development and initial validation of a procedural and distributive just world measure

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Personality and Individual Differences 43 (2007) 71–82

Development and initial validation of a proceduraland distributive just world measure

Todd Lucas a,*, Sheldon Alexander b, Ira Firestone b, James M. LeBreton b

a Center for Behavioral and Decision Sciences in Medicine, University of Michigan,

300 North Ingalls, 7C27, Ann Arbor, MI 48109-0429, United Statesb Department of Psychology, Wayne State University, United States

Received 6 February 2006; received in revised form 30 October 2006; accepted 3 November 2006Available online 19 December 2006

Abstract

Belief in a Just World Theory proposes that individuals need to believe that people get what theydeserve. We suggest that individual differences in just world beliefs may reflect perceived deservedness ofboth procedural and distributive justice criteria. Two samples were used to analyze the reliability, factorstructure, invariance and validity of a newly proposed just world measure. In both samples, there wasstrong evidence of distinct procedural and distributive just world beliefs, suggesting that individuals maybe characterized not only by their consideration for the deservingness of outcomes, but also rules, processesor interpersonal treatment. This new focus on a procedural–distributive just world distinction can bridgeindividual differences issues with other areas of justice research, where the importance of procedural justicejudgments is well recognized.� 2006 Elsevier Ltd. All rights reserved.

Keywords: Just world; Procedural justice; Distributive justice; Interactional justice

0191-8869/$ - see front matter � 2006 Elsevier Ltd. All rights reserved.doi:10.1016/j.paid.2006.11.008

* Corresponding author. Tel.: +1 734 615 8377.E-mail address: [email protected] (T. Lucas).

mailto:[email protected]

72 T. Lucas et al. / Personality and Individual Differences 43 (2007) 71–82

1. Introduction

Lerner (1980) suggested that individuals want to believe they live in a world that is fair. Thisneed is manifested in how individuals cope with, and react to, experiences of justice andinjustice. Lerner’s ideas comprise the well known Belief in a Just World (BJW) Theory, whichsuggests that individuals strive to believe that they live in a world in which people generally getwhat they deserve (Lerner & Miller, 1978). BJW has garnered considerable attention as anindividual difference construct, where attempts to characterize one’s stable endorsement of a fairand just world are undertaken. This research has generated both a multitude of theoreticalconceptualizations and a host of accompanying self report instruments (Furnham, 2003).Despite the vast array of measures now available, some potentially important conceptualiza-tions of BJW remain undeveloped. In particular, measures have not yet been formally construedin terms of one universally established tenet of justice theory and research; the distinction be-tween procedural and distributive justice. In this study, we report on the development of anew measure in which we distinguish between procedural and distributive just world beliefs.We suggest that it is both possible and useful to measure BJW utilizing these distinct justicedomains.

1.1. Individual differences measures of just world beliefs

The flagship individual differences measure of BJW is the (Rubin & Peplau (1975)) twenty-iteminstrument, which is often used to relate just world beliefs to important behaviors or evaluations.Despite its popularity, this measure has been limited by undesirable and inconsistent psychometricproperties, such as low to moderate internal consistency estimates and inconsistent factor solu-tions (Furnham, 2003; Mudrack, 2005). Researchers have therefore attempted to develop im-proved BJW measures. Accordingly, there are now over a dozen published instruments, andthese include both unidimensional (e.g. Dalbert, Montada, & Schmitt, 1987; Lipkus, 1991) andmultidimensional (e.g. Furnham & Procter, 1992; Maes, 1998) measures. In addition to their im-proved psychometric qualities, some new measures have expanded the theoretical underpinningsof BJW. For example, (Maes (1998)) used a temporal dimension to distinguish between immanent(just payment for prior occurrences) and ultimate (just payment will eventually occur) just worldbeliefs.

