Psychology, Crime & Law, June 2004, Vol. 10(2), pp. 205 Á/219ATTITUDES TOWARDS SEVERITY OF PUNISHMENT: A CONJOINT ANALYTIC APPROACH ¨ MICHAELA BROCKE*, CHRISTIAN GOLDENITZ, HEINZ HOLLING and WOLFGANG BILSKY$Westfalische Wilhelms-Universitat Munster, Psychologisches Institut IV, Fliednerstraße 21, ¨ ¨ ¨ 48149 Munster, Germany ¨Past research suggests that attitudes towards severity of punishment are affected by crime-specific factors. Theimpact of such factors has usually been investigated by between-subjects designs. The studies reported in thispaper, however, are based on within-subjects designs, using conjoint analysis for data collection and analysis.Study 1 employs a rape scenario for investigating the impact of the victim Á/offender relationship and of twovictim characteristics Á/ provocative behavior and intoxication. Study 2 uses a theft and an assault scenario foranalyzing the influence of several offender and crime characteristics on sanctioning: offender’s age, readinessto confess, previous convictions, and severity of the offense. Results from both studies are reported anddiscussed in terms of utility values. These values represent the importance placed on the case characteristicsfocused upon. In addition to the general evaluation of case characteristics, inter-individual differences areanalyzed by means of hierarchical cluster analysis. Advantages of the conjoint analytic approach overconventional research methods on sanctioning behavior are discussed.Keywords: Sentencing; Severity of Punishment; Conjoint Analysis; Within-subjects DesignINTRODUCTIONAttitudes towards sanctioning in general and towards severity of punishment in particularhave been repeatedly in the focus of research on crime and delinquency. The same applies toactual sanctioning behavior (e.g. McFatter, 1986; Ouimet and Coyle, 1991; Pfeiffer andOswald, 1989). Beside differences between professionals, differences between lay personsreceived considerable attention (e.g. Carroll and Payne, 1977; Gabriel and Greve, 1996).These latter differences seem relevant with regard to public opinion (e.g. Durham, 1993).However, the mere observation of differences is of little importance. What counts is theknowledge of factors causing divergent orientations towards sanctioning. Explaining laymen’s differences in the evaluation and appraisal of punishment proves tobe a difficult and complex task. This is true because people do not show a uniform andundifferentiated orientation towards this topic. It is possible, of course, to identifydifferences between individual attributions and attitudes towards crime and sanctioningon a general level (Furnham, 1988). However, there is ample evidence that the individual’sorientation towards (the severity of) punishment is affected by very different factors. Thus,in order to get below the surface of a global analysis, the type of offense as well as situation- *Corresponding author. E-mail: firstname.lastname@example.org$ E-mail: email@example.comISSN 1068-316X print/ISSN 1477-2744 online # 2004 Taylor & Francis LtdDOI: 10.1080/10683160310001614793 205
206 M. BROCKE et al .and offense-specific factors should be taken into account (Carroll and Payne, 1977; Hollinand Howells, 1987). Similarly, offender characteristics like sex, age, socio-economic status,or previous conviction proved to be relevant (Gabriel and Greve, 1996; Hagan, 1989).Especially in rape cases (Krahe, 1991), two further factor groups have to be considered: ´victim characteristics and victim behavior, and the offender Á/victim relationship (e.g. Bestand Demmin, 1982; Hammock and Richardson, 1997; Workman and Freeburg, 1999). It isobvious, then, that a general, undifferentiated approach for assessing attitudes towardsseverity of punishment is unlikely to go far enough in explaining judgmental differences. In our own research on attitudes towards severity of punishment, we recently conductedtwo studies, varying several factors in a quasi-experimental design. These factors includedoffender characteristics (prior conviction and employment) within short scenarios of theftand assault (Reichert, 1999; Reichert and Bilsky, 2001), and victim characteristics(intoxication, provocativeness) as well as the offender Á/victim relationship in a rape scenario(Brocke et al ., 2001; Bus, 2001). Several observer characteristics like sex (both studies), age(first study), generalized attitude towards sanctioning, role orientation, rape myths, empathywith victim and offender, and direct or indirect experience of victimization (second study)were measured additionally, in order to control their influence on individual