severidad de las penas

ATTITUDES TOWARDS SEVERITY OF PUNISHMENT:

A CONJOINT ANALYTIC APPROACH

MICHAELA BROCKE*, CHRISTIAN GOLDENITZ, HEINZ HOLLING andWOLFGANG 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�/offender relationship and of twovictim characteristics �/ 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 Design

INTRODUCTION

Attitudes towards sanctioning in general and towards severity of punishment in particular

have been repeatedly in the focus of research on crime and delinquency. The same applies to

actual sanctioning behavior (e.g. McFatter, 1986; Ouimet and Coyle, 1991; Pfeiffer and

Oswald, 1989). Beside differences between professionals, differences between lay persons

received 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 the

knowledge of factors causing divergent orientations towards sanctioning.

Explaining laymen’s differences in the evaluation and appraisal of punishment proves to

be a difficult and complex task. This is true because people do not show a uniform and

undifferentiated orientation towards this topic. It is possible, of course, to identify

differences between individual attributions and attitudes towards crime and sanctioning

on a general level (Furnham, 1988). However, there is ample evidence that the individual’s

orientation 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-

ISSN 1068-316X print/ISSN 1477-2744 online # 2004 Taylor & Francis Ltd

DOI: 10.1080/10683160310001614793

*Corresponding author. E-mail: [email protected]$E-mail: [email protected]

Psychology, Crime & Law, June 2004, Vol. 10(2), pp. 205�/219

205

and offense-specific factors should be taken into account (Carroll and Payne, 1977; Hollin

and 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�/victim relationship (e.g. Best

and Demmin, 1982; Hammock and Richardson, 1997; Workman and Freeburg, 1999). It is

obvious, then, that a general, undifferentiated approach for assessing attitudes towards

severity of punishment is unlikely to go far enough in explaining judgmental differences.

In our own research on attitudes towards severity of punishment, we recently conducted

two studies, varying several factors in a quasi-experimental design. These factors included

offender characteristics (prior conviction and employment) within short scenarios of theft

and assault (Reichert, 1999; Reichert and Bilsky, 2001), and victim characteristics

(intoxication, provocativeness) as well as the offender�/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, empathy

with 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 the

case in research on the impact of crime characteristics on sanctioning.

In our first study, prior conviction of the offender revealed considerable impact on

severity of punishment in an analysis of covariance, explaining 19.5% of variance in theft,

F (1,202)�/52.69, pB/0.001, and 24.4%, F (1,202)�/75.65, pB/0.001, in bodily harm. The

influence of employment was low but significant, too, resulting in F (1,202)�/5.51, pB/0.05

for theft, and F (1,202)�/4.02, pB/0.05 for bodily harm. Furthermore, a significant

interaction was identified for bodily harm, F (1,202)�/28.28, pB/0.001, revealing harder

punishment for an offender without employment and prior conviction. In our second study

using a rape scenario, multivariate analysis of covariance revealed only a marginal effect of

the victim�/offender relationship, F (2,313)�/2.95, p�/0.05. None of the remaining factors

and interactions came close to significance.

While findings from both studies seem interesting with respect to attitudes towards

severity of punishment, the between-subjects designs procedure employed in these

investigations reveals several shortcomings. A main disadvantage of this design refers to

the extensive sampling that is required when a high number of case characteristics is under

study. The higher the number of case characteristics, the more vignettes result and the more

respondents are needed. Thus, between-subjects designs are rather inefficient for investigat-

ing the impact of several crime characteristics simultaneously. Besides, between-subjects

designs require extensive data collection , since observer characteristics have to be measured

additionally. Moreover, with a between-subjects design only indirect conclusions about

factors influencing sanctioning decisions, drawn from inter-individual differences , are

provided. Thus, analyses of individual ‘‘sanctioning patterns’’, that is intra-individual

comparisons 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 experimental

within-subjects design was used. This design was realized by means of conjoint analysis .

Conjoint analysis provides reliable estimates of intra-individual weights, even with complex

factorial designs, without extensive sampling as usually required for within-subjects designs.

Actually, conjoint analysis is a data collection and analysis technique, which has become

206 M. BROCKE et al .

popular 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 objects

repeatedly with regard to their attractiveness. Other things being equal, these objects are

varied systematically with respect to some well-defined factors supposed to influence their

evaluation. Respondents’ answers are then decomposed by data analysis, assigning different

weights to the factors under study.

