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Appetite 47 (2006) 324332

Research Report

Lay peoples perception of food hazards: Comparing aggregated data

and individual data

Michael Siegrista,b,, Carmen Kellerb, Henk A.L. Kiersc

aHumanEnvironment Interaction, ETH, Zurich, ETH Centre CHN J75.1, CH-8092 Zurich, SwitzerlandbDepartment of Psychology, University of Zurich, Zurich, Switzerland

cHeymans Institute, University of Groningen, Groningen, The Netherlands

Received 14 October 2005; received in revised form 25 April 2006; accepted 5 May 2006

Abstract

The psychometric paradigm has been used to explain the perception of food hazard risks. In past studies, only aggregated data were

analyzed, and individual differences were neglected. In the present study, both aggregated data and non-aggregated data are analyzed.

Data stem from a mail survey conducted in Switzerland ( N 448). Analyzing aggregated data, results of past studies were successfully

replicated. The PCA analysis revealed the two factors unknown risk and dread risk. Results of a three-way component analysis

(3MPCA) suggest, that two components explain individual differences in the perception of food hazards. The two components were

labeled unobservable hazards, and familiar hazards. Individual differences in the cognitive representation of hazards were

correlated with attitudes toward natural foods. Results suggest that people who prefer natural foods differ in perceived risks from people

who do not prefer natural foods. Results show that methods permitting individual differences are crucial for a better understanding of

the cognitive representation of food hazards.

r 2006 Elsevier Ltd. All rights reserved.

Keywords: Food hazards; Psychometric paradigm; Risk perception; Three-way component analysis; Natural hazards

Introduction

The psychometric approach has been utilized to examine

lay peoples attitudes toward food hazards (Hansen, Holm,

Frewer, Robinson, & Sandoe, 2003). Psychometric studies

identify factors that influence the perception of various

hazards (Fischhoff, Slovic, Lichtenstein, Read, & Combs,

1978). This research approach has been used to study a

broad range of hazards including technological risks,

activities, and food hazards (Slovic, 1987). Studies focusing

on the perception of food-hazard risks (Fife-Schaw &

Rowe, 1996, 2000; Sparks & Shepherd, 1994) have

replicated the results found in the initial studies examining

a heterogeneous set of hazards. In the psychometric

paradigm, hazards are located on a cognitive map,

which depicts the hazards in a space of two or more

dimensions. The psychometric approach has been criti-

cized, however, for its neglect of individual differences in

risk perception (Sjo berg, 1996). Recent research has shown

that there are substantial individual differences in risk

perception that are not captured in the traditional

psychometric paradigm (Siegrist, Keller, & Kiers, 2005).

In the present study, we examine lay peoples perception of

various food hazards utilizing a method that allows for

individual differences.

The psychometric paradigm

In studies based on the psychometric paradigm, partici-

pants evaluate a variety of attribute rating scales for a set

of hazards (Fischhoff et al., 1978; Sparks & Shepherd,

1994). Participants assess, for example, how dreadful the

hazards are, whether the risks are known to science,

whether people have control over their exposure to the

hazard. The number of rating scales varies from study to

study. In most studies utilizing the psychometric paradigm,

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0195-6663/$ - see front matterr 2006 Elsevier Ltd. All rights reserved.

doi:10.1016/j.appet.2006.05.012

Corresponding author. Department of Psychology, University of

Zurich, Switzerland.

E-mail address: [email protected] (M. Siegrist).
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averages are taken across all participants, and the data

matrix (hazards rating scales) is submitted to a principal

component analysis (PCA).

In a study by Sparks and Shepherd (1994), lay peoples

perceptions of 25 food hazards were examined. Partici-

pants assessed these hazards with respect to 23 rating

scales. The authors found that three factors accounted forthe correlations among the rating scales. The first

component was labeled severity. The following rating

scales were highly correlated with the first principal

component: concern, dread potential, seriousness for

future generations, and threatening widespread disastrous

consequences. The second component was labeled un-

known, and the rating scales known to science and known

by people exposed were highly correlated with it. The third

component, called number of people exposed, accounted

for much less variance than the first two components. Food

hazards, such as genetic manipulation or pesticide residues,

were perceived as unknown and high-severe risks. Alcohol

and excessive calories, for example, were perceived as

known and low-severe risks. Some of these findings were

successfully replicated (Fife-Schaw & Rowe, 1996, 2000;

Kirk, Greenwood, Cade, & Pearman, 2002).