judgments.Scenarios were presented as short vignettes in between-subject designs, as it is usually thecase in research on the impact of crime characteristics on sanctioning. In our first study, prior conviction of the offender revealed considerable impact onseverity of punishment in an analysis of covariance, explaining 19.5% of variance in theft,F (1,202)0/52.69, p B/0.001, and 24.4%, F (1,202) 0/75.65, p B/0.001, in bodily harm. Theinfluence of employment was low but significant, too, resulting in F (1,202) 0/5.51, p B/0.05for theft, and F (1,202) 0/4.02, p B/0.05 for bodily harm. Furthermore, a significantinteraction was identified for bodily harm, F (1,202)0/28.28, p B/0.001, revealing harderpunishment for an offender without employment and prior conviction. In our second studyusing a rape scenario, multivariate analysis of covariance revealed only a marginal effect ofthe victim Á/offender relationship, F (2,313) 0/2.95, p0/0.05. None of the remaining factorsand interactions came close to significance. While findings from both studies seem interesting with respect to attitudes towardsseverity of punishment, the between-subjects designs procedure employed in theseinvestigations reveals several shortcomings. A main disadvantage of this design refers tothe extensive sampling that is required when a high number of case characteristics is understudy. The higher the number of case characteristics, the more vignettes result and the morerespondents are needed. Thus, between-subjects designs are rather inefficient for investigat-ing the impact of several crime characteristics simultaneously. Besides, between-subjectsdesigns require extensive data collection , since observer characteristics have to be measuredadditionally. Moreover, with a between-subjects design only indirect conclusions aboutfactors influencing sanctioning decisions, drawn from inter-individual differences , areprovided. Thus, analyses of individual ‘‘sanctioning patterns’’, that is intra-individualcomparisons in weighting the impact of different factors, are not possible. As a consequence,examination of inter-individual differences in sanctioning structures is unfeasible, too. To overcome these shortcomings, in the studies to be reported here, an experimentalwithin-subjects design was used. This design was realized by means of conjoint analysis .Conjoint analysis provides reliable estimates of intra-individual weights, even with complexfactorial designs, without extensive sampling as usually required for within-subjects designs.Actually, conjoint analysis is a data collection and analysis technique, which has become
SEVERITY OF PUNISHMENT 207popular in decision analysis and in market research (Green and Srinivasan, 1978, 1990;Green et al. , 2001). In this type of research, participants are asked to judge objectsrepeatedly with regard to their attractiveness. Other things being equal, these objects arevaried systematically with respect to some well-defined factors supposed to influence theirevaluation. Respondents’ answers are then decomposed by data analysis, assigning differentweights to the factors under study. In the present research context, assessment differed in that respondents had to assignsubjectively adequate degrees of punishment to offenses presented in different crimescenarios. These scenarios varied with respect to factors supposed to influence the perceivedseriousness of the criminal act under study. Conjoint analytic estimates gained from thesejudgments, then, represent the importance placed on the different case characteristics withrespect to severity of punishment. In other words, the results reflect the relative impact ofcase characteristics on preferred sentencing. To the best of our knowledge, this type ofanalysis has not been used for analyzing sentencing behavior in the past. Interestingly,however, for measuring the seriousness of different types of offenses the paired comparisonmethod and extensive measurement, both sharing kinship with conjoint analysis, have yetbeen applied (Thurstone, 1927; Levi, 1974; Francis et al. , 2001). In the following, two conjoint analytic studies on factors influencing the severity ofpunishment are reported. Both studies are exploratory to the extent that they test theapplicability of conjoint analysis in this special research context. The first study used a rapescenario for investigating the influence of victim characteristics and of the victim Á/offenderrelationship on sanctioning. The second study concentrated on the impact of offender as wellas context characteristics in an assault and a theft scenario . For each study, the design ispresented