Defining and Evaluating Perceptions of Victim Blame in Antigay Hate Crimes

Abstract

Victimology research often hinges on attribution of blame toward victims despite a lack of conceptual agreement on the definition and measure of the construct. Drawing on established blame attribution and intent literature, the present study evaluates psychometric properties of the Perceptions of Victim Blame Scale (PVBS) using mock jury samples in a vignette-based capital murder antigay hate crime context. Factor analyses show support for a three-factor structure with the following perceptions of victim blame subscales: Malice, Recklessness, and Unreliability. All factors displayed expected positive associations with homonegativity and authoritarianism. Likewise, all factors displayed null relations with trait aggression and social desirability. Only the Malice factor predicted sentencing decisions after controlling for crime condition and support for the death penalty. Results are reviewed with respect to blame attribution theory and practical application of a revised PVBS.

Keywords

victim blame hate crime jury homonegativity authoritarianism

Attribution of blameworthiness is salient in the victimology literature, both as a direct outcome variable (e.g., Davies, Pollard, & Archer, 2006; Fox & Leicht, 2005) and perceiver judgment influencing other dependent measures such as perceptions of an offender (e.g., Osofsky, Bandura, & Zimbardo, 2005; Plumm, Terrance, Henderson, & Ellingson, 2010). From an applied standpoint, victim blame is an important phenomenon to understand as the negative outcomes of blame on victims can be detrimental, including developing greater self-blame, psychological sequelae, higher risk of revictimization, and undesirable legal consequences (e.g., Costanzo & Costanzo, 1992; Miller, Markman, & Handley, 2007; Ullman, 1996). Although it is clear that experiencing victim blame is negative, it is less clear what “blame” specifically consists of. Much literature on victim blame addresses the conceptual definition of blameworthiness, but there is not a widely accepted consensus of what blame is. Partly owing to this, there is yet to be a widely accepted measure of blame, and instead “blame” is usually operationalized with single- or multi- items used interchangeably (e.g., blame, responsibility, culpability).

For the current study, we review the extant literature with emphasis on defining victim blame, clarifying its association with other blame terminology, reviewing major blame attribution theory, considering applications of victim blame, and assessing limitations of how victim blame has been measured to date. We then establish antigay hate crimes as a framework to investigate the nature of victim blame.

What is Blame?

Extant literature has demonstrated a multitude of times that victim blame has a detrimental impact on victims of numerous types of crimes, including violent crimes, sexual assault, and hate crimes (e.g., Abrams, Viki, Masser, & Bohner, 2003; Masser, Lee, & McKimmie, 2010). At the same time, scholarly literature offers discordant perspectives of blame (Robbennolt, 2000). Unfortunately, at the present time the literature appears to have more questions than answers in this regard.

While blame is often assessed with a single item, some research suggests blame is multidimensional, grounded in notions such as inferred intent, and that responsibility is a subcomponent of blame (e.g., Mikula, 2003; Scanlon, 2008). To this end, Shaver and Drown (1986) argue that the construct of blame comprises causality (judgments of how a victim caused the negative event), responsibility (purely behavioral without intent), and blameworthiness (how deserving of blame a victim is). Although some literature suggests that responsibility is a component of, or synonymous with, blame (e.g., Mikula, 2003; Whatley & Riggio, 1993), Chockler and Halpern (2004) argue that blame and responsibility are different constructs in that blame must have intent and responsibility is purely behavioral (i.e., purely based on action without intent). Moreover, morality is often framed as component of blame (Nadelhoffer, 2006; Wiley, 1999).

Although there is little general consensus concerning defining features of blame, its purposes seem clearer. It has been suggested that the functions of assigning blame are to understand other persons’ misfortunes, maintain a sense of personal control as an attempt to decrease perceiver’s own feelings of susceptibility to victimization, and to serve as a consequence of negative outcomes resulting from injurious conduct (e.g., Capezza & Arriaga 2008; Shaver & Drown, 1986). Given the proposed functions of blame, it is important to ground assumptions in theoretical conceptualizations. Two commonly investigated theories of blamed are outlined below.

Theoretical Conceptualizations and Applications of Blame

Several theoretical models exist explicating the process of attribution of blame. For example, just world theory postulates that individuals are motivated to preserve their belief in a just world; that individuals get what they deserve, thus bad things happen to bad people. Because of this, individuals must assume the victim is “bad” and assign high blame for their misfortune, in order to decrease their own vulnerability to such a misfortune (Aguiar, Vala, Correia, & Pereira, 2008, Amacker & Littleton, in press; Lerner, 1980).