1.2. Procedural and distributive justice

Although newer measures offer improvements, individual differences research on BJW contin-ues to intersect only minimally with some other important theories and research on psycholog-ical justice. A possible explanation is that currently available BJW measures do not apply someuniversally accepted theories of justice. Specifically, it is most curious that no individual differ-ence measure of BJW has attempted to formally distinguish between procedural and distributivejustice; perhaps the most ubiquitous concept within the study of psychological justice. Distrib-utive justice involves evaluation of the fairness of outcomes, allocations or distribution ofresources (Adams, 1965; Walster, Walster, & Berscheid, 1978), while procedural justice is

T. Lucas et al. / Personality and Individual Differences 43 (2007) 71–82 73

concerned with the evaluations of the fairness of decision processes (Thibaut & Walker, 1975;Lind & Tyler, 1988). Research suggests that individuals make unique judgments about the fair-ness of outcomes versus procedures, and that both types of judgments can predict importantbehaviors, decisions or evaluations (Tyler & Smith, 1998). Individuals may concern themselveswith procedural justice to the extent that it can affect distributions or allocations (Thibaut &Walker, 1975), or that it can convey information to the individual about group valuation orsocial standing (Lind & Tyler, 1988; Tyler & Smith, 1998). In this vein, a major contributionof justice research has been to suggest that procedural justice is often at least as importantto individuals as distributive justice.

1.3. The present research

Just world beliefs may be rooted not only in perceived deservingness of outcomes, but also pro-cesses, rules, or interpersonal treatment. Interestingly, while individual differences measures ap-pear to be somewhat universal in assessing distributive aspects of BJW, procedural aspectshave eluded formal measurement. A procedurally just world could reflect individual differencesin the deservedness of formal rules and processes (Leventhal, 1980) or interpersonal treatment(Tyler & Bies, 1990). In the present research, we report on the development of a new self reportmeasure of BJW. In accordance with established justice theories, we hypothesized distinct proce-dural (PJW) and distributive (DJW) just world components. In addition, we assessed the reliabil-ity, validity and invariance of our PJW–DJW concept.

We examined discriminant and convergent validity of our PJW and DJW measures using singleadministration and multiple-group validity methods. For multiple-group validity, we correlatedPJW and DJW with a well known health attribution measure. Numerous studies have suggestedthat just world beliefs relate positively to attributions of responsibility. We thus expected thatPJW and DJW would be positively associated with internal attributions for health. In addition,we expected that PJW would reflect interpersonal aspects of health responsibility (e.g. interactionswith one’s doctor) while DJW would reflect health and illness outcomes (e.g. whether one getssick). Finally, we examined the incremental validity of PJW and DJW in predicting healthattributions.

2. Method

2.1. Participants

Two samples of undergraduate psychology students from colleges in the same Midwestern statewere recruited for this study. The first sample (N = 152) was from a small liberal arts college andwas comprised of predominantly Caucasian students from relatively affluent backgrounds. Thesecond sample (N = 274) was from a large urban university and was comparatively diverse.Demographic and socioeconomic characteristics of both samples are summarized in Table 1.All participants received research credit for their participation.

Table 1Sample demographic characteristics

Characteristic Sample 1 (N = 152) Sample 2 (N = 274)

n % n %

GenderMale 54 35.5 83 30.3Female 98 64.5 191 69.7

EthnicityCaucasian 141 92.8 138 50.5African-American 4 2.6 79 28.9Hispanic 1 0.7 8 2.9Asian-American 2 1.3 18 6.6Middle-eastern 1 0.7 19 7.0Other 2 0.7 12 4.0

AgeLess than 20 89 58.6 82 29.920 to 30 63 41.4 158 57.730 or older 0 0 34 12.4

Family income< $20,000 8 5.3 17 6.2$20,000–$39,999 19 12.5 55 20.1$40,000–$59,999 30 19.7 70 25.5$60,000–$79,999 32 21.1 57 20.8$80,000–$99,999 22 14.5 31 11.3>$100,000 41 27.0 44 16.1


2.2. Measures

2.2.1. Belief in a just worldPrior to the present research, we conducted several pilot studies at the sample 2 university.

These studies provided an initial assessment of potential items to include in our measure. Basedon pilot work, we selected five items each to measure PJW and DJW (Table 2). All items wereanswered using a 7-point Likert-type scale ranging from ‘strongly disagree’ to ‘strongly agree’.We also administered the (Rubin & Peplau (1975)) BJW instrument, which includes 20 items an-swered on a six-point Likert-type scale.