first, together with some information about former research on the variablesunder study. Then, the conjoint analytic method and the results are sketched out. Since theuse of conjoint analysis is uncommon in this research context, data collection and dataanalysis are described in some detail.A RAPE SCENARIO (STUDY 1)In our first study, the impact of two victim characteristics on the severity of punishment,intoxication and provocative behavior , was analyzed in a rape scenario, together with thevictim Á/offender relationship . These factors were combined in a 2)/2 )/3 factorial design asoutlined in Table 1. While the factors intoxication and provocative behavior comprised twolevels each (present/absent or yes/no, respectively), the victim Á/offender relationship wasoperationalized by three factor levels: the categorical distinction between noddingTABLE 1 Factors and factor levels of Study 1.Factor Factor levelBehavior of victim no provocation provocationIntoxication victim is sober victim is tipsyRelation of victim and offender nodding acquaintance ex-couple, offender parted ex-couple, victim parted
208 M. BROCKE et al .acquaintance and former partnership was supposed to represent different degrees ofintimacy between victim and offender; this latter category was further specified by indicatingwho quit the former partnership Á/ the victim or the offender. The effects of the above factors have been studied repeatedly in connection with rape (e.g.Brocke et al ., 2001; Bus, 2001). For intoxication and provocative behavior of the victim, amitigating influence on sentencing has been confirmed by former research. Thus, studiesabout the influence of a woman’s consumption of alcohol prior to rape unanimously showthat the offender is considered less guilty, if the victim is intoxicated. Accordingly, a lowerdegree of punishment is chosen (e.g. Hammock and Richardson, 1997; Stormo et al ., 1997;Schuller and Wall, 1998). Other studies support the assignment of a higher degree of guilt toa victim if she is blamed for having shown provocative behavior prior to her victimization(e.g. Acock and Ireland, 1983; Schult and Schneider, 1991); this holds true for differentoperationalizations, for instance, wearing a provocative dress (e.g. Furnham and Boston,1996; Workman and Freeburg, 1999), or showing improvident and role-discrepant behavior(e.g. Best and Demmin, 1982). The importance of the victim Á/offender relationship has also been considered in variousstudies; results, however, proved inconsistent. Thus, a friendship between victim andoffender may cause higher (e.g. Bridges, 1991; Szymanski et al ., 1993) or lower attribution ofguilt to the victim (e.g. Bolt and Caswell, 1981; Krulewitz, 1982). These discrepancies mayindicate that it is not the mere acquaintance but the degree of intimacy and trust betweenoffender and victim that has to be taken into consideration (Sczesny and Krauel, 1996). Thedegree of closeness had been considered when designing Study 1, therefore.MethodProcedureSeventy-five students (62 female and 13 male), aged 19 Á/44 (median 0/ 20), participated inthis study. Aside from some general information about the investigation, participantsreceived detailed instruction of how to deal with the conjoint analytic tasks. Sinceconsecutive trials of the experimental variation of factor levels were displayed on a screenin a brief and standardized form only, the rape scenario within which to interpret thisinformation was presented first. Furthermore, it was stressed that offenses vary only withrespect to the variables displayed. Compliance with this instruction is of considerableimportance to guarantee the validity of the subsequent judgments. Subsequently, the presentation of the experimental variables and the recording ofparticipants’ reactions were conducted in a computer-assisted form, using Alasca (Hollinget al. , 2000). In order to get acquainted with the case characteristics under study, theparticipants had to rank order the levels of each factor first. Then, they worked on 25 gradedpaired comparison and eight rating tasks, as described next. Each graded paired comparison task consisted of two rape offenses differing in the factorlevels under study. Participants had to compare the seriousness of these offenses and to stateon a seven-point rating scale whether and to which extent one of them should be punishedharder than the other (cf. Figure 1). Offenses were first described by two (10 tasks), and then by three factor levels (15 tasks)each. Aside from the first pair, presentation of each of the following depended on theprevious judgments. This form of data collection, called adaptive conjoint analysis (Johnson,1987), ensures that every response yields a high level of information about the influence of