Extending the process of how blame occurs beyond being for self-protection, the Culpable Control Model (Alicke, 2000; CMM) asserts that a central role in assigning blame is the perceiver’s spontaneous and automatic emotional reaction following learning of a victim’s misfortune. This theory posits that individuals have a tendency to blame individuals that evoke negative emotions; therefore, if negative affective reactions are made toward the victim, these emotions will fuel victim blame attributions (Alicke, Buckingham, Zell, & Davis, 2008; Miller, Markman, Amacker, & Menaker, 2011).

Application of blame attribution has spread from bystanders to legal decision-makers. This emergent body of literature includes, but is not limited to, personal and professional individuals that victims disclose to (e.g., Ahrens, Campbell, Ternier-Thames, Wasco, & Sefl, 2007; Littleton & Radecki Breitkopf, 2006), witnesses of crimes (e.g., Rayburn, Mendoza, & Davison, 2003), mock and actual jurors (e.g., Costanzo & Costanzo, 1992), and law enforcement personnel (e.g., Osofsky et al., 2005). From an applied legal standpoint, the determination of blame and related constructs is particularly important because these perceptions can influence criminal culpability and, in turn, verdicts and sentencing outcomes for a range of cases (e.g., hate crime and sexual assault cases; Cramer, Chandler, & Wakeman, 2010; Finch & Munro, 2004). As such, the Perceptions of Victim Blame Scale (PVBS: Rayburn et al., 2003) is reviewed in the next section with particular focus in perceptions of hate crimes.

The PVBS

The PVBS, first utilized by Rayburn et al. (2003), contains 14 bipolar adjectives (e.g., violent–nonviolent and malicious-kind), each rated on a 7-point scale and summed for a total score. Each of the 14 items attempts to capture different aspects of perceived blame. The PVBS has been used alternatively as a moderating and dependent variable in several studies examining perceptions of hate crimes (e.g., Cramer et al. 2010; Rayburn et al., 2003). Ratings in the composite victim blame score demonstrate consistent differences by type of hate crime, as well as moderating impacts on sentencing outcomes (Cramer et al., 2010; Rayburn et al., 2003).

Several strengths of the measure should be noted. As with past attempts to measure blame, these strengths include incorporation of responsibility and blameworthiness as items and acceptable internal consistencies. Beyond these aspects, the scale utilizes different victim judgments that may be components of blame (e.g., how malicious and reckless the victim is), leading one to assume that blame may indeed be multifaceted. However, despite blame attribution theory suggesting potential factors of blame, Rayburn and colleagues (2003) failed to conduct factor analyses on the PVBS to assess whether the construct of perceived victim blame contains subcomponents.

The Present Study

Perceptions of Victims of Antigay Hate Crimes

To begin testing multifaceted conceptualizations of blame we selected a mock jury paradigm where perceivers provided judgments of an antigay hate crime. An antigay hate crime appears to be a logical context given that support for hate crime legislation has been found to hinge upon whether sexual minority persons were included (Johnson & Byers, 2003), and that unidimensional conceptualizations of blame have been shown to influence perceptions of antigay hate crimes (Plumm et al., 2010). In conducting a theory test such as the nature of victim blame, it is consistent with a philosophy of science perspective to begin using a conceptual framework where the theory holds potential utility (Popper, 1959). Again, it appears that victim blame possesses predictive utility in the perception of antigay hate crimes (Plumm et al., 2010). In regard to sample selection, it has been suggested that legally related theory tests begin with student mock jury samples for additional test under varying conditions (Haney, 1993; Wiener, Krauss, & Lieberman, 2011).

Research Questions

Research and models of blame establish a conceptual rationale to empirically test whether specific attribution of victim blame is a unitary or multifaceted construct. This is accomplished in the present study through systematic psychometric evaluation of the Perceptions of Victim Blame Scale (Rayburn et al., 2003) using archival data from a previous publication on blame in a hate crime scenario (see Cramer et al., 2010). The previous work focused on the function of a unidimensional conception of blame in perceptions of antigay hate crimes. The present study builds on this previous knowledge. Three sets of analyses are presented to evaluate: (a) whether a single or multifactor model of perceptions of victim blame display the best statistical fit, and (b) psychometric qualities (i.e., internal consistency, convergent, predictive, and divergent validity) of the best fit model.