2.2.2. Health locus of controlWe administered the multidimensional health locus of control scale (Wallston, Wallston, & DeV-

ellis, 1978). This eighteen-item health attribution measure contains three well-validated subscales.‘Internal’ measures one’s belief that internal (personal) factors are responsible for well being, thusconstruing health and illness as outcomes. ‘Powerful others’ measures one’s belief that health isdetermined by important others such as doctors, thus invoking perceived interpersonal treatment.‘Chance’ measures one’s belief that health and illness are matters of luck, thus invoking perceptionsof either outcomes or processes. Since ‘powerful others’ is a decidedly interpersonal framing of

Table 2Exploratory principal axis factor analysis for sample 1 (N = 152)

Initial analysis Final analysis

Firstfactor

Secondfactor

Firstfactor

Secondfactor

Distributive just world1. I feel that people generally earn the rewards and punishments

that they get in this world.936 �.216 .996 �.270

2. People usually receive the outcomes that they deserve 1.220 �.445 1.294 �.5233. Regardless of the processes used, people usually receive fair outcomes .589 .222 – –4. People generally deserve the things that they are accorded .994 �.182 1.021 �.2165. I feel that people usually receive the outcomes that they are due 1.027 �.161 1.064 �.204

Procedural just world1. I feel that people are generally treated according to fair processes .226 .544 – –2. People usually use fair procedures in dealing with others �.310 1.047 �.352 1.0733. I feel that people generally use methods that are fair in their

evaluations of others�.303 1.060 �.349 1.102

4. Regardless of the outcomes they receive, people aregenerally subjected to fair procedures

�.201 1.045 �.266 1.113

5. People are generally subjected to processes that are fair �.204 1.035 �.274 1.107

Note: Direct oblimin rotation with Delta value of 0.6 performed for both analyses.Eigenvalues for initial analysis = 5.99 (DJW) and 1.33 (PJW).Eigenvalues for final analysis = 1.57 (DJW) and 4.85 (PJW).Final analysis excludes DJW Item 3 and PJW Item 1.


locus of control, we hypothesized that it should relate more to procedural justice. Alternatively,since ‘internal’ relates to specific health outcomes, we expected it to reflect distributive justice.

2.3. Procedure

Measures were administered anonymously as paper and pencil surveys to participants during aregularly scheduled class meeting. Participants read instructions for each questionnaire, and gen-erally took between 5 and 15 min to complete the entire battery.

3. Results

3.1. Exploratory factor analysis

Exploratory principal axis factoring was used to identify the sample 1 factor structure. Based ontheoretical rationale, and on strong PJW/DJW latent variable correlations that we present subse-quently, we used oblique rotation and an a priori delta value of 0.6. Factors with eigenvalues great-er than 1.0 were interpreted, and this procedure was verified by visual examination of a scree plot.

As seen in Table 2, exploratory factor analysis supported a PJW–DJW measurement model.Two factors had eigenvalues greater than one, and together accounted for 70.28% of the total


variance. Five items measuring DJW loaded onto factor one, while five items measuring PJWloaded onto factor two. Although above traditionally acceptable benchmarks, two item loadingswere comparatively weak. The first item was distributive (‘regardless of the processes used, peopleusually receive fair outcomes’) while the second item was procedural (‘I feel that people are gen-erally treated according to fair processes’). The overall result was highly similar when we reran theexploratory analyses excluding these two less robust items. We thus settled on a final exploratorysolution consisting of two distinct factors, each indicated by four items with strong and orthog-onal factor loadings.

3.2. Confirmatory factor analysis

We used confirmatory factor analysis to evaluate the fit of the sample 1 factor solution insample 2. Confirmatory analyses were performed using LISREL 8.30 (Joreskog & Sorbom,1999) and maximum likelihood estimation. In all instances, the covariance matrix was analyzed,and scale was set using the disturbance term of the PJW–DJW latent variables. Because of themoderately large sample size, we expected a significant v-square statistic (Marsh, Balla, &McDonald, 1988). We therefore examined several additional fit indices, including both thenormed (NFI) and non-normed (NNFI) fit index (Bentler & Bonett, 1980), the comparative fitindex (CFI; Satorra & Bentler, 1994), and the root-mean-square error of approximation(RMSEA; Browne & Cudeck, 1993). Values of .90 or higher are thought to indicate good overallfit for the NFI, NNFI and the CFI, while values between .05 and .08 are generally consideredacceptable for the RMSEA.