In the studies reviewed, the data cube (hazards rating

scalesparticipants) was reduced to a matrix (ha-

zards rating scales). Factor loadings were computed for

the rating scales, and factor scores were computed for the

hazards. Because aggregated data were used, we do not

know whether the model neglects individual differences in

risk perception. In some studies (Bronfman & Cifuentes,

2003; Marris, Langford, Saunderson, & ORiordan, 1997;

Willis, DeKay, Fischhoff, & Morgan, 2005), differentmatrices were examined (e.g., person rating scales,

hazards rating-scales). Such an approach produces mis-

leading results, however, when substantial three-way inter-

actions are present. Only a few studies have reported results

of non-aggregated data (Siegrist et al., 2005; Vlek & Stallen,

1981), but these studies did not specifically focus on food

hazards. The study conducted by Siegrist et al. (2005), used

a generalization of PCA, three-way principal component

analysis (3MPCA; see Kiers & Van Mechelen, 2001), which

is appropriate for the analysis of three-way data. Results of

that study demonstrated that analyzing the non-aggregated

data sets of the psychometric paradigm provides additional

insight into individual differences in risk perception.

Three-way principal component analysis

Three-way data are data that can be arranged in a three-

dimensional array. In the context of our study, the three

dimensions are the individuals, the hazards and the rating

scales. The three sets of entities associated with such three-

way data sets are called the three modes of the array. It

can be tempting to either analyze three-way data after

aggregating over one of the three modes or to separately

analyze all two-way data sets contained in the three-way

data set. It should be noted, however, that such approaches

do not yield an explicit description of the three-way

interaction in the data and therefore can lead to conclusions

that are incomplete at best. The strength of 3MPCA

techniques is that they summarize all the information in a

large, pre-processed three-way data set (i.e., all main effects

and all interactions together), and they do so efficiently.

Specifically, three-way methods summarize the entities ofeach mode by means of a few components and describe the

relations between these components. The present study takes

participants scores on rating scales with respect to different

hazards and captures the (most salient) relations between

individuals, hazards, and rating scales by the relations

between components summarizing the individuals (indivi-

dual-mode), hazards (hazard-mode), and rating scales

(attribute-mode). This is particularly useful in the presence

of a three-way interaction: Without the use of summarizing

components, a full description of a three-way interaction

may require as many terms as there are data points, which is

not feasible unless the three-way data set is very small. For

the reconstruction of the original data from the component

variables related to individuals, hazards and rating scales, a

small three-way array is used. This array is called the core

array. The core array indicates the extent to which persons

who score high on some person component give ratings that

have high weights on some rating scales component, to

hazards that have high weights on some hazard component.

Various techniques for analyzing three-way data sets

have been proposed; we selected three-mode principal

component analysis (3MPCA) for the present study

(Kiers & Van Mechelen, 2001; Kroonenberg & De Leeuw,

1980; Tucker, 1966). As in PCA, in 3MPCA several choices

have to be made (for example, how the data are to bepreprocessed, how many components are to be used, and

what simple structure rotations are to be used). For details,

we refer the reader to Kiers and Van Mechelen (2001).

Simple examples explaining the logic of the 3MPCA

procedure in some more detail can be found elsewhere

(Siegrist et al., 2005; Van Mechelen & Kiers, 1999).