In the present research context, assessment differed in that respondents had to assign

subjectively adequate degrees of punishment to offenses presented in different crime

scenarios. These scenarios varied with respect to factors supposed to influence the perceived

seriousness of the criminal act under study. Conjoint analytic estimates gained from these

judgments, then, represent the importance placed on the different case characteristics with

respect to severity of punishment. In other words, the results reflect the relative impact of

case characteristics on preferred sentencing. To the best of our knowledge, this type of

analysis has not been used for analyzing sentencing behavior in the past. Interestingly,

however, for measuring the seriousness of different types of offenses the paired comparison

method and extensive measurement, both sharing kinship with conjoint analysis, have yet

been applied (Thurstone, 1927; Levi, 1974; Francis et al. , 2001).

In the following, two conjoint analytic studies on factors influencing the severity of

punishment are reported. Both studies are exploratory to the extent that they test the

applicability of conjoint analysis in this special research context. The first study used a rape

scenario for investigating the influence of victim characteristics and of the victim �/offender

relationship on sanctioning. The second study concentrated on the impact of offender as well

as context characteristics in an assault and a theft scenario . For each study, the design is

presented first, together with some information about former research on the variables

under study. Then, the conjoint analytic method and the results are sketched out. Since the

use of conjoint analysis is uncommon in this research context, data collection and data

analysis 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 the

victim �/offender relationship . These factors were combined in a 2�/2�/3 factorial design as

outlined in Table 1. While the factors intoxication and provocative behavior comprised two

levels each (present/absent or yes/no, respectively), the victim �/offender relationship was

operationalized by three factor levels: the categorical distinction between nodding

TABLE 1 Factors and factor levels of Study 1.

Factor Factor level

Behavior of victim no provocationprovocation

Intoxication victim is sobervictim is tipsy

Relation of victim and offender nodding acquaintanceex-couple, offender partedex-couple, victim parted

SEVERITY OF PUNISHMENT 207

acquaintance and former partnership was supposed to represent different degrees of

intimacy between victim and offender; this latter category was further specified by indicating

who quit the former partnership �/ 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, a

mitigating influence on sentencing has been confirmed by former research. Thus, studies

about the influence of a woman’s consumption of alcohol prior to rape unanimously show

that the offender is considered less guilty, if the victim is intoxicated. Accordingly, a lower

degree 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 to

a 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 different

operationalizations, 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 �/offender relationship has also been considered in various

studies; results, however, proved inconsistent. Thus, a friendship between victim and

offender may cause higher (e.g. Bridges, 1991; Szymanski et al ., 1993) or lower attribution of

guilt to the victim (e.g. Bolt and Caswell, 1981; Krulewitz, 1982). These discrepancies may

indicate that it is not the mere acquaintance but the degree of intimacy and trust between

offender and victim that has to be taken into consideration (Sczesny and Krauel, 1996). The

degree of closeness had been considered when designing Study 1, therefore.

Method

Procedure

Seventy-five students (62 female and 13 male), aged 19�/44 (median�/20), participated in

this study. Aside from some general information about the investigation, participants

received detailed instruction of how to deal with the conjoint analytic tasks. Since

consecutive trials of the experimental variation of factor levels were displayed on a screen

in a brief and standardized form only, the rape scenario within which to interpret this

information was presented first. Furthermore, it was stressed that offenses vary only with

respect to the variables displayed. Compliance with this instruction is of considerable

importance to guarantee the validity of the subsequent judgments.

Subsequently, the presentation of the experimental variables and the recording of

participants’ reactions were conducted in a computer-assisted form, using Alasca (Holling

et al. , 2000). In order to get acquainted with the case characteristics under study, the

participants had to rank order the levels of each factor first. Then, they worked on 25 graded

paired comparison and eight rating tasks, as described next.

Each graded paired comparison task consisted of two rape offenses differing in the factor

levels under study. Participants had to compare the seriousness of these offenses and to state

on a seven-point rating scale whether and to which extent one of them should be punished

harder 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 the

previous 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


the factor levels on the degree of punishment. More concretely, numerical estimates of the

impact of the different factor levels are calculated after every response, and an algorithm is

used to determine the combination of factor levels to be presented next, in order to gain the

maximum information required for stabilizing the estimates. This is why adaptive conjoint

analysis 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 as

described by varying levels of the three factors under study (cf. Figure 2).

Data Analysis

In conjoint analysis, two kinds of estimates describing the influence of the independent

variables are available. One of them is related to the factor levels and termed part-worths . In

the present context, the part-worths can be interpreted as ‘‘penalty-worths’’, indicating the

extent to which the presence of a certain factor level is perceived as aggravating from a

participant’s point of view. To gain the part-worths, weights for the factor levels are

estimated from the paired comparisons by ordinary least squares (OLS) regression. This

estimation method is usually used in conjoint analysis, because it is equivalent (e.g. Wittink

and Cattin, 1981) or superior (e.g. Cattin and Bliemel, 1978) to nonmetric methods. If the

independent (categorical) variables are dummy coded, OLS regression is analogous to

analysis of variance, as typically used in between-subjects design studies.