Although v-square was significant, v2 (19, N = 274) = 163.21, p < .001, most other indices ap-proached benchmarks for acceptable fit, NFI = .91, NNFI = .88, CFI = .92, RMSEA = .16 (90%confidence interval: 0.14, 0.19). Item loadings were substantial for both DJW (.77–.89) and PJW(.76–.91), and all were significant at p < .001. The PJW and DJW latent constructs were signifi-cantly correlated, .58, p < .001. In addition, the two factor structure was superior in fit to a modelthat estimated only a single undifferentiated just world factor; Dv2 (1, N = 274) = 434.92, p < .001,NFI = .65, NNFI = .51, CFI = .653, RMSEA = .36 (90% confidence interval: 0.34, 0.38). Ourlargest modification indices were for three error covariances (DJW1, DJW2; DJW3, DJW4;PJW1, PJW2), v2 (3, N = 274) = 158.97, p < .001. Although a tenuous solution to moderate fitindices, overall fit improved if the two factor model was respecified to include these pairs as freelyestimated parameters, NFI = .97, NNFI = .97, CFI = .98, RMSEA = .07 (90% confidence inter-val: 0.05, 0.10), v2 (16, N = 274) = 43.06, p < .001. No other modification indices were substantial.

3.3. Gender and ethnic invariance

Next, we examined the proposed measurement model for gender and ethnic invariance (i.e.measurement equivalency). We assessed equivalency of factor loadings using a global test ofcovariance structure equality (Vandenberg & Lance, 2000). We initially ran an unconstrainedmodel, in which subgroup parameters were estimated independently. We then ran a second modelin which parameters were constrained to be equal across groups (e.g., corresponding male and fe-male factor loadings are equal). If overall fit was retained in the second model, we concluded gen-der/ethnic invariance.


For gender, we constrained female estimates to equal those of males. The initial model fit well,NFI = .96, NNFI = .95, CFI = .97, RMSEA = .10 (90% confidence interval: 0.07, 0.13), v2 (31,N = 274) = 73.21, p < .001. In addition, though v-square difference was significant, Dv2 (8,N = 274) = 26.93, p < .001, there were no substantial reductions in other fit indices in the con-strained model, NFI = .94, NNFI = .94, CFI = .96, RMSEA = .10 (90% confidence interval:0.08, 0.13), v2 (39, N = 274) = 100.14, p < .001. The largest suggested modification was for errorcovariance of PJW item 3 (‘regardless of the specific outcomes they receive, people are generallysubjected to fair procedures’). Releasing this constraint did improve the fit, Dv2 (1,N = 274) = 4.59, p < .05. However, no improvements to fit were suggested by independently esti-mating any single factor loading. We thus concluded gender invariance.

For ethnicity, we constrained African-American estimates to equal those of Caucasians. Theinitial model again fit well, NFI = .95, NNFI = .96, CFI = .98, RMSEA = .08 (90% confidenceinterval: 0.04, 0.12), v2 (31, N = 274) = 55.08, p < .001. Although v-square difference was signif-icant Dv2 (8, N = 274) = 18.79, p < .05, fit was generally retained in the constrained model,NFI = .94, NNFI = .95, CFI = .97, RMSEA = .09 (90% confidence interval: 0.05, 0.12), v2 (39,N = 274) = 73.87, p < .001. The largest suggested modification was for PJW item 2 (‘I feel thatpeople generally use methods that are fair in their evaluations of others’). Removing this loadingconstraint improved model fit Dv2 (1, N = 274) = 10.10, p < .001. We thus concluded ethnicinvariance, with the possible exception that PJW item 2 could be estimated independently.

3.4. Internal consistency

Cronbach’s (1951) alpha was used to examine internal consistency. For sample 1, the four-itemPJW (a = .92) and DJW (a = .92) measures were consistent. PJW (a = .89) and DJW (a = .88)measures were also reliable in sample 2. PJW–DJW Pearson correlations were strong and positivein both sample 1 (r = .51, p < .001) and sample 2 (r = .48, p < .001).

3.5. Convergent–discriminant validity analysis

To examine convergent and discriminant validity, we first performed a multitrait scalinganalysis. In this method, convergent validity exists when corrected item-total correlations exceed.40 (Hays, Hayashi, Carson, & Ware, 1988), while discriminant validity exists when single-itemcorrelations are substantially stronger with hypothesized than with non-hypothesized constructs.Table 3 shows that item-total correlations were acceptable for both PJW (0.72–0.78) andDJW (0.70–0.76). In addition, correlations between single-items and congruent measures weresubstantial (PJW = 0.81–0.89; DJW = 0.83–0.86), while correlations with incongruent measureswere low (PJW = 0.40–0.53; DJW = 0.37–0.55). Convergent and discriminant validity were thussupported.

