor (a) retribution over reconciliation and (b) striking first as a means toavoid being attacked and becoming a victim. Alternative b is the NA alternative. It istargeted to appeal to nonaggressive individuals’ desire for a prosocial counterbalanceto the antagonistic and provocative tenor of the AG alternative.

Conditional reasoning test for aggression (CRT-A). The conditional reasoning testfor aggression, referred to as the CRT-A, is composed of 22 conditional reasoning(CR) problems such as the two illustrated in Table 2 (James & McIntyre, 2000). The 22CR problems in the CRT-A evolved over a series of developmental studies and weresubjected to intensive psychometric analyses over a number of years. Each CR prob-lem was based on one or more of the six JMs. Several methods were examined for scor-ing the 22 CR problems. These alternatives provided highly correlated scores andessentially equal correlations with external variables. The method described belowproduced the most interpretable factor structure.

Respondents are given a “+1” for every AG alternative they select, a “0” for everylogically incorrect alternative they select (an infrequent event), and a “–1” for everyNA alternative they select. These scores are summed within individuals to furnishcomposite scores, which are then linearly transformed (so as to preserve the between-person distribution) into a standard scale that has a mean of 6.0 and a standard devia-tion of 1.67. Respondents obtain high scores on this scale by selecting a comparativelylarge number of AG alternatives to solve the CR problems. This strategy indicates thatJMs for aggression are instrumental in shaping their reasoning. According to the ratio-nale for measurement presented earlier (see Figure 1), these respondents are preparedto justify (rationalize) the expression of a strong (implicit) motive to harm others andyet still think of themselves as moral and prosocial. We shall, therefore, refer to theseindividuals as “justifiers.” The scale is referred to as the Justification of AggressionScale (JAGS).

A low score on the JAGS indicates that JMs are not instrumental in shaping arespondent’s reasoning. The absence of an implicit cognitive system to justify aggres-sion suggests that the aggression motive is weak and that these respondents areunlikely to engage in aggression in the future (or to have engaged in aggression in thepast). Scores ranging between the weak and strong poles on the JAGS indicate thatJMs for aggression are only sporadically instrumental in shaping reasoning. Implicitdefenses for justifying aggression are not well developed, which portends a weak pro-clivity to engage in aggressive acts, either in the past or future.

Verbal/visual conditional reasoning test (VCRT). For less adept readers, a testbased on a “verbal-visual” version of a subset of the CR problems was designed tohave a threshold reading level of approximately the fifth to sixth grade (Green &James, 1999). Referred to as the VCRT, this test consists of bare-bones versions of CRproblems. The problems are presented both verbally and in written form using a VCRand television. The written component consists of simplified prose, which is overlaid




on a photograph consistent with the basic theme of the CR problem. The currentVCRT contains 14 CR problems, 12 of which are shared with the CRT-A. Work con-tinues on converting CRT-A problems to the VCRT format. Scoring of the VCRTproblems is based on the same procedures as described above for the JAGS.

Discussion of Principle 1

A strong effort has been made to satisfy Ozer’s (1999) Principle 1 by grounding theconditional reasoning measurement system in rational psychological theories relatingto conflicting motives, defense mechanisms and rationalization, and implicit self-protective biases, which we refer to as JMs. Additional theorizing is desired on a num-ber of fronts. For example, models are needed that specify the mental processes bywhich JMs affect the cognitive operations involved in framing and analyses. Theoriesare also desired regarding alternative, indirect ways to measure JMs so that tests ofconvergent validity may be conducted. These issues will be addressed in the future.

We proceed now to reports of empirical tests of the “goodness” (Ozer, 1999) of theconditional reasoning approach.

Principle 2: The internal structure of items in a measure should match the requirements ofboth the relevant psychological theory and the measurement model.

Principle 3: The measure should have demonstrably high validities for the most theoreti-cally relevant inferences.

Principle 2 requests that the item characteristics, scale characteristics, and factorstructure of the CR problems be consistent with the psychological theory used to buildthe problems. Principle 3 focuses on estimates of validity, where validity is viewedthrough the broad lens of construct validity. At issue is the validity of inferences re-garding (a) the reliability of the instrument (e.g., internal consistency, stability), (b) theinstrument’s relations with important, definitional outcomes (e.g., behavioral crite-ria), and (c) the instrument’s relations with measures of other theoretical constructs ofinterest (e.g., self-report personality variables, potential confounds such as gender andrace).

Fourteen samples from diverse populations furnished the types of evidence speci-fied in Principles 2 and 3. Item characteristics, scale distributions, and a factor analysiswere based on a combination of four samples (n = 1,603). Analyses relating to variousaspects of validity were based on meta-analyses of results obtained on subsets of thesamples from the 14 that had data relevant to a research question. The samples thatwere entered into each analysis are overviewed in Table 3. A more extensive presenta-tion of the 11 samples that were entered into the meta-analysis of empirical validities ispresented in Table 4.

The meta-analyses offered an efficient means to summarize multiple studiesinvolving multiple samples. One consequence of this approach is that it is not possibleto describe particular samples or studies in depth. For readers desiring more detail,Samples 1 through 8 are described in greater depth in the test manual for the CRT-A(James & McIntyre, 2000), and all studies have been reported in national conferences(Academy of Management, Society for Industrial & Organizational Psychology) byone or more of the authors (reports are available from the senior author).