Variables affecting risk perception

External variables can be used in the explanation of

individual differences in risk perception. Results of the

study by Siegrist et al. (2005) showed that general trust

influenced how hazards were perceived. In the domain of

food, attitudes toward the naturalness of foods seems to be

important (Rozin et al., 2004). Results of past research

suggest that perceived naturalness is related to the

acceptance of novel food (Tenbu lt, de Vries, Dreezens, &

Martijn, 2005). In addition, self-reported purchase of

organic foods was related to perceived benefits for human

health (Magnusson, Arvola, Hursti, Aberg, & Sjoden,

2003). Based on these results, we hypothesized that

attitudes toward naturalness shape risk perception. Speci-

fically, persons who show a preference for natural foods

perceive food hazards differently compared with persons

who do not show a preference for natural foods.

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Rationale of the present study

The aim of present study is to better understand

individual differences in laypeoples perceptions of food

hazards. We examined how well results obtained by

analyzing aggregated data sets represent individual risk

perception in the domain of food risks. The first goal wasto analyze the aggregated data set and to compare the

results with those from earlier studies (Fife-Schaw & Rowe,

1996, 2000; Sparks & Shepherd, 1994). The second

objective was to utilize a 3MPCA to analyze the individual

variation that is not covered by the PCA of the aggregated

data. The third objective was to describe the extent to

which individual differences in various types of risk

perception are related to a preference for natural foods.

Method

Participants

The data for the present study come from a mail survey

conducted in the German-speaking part of Switzerland. A

questionnaire and accompanying letter were sent to a

random sample of addresses from the telephone book. The

letter and first page of the questionnaire asked that the

questionnaire be completed by the person in the household

who was over 18 years of age and who was next in line for

their birthday. A reminder letter was sent out some time

later. A second questionnaire was sent to persons who did

not respond to the letter or reminder. Four hundred and

forty-eight people completed the questionnaire, with a

response rate of 37.5%.Fifty-two percent (n 233) of the respondents were

women, and 48% (n 213) were men. Two participants

did not report their gender. The mean age was 50.0

(SD 16.0). Seventeen persons did not report their age.

Education and income were not measured. Therefore, we

do not know whether better-educated people were more

likely to fill out the questionnaire.

Questionnaire

Participants were asked to rate 24 possible hazards on

nine five-point scales. The hazards were similar to those

used by Sparks and Shepherd (1994), with the addition of

several new items including diet products, convenience

foods and vitamin-enriched food. These new items were

added because both functional foods (van Kleef, van Trijp,

& Luning, 2005; Verbeke, 2005), and convenience foods

(Verlegh & Candel, 1999) are increasingly important for

the food sector. The nine rating scales were adapted from a

study by Fischhoff and colleagues (Fischhoff et al., 1978):

(1) Voluntariness of risk (1 voluntary; 5 involuntary).

(2) Immediacy of effect (1 immediate; 5 delayed).

(3) Knowledge of risk to those who are exposed

(1 known precisely; 5 not known).

(4) Scientific knowledge (1 little; 5 much).

(5) Control over risk (1 uncontrollable; 5

controllable).

(6) Newness (1 new; 5 old).

(7) Harm for health (1 not at all; 5 very strong).

(8) Peoples worries (1 not worried; 5 very much

worried).(9) Probability of health damage (1 not probable;

5 probable).

In addition to sociodemographic variables, the ques-

tionnaire included five items designed to measure pre-

ference for natural foods (e.g., Whenever possible I buy or

consume organic food, I feel good when I eat natural

foods). Respondents were asked to express their agree-

ment or disagreement with these items using a value

between 1 (dont agree at all) and 5 (agree absolutely).

Data analysis

(a) Data imputation: Data from 34 participants were not

used because these participants failed to answer more

than ten questions. For the remaining 414 respondents,

missing values on an item were replaced by the mean

value. More sophisticated data imputation procedures

yielded virtually identical results.

(b) Response scale recoding: Variables of rating scales 4

(scientific knowledge), 5 (control over risk), and 6

(newness) were recoded, so that a high value indicates

great risk severity.

(c) Two-way PCA on aggregated data: To replicate the

results reported in earlier studies (Fife-Schaw & Rowe,2000; Sparks & Shepherd, 1994), a two-way PCA was

carried out on the data averaged across the individuals.