We also examined validity using a multitrait–multiethnicity correlation matrix. Specifically, wecorrelated PJW and DJW with the Rubin and Peplau measure and the three health locus of con-trol subscales for the entire study 2 sample, as well as separately for Caucasians and African-Americans. Convergent validity was suggested to the extent that (1) PJW correlated positivelywith the ‘powerful others’ subscale; (2) DJW correlated positively with the ‘internal’ subscale;(3) both PJW and DJW correlated positively with the Rubin and Peplau measure. Discriminant

Table 3Multitrait scaling analysis of procedural and distributive just world scales (N = 274)

Internal consistency(if item deleted)

Itemconvergence

Item discrimination(other scale correlation)

M SD Skew Kurtosis

Procedural just world .89 19.39 5.60 0.12 �1.00Item 1 (.86) .75 .85 (.37) 3.81 1.32 0.87 �2.63Item 2 (.88) .72 .81 (.37) 3.73 1.32 0.80 �2.29Item 3 (.85) .79 .86 (.55) 3.91 1.29 �0.11 �2.47Item 4 (.85) .78 .89 (.52) 4.00 1.31 �0.73 �2.48

Distributive just world .88 21.99 5.91 �3.36 �0.03Item 1 (.85) .73 .84 (.40) 4.71 1.58 �3.95 �1.61Item 2 (.83) .76 .85 (.41) 4.53 1.43 �2.99 �1.46Item 3 (.86) .70 .83 (.49) 4.44 1.31 �2.78 �0.90Item 4 (.83) .77 .86 (.53) 4.39 1.42 �2.86 �1.99


validity was indicated to the extent that correlations among the three just world measures werestronger than those between just world and health locus of control measures. Convergent and dis-criminant validity were bolstered to the extent hypothesized relationships were observed in boththe total sample and ethnic sub-samples.

As expected, the three just world measures were significantly and positively correlated (Table4). Interestingly, the Rubin and Peplau measure was more strongly associated with DJW thanPJW in the Caucasian sub-sample, z = 1.90, p < .05, and in the total sample, z = 1.46, p = .07.Though directionally similar, this result was not significant for the African-American sub-sample,z = 0.35, p = .35. For all three samples, PJW was notably correlated with the ‘powerful others’subscale, while DJW was most strongly correlated with the ‘internal’ subscale. Discriminant valid-ity was evident in that, the three just world measures correlated more strongly with one another,than with the health locus of control measures, and this overall pattern was generally consistent inboth ethnic sub-groups.

3.6. Incremental validity and relative importance

To examine incremental validity, we performed a series of three hierarchical multiple regres-sions. The Rubin and Peplau measure was entered into the first step of each regression, whilePJW and DJW were added in the second step. Each health locus of control subscale served asa criterion. We expected that DJW would improve prediction of the ‘internal’ subscale, whilePJW would improve prediction of the ‘powerful others’ subscale. In both cases, our hypotheseswere confirmed. The prediction of ‘internal’ locus improved in the second step of the regression,DR2 = .06, p < .001, and DJW was a significant predictor, b = .28, p < .001, while PJW was not,b = �.03, p = .69. For ‘powerful others,’ prediction also improved when PJW and DJW wereadded, DR2 = .07, p < .001. PJW was a significant predictor, b = .32, p < .001, while DJW wasnot, b = �.09, p = .24. For the ‘chance’ subscale, there was marginal support for incrementalvalidity, DR2 = .02, p = .08. Individually, neither PJW, b = .05, p = .51, nor DJW, b = .12,p = .13, were significant predictors. However, a non-significant result was also obtained for the

Table 4Multitrait–multiethnic correlates of procedural and distributive just world

Caucasians (N = 138) 1 2 3 4 5 6

1. PJW .872. DJW .29*** .853. RPBJW .19* .40*** .594. HLC-I .09 .16 .11 .785. HLC-C .02 .13 .02 �.45*** .716. HLC-O .22** .03 .19* �.16** .33*** .63

African-Americans (N = 79)1. PJW .922. DJW .59*** .903. RPBJW .33** .38*** .624. HLC-I .10 .28* .16 .635. HLC-C .23* .02 .01 �.24* .506. HLC-O .29** .09 .14 �.04 .28* .65

Total Sample (N = 274)1. PJW .892. DJW .48*** .883. RPBJW .29*** .40*** .594. HLC-I .14* .25*** .13* .725. HLC-C .11 .11* �.01 �.25*** .636. HLC-O .31*** .14* .19** �.01 .34*** .66

Note: PJW = Procedural just world; DJW = Distributive just world; RPBJW = Rubin and Peplau just world measure;HLC-I = Internal subscale; HLC-C = Chance subscale; HLC-O = Powerful others subscale. Cronbach’s alpha givenon diagnol.