80

Tabl

e 3

Sam

ple

Info

rmat

ion

Sam

ple

inN

umbe

r of

Ana

lysi

s of

Con

ditio

nal

Dis

trib

utio

nsG

ende

rR

easo

ning

and

Fact

orE

mpi

rical

Inte

llect

ual

and/

orS

elf-

Sam

ple

Com

posi

tion

nIn

stru

men

tP

robl

ems

Str

uctu

reR

elia

bilit

yV

alid

ityS

kills

Rac

eR

epor

ts

1P

atro

l offi

cers

140

Dev

elop

men

tal C

RT

a6

X2

Und

ergr

adua

tes

188

Dev

elop

men

tal C

RT

15X

XX

3U

nder

grad

uate

s60

Dev

elop

men

tal V

CR

T12

XX

XX

4N

ucle

ar fa

cilit

y op

erat

ors

97D

evel

opm

enta

l CR

T16

X5

Und

ergr

adua

tes

276

Dev

elop

men

tal C

RT

25X

Dev

elop

men

tal V

CR

T12

6U

nder

grad

uate

s22

5D

evel

opm

enta

l VC

RT

14X

XX

XX

7R

esta

uran

t em

ploy

ees

135

Dev

elop

men

tal C

RT

22X

X8

Pac

kage

han

dler

s10

5C

RT-

Ab

22X

XX

X9

Tem

pora

ry e

mpl

oyee

s11

1C

RT-

A22

XX

XX

10U

nder

grad

uate

s80

2C

RT-

A22

XX

11C

usto

mer

con

trac

t ass

ocia

tes

585

CR

T-A

22X

X12

Und

ergr

adua

tes

95C

RT-

A22

XX

X13

Und

ergr

adua

tes

191

CR

T-A

22X

X14

Und

ergr

adua

tes

191

CR

T-A

22X

X

Not

e:C

RT

= c

ondi

tiona

l rea

soni

ng te

st;V

CR

T =

ver

bal/v

isua

l con

ditio

nal r

easo

ning

test

;CR

T =

con

ditio

nal r

easo

ning

test

for

aggr

essi

on.

a.E

arly

ver

sion

s of

con

ditio

nal r

easo

ning

test

s th

at in

clud

ed tr

ial c

ondi

tiona

l rea

soni

ng p

robl

ems.

b.F

inal

ver

sion

of c

ondi

tiona

l rea

soni

ng te

st d

escr

ibed

in te

xt.

Sam

ple

in M

eta-

Ana

lysi

s




Table 4Criterion and Hypotheses for 11 Validation Samples

Sample Criterion Hypothesis

140 officers whopatrolled federal parksin a southeastern state;almost all officers weremale, in their 40s and50s, and White

Supervisory performanceratings; M = 3.48 on a 5-pointscale (1 = not at all effective,5 = extremely effective), SD =0.87

Aggressive officers would receivelower ratings because oftendencies to be hostile to thepublic, engage in dominancecontests with supervisors, andclash with coworkers

188 undergraduatesfrom a southeasternuniversity; 34% werefemale, and 10% wereAfrican American (theother 90% were White)

Habitual absences—lack ofclass attendance; 37% ofstudents had high absences,low attendance

Aggressive students would framethemselves as victims ofinequitable treatment bypowerful others (e.g.,professors); being victimizedwould serve as a justification forhabitual absenteeism, which isoften a form of passive-aggressiveness used by peoplein low power positions to defy orto resist oppressors (see Buss,1961).

60 undergraduates froma southeasternuniversity; 60% werefemale, and almost allwere White

Lack of truthfulness aboutextra credit deserved fromparticipating in a 55-minuteexercise, which consisted ofcompleting the verbal/visualconditional reasoning test foraggressiveness and theJackson Personality Profile.Fifteen percent of studentsstated that they deserved 10points (experiment lastedmore than 1 hour) when theydeserved 5 points (experi-ment lasted 1 hour or less)

Being kept waiting 10 minutes forexperiment to start, beingsurprised with an announcementof having only 15 minutes tofinish experiment, and beingconsistently reminded to workquickly would trigger anger andresentment (e.g., hostileattributions, victimization) amongaggressive students. Aggressivestudents would attempt to “fightback” or “get even” byoverstating extra credit theydeserved.

97 nuclear facilityoperators; mean agewas 44.42 years, meantenure in company was15.29 years (SD =9.05), 15% werefemale (data for racewere not available),and education variedbetween high schoolgraduate with nocollege (23%) tocollege graduate (31%)

Absences—lack of workattendance for the past 2½years. Lack of attendanceconsisted of nonoccupational,full-day absences.

A pronounced positive skewcompounded by severalcomparatively extreme scoresin the absence data resultedin a natural logtransformation.

Aggressive operators wouldbelieve that the standards in thishigh-reliance work setting wereoverly strict, serving to renderthem submissive rather than justensuring safety and reliability.One of the few legitimate meansavailable to these individuals toseek justifiable redress for beingdominated was a subdisciplinarylevel of habitual absenteeism (aform of passive-aggressiveness).Thus, the higher the indicatedaggressiveness, the greater theabsenteeism.

(continued)



Item Characteristics of CR Problemsand Distributions of Scores on the JAGS

The mean p value for the 22 AG alternatives comprising the JAGS for the CRT-Atest was .16. The 14 AG alternatives in the JAGS for the VCRT had a mean p value of.17. These results signified that JMs were instrumental in shaping the reasoning ofonly a small proportion of respondents on a given CR problem. The part-whole corre-


225 undergraduates froma southeasternuniversity; 49% werefemale, and almost allwere White

Conduct violations, whichincluded formal sanctions forcheating, plagiarism, forgery,vandalism, physical violence,theft, possession of illicitdrugs, public drunkenness,and misuse of computeraccounts; 7 students insample (03%) had a violation.Type of violation notdisclosed by university.

Aggressive students would framethemselves as victims of adomineering and uncaringacademic bureaucracy thatdemands submission to arbitraryrules and regulations. Miscon-duct is an act of defiance andbravery that sends a messagethat the powerless have meansto thwart domination andoppression. Consequently, thegreater the aggression, thehigher the probability ofengaging in misconduct.

135 new restaurantemployeesa; mean agewas approximately 24years, 66% werefemale, and companydid not keep records onrace

Attrition; the minimum lengthof stay to be considered a“successful hire” by thecompany was 30 days. Thefollowing dichotomouscriterion was created toconform to this standard: 0 =newly hired worker stayed onthe job for at least 30 days(62% of sample), 1 = newlyhired worker left job beforeend of 30-day period (38% ofsample).

The vast majority of samplemembers were hosts and serverswho had to deal with assertive,difficult, and at times unrulycustomers. These factors wereexpected to trigger the motive toaggress. One key means toexpress the aroused aggressionwas to quit soon after being hired(i.e., disruptive attrition). It wasthus expected that aggressiveemployees would be more likelyto quit early.

105 new packagehandlers in aninternationalorganization thatspecializes in the rapiddelivery of mail andpackages. Mean agewas 23.25, 74% weremale, 52% were AfricanAmerican, 24% wereHispanic, and 23%were White.