Part-worths are calculated for every participant. To obtain inter-individual comparability

of these estimates, part-worths are standardized. This is accomplished for each factor by

setting the factor level with the highest part-worth to one, and the lowest to zero. Part-

FIGURE 1 Paired comparison 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 between

participants or subgroups (Backhaus et al ., 2000), classification methods can be applied

to the data. To test for subgroups that differ in judging the seriousness of an offense, a

hierarchical cluster analysis on the individual, standardized part-worths was conducted in

the present study, using Ward’s method and squared Euclidean distances as a measure of

similarity between participants.

The second kind of estimate is called relative importance (score ). It refers to the factors

and specifies their influence on the judgment on the whole. For each factor, it is determined

by the difference between the highest and the lowest standardized part-worth. Thus, if the

part-worths of a two-level factor are similar, for example, a low relative importance score

results. This score indicates that judging the severity of an offense does not change

substantially, whether one or the other level of this factor is considered. For simplicity of

interpretation, importance scores are transformed into percentages by dividing each factor’s

range by the sum of the ranges of all factors (cf. Hair et al ., 1998).

Two methods exist for aggregating relative importance scores. First, relative importance

can be determined by the span of aggregated part-worths. This measure reveals the influence

of 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 aggregated

afterwards. By applying this procedure, the mean influence of the factor levels on the

individual level is determined. If the constellations of individual part-worths diverge, the two

FIGURE 2 Rating task in Alasca .


measures may differ. Since they complement each other, both are reported and compared for

a rough check of heterogeneity.

Besides the part-worths and the relative importance scores, measures of reliability and

validity 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 are

considered as a measure of internal consistency because goodness of fit is essentially

determined by the consistency of a participant’s responses. For all participants, R2 values are

checked, and participants with values lower than R2�/0.5 are excluded from further

analyses. This seems adequate, because part-worths calculated for these cases are not

supposed to be sufficiently reliable. For the remaining participants, the mean of the Fisher z

transformed individual R2 values is reported.

The second measure, the hit rate, indicates the predictive validity of the part-worths. It is

calculated from the data of the rating part of the conjoint analysis. All pairwise

combinations of the offenses presented in the rating part were inspected with respect to

possible differences in rating. For each pair differing it was checked, whether the part-worths

predict correctly to which of the two offenses a higher degree of punishment is assigned. The

relative frequency of correct predictions is transformed into a percentage value for each

participant and aggregated afterwards. As a rule of thumb, hit rates above 80% are usually

regarded as high (Teichert, 2000).

It should be noted that the values for internal consistency and predictive validity also offer

information about the independence and the additivity of the factor levels underlying the

regression model adopted. Both measures indicate the degree of concordance between the

predicted and the actual responses in the paired comparison and the rating part.

Results

Five participants showed goodness of fit values of the respective regression lower than R2�/

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 was

R2�/0.87. Hit rates resulted in 80.2% on average. According to the criterion mentioned

above, this percentage indicates a high level of predictive validity.

The means of the standardized individual part-worths are depicted in Figure 3. They

illustrate 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.


factor. On an aggregated level, provocative behavior has the strongest influence on the

assessment of punishment (79.8% of relative importance), since the difference between the

levels of this factor is higher than between the levels of the remaining factors. Here, a higher

degree of punishment is assessed to the offender, if the victim is not blamed for provocative

behavior. In other words, provocative behavior of the victim is likely to concede extenuating

circumstances to the offender. The second-highest importance (17.4% of relative impor-

tance) is attached to the victim �/offender relationship . For this factor, the part-worth for a

nodding 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 factor

levels of intoxication is negligible, this factor is supposed to play a minor role in the present

context (2.8% of relative importance). Nevertheless, part-worths imply the assignment of a

slightly 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, the

influence of provocative behavior (49.3% of relative importance) diminishes. On the other

hand, higher relative importance scores result for the victim �/offender relationship (29.7% of

relative importance) and for intoxication (21.0% of relative importance).