SEVERITY OF PUNISHMENT 209 FIGURE 1 Paired comparison task in Alasca .the factor levels on the degree of punishment. More concretely, numerical estimates of theimpact of the different factor levels are calculated after every response, and an algorithm isused to determine the combination of factor levels to be presented next, in order to gain themaximum information required for stabilizing the estimates. This is why adaptive conjointanalysis represents a highly efficient way of data collection in a within-subjects design. Finally, additional rating tasks were presented in an adaptive manner. In these tasks,respondents had to rate the seriousness of eight offenses on a nine-point rating scale asdescribed by varying levels of the three factors under study (cf. Figure 2).Data AnalysisIn conjoint analysis, two kinds of estimates describing the influence of the independentvariables are available. One of them is related to the factor levels and termed part-worths . Inthe present context, the part-worths can be interpreted as ‘‘penalty-worths’’, indicating theextent to which the presence of a certain factor level is perceived as aggravating from aparticipant’s point of view. To gain the part-worths, weights for the factor levels areestimated from the paired comparisons by ordinary least squares (OLS) regression. Thisestimation method is usually used in conjoint analysis, because it is equivalent (e.g. Wittinkand Cattin, 1981) or superior (e.g. Cattin and Bliemel, 1978) to nonmetric methods. If theindependent (categorical) variables are dummy coded, OLS regression is analogous toanalysis of variance, as typically used in between-subjects design studies. Part-worths are calculated for every participant. To obtain inter-individual comparabilityof these estimates, part-worths are standardized. This is accomplished for each factor bysetting the factor level with the highest part-worth to one, and the lowest to zero. Part-
210 M. BROCKE et al . FIGURE 2 Rating task in Alasca .worths of intermediate levels are transformed accordingly. In order to aggregate results,means of the standardized part-worths are calculated. As aggregation may level differences in the constellation of part-worths betweenparticipants or subgroups (Backhaus et al ., 2000), classification methods can be appliedto the data. To test for subgroups that differ in judging the seriousness of an offense, ahierarchical cluster analysis on the individual, standardized part-worths was conducted inthe present study, using Ward’s method and squared Euclidean distances as a measure ofsimilarity between participants. The second kind of estimate is called relative importance (score ). It refers to the factorsand specifies their influence on the judgment on the whole. For each factor, it is determinedby the difference between the highest and the lowest standardized part-worth. Thus, if thepart-worths of a two-level factor are similar, for example, a low relative importance scoreresults. This score indicates that judging the severity of an offense does not changesubstantially, whether one or the other level of this factor is considered. For simplicity ofinterpretation, importance scores are transformed into percentages by dividing each factor’srange by the sum of the ranges of all factors (cf. Hair et al ., 1998). Two methods exist for aggregating relative importance scores. First, relative importancecan be determined by the span of aggregated part-worths. This measure reveals the influenceof the factor levels on the sample as a whole and resembles the results achieved in between-subjects designs. Second, importance can be calculated on an individual level and aggregatedafterwards. By applying this procedure, the mean influence of the factor levels on theindividual level is determined. If the constellations of individual part-worths diverge, the two