(d) Variance component estimation: Before carrying out a

3MPCA, we checked to see whether the data could be

analyzed reasonably well by means of a two-way PCA

on aggregated data. Estimates of the percentages of

variance for main effects of individuals, hazards, and

rating scales, as well as for all pairwise interactions,

were obtained from a fixed effect analysis of variance

with repeated measures on the 414 24 9 three-way

data table.

(e) Three-way component analysis (3MPCA): This type of

analysis entails three kinds of choices related to: (1) a

possible preprocessing of the data prior to the actual

analysis, (2) the choice of the number of the compo-

nents for each of the three modes, and (3) the choice of

a simple structure rotation (see Kiers & Van Mechelen,

2001). The following summarizes the decisions we

made for the analysis of the present data.

First, regarding preprocessing of the data: due to the

incomparability of the labels of the response dimen-

sions a kind of standardization of the data is required.

To eliminate the influence of (unknown) neutral points,

data were centered across individuals (person mode),

that is, for each hazard, for each rating scale, the

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average score across persons is subtracted from the

scores. An added benefit of this centering is that it takes

out the very same information that was analyzed by

the PCA on aggregated data. Thus, the results of the

3MPCA are fully complementary to the results of the

PCA. Moreover, given the difference in rating scale

labels, a correction for artificial differences betweenresponses in scale range is also preferable. Therefore,

data were scaled within rating scales (attribute mode)

by dividing each entry by the standard deviation of the

scores on the corresponding rating scale.

Second, regarding the choice of the number of

components (which may differ for individuals, hazards,

and rating scales), several criteria were used. These

included percentage of variance accounted for, a

generalized scree test (Timmerman & Kiers, 2000),

stability of analyses across two random splits of the

data, and interpretability of the components. The

generalized scree test relies on differences in percentage

of variance accounted for between neighboring solu-

tions, which in the two-way component analysis case

correspond to eigenvalues.

Third, as to the choice of a rotation for the present

data, we decided to use varimax rotations in order to

obtain a simple structure primarily for the hazards and

rating scales; the remaining rotational freedom was

used for varimax rotation of the core.

(f) Reliability check: The reliability of the preference for

natural food scale was checked by calculating Cron-

bachs alpha. The scale had good internal consistency

(a :81).

(g) Correlation with external scales: Correlations acrosspersons were calculated between the 3MPCA person

component scores and the attitudes toward natural-

ness.

Results

Analysis of the aggregated data

A two-way PCA of the aggregated data was conducted.

As Table 1 shows, the first of the two orthogonal

components of the unrotated factor loadings is highly

correlated with immediacy of effect, knowledge about risk

by exposed persons, knowledge to science, and newness.

This component is labeled unknown risk. The second

component is associated with the rating scales voluntari-

ness, harm for health, peoples worries, and probability of

health damage. This component is labeled dread risk.

These two components explain 89% of the variance. The

factor scores of the 24 hazards were computed. Fig. 1 gives

a two-dimensional plot of these hazards, using factor

scores as coordinates. Antibiotics in meat, GMO, and food

irradiation are located high on unknown, and relatively

high on dread. Salmonella, botulism, and bacterial

contamination are located high on dread, and relatively

low on unknown. Alcohol and caffeine are located low

on unknown, and relatively low on dread.

Three-mode principal component analysis

Before carrying out a 3MPCA, an estimation of the

variance components should be conducted to indicate

whether the data could also be analyzed reasonably well by

means of a two-way PCA on aggregated data. For

example, if all individuals showed roughly the same

response patterns with respect to all rating scales and

hazards, one could simply take the averages across

individuals and analyze the patterns of average responses

to all hazards with respect to all rating scales. Three-wayanalysis is indicated when the data contain a non-negligible

three-way interaction; that is, when individuals differ in

their risk perceptions of different hazards. Table 2 presents

the results of the variance component estimation. The

results show that the data averaged across individuals,

which only represent the rating scales and hazards main

effects, as well as their interaction, explain only 42.1%

(11.7%+12.6%+17.8%) of the variance of the original

data, and 57.9% of the variance is related to individual

differences. There are two sizable two-way interaction

terms, as well as the three-way-interaction-plus-error term,

which cannot be ignored. Although we do not know what

part of this term is error, it is clear that an important three-

way interaction is present in the data in addition to two

important two-way interactions. These results indicate that

individuals differ in terms of their risk perceptions of

different hazards. Therefore, in order to examine how

individuals differ in their risk perceptions, a three-way

interaction is clearly indicated.