The differences found in relative importance on the aggregated and on the individual level

indicate disagreement between participants. This is also reflected by a hierarchical cluster

analysis computed for the individual standardized part-worths. The dendrogram of this

analysis suggested a four-cluster solution. In Figure 4, profiles of the mean part-worths are

shown for each of these clusters. They illustrate the difference in weighting applied by these

subgroups. 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 : respondents

belonging to clusters 1 and 2 (n�/23) attach a higher part-worth to the intoxication of the

victim, whereas those of clusters 3 and 4 (n�/47) do not. Finally, groups disagree in

weighting the different levels of the victim �/offender relationship : while rape by a nodding

acquaintance, as compared to a former partner, is regarded as more serious by the majority

of our participants (n�/60), this does not hold for the respondents of cluster 4 (n�/10).

FIGURE 4 Mean part-worths of the factor levels for subgroups determined by cluster analysis.


Furthermore, information about who quit the former partnership influences the assignment

of punishment, too; clusters differ with respect to the weighting of the respective alternatives.

Thus, participants of clusters 2 and 4 (n�/21) punish less hard if the offender has separated,

while those of cluster 1 and 3 (n�/49) assign opposite weights.

DISCUSSION

With respect to the criteria mentioned, the results of the conjoint analysis in our first study

proved 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 previous

studies using a within-subjects design. This applies to the whole sample (i.e. on an

aggregated level) and to the different subgroups.

For intoxication on an aggregated level, on the other hand, part-worths suggest a

direction opposite to former results, although, factor levels were nearly equal in height.

However, differences in weighting this latter factor could be demonstrated between two

subgroups by means of cluster analysis: the majority of the participants is likely to interpret

the intoxication of the victim in the expected sense, that is, in the sense of shared

responsibility. The second, smaller group, in contrast, tends to interpret the offender’s

behavior as taking advantage of the victim’s handicap in this special situation. Two factors

may 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 to

other judgmental processes than the evaluation of one of the levels alone. Second, changing

role expectations, that tolerate more and more the consumption of alcohol by women, may

have come into effect.

Finally, for the impact of the victim �/offender relationship on punishment a high degree of

intimacy is regarded as mitigating on an aggregated level. However, results of the cluster

analysis reveal inter-individual differences for this factor, too. These differences may have

contributed 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 assault

and a theft scenario: offender’s age , confession , severity of the offense and previous

conviction . For the latter factor, pertinence was considered by two factor levels. In both

scenarios, age and confession comprised two, severity and previous conviction three factor

levels, resulting in a 2�/2�/3�/3 factorial design. Factor levels were the same for both

scenarios, except for severity , which had to be adapted to the different types of crime (see

Table 2).

Former studies on the effect of offender’s age , severity of offense and previous conviction

have shown significant effects on sanctioning for several crimes. Thus, more lenient forms of

punishment, for instance, have been assigned to young perpetrators without previous

conviction, 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). The

same holds for the mitigating influence of a confession on punishment.


METHOD

Procedure and Data Analysis

Sixty students (45 female and 15 male), aged 19�/54 (median�/22), participated in Study 2.

Data collection was similar to that in our first study. However, because of the greater

number of factors and factor levels in the present study, pairs of offenses presented were

described 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 four

levels each. Furthermore, participants had to run through the conjoint analytic procedure

twice�/once for theft, and once for assault. The sequence of these offenses was balanced

between participants. Methods applied for data analysis were the same as in Study 1.

RESULTS

The goodness of fit values for the regressions were lower than R2�/0.5 for four participants.

For the remaining 56 participants, means of the goodness of fit indices were R2�/0.86 for

both offenses. Hit rates were 89.0% on average for theft and only slightly lower (88.2%) for

assault. 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% of

relative importance): the difference between no previous conviction and previous conviction

is higher than the differences between the levels of the remaining factors. A much higher

degree 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% of

relative importance), confession (20.3% of importance), and age (16.8% of relative

importance), 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 previous

conviction was of utmost importance for theft, severity of the offense had the strongest

impact on sanctioning for assault (41.9% of relative importance). Nevertheless, previous

convictions proved to be an influential factor, too (34.5% of relative importance). While the

TABLE 2 Factors and factor levels of Study 2.

Factor Factor level

Age 17 years30 years

Confession offender confessesoffender denies

Previous conviction no convictionconviction for theftconviction for assault

Severity of offence: theft 250 t loss750 t loss1250 t loss

Assault several punchespunches, knock-downpunches, knock-down, additional kicks


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 the

remaining 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 a

pertinent in comparison to a non-pertinent conviction for assault. In sum, for these two

factors the relative importance scores as well as the profile of part-worths are different. With

respect 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 minor

importance for sanctioning. However, mitigating effects of both factors are even less

pronounced for assault than for theft.

For both theft and assault, the relative importance scores calculated on the individual level

do not differ substantially from those on the aggregated level: for theft, the mean individual

relative importance scores differ from the aggregated relative importance scores by only

FIGURE 5 Mean part-worths of the factor levels for theft.