Proposed Correlates

Perceptions of victims are commonly linked to personality traits and attitudes, which provide important validity data. Two such attitudes that may be related to legalistic evaluations of victim blame are authoritarianism and sexual prejudice (i.e., homonegativity). Literature supports positive relations between blame toward victims thought of as weak (e.g., women, gay males) with both authoritarianism (e.g., Altemeyer, 1996, 2004) and sexual prejudice (e.g., Hill, 2000; Wall, 2000). As such, we anticipate positive associations between perceptions of victim blame with both of these attitudes because the present study utilized variations in victim sexual orientation in case vignettes.

A wealth of evidence suggests that perceptions of blameworthiness and similar ratings impact decision-making and behavior specifically in legal situations (e.g., Bright & Goodman-Delahunty, 2006). As such, we also expect predictive validity in terms of a significant relationship between victim blame subscales and likelihood of assigning the death penalty while controlling for general death penalty attitudes and vignette crime type.

Literature shows that social desirability is negatively associated with conviction proneness among jury eligible community members (Moran & Comfort, 1982), yet is uncorrelated with jury-specific measures such as juror bias (Kassin & Wrightsman, 1983). Given this, we expect social desirability to demonstrate nonsignificant associations with perceptions of victim blame.

Theoretical and empirical literatures portray a significant association between perceiver trait emotion (e.g., anger) and subsequent perceptions of blame and responsibility (e.g., Alicke, 2000; Haidt, 2001). A common explanation for this association is a quick, automatic response to situations based on high trait emotions (e.g., Pacini & Epstein, 1999). However, in their review of the link between trait emotion and perceptions of blame/responsibility, Feigenson and Park (2006) point out an exception in that trait emotions may not impact perceptions of blame when an injustice has been committed (e.g., Goldberg, Lerner, & Tetlock, 1999). Placed in the context of a hate crime, perceptions of victim blame should be unassociated with measures of trait anger if participants perceive murder of the victim as an injustice. Therefore, we hypothesize that perceptions of victim blame will show divergent (i.e., nonsignificant) associations with trait aggression. Social desirability is also hypothesized to show null relationships with PVBS subscales.

Method

Participants

The demographics form from the original data collection included standard demographic characteristics such as age, gender, ethnicity, and religion. Also, support for the death penalty was measured on a 10-point Likert item (range 1 to 10) with higher scores reflecting a greater support for the death penalty, and included as a control variable in order to enhance ecological validity. Attitudes toward the death penalty invariably impact capital trials (i.e., those where the death penalty is a sentencing option; e.g., O’Neil, Patry, & Penrod, 2004). Because the present study uses a capital murder paradigm, Witherspoon death-qualification criteria are also adopted in order to closely replicate capital murder trial procedures. The sample demographics are as follows:

Sample I. Exploratory procedures used a sample from a large public university in the southeastern United States (N = 246). The sample was predominantly female (n = 150, 61.00%), Caucasian (n = 201, 81.71%), Protestant (n = 141, 58.02%), and had a mean age of 18.19 years (SD = 1.05).

Sample II. Participants for confirmatory procedures were drawn from a large public university in the Mid-Atlantic United States (N = 199). They possessed a mean age of 20.68 years (SD = 5.27). Predominant demographic characteristics were a female gender (n = 143, 71.86%), Caucasian ethnicity (n = 107, 54.31%), and Catholic religion (n = 62, 31.16%).

Sample III. Participants (N = 155) were drawn from undergraduate psychology courses at a large public university in the southeastern United States. The sample (M age = 19.39, SD = 1.23) was predominantly male (n = 82, 52.90%), Caucasian (n = 121, 78.06%), and Protestant/Southern Baptist (n = 119, 77.27%).

Measures

Homonegativity (Samples I & II): Homonegativity, or sexual prejudice, was assessed with the Modern Homonegativity Scale- Gay Male version (MHS-G; Morrison & Morrison, 2002). The MHS-G is a 12-item Likert measure (scale 1 to 5; strongly disagree to strongly agree). Three items are reverse scored, and higher total scores indicate greater levels of homonegativity. Cronbach’s α has been shown to be 0.91 (Morrison & Morrison, 2002).