In selecting the 3MPCA model, several models with

varying numbers of components were evaluated. For the

present data, a P 4 (person components), Q 2 (hazard

components), R 2 (rating scales components) solution

seems appropriate. It accounts for 19% of the variance not

explained by the 2-PCA model utilizing aggregated data.

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Table 1

Loadings from two-way principal components analysis over nine rating

scales averaged across individuals (unrotated solution)

Rating scale Unknown

risk

Dread risk

Voluntariness (1 voluntary) .51 .80

Immediacy (1 immediate) .75 .32

Knowledge of risk to those exposed

(1 known precisely)

.96 .16

Scientific knowledge (1 much) .96 .14

Control over risk (1 controllable) .62 .72

Newness (1 old) .93 .05

Harm for health (1 not at all) .41 .88

Peoples worries (1 not worried) .00 .96

Probability of health damage (1 not

probable)

.35 .89

Variance (%) 46.81 42.35



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Including more components would result in models that

would be difficult to interpret. That is not to say that such

models would be meaningless, but only that we have

chosen to limit the model to the smallest number of

dimensions that describes the largest differences across

individuals, hazards and scales. Overall, the selected model

also appears to be reasonably stable in the split-half

procedure.

The component weights for the hazards (hazard mode)

are presented in Table 3, with weights above .20 set in bold

face. The 95% confidence intervals were obtained by a

bootstrap procedure using the optimal target rotations

method (Kiers, 2004) with 1000 bootstrap samples. These

indicate the stability of the component weights. Food

hazards scoring high on the first hazard componentwith

highest weights for genetically modified plants and animals,

irradiated food and pesticide residues on foodrefer to

hazards with unobservable consequences. The second

component was labeled familiar hazards. The foodhazards with the highest loadings on this component were

high-sugar diet, excessive calorie intake, high-fat diet,

alcohol and caffeine. These are food hazards people are

familiar with.

The component weights for the rating scales (attribute

mode) are depicted in Table 4, with weights above .30 set in

bold face. Similar to the results obtained analyzing

aggregated data, the first component is associated with

voluntariness, knowledge of risk to those exposed, scientific

knowledge, control over risk and newness. Therefore, this

component was labeled as unknown risk. The second

component is associated with probability of health

damage, peoples worries, and harm for health. This

component was labeled as dread risk. Results indicate

that the same components can be used in describing both

the data related to individuals and the aggregated data (the

data across individuals).

The chosen 3MPCA model also involved four stable

person components. This suggests that there are at least

these four dimensions along which individuals differ

regarding risk perception of food hazards. Using orthogo-

nal rotations only (thus leaving the component scores

uncorrelated) results in a core array that is fairly, but not

ideally, simple. In the present case, where we have as many

person components as there are combinations of hazards

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Fig. 1. Location of the food hazards within the two-component space.

Table 2

Estimated variance components and variance percentages

Effect SS Percent

Individuals 6652.51 3.5

Rating scales 22 163.43 11.7

Hazards 23 898.85 12.6

Individuals by rating scales 24 546.46 12.9

Individuals by hazards 14 285.26 7.5

Rating scales by hazards 33 771.17 17.8

I ndi vidu als by r atin g scal es b y haz ards 64 8 64. 94 34.1

Total 19 0182.62 100

Note. SS Sum of squares.