SEVERITY OF PUNISHMENT 211measures may differ. Since they complement each other, both are reported and compared fora rough check of heterogeneity. Besides the part-worths and the relative importance scores, measures of reliability andvalidity can be determined in conjoint analysis, too. From the measures that exist (cf.Bateson et al ., 1987), two commonly used are calculated here. The first is the R2 of the OLS regressions conducted on an individual level. R2 values areconsidered as a measure of internal consistency because goodness of fit is essentiallydetermined by the consistency of a participant’s responses. For all participants, R2 values arechecked, and participants with values lower than R2 0/0.5 are excluded from furtheranalyses. This seems adequate, because part-worths calculated for these cases are notsupposed to be sufficiently reliable. For the remaining participants, the mean of the Fisher ztransformed individual R2 values is reported. The second measure, the hit rate, indicates the predictive validity of the part-worths. It iscalculated from the data of the rating part of the conjoint analysis. All pairwisecombinations of the offenses presented in the rating part were inspected with respect topossible differences in rating. For each pair differing it was checked, whether the part-worthspredict correctly to which of the two offenses a higher degree of punishment is assigned. Therelative frequency of correct predictions is transformed into a percentage value for eachparticipant and aggregated afterwards. As a rule of thumb, hit rates above 80% are usuallyregarded as high (Teichert, 2000). It should be noted that the values for internal consistency and predictive validity also offerinformation about the independence and the additivity of the factor levels underlying theregression model adopted. Both measures indicate the degree of concordance between thepredicted and the actual responses in the paired comparison and the rating part.ResultsFive participants showed goodness of fit values of the respective regression lower than R2 0/0.5, indicating a low level of internal consistency. They were excluded from further analysis,therefore. For the remaining 70 participants, the mean of the goodness of fit indices wasR2 0/0.87. Hit rates resulted in 80.2% on average. According to the criterion mentionedabove, this percentage indicates a high level of predictive validity. The means of the standardized individual part-worths are depicted in Figure 3. Theyillustrate both the different influence of the factors and the relations among the levels of each FIGURE 3 Mean part-worths of the factor levels for rape.
212 M. BROCKE et al .factor. On an aggregated level, provocative behavior has the strongest influence on theassessment of punishment (79.8% of relative importance), since the difference between thelevels of this factor is higher than between the levels of the remaining factors. Here, a higherdegree of punishment is assessed to the offender, if the victim is not blamed for provocativebehavior. In other words, provocative behavior of the victim is likely to concede extenuatingcircumstances to the offender. The second-highest importance (17.4% of relative impor-tance) is attached to the victim Á/offender relationship . For this factor, the part-worth for anodding acquaintance is higher than for a former partnership of offender and victim. Thus,a high degree of intimacy is regarded as mitigating. Since the difference between the factorlevels of intoxication is negligible, this factor is supposed to play a minor role in the presentcontext (2.8% of relative importance). Nevertheless, part-worths imply the assignment of aslightly higher degree of punishment to the offender, if the victim is intoxicated. On an individual level, different relative importance scores result for all factors. Thus, theinfluence of provocative behavior (49.3% of relative importance) diminishes. On the otherhand, higher relative importance scores result for the victim Á/offender relationship (29.7% ofrelative importance) and for intoxication (21.0% of relative importance). The differences found in relative importance on the aggregated and on the individual levelindicate disagreement between participants. This is also reflected by a hierarchical clusteranalysis computed for the individual standardized part-worths. The dendrogram of thisanalysis suggested a four-cluster solution. In Figure 4, profiles of the mean part-worths areshown for each of these clusters. They illustrate the difference in weighting applied by thesesubgroups. While there did not result any differences with respect to provocative behavior ,the four clusters can be divided into two groups with regard to intoxication : respondentsbelonging to clusters 1 and 2 (n0/23) attach a higher part-worth to the intoxication of thevictim, whereas those of clusters 3 and 4 (n0/47) do not. Finally, groups disagree inweighting the different levels of the victim Á/offender relationship : while rape by a noddingacquaintance, as compared to a former partner, is regarded as more serious by the majorityof our participants (n0/60), this does not hold for the respondents of cluster 4 (n 0/10). FIGURE 4 Mean part-worths of the factor levels for subgroups determined by cluster analysis.