Habitual absences—lack ofwork attendance during thefirst 90 days of employment,which is used by thecompany to determine ifemployees should beretained. Rate of absentee-ism was quite high (M = 4.9days, SD = 5.2 days) and,uncharacteristically (for thestudies here), had a slightnegative skew.

Work was well known forengendering quantitativeoverload via its physicaldemands (e.g., lifting heavypackages) and rigid, rapidlypaced time schedules.Aggressive individuals wouldattribute overload to beingexploited and victimized in theinterest of profit and theirabsences as a way to retaliateby impeding the organization’sproductivity.

(continued)

Table 4 (continued)




lations averaged .41 for the CRT-A and .51 for the VCRT. These correlations are con-sistent with those expected for reasoning problems sampled from the same general


111 people selected tobecome members of apool for temporary,entry-level jobs in localbusinesses in asouthern town. Meanage was 29.64 years,64% were male, and82% were White (allremaining individualswere AfricanAmericans).

Supervisory ratings of“reliability” on the firstassigned temporary job.Psychometric analysesprovided the following 3-pointscale: 0 = performed reliably(34% of sample);1 = performed unreliably(e.g., accepted offer oftemporary employment butfailed to show up forassignment, did not completeassignment; 61% of sample);2 = engaged in blatantlyunreliable behavior (e.g.,threatened supervisor, falselyclaimed to be injured; 05% ofsample).

Aggressive people are predisposedto engage in deviant, delinquent,retaliatory, dysfunctional, andobstructive behaviors, whichtypically earns them the label ofunreliable (Hogan & Hogan,1989). Aggression is an intrinsiccomponent of unreliabilitybecause deviance, delinquency,retribution, and the like ofteninvolve intentionally hostileattempts to harm an organizationby exacting revenge andretaliation in ways that disruptwork schedules, impedeproductivity, weaken morale,undermine authority, andencourage rebelliousness(James, 1998).

95 undergraduates froma northwesternuniversity; 55% werefemale, 38% wereAsian, 0.4% wereHispanic, 0.1% wereAfrican American, and57% were White.

Theft of prizes reserved formembers of the winninggroup (Group A; n = 47) bymembers of the losing group(Group B; n = 95). Aftercompleting conditionalreasoning tests foraggression and psychologicalprofiling tasks, members ofGroup A were given prizes(e.g., CD-ROMs) for correctlyidentifying a greaterproportion of antisocialprofiles. After members ofGroup A and theexperimenter left the room, 6members of losing Group B(6.3%) stole a prize(witnessed by confederate inGroup B).

Profiling task for winning Group Awas clearly easier than profilingtask for losing Group B, whichwould fuel feelings of inequityand anger among members ofGroup B. Aggressively disposedGroup B members wouldretaliate (e.g., get revenge)against experimenter by stealinga prize.

191 undergraduatemales from asoutheastern universitywho participated inintramural basketball.Almost all participantswere White.

Number of hard foulscommitted (personal foul inwhich opponent is knocked toground) and fights started bya player. Fifteen players(08%) engaged in at leastone such physical altercationduring five-game season.

Aggressive individuals are morelikely than nonaggressiveindividuals to engage in physicalattacks.

Table 4 (continued)


(continued)



domain of content (Nunnally & Bernstein, 1994). The higher correlation for the VCRTwas partially a function of one rather high item-total correlation of .79.

The distributions of scores on the JAGS for the CRT-A and the VCRT were similar(see Table 5). Noticeable features of both distributions are (a) positive skews and (b)leptokurtosis, with a peak occurring at a score of approximately 5 to 6. Statistical testsof skew and kurtosis are reported at the bottom of Table 5. The test values (based on Zvalues) all exceeded their respective standard errors by margins indicating signifi-cance (p < .05). (The larger Z values for the VCRT likely reflect comparatively greatersampling error.) These results indicate that JMs for aggression were instrumental inshaping the reasoning of approximately 15% of the respondents (approximately 85%of respondents had scores of less than +1 standard deviation on the JAGS). This per-centage is consistent with general findings that aggressive personalities are notwidespread in the general population (see Millon, 1990).

Factor Analysis

An exploratory factor analysis of the CR problems sought to discern their latentstructure. We hoped that this structure would reflect the JMs used to construct the


191 undergraduates froma southwesternuniversity; 70% werefemale and most wereWhite.

Lack of truthfulness andcheating on an Internet-based simulation (IBS).Students completed anonline, multiple-choice mathtest, ostensibly to evaluate anew assessment procedure.Unknown to students, theprocedure was controlled byan IBS that frustrated theirattempts to take the test bycreating “program errors”(e.g., login failures, sendingfalse messages, failing torespond to commands). TheIBS also recorded retaliatoryactions, including whetherstudents (a) stated that theyhad read the instructionswhen they had not, (b) used atest key (they thought wasmistakenly provided) tochange answers, and/or (c)falsely reported that they hadcompleted the test (to endtheir participation).

Aggressive students would befrustrated and angry about beingrepeatedly subjected to programerrors, which would trigger adesire to retaliate against theInternet test. Aggression wouldbe expressed by lying about theextent of their participation and/or taking advantage of theopportunity to cheat.Approximately 7% of thestudents engaged in two of thepossible actions, while anadditional 31% engaged in oneof the possible actions. Sixty-twopercent did not reactaggressively.

a. No conditional reasoning test was used for selection in any of the industrial validation studies.Tests were generally administered during the hiring process or just after selection during orientation/initial training.

Table 4 (continued)




problems. However, we were aware that one or more of the JMs might not be empiri-cally distinguishable and define a separate latent variable. Another possibility was thatthe CR problems would factor by JMs, but the factor structure would be complex inunpredictable ways. Given these uncertainties, we decided to use exploratoryprocedures.