Authoritarianism (Samples I & II): Authoritarianism was measured using a modified format of the Right Wing Authoritarianism (RWA) Scale (Altemeyer, 1981, 1996), which consists of 30 additional items assessing authoritarian beliefs and attitudes (i.e., politically conservative and traditional beliefs and values). The 9-point rating scale ranges from −4 (very strongly disagree) to 4 (very strongly agree). Fifteen items are reverse scored and a total score is obtained for the 30 items. Previous internal consistency values have been high, reported at 0.88-0.94 (Altemeyer, 1981).

Perceptions of Victim Blame (Samples I, II, III): The PVBS (Rayburn et al., 2003) evaluates perceivers’ attribution of victim blame in a given scenario. It consists of 14 bipolar (e.g., malicious-kind) adjectives on a 7-point scale (1 to 7) that are totaled for a composite victim blame score. Rayburn and colleagues (2003) reported an internal consistency of 0.90. Pertinent items were recoded in a consistent direction with higher scores reflecting greater agreement and higher levels of blame. For the present study, the PVBS was factor analyzed for the identification of appropriate subscales.

Sentencing Decision (Samples I & II): Sentencing decision, defined here as the likelihood of assigning the death penalty, was assessed on a single 10-point Likert item with higher scores suggesting a greater likelihood of assigning the death penalty. Likelihood of assigning the death penalty was selected because all crime scenarios featured capital murder, a scenario where the death penalty becomes a potential sentencing choice.

Trait Aggression (Sample III): Aggression was assessed using the Aggression Questionnaire (AQ; Buss & Perry, 1992). The scale consists of 29 statements, each scored on a 5-point Likert type-scale (ranging from extremely uncharacteristic of me to extremely characteristic of me). The AQ assesses four subscales of aggression: Physical aggression, verbal aggression, anger, and hostility. These are then aggregated for a total score. Buss and Perry (1992) noted Cronbach’s α scores of 0.85 for physical aggression, 0.72 for verbal aggression, 0.83 for anger, 0.77 for hostility, and 0.89 for total aggression.

Social Desirability (Sample III): Social Desirability was evaluated with the Marlowe-Crowne Social Desirability Scale (MCSDS; Crowne & Marlowe, 1960). The scale consists of 33 true–false items, 15 of which are reverse scored. True answers are totaled for a composite social desirability score Cronbach’s α levels range from 0.73 to 0.88 (Crowne & Marlowe, 1960).

Procedure¹

In samples I and II, all participants received a counterbalanced, randomly assigned questionnaire packet including a manipulated crime vignette scenario and associated dependent measures, as well as self-reported characteristics (e.g., sexual prejudice, authoritarianism). Manipulated information between conditions pertained to victim sexual orientation and hate crime status; specifically, four conditions existed in all: a control condition with no victim sexual orientation stated, a “heterosexual condition” in which the victim’s sexual orientation is stated to be heterosexual, a “gay condition” in which the victim’s sexual orientation was stated as gay, and a “hate crime condition” in which a brief rationale for of rage toward the victim’s sexual minority status was provided as a rationale for the crime (Cramer et al., 2010). All scenarios occurred in the context of capital murder trials, and all other vignette information was held constant (e.g., location of the crime, lack of perpetrator criminal history; Cramer et al., 2010). It is important to note that main and moderating effects of manipulations are reported in previous work, and are therefore only of interest as a control variable in the present analysis. All procedures were identical for sample III with one exception. Manipulated information between conditions pertained to victim and perpetrator sexual orientation and hate crime evidence (see Cramer et al., 2010 for further details).

Results

Sample I: Exploratory Factor Analysis (EFA), Convergent and Predictive Validity

EFA was used to evaluate potential multidimensional factor structure of the scale in the first sample. Only participants with complete data were used (N = 240); however, between-groups comparisons are not reported for participants dropped from the sample because only six participants failed to provide complete data. Such a small sample would not allow for meaningful between-groups comparisons between those six participants and the sample size of the EFA analysis (N = 240). EFA specification featured principal factors, oblique promax rotation with Kaiser normalization. These parameters were selected to evaluate the possibility of oblique factors and to optimally determine simple structure, since it is feasible that latent constructs contributing to victim blame are very likely to be intercorrelated (see Hendrickson & White, 1964). Loadings are presented in Table 1. As is consistent with scale development literature in a jury paradigm (e.g., Brodsky, Griffin, & Cramer, 2010), a factor loading cutoff of 0.40 was used for subscale derivation. Three factors emerged from the original 14-item scale. Nine of the 14 items loaded on factor one (loadings range = 0.49 to 0.87, eigenvalue = 7.77), two items loaded on a second factor (0.75 and 0.78, eigenvalue = 6.41), and two items loaded on a third factor (0.78 and 0.79, eigenvalue = 6.25). A fourth factor was also identified with only a single item loading, but this factor was excluded from the final model specification because its variance proportion was minimal compared to the other identified factors (i.e., variance proportion = 0.09). From these results, we conclude that three underlying subcomponents exist, namely Malice (α = 0.95), Recklessness (α = 0.88), and Unreliability (α = 0.90), respectively. The overall internal consistency for global Perceptions of Victim Blame Scale featuring all 13 available items was 0.96.