SEVERITY OF PUNISHMENT 213Furthermore, information about who quit the former partnership influences the assignmentof punishment, too; clusters differ with respect to the weighting of the respective alternatives.Thus, participants of clusters 2 and 4 (n 0/21) punish less hard if the offender has separated,while those of cluster 1 and 3 (n0/49) assign opposite weights.DISCUSSIONWith respect to the criteria mentioned, the results of the conjoint analysis in our first studyproved reliable and valid, as indicated by the internal consistency and the predictive validity.In addition, these results demonstrate the appropriateness of the conjoint analytic model. For provocative behavior , the part-worths are in accordance with the results of previousstudies using a within-subjects design. This applies to the whole sample (i.e. on anaggregated level) and to the different subgroups. For intoxication on an aggregated level, on the other hand, part-worths suggest adirection opposite to former results, although, factor levels were nearly equal in height.However, differences in weighting this latter factor could be demonstrated between twosubgroups by means of cluster analysis: the majority of the participants is likely to interpretthe intoxication of the victim in the expected sense, that is, in the sense of sharedresponsibility. The second, smaller group, in contrast, tends to interpret the offender’sbehavior as taking advantage of the victim’s handicap in this special situation. Two factorsmay have contributed to this difference between the present and the former results. First,direct comparison of two factor levels, as simultaneously presented in our study, may lead toother judgmental processes than the evaluation of one of the levels alone. Second, changingrole expectations, that tolerate more and more the consumption of alcohol by women, mayhave come into effect. Finally, for the impact of the victim Á/offender relationship on punishment a high degree ofintimacy is regarded as mitigating on an aggregated level. However, results of the clusteranalysis reveal inter-individual differences for this factor, too. These differences may havecontributed to inconsistent results obtained with between-subjects designs in the past.Assault and Theft (Study 2)In our second study, effects of four factors on sanctioning were investigated in an assaultand a theft scenario: offender’s age , confession , severity of the offense and previousconviction . For the latter factor, pertinence was considered by two factor levels. In bothscenarios, age and confession comprised two, severity and previous conviction three factorlevels, resulting in a 2)/2 )/3)/3 factorial design. Factor levels were the same for bothscenarios, except for severity , which had to be adapted to the different types of crime (seeTable 2). Former studies on the effect of offender’s age , severity of offense and previous convictionhave shown significant effects on sanctioning for several crimes. Thus, more lenient forms ofpunishment, for instance, have been assigned to young perpetrators without previousconviction, who committed less serious types of crime (Gabriel and Greve, 1996; Reichert,1999). This is in line with both jurisdiction as established in law in many countries (Hagan,1989), and with actual sentencing behavior of judges (Steffensmeier and Hebert, 1999). Thesame holds for the mitigating influence of a confession on punishment.