FIGURE 6 Mean part-worths of the factor levels for assault.


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 analysis

differ only slightly and are therefore not depicted graphically.

DISCUSSION

On the whole, the goodness of fit indices and the individual hit rates for both offenses

suggest again the appropriateness of the additive main effects models used to calculate the

part-worths. Part-worths show that the factor levels influence sanctioning uniformly in the

expected direction in both offenses: A young perpetrator with no previous conviction, who

commits a minor offense and confesses is punished less hard than a previously convicted

older person, who commits a major offense and refuses to confess. However, differences in

the profiles of the part-worths and in the relative importance of factors were found for

assault and theft.

SUMMARY AND CONCLUSIONS

For a differentiated analysis of attitudes towards severity of punishment, a number of

features of a crime has to be considered. Compared to between-subjects designs, within-

subjects designs realized by conjoint analysis represent a more accurate and parsimonious

approach in this context. We reported on two studies using conjoint analysis for data

collection and analysis. In a first study, we aimed at the description of the sanctioning

structure applied to rape; in a second study, we focused on the (comparison of) sanctioning

patterns applied to theft and assault. All in all, findings from both studies suggest that

detailed insights into the process of sanctioning can be gained by using a within-subjects

design and conjoint analysis, respectively. The various benefits of this approach in the

present 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 factor

levels on the degree of punishment is determined on an individual level . In other words, the

influence of factor levels on the dependent variable need not be inferred indirectly by

comparing experimental groups; rather, participants serve as their own control in a repeated

measurements design. This implies, that, unlike between-subjects designs, a within-subjects

design does not require recording of individual characteristics in order to control for

between-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 judgmental

styles 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 conjoint

analysis. Complex research designs can be employed, because the evaluation of subsets of

stimuli within fractional factorial or adaptive designs still provides reliable estimates of

parameters. The main advantage of complex designs is that a trade-off of several different

features of an offense is possible, thus providing a more differentiated analysis of attitudes

towards severity of punishment.

As offenses are judged in pairs, the standards of comparison and the factor levels under

study are always evident in conjoint analysis. This is important for methodological as well as


content reasons. From a methodological point of view, ratings in between-subjects designs

may result opposite to those in within-subjects designs because of unclear standards of

comparison. Birnbaum (1999) provides an instructive experimental illustration for mislead-

ing conclusions resulting from between-subjects designs. He also discusses this problem with

respect to studies on rape. As regards content, presenting a standard of comparison seems

reasonable, too. Thus, in a vignette approach, about one half of the respondents stated to be

unable 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, though

unusual in the given context, conjoint analysis provides a means for studying intra-

individual preferences and patterns of sanctioning as well as inter-individual differences in

an economic manner.

Of course, in order to use this approach efficiently, the procedure has to be adapted to the

given research context. Therefore, we finally outline some recommendations for research

design and data collection in conjoint analysis as emphasized in the literature (e.g. Hair et

al ., 1998).

As regards the research design, the factors to be studied have to be well-selected, since the

relative importance scores of the factors are mutually dependent. In this respect conjoint

analysis closely resembles ipsative measurement. For example, in a conjoint analysis with

unimportant factors only, the relative impact of a single factor may be overestimated; if

more important factors were included, the impact of the first factors would certainly

diminish.

In addition, descriptions used for the factor levels have to be specified carefully, since

semantic variations may influence the results considerably. The range of the factor levels

under study has to be taken into consideration as well. A wide range may lead to higher

relative importance scores than a small range. Thus, to ensure the comparability of the

relative importance scores, it is recommended to use a similar range for all factors, and to

balance the number of levels across factors if possible.

Furthermore, considerable attention has to be paid to the experimental design used for

data collection. For example, in order to test for interaction effects of factor levels, designs

that allow for the estimation of interaction terms have to be constructed.

In view of the encouraging findings of our investigations, further studies should be

conducted to test other applications of conjoint analysis in the research on punishment and

sanctioning. Research on individual weighting schemes applied in judging the seriousness of

offenses using complex designs, for example, seem especially promising. In addition, the

relation of judgments relating to case characteristics and person variables could be

investigated on a more differentiated level, for instance the interdependence of evaluations

of 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 by

judges and laymen in the context of sanctioning and sentencing, are another possible field of

research. Finally, the predictive validity of factors identified in conjoint analysis needs

examination, if their usefulness is supposed to go beyond mere attitudinal research.

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severidad de las penas

Education

victim offender relationship

offender victim relationship

victim behavior

appraisal of punishment

crimespecific factors

observer characteristics

empathywith victim

toactual sanctioning