Table 1.

Perceptions of Victim Blame Scale EFA Factor Loadings.

PVBS Item	Malice	Recklessness	Unreliability
Violent/nonviolent*	0.87	—	—
Gentle/forceful	0.75	—	—
Maniacal/sane*	0.67	—	—
Good natured/vicious	0.83	—	—
Malicious/kind*	0.85	—	—
Blameless/blameworthy	0.49	—	—
Fault/faultless*	0.51	—	—
Harmful/harmless*	0.58	—	—
Hurtful/innocuous*	0.56	—	—
Responsible/irresponsible	—	—	—
Careful/reckless	—	0.75	—
Conscientious/careless	—	0.78	—
Reliable/unreliable	—	—	0.78
Dependable/undependable	—	—	0.79

Note. EFA = Exploratory factor analysis. PVBS = Perceptions of Victim Blame Scale. *Reverse-scored item.

Table 2 summarizes convergent validity information for the Malice, Recklessness, and Unreliability subscales. All three subscales displayed expected significant positive convergent validity patterns with measures of participant authoritarianism (rs ranged from 0.18 to 0.27 for the three subscales, 0.22 for the overall scale) and homonegativity (rs ranged from 0.20 to 0.25 for the three subscales, 0.23 for the overall scale). As expected, the three main PVBS subscales were highly intercorrelated (rs ranged from 0.67 to 0.75).

Table 2.

Correlation Matrices by Sample I, II, & III

Sample I

	Malice	Recklessness	Unreliability	PVBS Scale
Malice subscale	—
Recklessness subscale	0.76***	—
Unreliability subscale	0.70***	0.67***	—
PVBS total score	0.98***	0.84***	0.79***	—
Authoritarianism	0.18***	0.24***	0.27***	0.22***
Homonegativity	0.20***	0.21***	0.25***	0.23***

Note. PVBS = Perceptions of Victim Blame Scale. ***p < .001.

Sample II

	Malice	Recklessness	Unreliability	PVBS Scale
Malice	—
Recklessness	0.74***	—
Unreliability	0.67***	0.69***	—
PVBS total score	0.98***	0.83***	0.77***	—
Authoritarianism	0.09	0.07	0.06	0.09
Homonegativity	0.12	0.13	0.09	0.13

Note. PVBS = Perceptions of Victim Blame Scale. ***p < .001.

Sample III

	Malice	Recklessness	Unreliability	PVBS Scale
Malice	—
Recklessness	0.69***	—
Unreliability	0.62***	0.71***	—
PVBS total score	0.97***	0.82***	0.76***	—
Social desirability	0.00	−0.05	0.00	−0.01
Hostility	0.03	0.03	−0.08	0.02
Physical aggression	−0.08	0.01	−0.01	−0.06
Verbal aggression	0.00	0.06	0.12	0.03
Anger	0.06	0.00	0.03	0.05
Aggression total score	−0.01	0.03	0.01	0.00

Note. PVBS = Perceptions of Victim Blame Scale. ***p < .001.

General Linear Model (GLM) procedures were implemented to examine the ability of PVBS subscales to predict sentencing decision above and beyond manipulated condition and support for the death penalty. Consistent with the Witherspoon² criteria in death penalty cases, participants who indicated an inability to assign the death penalty under any circumstances were dropped from analyses. The overall model was significant, F(7, 211) = 20.94, p < .001, R² = .40. Table 3 summarizes statistics for the full model. Of the PVBS subscales, Malice was significantly positively associated with likelihood of assigning the death penalty. Neither Recklessness nor Unreliability were significantly related to assigning the death penalty. Notably, death penalty support was significantly and positively associated with a likelihood of assigning the death penalty. Also, the hate crime condition yielded greater likelihood of assigning the death penalty compared to the gay victim condition, although additional details of this pattern can be found in the original article (see Cramer et al., 2010).