A principal components (1.0s in diagonal) of polychoric correlations was con-ducted, followed by an oblique (Harris-Kaiser II) rotation. Standard criteria indicatedadoption of the five-factor solution shown in Table 6 (eigenvalue of factor >1.0, alphacoefficient of factor >.70, at least three variables with loadings of .30 or greater, and ascree test). (Recent discussions of the criteria for deciding how many factors to retainare presented in Conway & Huffcutt [2003] and Fabrigar, Wegener, MacCallum, &Strahan [1999].) As hoped, the CR problems factored by JMs, with approximately a70% congruency between the loadings and the JM(s) we used to build each problem.Approximately one half of the CR problems (i.e., 10) loaded on more than one factor,which indicates that, as illustrated by the problems in Table 2, conditional reasoning isoften shaped by multiple JMs. However, no general pattern of multiple shapingemerged, as indicated by the significant but modest correlations among the factors atthe bottom of Table 6.

One JM, the derogation of target bias, is not represented in the five factors. This islikely due to the fact that only one CR problem (Problem 19) based partially on this JMsurvived the test construction. We will likely drop this JM in the future.

A condensed version of the conclusion underlying the AG response is indicated foreach CR problem in Table 6. (Please remember that these are inferences used to solvereasoning problems, not attitude statements.) The themes running through these infer-ences combined with our knowledge of how the problems were constructed suggestedthe following designations for the factors: Factor 1, Social Discounting Bias; Factor 2,


Table 5Distributions of Scores on the JAGS for CRT-A and VCRT

CRT-A VCRT

Score Frequency % Cumulative % Frequency % Cumulative %

3.00-3.99 230 14.4 14.4 26 11.6 11.64.00-4.99 224 14.0 28.4 54 24.0 35.65.00-5.99 391 24.4 52.8 49 21.8 57.46.00-6.99 424 26.4 79.2 38 16.9 74.37.00-7.99 144 9.0 88.2 30 13.3 87.68.00-8.99 110 6.9 95.1 17 7.6 95.29.00-9.99 37 2.3 97.4 5 2.2 97.410.00-10.99 31 1.9 99.3 3 1.3 98.711.00-11.99 9 0.6 99.9 0 012.00-12.99 2 0.1 100 3 1.3 10013.00-13.99 1 0.01 100Total 1,603 100 100 225 100 100Test of skewness 0.68 1.24

Standard error 0.06 0.16Test of kurtosis 0.52 2.33

Standard error 0.12 0.32

Note:JAGS = Justification of Aggression Scale;CRT-A = conditional reasoning test for aggression;VCRT = verbal/visual conditional reasoning test.



Victimization by Powerful Others Bias; Factor 3, Retribution Bias; Factor 4, HostileAttribution Bias; and Factor 5, Potency Bias.

In sum, results of the factor analysis were generally consistent with the psychologi-cal theory used to build the CR problems.

Estimates of Reliability

Three types of reliability were estimated, which cumulatively point to the internalconsistency and stability of the conditional reasoning instruments. The first type ofreliability involved internal consistency estimates (alpha coefficients) for each of thefive factors. As reported in Table 6, the estimates varied from .87 to .74 (n = 1,603).Internal consistencies for scores on the CRT-A and the VCRT were also estimated. Theestimate of reliability for the 22-problem JAGS from the CRT-A was based on aKuder-Richardson (Formula 20) coefficient, which used the average item-total


Table 6Factor Analysis of 22 Conditional Reasoning Problems

Factor Loadings

Problem Conclusions Underlying Aggressive Response 1 2 3 4 5

1 Tardiness is a way to show disrespect .32 –.302 To be successful, salespeople must be disliked .383 Peacetime generals do poorly in war .774 Eye for an eye precludes first strike .47 .575 Bosses fear strong employees –.35 .576 No reason to be dependable in technological society .307 Boy/Girl Scouts prepare people to be good followers .598 Rich people fear the homeless .40 .399 Companies are not concerned with rank-and-file employees .38

10 Duels help identify good leaders .42 –.4711 Scrutiny implies rivalry .6512 Gun carriers will shoot if attacked .3113 Americans built cars to wear out .32 .4914 Surveillance is an excuse to bother people .40 .6815 Bonuses are used to control employees .6516 Searches fail to uncover dishonest employees .3917 Police are ambivalent about deaths of gang members .3518 Unlike wild animals, successful people can be weak .7019 Victims are intentionally hurt by violent criminals .5720 Hiring a lawyer implies will not play fair in divorce .4921 Employees conform because afraid to retaliate .41 .51 .3122 Small countries can be made to follow agreements .32 .58

Factor reliabilities (α) .87 .82 .81 .76 .74Factor intercorrelations

Factor 1 1.00Factor 2 .25* 1.00Factor 3 .18* .20* 1.00Factor 4 .07* .10* .06* 1.00Factor 5 .19* .16* .16* .08*1.00

*p < .05.



polyserial correlation (cf. Gulliksen, 1950, chaps. 16 and 21). The estimate was .76,which is based on the same combination of samples as used in the factor analysis (n =1,603). The internal consistency estimate obtained for the 14-problem JAGS from theVCRT using the same procedure was .78 (Sample 6, n = 225). These results indicatethat the total scores on the JAGSs for the CRT-A and VCRT are reliable estimates ofthe true scores that would be obtained if all possible CR problems from a heteroge-neous domain of CR problems for aggression were answered.

The third and final estimate of reliability was based on a hybrid alternative formsanalysis. Undergraduates in a development sample (Sample 5 in Table 3; n = 276) weregiven an early version of a CRT for aggression (25 problems) during the 1st week of asemester. Two months later, they were given a VCRT, which at that time had 12 of the25 CRT problems translated into the VCRT format. Percentage agreement was com-puted for each of the 12 problems. These values ranged from 64.9% to 94.6%, with amean of 81.4% agreement. The estimated correlation between the total score on the12-problem VCRT and a composite score based on the 12 CRT problems shared withthe VCRT was .82. This correlation is suggestive of a reasonable degree of stability inresponses to CR problems as well as a reasonable degree of comparability in the scoresproduced by a CRT format and a VCRT format.

Empirical Validities

Results of 11 empirical validation studies are presented in Table 7. Before discuss-ing these findings, however, it is necessary to address the criteria we used as behavioralindicators of aggression.