214 M. BROCKE et al .TABLE 2 Factors and factor levels of Study 2.Factor Factor levelAge 17 years 30 yearsConfession offender confesses offender deniesPrevious conviction no conviction conviction for theft conviction for assaultSeverity of offence: theft 250 t loss 750 t loss 1250 t loss Assault several punches punches, knock-down punches, knock-down, additional kicksMETHODProcedure and Data AnalysisSixty students (45 female and 15 male), aged 19 Á/54 (median0/22), participated in Study 2.Data collection was similar to that in our first study. However, because of the greaternumber of factors and factor levels in the present study, pairs of offenses presented weredescribed by two (10 tasks), three (10 tasks), and four factor levels (five tasks), respectively,in the paired comparison tasks. The offenses displayed in the rating part consisted of fourlevels each. Furthermore, participants had to run through the conjoint analytic proceduretwice Á/once for theft, and once for assault. The sequence of these offenses was balancedbetween participants. Methods applied for data analysis were the same as in Study 1.RESULTSThe goodness of fit values for the regressions were lower than R2 0/0.5 for four participants.For the remaining 56 participants, means of the goodness of fit indices were R2 0/0.86 forboth offenses. Hit rates were 89.0% on average for theft and only slightly lower (88.2%) forassault. Thus, the predictive validity of the part-worths proved extremely high. The means of the standardized individual part-worths for theft are depicted in Figure 5.On an aggregated level, previous conviction proved most important for sentencing (38.9% ofrelative importance): the difference between no previous conviction and previous convictionis higher than the differences between the levels of the remaining factors. A much higherdegree of punishment is assigned to an offender who has been previously convicted,independently of pertinence or non-pertinence. For the other factors, severity (23.9% ofrelative importance), confession (20.3% of importance), and age (16.8% of relativeimportance), a lower influence on the assignment of punishment results. Young age,confession, and low severity of the offense proved mitigating, as expected. For assault a different weighting scheme results (see Figure 6). Whereas previousconviction was of utmost importance for theft, severity of the offense had the strongestimpact on sanctioning for assault (41.9% of relative importance). Nevertheless, previousconvictions proved to be an influential factor, too (34.5% of relative importance). While the
SEVERITY OF PUNISHMENT 215 FIGURE 5 Mean part-worths of the factor levels for theft. FIGURE 6 Mean part-worths of the factor levels for assault.direction of influence was the same for both offenses, the profile of part-worths is different:part-worths for the levels of severity are graded uniformly for theft. For assault, however,the level representing the highest severity is judged much more aggravating than theremaining two. Furthermore, whereas the difference between a pertinent and a non-pertinent conviction was of no importance for theft, a much higher part-worth results for apertinent in comparison to a non-pertinent conviction for assault. In sum, for these twofactors the relative importance scores as well as the profile of part-worths are different. Withrespect to the two remaining factors, results resemble the findings for theft: confession(13.8% of relative importance) and age (9.9% of relative importance) are both of minorimportance for sanctioning. However, mitigating effects of both factors are even lesspronounced for assault than for theft. For both theft and assault, the relative importance scores calculated on the individual leveldo not differ substantially from those on the aggregated level: for theft, the mean individualrelative importance scores differ from the aggregated relative importance scores by only
216 M. BROCKE et al .3.6% on average. For assault, differences are even smaller, with a mean of 2.4%.Accordingly, the part-worth profiles of subgroups identified by hierarchical cluster analysisdiffer only slightly and are therefore not depicted graphically.DISCUSSIONOn the whole, the goodness of fit indices and the individual hit rates for both offensessuggest again the appropriateness of the additive main effects models used to calculate thepart-worths. Part-worths show that the factor levels influence sanctioning uniformly in theexpected direction in both offenses: A young perpetrator with no previous conviction, whocommits a minor offense and confesses is punished less hard than a previously convictedolder person, who commits a major offense and refuses to confess. However, differences inthe profiles of the part-worths and in the relative importance of factors were found forassault and theft.SUMMARY AND CONCLUSIONSFor a differentiated analysis of attitudes towards severity of punishment, a number offeatures of a crime has to be considered. Compared to between-subjects designs, within-subjects designs realized by conjoint analysis represent a more accurate and parsimoniousapproach in this context. We reported on two studies using conjoint analysis for datacollection and analysis. In a first study, we aimed at the description of the sanctioningstructure applied to rape; in a second study, we focused on the (comparison of) sanctioningpatterns applied to theft and assault. All in all, findings from both studies suggest thatdetailed insights into the process of sanctioning can be gained