Table 3.

GLM Analyses Predicting Sentencing Decision in Samples I & II.

	Sample I			Sample II
Predictor	β (SE)	p-value	$η_{p}^{2}$	β (SE)	p-value	$η_{p}^{2}$
Malice	.03 (.02)	.03	.02	.04 (.02)	.03	.03
Recklessness	−.11 (.07)	.11	.01	−.11 (.09)	.22	.01
Unreliability	−.03 (.07)	.68	.001	−.06 (.09)	.50	.003
Control condition*	−.06 (.41)	.89	<.001	−.40 (.53)	.44	.004
Hate crime condition*	1.14 (.41)	.006	.04	.80 (.54)	.14	.01
Heterosexual victim condition*	−.12 (.41)	.77	<.001	.04 (.52)	.94	<.001
Death penalty support	.74 (.07)	<.001	.38	.60 (.08)	<.001	.25

Note. GLM = General Linear Model. *gay victim condition was coded as the reference group for between-groups comparisons.

Sample II: Confirmatory Factor Analysis (CFA), Convergent and Predictive Validity

CFA was used to compare the three-factor solution with Rayburn and colleagues’ (2003) single-factor total score model. Both confirmatory models was estimated using only complete data from the second sample (n = 196). As with sample I, between-groups comparisons were not reported for sample II participants dropped from the sample because only three participants failed to provide complete data. Meaningful between-groups comparisons assessing difference in these three participants would therefore not be interpretable. The single-factor CFA contained all original 14 PVBS items and displayed inadequate or poor statistical fit, (χ² [77] = 509.54, p < .001; RMSEA = 0.17; Tucker-Lewis Index = 0.70; Comparative Fit Index = 0.75; see DeCoster, 2009; Kenny, 2010 for fit index interpretations). The CFA for the three-factor PVBS solution allowed for the three factors to correlate based upon initial convergent validity data. The three-factor model converged, and showed adequate model fit (χ² [62] = 196.16, p < .001; RMSEA = 0.10; Tucker-Lewis Index = 0.92; Comparative Fit Index = 0.93; see DeCoster, 2009; Kenny, 2010 for fit index interpretations). Scale reliabilities were high for Malice (α = 0.94), Recklessness (α = 0.86), and Unreliability (α = 0.95), as well as for total Victim Blame (α = 0.95). Fit indices suggest a support a three-factor model of the PVBS. In line with model fit results, only the three-factor model was used in subsequent validity analyses.

Concerning convergent validity, the associations between the PVBS subscales and measures of authoritarianism and homonegativity were consistently positive but were all nonsignificant. As expected, the subscales themselves were highly intercorrelated. The correlation matrix is presented in Table 2.

GLM was again used to evaluate PVBS subscales as predictors of assigning the death penalty. The sample was again death-qualified (see above criteria from Witherspoon v. Illinois, 1968 ). Crime condition and support for the death penalty were used as control variables. Table 3 summarizes the GLM results. The overall model was significant, F(7, 159) = 9.13, p < .001, R² = .29. Of the PVBS subscales, Malice again was significantly positively associated with likelihood of assigning the death penalty. Neither Recklessness nor Unreliability were significantly related to assigning the death penalty. Support for the death penalty also displayed a significant positive relation with likelihood of assigning the death penalty.

Sample III: CFA and Divergent Validity

CFA was used to replicate the previously validated three-factor structure and test potential fit of the single-factor model in this data set. Identical parameters for each PVBS CFA model were employed as with sample II. The single-factor PVBS model again displayed inadequate or poor fit, χ² [77] = 458.11, p < .001; RMSEA = 0.18; Tucker-Lewis Index = 0.62; Comparative Fit Index = 0.68. The three-factor model converged, and showed adequate model fit (χ² [62] = 154.70, p < .001; RMSEA = 0.10; Tucker-Lewis Index = 0.92; Comparative Fit Index = 0.94)³. As in the prior two samples, scale reliabilities were high for Malice (α = 0.93), Recklessness (α = 0.92), and Unreliability (α = 0.92), as well as for total score of victim blame (α = 0.94).