Behavioral manifestations of aggression. The most dramatic manifestations ofaggression are violent acts meant to be highly injurious to a target. However, acts suchas physical assault and homicide have low base rates and constitute only a fraction ofthe behaviors that are meant to harm others (cf. Baron & Richardson, 1994; Borum,1996). In this regard, Folger and Baron (1996) indicated that

some workplace aggression research concentrates on physical forms of assault of anactive and direct nature. . . . Such assaults have the flavor of high drama, but they donot adequately represent the full gamut of workplace aggression. Clearly, additionalresearch should address other forms of aggression, such as the passive and indirectvariety. (p. 70)

Folger and Baron (1996, p. 70) suggested that one of the reasons that research hasfailed to devote attention to the passive and indirect forms of aggression is that investi-gators have “failed to view these behaviors as aggressive.” Of particular concern hereis that hostile intentions are easily concealed, and thus what is truly an aggressive be-havior (e.g., theft, not showing up for work) is attributed to nonhostile motives, such aspersonal gain or laziness. However, the behavior may in fact be intended to harm (e.g.,to exact retaliation or revenge for a perceived injustice by impeding productivity or un-dermining authority; cf. Skarlicki & Folger, 1997) and thus can certainly be fully orpartially an act of aggression for many people. Neuman and Baron (1998) make thispoint rather forcefully with respect to a manifestation of aggression they refer to as“obstructionism.”




88

Tabl

e 7

Unc

orre

cted

Val

iditi

es fo

r S

core

s on

the

JAG

S

Sam

ple

Num

ber

and

Crit

erio

nn

Sam

ple

Inst

rum

ent

Exp

erim

enta

l Des

ign

Unc

orre

cted

Val

idity

a

1.S

uper

viso

ry r

atin

g, o

vera

ll pe

rfor

man

ce14

0P

atro

l offi

cers

CR

TC

oncu

rren

t–.

492.

Abs

ence

s, la

ck o

f cla

ss a

ttend

ance

188

Und

ergr

adua

tes

CR

TP

redi

ctiv

e.3

73.

Lack

of t

ruth

fuln

ess

abou

t ext

ra c

redi

t60

Und

ergr

adua

tes

VC

RT

Exp

erim

ent

.49

4.A

bsen

ces,

lack

of w

ork

atte

ndan

ce97

Nuc

lear

faci

lity

oper

ator

sC

RT

Pos

tdic

tive

.42

6.S

tude

nt c

ondu

ct v

iola

tions

225

Und

ergr

adua

tes

VC

RT

Pos

tdic

tive

.55

7.A

ttriti

on13

5R

esta

uran

t em

ploy

ees

CR

TP

redi

ctiv

e.3

28.

Abs

ence

s, la

ck o

f wor

k at

tend

ance

105

Pac

kage

han

dler

sC

RT-

AP

redi

ctiv

e.3

49.

Wor

k un

relia

bilit

y11

1Te

mpo

rary

em

ploy

ees

CR

T-A

Pre

dict

ive

.43

12.T

heft

95U

nder

grad

uate

sC

RT-

AE

xper

imen

t.6

413

.Har

d fo

uls

and

fight

s in

intr

amur

al b

aske

tbal

l19

1U

nder

grad

uate

sC

RT-

AP

redi

ctiv

e.3

814

.Lyi

ng a

nd c

heat

ing

in In

tern

et-b

ased

sim

ulat

ion

191

Und

ergr

adua

tes

CR

T-A

Pre

dict

ive

.40

Not

e:JA

GS

=Ju

stifi

catio

nof

Agg

ress

ion

Sca

le;C

RT

=co

nditi

onal

reas

onin

gte

st;V

CR

T=

verb

al/v

isua

lcon

ditio

nalr

easo

ning

test

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With respect to obstructionism, people do fail to return phone calls, show up late formeetings, absent themselves from work, and delay action on important matters forreasons totally unrelated to aggression. However, when these acts are motivated bymalicious intent (as may often be the case), their effects can be quite damaging to indi-viduals and organizations. (p. 399)

In addition to obstructionism (e.g., unresponsiveness, habitual absenteeism, habit-ual tardiness, and procrastination), easily concealed, nondramatic forms of passiveand/or indirect aggression include early and disruptive attrition, theft, lying, low jobperformance, unreliability, asocial conduct, subtle sabotage of projects or machinery,vandalism, spreading rumors, and failure to issue timely warnings of impendingphysical or financial danger (cf. Ambrose, Seabright, & Schminke, 2002; Baron &Richardson, 1994; Buss, 1961; Iverson & Deery, 2001; Neuman & Baron, 1998;O’Leary-Kelly, Griffin, & Glew, 1996). Skarlicki and Folger (1997) included many ofthese behaviors in what they designated “organizational retaliatory behaviors,” whichare negative workplace behaviors “used to punish the organization and its representa-tives in response to perceived unfairness” (p. 435; see also Greenberg, 1990, 1996,2002). Not only are aggressive individuals more likely to perceive unfairness(Douglas & Martinko, 2001), but they are also more likely to experience the emotionsof anger, outrage, and resentment that engender the desires to punish (i.e., to impose“harmful consequences” or to “respond destructively” toward) that sustain the indirectand covert seeking of retribution (Skarlicki & Folger, 1997).

Additional lines of research further support our position that aggression is one ofthe explanatory constructs underlying our criteria. One such line is “deviant work-place behavior,” which Bennett and Robinson (2000) define as “voluntary behaviorthat violates significant organizational norms and, in so doing, threatens the well-being of the organization or its members, or both” (p. 349; Robinson & Bennett,1995). Included among the developmental deviance measures were theft, absences,falsification of receipts (lying), and low and unreliable job performance. Our criteriacan also be linked to aggression via studies of (a) “counterproductive performance,”which is defined as “voluntary behavior that harms the well-being of the organization”and includes behaviors such as absences, theft, and low productivity (Rutundo &Sackett, 2002, p. 69); (b) “dysfunctional resistance tactics,” which consist of passive-aggressive behaviors such as acting like one did not hear a request (Tepper, Duffy, &Shaw, 2001, p. 975); and (c) displaced aggression, which consists of attempts to harmorganizations by engaging in passive-aggressive behaviors such as absenteeism,tardiness, and turnover (especially early withdrawal; Pearson, 1998).