* p < .05.** p < .01.*** p < .001.


Rubin and Peplau measure at both the first, b = �.01, p = .82, and second step, b = �.08, p = .24,of the ‘chance’ regression.

To examine relative importance, we conducted a general dominance analysis of the three justworld measures for each health locus of control subscale. General dominance analysis is esti-mated as the mean of each predictor variable’s squared semipartial correlation across all pos-sible subset regression models (Budescu, 1993; Johnson & LeBreton, 2004). Relativeimportance is assessed by comparing the mean-squared semipartial correlations of predictors,which indicates the proportion of the total explained variance that each accounts for. For ‘inter-nal’, PJW and DJW accounted for 13.5% and 74.6% of the explained variance, respectively,while the Rubin and Peplau measure accounted for 11.8%. For ‘chance’, PJW and DJW ac-counted for 42.3% and 46.0% of the explained variance, while the Rubin and Peplau accountedfor 11.7%. Finally, for ‘powerful others’, PJW and DJW accounted for 72.7% and 7.4% of theexplained variance, while Rubin and Peplau accounted for 19.9%. Thus, PJW and DJW weregenerally superior to the Rubin and Peplau measure, especially when predicting a theoreticallycongruent health locus.


4. Discussion

Overall, results supported the proposed PJW–DJW measure, suggesting that individuals maybe characterized by their belief not only in a world with fair outcomes but also fair rules, pro-cedures and interpersonal treatment. Given the ubiquitous presence of procedural fairness, thenewly proposed measures could further bridge BJW with other areas of justice theory and re-search. For instance, Lind and Tyler (1988) suggest that procedural justice is important becauseit may convey information to the individual about group valuation and social standing, and itcould be that PJW beliefs are reflective of this. Thus, the PJW conceptualization could affordresearchers opportunities to apply relational theories of psychological justice to individualdifferences.

The PJW–DJW distinction might also enhance other conventional areas of justice research. Forinstance, studies have shown that satisfaction with authorities improves when individuals perceivethe use of fair procedures (Alexander & Ruderman, 1987; Tyler, 1994). This occurrence might beespecially true for high PJW individuals. Alternatively, the fairness of outcomes determined byauthorities might matter more than the use of fair processes to high DJW individuals. In essence,mainstream justice researchers could explore the potential of PJW and DJW to moderate otherwell-established justice phenomena.

Future directions suggested by the use of health locus of control to validate the PJW–DJWmeasures also warrant discussion. The relationship of PJW and DJW to measures of healthattribution demonstrated the general validity of our proposed measures. However, it is alsonoteworthy that both PJW and DJW were associated with different types of theoretically con-gruent health attributions. Previous research has demonstrated that BJW may relate positivelyto attributions of responsibility. However, less has been done to explicate potential ramificationsof these attributions for health and well being. A potential avenue of research could be to ex-plore ways in which health attributions predicted by PJW and DJW impact health cognitionand behavior.

While both new measures were positively correlated with the Rubin and Peplau measure, it isinteresting that this association was stronger for DJW than PJW. In addition to other docu-mented criticisms, this historically influential measure of BJW could also be limited by its insen-sitivity to procedural aspects of BJW. Future studies will be needed to discern the extent towhich other BJW measures convey procedural versus distributive justice content. However,Mudrack (2005) has recently suggested that many popular measures of BJW can be readilycharacterized according to one or more distributive criteria (deservedness of positive versus neg-ative outcomes).

Two statistical characteristics of the PJW–DJW measures require attention. First, while PJWwas normally distributed, DJW was negatively skewed. Theoretically, this could suggest thatoutcome deservedness is more rigid or socialized than procedural deservedness. Functionally,researchers might consider variable transformations to normalize the DJW measure whenexamining relationships with other variables. Second, while both measures were largely invari-ant, PJW item 2 (‘I feel that people generally use methods that are fair in their evaluations ofothers’) might be estimated differently for Caucasians than African-Americans. However, be-cause of the relatively limited characteristics of sample 2, future multi-ethnic studies will beneeded.


Acknowledgement

We thank Claudia Dalbert for her comments on a previous version of this article.

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