As expected, the three victim blame subscales and the overall scale were nonsignificantly related to measures of trait aggression, suggesting support for divergent validity between these constructs (see Table 2). Total aggression (rs ranged from −0.01 to 0.03), hostility (rs ranged from −0.08 to 0.03), physical aggression (rs ranged from −0.08 to 0.01), verbal aggression (rs ranged from 0.00 to 0.12), anger (rs ranged from 0.00 to 0.06), and overall aggression (rs ranged from −0.01 to 0.03) all displayed negligible associations with Malice, Recklessness, and Unreliability. Likewise, social desirability was uncorrelated with any of the PVBS subscales (rs ranged from −0.05 to 0.00).

Discussion

Overall, perceptions of victim blame, as measured by the PVBS, appear multidimensional. Indeed, three intercorrelated subcomponents emerged, which we entitled Malice, Recklessness, and Unreliability. Moreover, all components of perceptions of victim blame displayed high internal consistency and expected directional (although varying in significance) relationships with homonegative and authoritarian attitudes. Nonsignificant associations for the PVBS subscales were observed with trait anger and social desirability as well. Finally, Malice appears to be the key facet of perceptions of victim blame as they pertain to understanding sentencing judgments in a capital murder situation.

The multifaceted nature of perceptions of victim blame is a conceptual advancement over previous work depicting victim blame as merely dichotomous or a unidimensional construct (e.g., Plumm et al., 2010; Rayburn et al., 2003). There are numerous benefits of disentangling complexities of victim blame. Benefits include, but are not limited to, more statistically sound evaluation of the continuum of victim blame and theoretical clarification of attribution theory in a legal context. Given initial evidence for its validation, we entitle the new scale the Perceptions of Victim Blame Scale-Revised (PVBS-R; see appendix for full scale).

Two elements in particular stand out from previous literature that may explain or elaborate on resulting PVBS subscales. The first is the role of perceived intention in evaluating victim blame. The role of intention in attribution of blame discussed by Scanlon (2008) and others is confirmed by the stable factor structure of three subscales: Unreliability (low perceived intent to harm), Recklessness (low to moderate perceived intent to harm), and Malice (high and overt intention to harm). This is further highlighted by just world theory, which posits that individuals are motivated to believe in a world where people get what they deserve and deserve what they get (Lerner, 1980). Thus, a victim having high and overt intention to harm (malice) may lead to victim “bad person” judgments that enforce victim blame. Likewise, exclusion of responsibility from the definition of victim blame is consistent with conceptual perspectives that blame and responsibility are separate, but perhaps correlated (e.g., Bandura, 2003; Wiley, 1999). Despite often being used interchangeably in the extent literature (e.g., Whatley & Riggio, 1993), attribution of victim blame appears a distinct process from responsibility, at least in this context.

A consistent finding emerged in that ratings of victim malice predicted likelihood of assigning the death penalty. This is somewhat unexpected because, at face value, it seems contrary to logic. Why would perceptions of victim malice—i.e., the victim having high and overt intention to harm the offender—be correlated with increased punishment for the offender? It is plausible that both the victim and offender were perceived as aggressive, and in turn, intent to harm was perceived for both persons. Rather than perceiving blame in this situation as a finite quantity to be divided among victim and offender, it is possible that the violent nature of the capital murder situation yielded high degrees of blame and punishment for all parties involved. This explanation is consistent with what the Culpable Control Model (Alicke, 2000) would predict, which highlights the role of emotion-laden automatic reactions in blame attributions. It is likely that disclosure of a violent crime spawned negative emotional reactions; the mere presence of negative arousal may have led to both higher victim malice judgments and harsher punishment for the offender.

Regarding validity, previous literature suggested PVBS subscales would display: (a) convergent associations with politically conservative attitudes (e.g., Altemeyer, 1996; Hill, 2000; Rye, Greatrix, & Enright, 2006); (b) divergent associations with trait aggression and social desirability (e.g., Goldberg et al., 1999); and (c) predictive associations with legal decisions (e.g., Osofsky et al., 2005; Plumm et al., 2010). All three of these associations were confirmed. In fact, the latter was clarified in that perception of victim malice (i.e., with intent to harm) in a capital murder was predictive of capital sentencing.