In sum, aggression is an integral component of organizational retribution, counter-productive performance, dysfunctional resistance tactics, obstructive behaviors,workplace deviance, and displaced aggression. This is because many if not most devi-ant, retaliatory, counterproductive, dysfunctionally resistant, obstructive, and dis-placed behaviors involve intentionally hostile attempts to harm an organization or itsconstituents by exacting retribution, revenge, and retaliation in ways that disrupt workschedules, impede productivity, weaken morale, undermine authority, encouragerebelliousness, and “get even” with a boss or coworkers. To avoid punishment, theseprocesses seldom involve outright violence or acts that are easily detectable as aggres-sion. Rather, they focus on indirect, passive-aggressive behaviors such as failing tocome to work or coming to work late, stealing from those seen as guilty of injustices




(to exact restitution), lying to authority figures (to regain face and obtain retributionfor being disrespected), and performing in poor, unreliable, or improper manners.

The preceding discussion indicates that it is reasonable to regard the criteria for thefirst nine studies in Tables 4 and 7 as relevant indicators of aggression. There is noclaim that aggression is the only latent construct underlying these behaviors or thatthese behaviors exhaust the domain of aggression. Nonetheless, these behaviors haveoften been used as behavioral indicators of workplace aggression and should suffice as“important external variables” (Ozer, 1999) on which to conduct the initial validationstudies for the conditional reasoning measures of aggression. It might also be men-tioned that these criteria are vital to research on human performance in organizations,and the facility to predict them is an important contribution of the conditional reason-ing approach. Finally, to the extent that these behaviors are shaped by factors otherthan aggression, the probability of finding comparatively strong validity coefficientsis reduced.

Results of 11 empirical validation studies. Returning now to Table 7, we predictedthat “justifiers” (people with high scores on the JAGS) would have a significantlygreater probability of engaging in aggressive acts (past, present, and future) thanwould people with low or moderate scores on the JAGS. Results supported this predic-tion. Individuals whose scores indicated that JMs were instrumental in shaping theirreasoning had significantly greater probabilities of engaging in behavioral manifesta-tions of aggression than did individuals whose scores indicated that JMs were notinstrumental in guiding reasoning. Included in this general finding is a recent study(Sample 13) that involved active and direct aggression as a criterion, namely, hardfouls and fights in college intramural basketball (men).

Validities ranged from a low of .32 to a high of .64 and had a mean of .44. If we wereto follow current convention and correct .44 for such things as unreliability in the crite-rion and range restriction in the predictor (e.g., Schmidt & Hunter, 1998), then the .44could surpass .60, depending on the assumptions used for corrections. However, theobserved values in Table 7 reflect what can be expected practically in regard to the useof the JAGS in applications such as selection and placement, and thus, we maintain afocus on observed values.

To put a mean validity of .44 in perspective, consider that uncorrected empiricalvalidities for single predictors against behavioral criteria rarely exceed .40 for aptitudemeasures and .30 for (primarily self-report) personality measures (see Hurtz & Dono-van, 2000; Mischel, 1968). The mean of .44 indicates that the JAGS is capable of gen-erating validities of comparatively large magnitudes for a single, psychologicalpredictor.

A supplement to the empirical validity analysis attempted to shed additional lighton the construct validity of the JAGS scores. The objective of this analysis was toascertain whether scores on the JAGS tended to have higher validities against criteriathat appeared to be stronger operationalizations of aggression. The criteria were sepa-rated into those most likely to be saturated with hostile intent versus those whose satu-ration by hostility was more probabilistic (i.e., alternative explanations were quitelikely for some people). The former criteria included behaviors that were directlyaggressive (hard fouls in basket ball games, Sample 13) as well as behaviors that indi-cated deviance from organizational norms (Bennett & Robinson, 2000) andcounterproductivity (Rutundo & Sackett, 2002) (conduct violations, Sample 6; lying,




Sample 3; theft, Sample 12; and lying and cheating, Sample 14). The mean validity forthese four samples was .50. The latter set of criteria included behaviors for which indi-rect aggression, deviance, and/or counterproductivity were among the possible causes(absences, Samples 2, 4, and 8; attrition, Sample 7; low performance, Sample 1; andunreliability, Sample 9). The mean validity for these six samples was .40.

The small samples of correlations (5 and 6) denote that only preliminary trendsshould be discussed. With this caveat in mind, using meta-analytic proceduresrecently described by Cohn and Becker (2003), we found the means to be significantlydifferent (Ti = 2.12, p .05). These results suggest that scores on the JAGS do tend tohave stronger validities against criteria that are more clearly saturated with aggression,which is consistent with the JAGS being a measure of aggression. The still reasonablyhigh average validity of .40 against criteria that were less clearly covered by theaggression construct suggests that a) whereas origin of behavior cannot be unequivo-cally established, b) aggression may be a frequent contributor to these behaviors aswell, as suggested by Neuman and Baron (1998) with respect to obstructionism.

Correlations Between Scores on theJAGS and Critical Intellectual Skills

Correlations between scores on the JAGS and critical intellectual skills wereobtained on four samples of undergraduates (see Table 3). Measurement of criticalintellectual skills was based on scores from tests developed by American College Test-ing (ACT scores). Scores were obtained from student records after obtaining informedconsent from the students. The overall mean for the sample was approximately 22 witha standard deviation of approximately 4.0.

Of initial note is that there is no theoretical reason to expect a correlation betweenconditional reasoning for aggression and intelligence. If a correlation were observed,then critical intellectual skills might in some way be confounded with responses to theCR problems. For example, NA alternatives might in some rational way, perceptibleby intelligent respondents, be “more logical” than the AG alternatives. However,nonsignificant correlations of –.06, –.05, –.08, and .02 indicated that no suchconfounding took place.

Correlations Between the JAGS and Gender and Race

Correlations between scores on the JAGS and gender are presented in the top por-tion of Table 8. Relationships with race are presented in the bottom part of this table.Results indicated that the JAGS did not correlate significantly with gender in five ofthe seven possible samples. Correlations in two samples indicated a tendency foryoung, adult, educationally motivated men to have slightly higher scores than young,adult, educationally motivated women. This tendency was not consistent across allundergraduate samples and did not extend to workplace samples. In all, a generallylow and nonsignificant correlation between gender and the JAGS is indicated.