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and rating scales components, namely 4, we can find an

oblique rotation of the person component scores such that

the core array becomes diagonal. The oblique rotation,

then, yields person components that are no longer

uncorrelated, but that have a very simple, one-to-one,

relation to the hazard and rating scale components, via the

core. The core resulting from this oblique rotation is given

in Table 5. The interpretation of each person component

now is given directly by the combination of hazard and

rating scale components associated with it, while the

associated value in the core indicates the importance of

the component: The highest core entries indicate the largest

individual differences. It can be seen, however, that

differences between the core elements are not big. As for

the interpretation, person component 1 distinguishes

individuals in terms of their perception of unobservable

hazards as unknown risk. Individuals scoring high on the

first person component perceive unobservable hazards as

highly unknowable, while people with low scores on this

person component perceive unobservable hazards as quite

knowable. Individuals scoring high on the second person

component perceive familiar hazards as highly unknow-

able, whereas people with low scores on this component

perceive familiar hazards as quite knowable. The third and

fourth components are related to the dreadfulness of the

unobservable hazards. Persons scoring high on the third

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Table 3

Hazard component weights

Hazard Unobservable hazards 95% Confidence intervals Familiar hazards 95% Confidence intervals

Pesticide residues in food 0.29 0.21 0.32 0.05 0.08 0.03

Genetically modified plants 0.38 0.33 0.40 0.11 0.13 0.06

Genetically modified animals 0.37 0.32 0.39 0.11 0.13 0.06

Natural toxicants in foods 0.24 0.17 0.27 0.02 0.01 0.09

BSE (mad cow disease) 0.17 0.08 0.22 0.01 0.04 0.09

Foods with bacterial contamination 0.14 0.03 0.23 0.05 0.04 0.16

Food additives 0.25 0.19 0.28 0.10 0.06 0.15

Salmonella 0.10 0.03 0.20 0.07 0.02 0.19

Irradiated food 0.34 0.29 0.35 0.05 0.07 0.01

Food colorings 0.25 0.17 0.30 0.11 0.05 0.18

Artificial sweeteners 0.13 0.07 0.21 0.20 0.13 0.26

Caffeine 0.04 0.06 0.01 0.35 0.31 0.37

Alcohol 0.07 0.10 0.01 0.34 0.28 0.36

Excessive calorie intake 0.06 0.09 0.01 0.38 0.33 0.40

High-sugar diet 0.05 0.07 0.00 0.39 0.34 0.40

High-fat diet 0.05 0.07 0.01 0.37 0.32 0.39

Antibiotics in meat 0.26 0.18 0.30 0.01 0.04 0.06

Preservatives 0.25 0.19 0.29 0.11 0.07 0.17

Botulism 0.10 0.01 0.19 0.08 0.01 0.19

Nitrates (e.g., in salad) 0.26 0.21 0.28 0.00 0.03 0.05

High-salt diet 0.05 0.03 0.09 0.28 0.25 0.30

Diet products 0.06 0.01 0.15 0.24 0.16 0.27

Convenience food 0.11 0.06 0.19 0.22 0.16 0.26

Vitamin enriched foods 0.15 0.08 0.23 0.17 0.10 0.23

Note. Weights larger than .20 set in bold face. Confidence intervals obtained by bootstrap procedure.

Table 4

Rating scale component weights

Dimension Unknown

Risk

95% Confidence intervals Dread risk 95% Confidence intervals

Voluntariness (1 voluntary) 0.42 0.30 0.48 0.15 0.08 0.23

Immediacy (1 immediate) 0.08 0.06 0.20 0.23 0.35 0.08

Knowledge of risk to those exposed

(1 known precisely)

0.44 0.35 0.49 0.15 0.23 0.07

Scientific knowledge (1 much) 0.50 0.31 0.63 0.01 0.14 0.13

Control over risk (1 controllable) 0.47 0.34 0.57 0.05 0.05 0.14

Newness (1 old) 0.39 0.27 0.46 0.02 0.13 0.08

Harm for health (1 not at all) 0.01 0.05 0.05 0.47 0.37 0.51

Peoples worries (1 not probable) 0.07 0.01 0.13 0.54 0.47 0.58

Probability of health damage (1 not

probable)

0.04 0.08 0.00 0.62 0.56 0.65

Note. Weights above .30 set in bold face. Confidence intervals obtained by bootstrap procedure.