by using a within-subjectsdesign and conjoint analysis, respectively. The various benefits of this approach in thepresent research context are briefly summarized and discussed in the following. The main advantage of the approach stems from the fact that the impact of different factorlevels on the degree of punishment is determined on an individual level . In other words, theinfluence of factor levels on the dependent variable need not be inferred indirectly bycomparing experimental groups; rather, participants serve as their own control in a repeatedmeasurements design. This implies, that, unlike between-subjects designs, a within-subjectsdesign does not require recording of individual characteristics in order to control forbetween-subjects variance that is undue to experimental manipulation. Furthermore,subgroups of participants, differing in judgmental styles or patterns, can be identified.With a between-subjects design, these inter-individual differences remain unconsidered.However, the cluster analytic results of our first study showed that disentangling judgmentalstyles may be revealing with respect to more fine-grained differences in sanctioning. Moreover, a large number of factors and factor levels can be studied with conjointanalysis. Complex research designs can be employed, because the evaluation of subsets ofstimuli within fractional factorial or adaptive designs still provides reliable estimates ofparameters. The main advantage of complex designs is that a trade-off of several differentfeatures of an offense is possible, thus providing a more differentiated analysis of attitudestowards severity of punishment. As offenses are judged in pairs, the standards of comparison and the factor levels understudy are always evident in conjoint analysis. This is important for methodological as well as
SEVERITY OF PUNISHMENT 217content reasons. From a methodological point of view, ratings in between-subjects designsmay result opposite to those in within-subjects designs because of unclear standards ofcomparison. Birnbaum (1999) provides an instructive experimental illustration for mislead-ing conclusions resulting from between-subjects designs. He also discusses this problem withrespect to studies on rape. As regards content, presenting a standard of comparison seemsreasonable, too. Thus, in a vignette approach, about one half of the respondents stated to beunable to make judgments on the basis of the information provided (Krahe, 1991). The ´qualitative prototype approach suggested by Krahe (1991), alternatively, resembles the ´conjoint approach inasmuch as both rely on the comparison of features. In sum, thoughunusual in the given context, conjoint analysis provides a means for studying intra-individual preferences and patterns of sanctioning as well as inter-individual differences inan economic manner. Of course, in order to use this approach efficiently, the procedure has to be adapted to thegiven research context. Therefore, we finally outline some recommendations for researchdesign and data collection in conjoint analysis as emphasized in the literature (e.g. Hair etal ., 1998). As regards the research design, the factors to be studied have to be well-selected, since therelative importance scores of the factors are mutually dependent. In this respect conjointanalysis closely resembles ipsative measurement. For example, in a conjoint analysis withunimportant factors only, the relative impact of a single factor may be overestimated; ifmore important factors were included, the impact of the first factors would certainlydiminish. In addition, descriptions used for the factor levels have to be specified carefully, sincesemantic variations may influence the results considerably. The range of the factor levelsunder study has to be taken into consideration as well. A wide range may lead to higherrelative importance scores than a small range. Thus, to ensure the comparability of therelative importance scores, it is recommended to use a similar range for all factors, and tobalance the number of levels across factors if possible. Furthermore, considerable attention has to be paid to the experimental design used fordata collection. For example, in order to test for interaction effects of factor levels, designsthat allow for the estimation of interaction terms have to be constructed. In view of the encouraging findings of our investigations, further studies should beconducted to test other applications of conjoint analysis in the research on punishment andsanctioning. Research on individual weighting schemes applied in judging the seriousness ofoffenses using complex designs, for example, seem especially promising. In addition, therelation of judgments relating to case characteristics and person variables could beinvestigated on a more differentiated level, for instance the interdependence of evaluationsof different rape scenarios and rape myth acceptance (cf. Payne et al. , 1999) or rape empathy(cf. Deitz et al ., 1982). Similarities and deviations in the weighting of factors, applied byjudges and laymen in the context of sanctioning and sentencing, are another possible field ofresearch. Finally, the predictive validity of factors identified in conjoint analysis needsexamination, if their usefulness is supposed to go beyond mere attitudinal research.ReferencesAcock, A. C. and Ireland, N. K. (1983). Attribution of blame in rape cases: the impact of norm violation, gender and sex role attitude. Sex Roles , 9, 179 Á/193.
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