A recent review of gender-aggression relationships indicated that (a) whereas menare generally more aggressive than women in neutral conditions, (b) when provoked,differences between men and women in levels of aggressiveness are small, withwomen generally reacting almost as aggressively as men (Bettencourt & Miller,1996). Our results are consistent with our findings. At the implicit level, women are at




most only slightly less cognitively prepared than men are to justify aggressive acts.The issue regarding gender differences appears to be whether a trigger or provocationis needed to unleash defensive processes, as well as perhaps how aggression is mani-fested (e.g., passive vs. active channels, verbal vs. physical channels; Lagerspetz,Bjorkqvist, & Peltonen, 1988).

Race was unrelated to the scores on the JAGS. The nonsignificant correlations inSamples 2 and 9 indicated that mean scores on the JAGS did not differ significantlybetween African Americans and Whites. The nonsignificant F tests in Samples 8 and12 had similar implications (a t test between Whites and African Americans in Sample8 was also nonsignificant). There was thus no indication that different races were moreor less likely to include justifiers.

Relationships Between the JAGS andSelf-Report Measures of Aggression

Self-report measures of conscious (explicit) cognitions have a history of low andoften nonsignificant correlations with various measures, typically projective, of


Table 8Relationships Between Scores on the Justification

of Aggression Scale and Gender and Race

PointProportion Biserial Biserial

Sample Composition Female Correlation Correlation

Correlations with gender (male = 0, female = 1)Sample 2 188 undergraduates 0.34 –.08 –.10Sample 3 60 undergraduates 0.60 –.22* –.29*Sample 6 225 undergraduates 0.49 –.20* –.25*Sample 7 120 restaurant employees 0.66 –.06 –.08Sample 8 105 package handlers 0.26 .04 .05Sample 9 111 temporary employees 0.36 .07 .09Sample 12 95 undergraduates 0.55 .00 .00

Sample Composition Proportion Race Relationship

Relationships with raceSample 2 188 undergraduates 0.90 White (0) r = .06a

.10 African American (1) (r = .10)b

Sample 8 105 package handlers 0.23 White F = 1.430.52 African American0.24 Hispanic0.01 Asian

Sample 9 111 temporary employees 0.82 White (0) r = .070.18 African American (1) (r = .10)

Sample 12 95 undergraduates 0.57 White F = 0.160.38 Asian0.04 Hispanic0.01 African American

a. Point-biserial correlation.b. Biserial correlation, which is recommended by Lord and Novick (1968).*p < .05.



implicit cognitions (Greenwald & Banaji, 1995; McClelland et al., 1989). Bornstein(2002) recently reviewed this literature and concluded that

a paradox has emerged in contemporary personality research. This paradox—whichmight usefully be termed the heteromethod convergence problem—is as follows:Even when self-report and projective measures of a given trait, motive, or need stateboth predict theoretically related features of behavior, scores on the two tests correlatemodestly with each other. (p. 47)

This pattern of modest relationships between explicit and implicit measures ex-tended to the JAGS (see Table 9). (Conditional reasoning is considered projective inthe sense that JMs are implicitly mapped or “projected” into reasoning [James, 1998].)Scores on the JAGS shared no more, and typically less than, 0.7% of their variancewith self-ascriptions of aggression from recognized self-report personality invento-ries. Based on Bornstein’s (2002) review, the likely reason for these modest rela-tionships is not, as psychometricians have mistakenly thought over the years, lack ofconvergent validity. Rather, self-reports and projective techniques measure comple-mentary aspects of traits, motives, and need states (i.e., explicit and implicit aspects),and there is no simple pattern to how these explicit and implicit components relate. In-deed, the explicit and implicit facets may conflict, one may compensate for the other,they may work in harmony, or they may work independently; it depends on the con-structs and the people.

This is clearly an issue for future research. Readers are referred to excellent newtheories regarding interfaces between explicit and implicit measurements in Bornstein(2002; process dissociations models) and Winter et al. (1998; channeling models).These models are overviewed and integrated with conditional reasoning measures inChapter 5 of James and Mazerolle (2002).

Multiple regression and dominance analyses. The data from Table 9 were enteredinto a multiple regression analysis (the ACT was added for Sample 3), the results ofwhich were interpreted in terms of a dominance analysis (see Table 10). By way ofbrief explanation, dominance analysis estimates the “relative importance of the p pre-dictors in a specific model” (Budescu, 1993, p. 549). These relative importance values


Table 9Correlations Between Scores on the Justification of Aggression Scale

and Self-Report Measures of Aggression

Sample Composition Alternative Measure Correlation

3 60 undergraduates PRF Aggression .14PRF Dominance .05PRF Impulsivity .11

6 225 undergraduates NEO-PI-R Angry Hostility .26*NEO-PI-R Dutifulnessa –.18*

12 95 undergraduates Aggression questionnaire .24*13 191 undergraduates NEO-PI-R Angry Hostility .002

Note: PRF = Personality Research Form (Jackson, 1967); NEO-PI-R = NEO Personality Inven-tory–revised (Costa & McCrae, 1992); aggression questionnaire (Buss & Perry, 1992).a. An indicator of low aggressiveness.*p < .05.



can be interpreted as the proportion or percentage that a particular variable contributesto the R2. For example, in Study 3, the proportional contribution of the JAGS to predic-tion (i.e., to R2) was approximately 83%. The Personality Research Form–based self-report of aggression had approximately an 8% proportional contribution to prediction.Thus, of these two predictors, the JAGS was the more important.

The findings of the dominance analyses denote that the JAGS was relatively moreimportant than the self-report measures in predicting the criteria in all four studies.The JAGS was also relatively more important than critical intellectual skills (ACTscores) in predicting the truthfulness criterion. The self-report measures split oversamples in regard to whether they had significant validities against the criteria. Inevery case, however, the proportional contribution made by the JAGS to the R2 wasgreater, typically substantially so, than the self-report measures.