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component perceive unobservable hazards as more dread-

ful than persons scoring low on this component. Finally,

persons scoring high on the fourth component perceive

familiar hazards as more dreadful, whereas persons with

low values on this component perceive unfamiliar hazards

as more dreadful. It can be seen that the two person

components associated with unknown risk are mildly

correlated (r :36), just as with the two components

related to dread risk (r :

48), while these components

mutually are hardly correlated (rp:11).

The person component scores were correlated with the

attitude preference for natural foods. A large correlation

was observed for preference for natural foods and person

component 3 (r :45, N 399, 95%CI: [.37, .52]).

Individuals, who show a preference for natural foods tend

to perceive unobservable hazards as more dreadful than

individuals who do not show a preference for natural

foods. The food attitude scale was also rather strongly

correlated with person component 4 (r :25, N 399,

95%CI: [.16, .34]). This means that the more preference for

natural food is expressed, the more that familiar hazardsare perceived as dreadful risks. Finally, a small but

statistically significant correlation between preference for

natural foods and person component 1 was observed

(r :14, N 399, 95%CI: [.04, .23]). This result suggests

that there is a small tendency for people with a preference

for natural foods to perceive unobservable hazards as more

unknown risks than people not having a preference for

natural foods. The correlation between person component

2 and preference for natural foods was very small and not

significant (r :05, N 399, 95%CI: [.05, .15]).

Discussion

The psychometric paradigm has been used to address the

question of why people perceive various hazards differ-

ently. In this paradigm, participants are asked to evaluate a

list of hazards with respect to a number of rating scales.

The big advantage of this approach is that perceptions of

different hazards can be compared with each other. Several

studies have examined the perceived risks associated with

potential food hazards (Fife-Schaw & Rowe, 1996, 2000;

Kirk et al., 2002; Sparks & Shepherd, 1994). One weakness

of these past studies was the neglect of individual

differences.

The present study employed an adaptation of the ratings

scales used by Fischhoff et al. (1978). The set of hazards

that was used is comparable to the hazards included in the

study by Sparks and Shepherd (1994). Initially, aggregated

data were analyzed as in most psychometric studies. The

PCA analysis of the rating scales revealed two factors that

were labeled as unknown risk and as dread risk. These

components are similar to those elicited by other research-

ers utilizing the psychometric paradigm to examine food

hazards (Fife-Schaw & Rowe, 2000; Kirk et al., 2002;

Sparks & Shepherd, 1994). In the present study, the

location of the food hazards within the two-component

space is very similar to the study by Sparks and Shepherd

(1994). This similarity is remarkable, given that the two

studies differ in a number of aspects. Data collection

occurred more than 10 years apart, and the two studies

used different rating scales. Results of the present study

suggest, therefore, that on an aggregated level the patterns

produced by the psychometric paradigm are very stable.

Results of the 3MPCA indicate that at least two

components can be used sensibly for explaining individualdifferences in the perception of hazards. The two compo-

nents were labeled unobservable hazards, and familiar

hazards. Food hazards such as genetically modified plants

or animals, irradiated food, and pesticide residues on food

loaded highly on the component unobservable hazards,

and hazards like alcohol, excessive calorie intake, and high-

sugar diet loaded low on this component. Hazards like

high-sugar diet, and caffeine had high loadings on the

component familiar hazards. Low loadings on this

component were observed for hazards such as GMO and

irradiated food. The rating scales can be described by the

two factors unknown risk, and dread risk. Results

suggest, therefore, that the same components that explain

aggregated data can also be used effectively to explain

individual differences in risk perception.

Four person components were used to describe the

individual differences. These four components are at odds

with the results of the two-way PCA, which assumes that

there are no individual differences. The results of the two-

way PCA indicate that people generally associate unobser-

vable hazards as unknown and dreadful risks, and familiar

hazards as known and not dreadful risks. Results of the

3MPCA indicate, instead, that for each combination

of a hazards component and a rating scales component, a

stable component can be found that describes individual

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Table 5

Core array

Unknown risk Dread risk

Person component Unobservable hazards Familiar hazards Unobservable hazards Familiar hazards

Component 1 60.0 0 0 0

Component 2 0 69.5 0 0Component 3 0 0 67.6 0

Component 4 0 0 0 61.8



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differences in the extent to which the associated type of

hazards are perceived as unknown risks or dreadful risks.