The present study contains noteworthy methodological and sample limitations offering fruitful ground for further evaluation of the blame attribution structure we found. Concerning the samples used, the predominant characteristics of undergraduate student, Christian and Caucasian, curtail the ability to generalize present findings. Indeed, both the education level potentially yielding heightened social awareness, as well as the restricted scope of the world views of a majority Christian/Caucasian sample, may narrow the perceptions of blame in a hate crime situation. In terms of the method, the capital trial scenario holds numerous limitations, as such cases are rare within the United States and do not extend to international or cross-cultural contexts. Moreover, in assessing victim blame in a trial, live testimony can influence such ratings, yet capital murder fails to offer this aspect of juror perception. As such, our three-factor blame structure is limited in that it is primarily inferred from trial details, as opposed to live observation.

Future application and research within the legal system can build upon the limits of this study in fully vetting a multifactor attribution of blame structure. Aspects of the legal system that may benefit from empirical testing or real world application include the sentencing phase of criminal trials, assignment of damages in civil trials, and social service decision-making. As attribution of victim blame appears nuanced based on perceived intent, legal guidelines may also demonstrate consideration of this fact. For example, civil litigation damages awarded to victimized parties could be lessened in scenarios where his or her blame possesses little intent to harm other involved parties. On the other hand, where intent or malice on the part of the victim is present, civil damages would be minimized. Of course, further empirical research is needed to test the three-factor structure of perceived victim blame in these varying contexts.

Empirical application of the PVBS-R itself also pertains to victimology research. Most literature using simplistic definitions of blame may be too face-valid and fail to fully capture the construct (e.g., Plumm et al., 2010). Future research should replicate and extend the new PVBS-R factor structure across types of crime severity (e.g., property crime) and victims (e.g., sexual assault). Additional insight may result from comparing the performance of the PVBS-R across capital and noncapital sentencing outcomes, given that the possibility to impose a death sentence may invoke alternative rationalizations or moral evaluations about victim or offender conduct, in addition to self-reflections about beliefs and values on the part of jurors. To broaden the validity of the PVBS-R, additional studies should be conducted using community samples. Finally, gradations of blame and intent should be investigated as they impact perpetrators of crimes.

Footnotes

Appendix

Perceptions of Victim Blame Scale—Revised (PVBS-R)

Instructions: Please rate the victim on the following characteristics. Please circle your answer.

violent	1	2	3	4	5	6	7	nonviolent*
gentle	1	2	3	4	5	6	7	forceful
maniacal	1	2	3	4	5	6	7	sane*
good natured	1	2	3	4	5	6	7	vicious
malicious	1	2	3	4	5	6	7	kind*
blameless	1	2	3	4	5	6	7	blameworthy
fault	1	2	3	4	5	6	7	faultless*
harmful	1	2	3	4	5	6	7	harmless*
hurtful	1	2	3	4	5	6	7	innocuous*
careful	1	2	3	4	5	6	7	reckless
conscientious	1	2	3	4	5	6	7	careless
reliable	1	2	3	4	5	6	7	unreliable
dependable	1	2	3	4	5	6	7	undependable

Note. PVBS = Perceptions of Victim Blame Scale. *Denotes a reverse-scored item.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Robert J. Cramer, PhD is an assistant professor of psychology (psychology-law specialization) at Sam Houston State University. His research interests include hate crimes and violence, jury perceptions, expert testimony, and suicide.

Matt R. Nobles, PhD is an assistant professor of criminal justice at Sam Houston State University. His research interests include violent and interpersonal crimes, GIS and spatial econometrics, criminological theory, and quantitative methods.

Amanda M. Amacker, PhD is a now a staff psychologist for the Bureau of Prisons. She attended Sam Houston State University at the time this research was completed. Her research interests include interpersonal victimization, victim culpability attributions, and psychological recovery following sexual victimization.

Lisa Dovoedo, MA is a doctoral student in clinical psychology at Sam Houston State University. Her research interests include victim blame, expert testimony, and jury decision-making.

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Defining and Evaluating Perceptions of Victim Blame in Antigay Hate Crimes

Abstract

Keywords

What is Blame?

Theoretical Conceptualizations and Applications of Blame

The PVBS

The Present Study

Perceptions of Victims of Antigay Hate Crimes

Research Questions

Proposed Correlates

Method

Participants

Measures

Procedure 1

Results

Sample I: Exploratory Factor Analysis (EFA), Convergent and Predictive Validity

Sample II: Confirmatory Factor Analysis (CFA), Convergent and Predictive Validity

Sample III: CFA and Divergent Validity

Discussion

Footnotes

Appendix

Declaration of Conflicting Interests

Funding

Notes

Author Biographies

References

Procedure¹