Discussion of Principles 2 and 3

The conditional reasoning measurement system is a work in progress. Resultsobtained at this point in time indicate that the factor structure of the CR problems isconsistent with the theoretical foundation of conditional reasoning and justificationprocesses, that scores on the JAGS are reliable, that the JAGS validly predicts a rangeof behavioral measures of aggression (which include key criteria of performance inorganizations), and that scores on the JAGS are not generally related to confoundingvariables (critical problem solving skills, gender, and race). In future studies, we willreport our ongoing research on tests of new models regarding interfaces betweenscores on the JAGS and self-reports of aggression, the general lack of susceptibility ofthe CRT-A to faking when given in normal circumstances, validations against addi-tional criteria, and extensions of the research into different populations.


Table 10Multiple Regression–Dominance Analyses

RelativeZero- Beta Importance

Sample Criterion Multiple R Variable Order r Weight (%)

3 Lack of truthfulness .55* JAGS .49* .51* 82.83about extra credit ACT –.07 –.12 2.89

PRF Aggression .16 .17 8.11PRF Impulsiveness .14 .14 6.05PRF Dominance .05 .04 .72

6 Student conduct .61* JAGS .55* .53* 77.60violations NEO Angry Hostility .26* .21* 16.00

NEO Dutifulness –.18* –.13* 6.4312 Theft .70* JAGS .64* .57* 71.7

Aggression questionnaire .44* .30* 28.313 Hard fouls and .45* JAGS .38* .38* 74

fights NEO Angry Hostility .22* .22* 26

Note:JAGS = Justification of Aggression Scale;ACT = American College Testing;PRF = Personal-ity Research Form; NEO = NEO Personality Inventory.*p < .05.



Conclusions

Greenwald and Banaji (1995) encouraged investigators to develop efficient, indi-rect approaches to measure implicit personality. We have described the results of anattempt to construct such an approach for aggression. When viewed as a work in prog-ress, it would seem fair to say that the conditional reasoning technique for measuringaggression receives passing marks on Ozer’s (1999) Principles 1 through 3. In thefuture, the method may or may not attain Ozer’s conception of an ideal personality test.For the present, however, it appears to have reasonably satisfied a number of the exact-ing standards required to build a valid and efficient indirect measurement system. Thissystem has many uses in science (e.g., studies of implicit cognitive processes) and inthe field (e.g., selection of nonaggressive employees). These uses will likely multiplyas researchers and professionals become comfortable with implicit measures and theopportunities they offer in theory development and application.

To illustrate, it is possible to contemplate how the conditional reasoning approachmight be extended beyond the present tests for aggression and achievement motiva-tion. This system is theoretically generalizable to any behavior that is subject to justifi-cation (rationalization) by at least some individuals. Included here are negative traitssuch as antisocial actions, hidden motives such as when a search for excellence isengendered by an obsessive quest for perfection, and biases in favor of one side of adialectic, such as when a leader consistently chooses, and justifies, personal decisionmaking over delegation of authority. Others will surely have creative ideas for howconditional reasoning might be used in the field and in research.

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Lawrence R. James is a professor of psychology at Georgia Institute of Technology in Atlanta, Georgia. Hereceived his B.S., M.S., and Ph.D. degrees from the University of Utah. He has been active in studying the ef-fects of organizational environments on individual adaptation, motivation, and productivity. His method-ological contributions have made possible tests of new models in areas such as personality, organizationalclimate, leadership, and personnel selection. He is a fellow in Division 5 (Evaluation and Measurement) andDivision 14 (Industrial and Organizational Psychology) of the American Psychological Association.

Michael D. McIntyre is a graduate of the Industrial-Organizational Psychology Program at the Universityof Tennessee and is currently a lecturer in the management department at UTK.

Charles A. Glisson is University Distinguished Research Professor and Director of the University of Tennes-see Children’s Mental Health Services Research Center (CMHSRC). He received his doctorate from Wash-ington University and has directed interdisciplinary NIMH-funded research continuously since founding theCMHSRC in 1987. In addition to funding from NIMH, the CMHSRC has received funding from the NationalInstitute of Drug Abuse, Health Resources Services Administration, DHHS Child and Maternal Health,




Casey Family Program, Silberman Fund, and the John D. and Catherine T. MacAurthur Foundation. Dr.Glisson’s research focuses on the organizational context and characteristics of mental health, child welfareand juvenile justice systems and on organizational and community development strategies for improvingtheir effectiveness.

Phillip D. Green is a graduate of the Experimental Psychology Program at the University of Tennessee and iscurrently a research assistant professor in the Children’s Mental Health Research Center at UTK.

Timothy W. Patton is a graduate of the Industrial-Organizational Psychology Program at the University ofTennessee, and is currently a consulting services manager for Development Dimensions International.

James M. LeBreton is an assistant professor of psychology at Wayne State University in Detroit, Michigan.He received his Ph.D. in industrial and organizational psychology with a minor in statistics from the Univer-sity of Tenneessee. He also received his B.S. in psychology and his M.S. in industrial and organizational psy-chology from Illinois State University. His research focuses on the application of social cognition to person-ality theory and assessment, applied psychometrics, and the application and development of new researchmethods and statistics to personnel selection and work motivation.

Brian C. Frost is an advanced doctoral student in the Industrial-Organizational Psychology Program at theUniversity of Tennessee.

Sara M. Russell is an advanced doctoral student in the Industrial-Organizational Psychology Program atthe University of Tennessee.

Chris J. Sablynski is an assistant professor of human resource management at California State University,Sacramento. He received his Ph.D. in human resource management and organizational behavior from theUniversity of Washington. His current research interests include employee retention, employer branding,and workplace aggression.

Terence R. Mitchell received his undergraduate degree from Duke in 1964, an advanced diploma in publicadministration from the University of Exeter in England in 1965 and a master’s and Ph.D. from Illinois in1969 in organizational psychology. He has been at the University of Washington since 1969. He was ap-pointed the Carlson Professor of Management in 1987. He has published numerous journal articles andbook chapters on the topics of motivation, leadership, and decision-making. He is a fellow of the Academy ofManagement and the American Psychological Association and in 1999 he received the SIOP DistinguishedScientific Contribution award.

Larry J. Williams was the editor of Organizational Research Methods and is the director of the Center for theAdvancement of Research Methods and Analysis at Virginia Commonwealth University. He received hisPh.D. in organizational behavior from the Indiana University.




a conditional reasoning measure for aggression

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