The person component scores were correlated with the

external variable preference for natural foods. Results

suggest that individuals who show a preference for natural

foods perceive unobservable food hazards as more dreadful

than individuals who do not show a preference for naturalfoods. Past research suggests that people who are

concerned with the naturalness of food are more likely to

buy organic food (Lockie, Lyons, Lawrence, & Grice,

2004). Results of the present research suggest that attitudes

toward natural food also shape risk perception of food

hazards. In other words, people for whom naturalness of

food is important perceive food risks differently from

people for whom naturalness of food is not important.

The present study examined the perception of food

hazards not included in other studies. Newer risks, such as

convenience food or functional foods (i.e., vitamin-

enriched food), are viewed as rather unknown risks with

low dreadfulness. Perceived risk seems not to be a problem

for these new foods.

Products or food technologies with tangible benefits for

the consumer are viewed as less dreadful (e.g., artificial

sweetener, convenience food) than products or food

technologies without obvious consumer benefits (e.g.,

GM foods, food irradiation). It might be tempting to

assume that consumers accept new food technologies when

products with desirable benefits are created. It should be

noted, however, that novel foods with clear benefits might

not be appealing to all consumers. Results of the present

research suggest that attitude preference for natural foods

shaped perception of food hazards. Therefore, attitudesmay also influence acceptance of novel foods with clear

benefits for consumers.

Past research suggests that psychometric studies based

on aggregated data do no fully explain individual risk

perception (Siegrist et al., 2005; Willis et al., 2005).

Analysis of variance results show that individual differ-

ences are responsible for a substantial part of the variance.

About 58% of the variance is associated with individual

differences or with measurement errors. Results of the

present study are in line with results of earlier studies,

therefore, in suggesting that laypeoples risk perception

cannot be explained by a model based on aggregated data

(Siegrist et al., 2005). Results of the analysis of variance

strongly suggest that techniques for analyzing three-way

data sets should be utilized in order to get a complete

description of the data.

The present study focusing on food hazards yielded very

similar results to the study by Siegrist et al. (2005) in which

risk perception of a very heterogeneous set of hazards was

examined. In the Siegrist et al. (2005) study the model

explained 13% of the variance, whereas in the present

study the proposed model explains 19% of the variance. In

the present study, participants responded to more specific

hazards, and this resulted in a larger part of the variance

being explained by the model. To some, an explained

variance of 19% may still seem low. However, the results

suggest that the present 3MPCA solution gives a usefully

interpretable, parsimonious, and stable description of

individual differences in risk perception. It should also be

emphasized that there were 216 hazard and rating scale

combinations on which individuals could differ. That only

four components account for as much as 19% of thevariance is therefore quite a surprising result.

Some limitations of the present study should be

addressed. The response rate was 37.5%, even though

households were contacted three times. It could be, that

better-educated people were more likely to fill out the

questionnaire. In view of the fact that participants were

being asked to respond to more than 250 items, the

response rate seems acceptable. Furthermore, in most

studies utilizing the psychometric method for studying risk

perception, convenience samples were examined. The

sample utilized in the present study is clearly less biased

than a pure convenience sample.

The psychometric method requires that the attributes

that might be important for risk perception be explicitly

stated. For measuring food hazards, we adapted rating

scales that were used to measure other hazards in earlier

studies (Fischhoff et al., 1978). It is possible, however, that

the attributes we included were not all relevant to the

perception of food hazards. Perceived naturalness is an

important factor in the domain of foods (Rozin, 2005), for

example, that was not included as an attribute in the

present study. Future studies should include additional

attributes that might be important for lay peoples risk

perception.

Results of the present study strongly support the notionthat three-way-component methods should be used for a

better understanding of lay peoples risk perception. In

future studies, the food hazards should be described in

greater detail. This might result in a higher proportion of

variance explained by the model. In the present research,

preference for natural food was used as an external

variable. Future studies should test other factors that

might explain individual differences in risk perception.

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