Development and validation of the Food Disgust Scale

Accepted Manuscript Development and validation of the Food Disgust Scale Christina Hartmann, Michael Siegrist PII: DOI: Reference:

S0950-3293(17)30171-4 http://dx.doi.org/10.1016/j.foodqual.2017.07.013 FQAP 3369

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Food Quality and Preference

Received Date: Revised Date: Accepted Date:

26 January 2017 25 July 2017 28 July 2017

Please cite this article as: Hartmann, C., Siegrist, M., Development and validation of the Food Disgust Scale, Food Quality and Preference (2017), doi: http://dx.doi.org/10.1016/j.foodqual.2017.07.013

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FOOD DISGUST SCALE

Development and validation of the Food Disgust Scale Christina Hartmann & Michael Siegrist ETH Zurich, Department Health Science and Technology (D-HEST), Consumer Behavior, Switzerland

Address for correspondence: Dr. Christina Hartmann ETH Zurich Department Health Science and Technology (D-HEST), Consumer Behavior Universitaetstrasse 22, CHN H75.3 CH-8092 Zurich Switzerland E-mail: [email protected] Telephone: +41 (0)44 632 97 47

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FOOD DISGUST SCALE

Development and validation of the Food Disgust Scale Abstract The function of disgust as a pathogen avoidance promoter in the food domain is not well understood. One problem is that no food-specific disgust scale is available. Thus, we developed and validated the Food Disgust Scale (FDS) through a series of five studies. The FDS is a self-report measure that enables the assessment of an individual’s emotional disposition to react with disgust to certain food-related (offensive) stimuli. Exploratory and confirmatory factor analyses were used to develop eight FDS subscales that represent unique types of food disgust: animal flesh, poor hygiene, human contamination, mold, decaying fruit, fish, decaying vegetables, and living contaminants. The subscales showed good internal consistencies across three different adult samples (N between 170 and 527). Alongside the 32item version, an 8-item composite measure was developed and tested. Validity was supported by correlational analysis between the revised version of the Disgust Scale, germ aversion, food neophobia, picky eating, and individuals’ number of food-borne illnesses in the last five years. In addition, two-week test-retest reliability was very good. Incremental validity was supported in an eating experiment (i.e., willingness to eat insect-based food). The new scale will not only help improve the understanding of how food disgust shapes people’s food behavior in a functional and dysfunctional way, but will also help enhance the understanding of consumer acceptance of new foods and food technologies.

Keywords: disgust sensitivity, food neophobia, food acceptance, food choice, food-borne disease

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1. Introduction

2

1.1. Functional domains of disgust

3

When considering evolutionary processes and the drivers of natural selection, one

4

would expect humans to have developed threat-detection systems and responses. Evidence

5

has suggested that a disgust reaction is one such highly functional system that aims to reduce

6

the contact with and thus the likelihood of infection from bacteria, parasites, and viruses.

7

Disgust is a regulatory human emotion thought to be a component of the behavioral immune

8

system because it cognitively triggers disease-preventive behavior to avoid health threats

9

(Terrizzi, Shook, & McDaniel, 2013). Besides the behavioral component, a feeling of disgust

10

is accompanied by specific physiological reactions and a characteristic facial expression.1

11

Food disgust, at its core, is a food-rejection emotion intended to prevent the ingestion

12

of potentially noxious and/or pathogen-laden substances (Chapman & Anderson, 2012; Haidt,

13

McCauley, & Rozin, 1994). Bitter tastes in particular are prototypically a stimulus for an

14

innate oral rejection, leading to spitting out the unpalatable, potentially toxic material

15

(Chapman & Anderson, 2012). Apart from distaste, disgust is triggered by cues that

16

symbolize hazardous items and the presence of pathogens including certain odors (e.g., smell

17

of decayed food), visual cues (e.g., mold), tactile cues (e.g., slime), and auditory input (e.g.,

18

hearing someone clear a throat full of mucus) (Curtis & Biran, 2001). Likewise, objects that

19

contact a disgusting object can become contaminated and a subsequent trigger of disgust.

20

Disgust elicitors can be not only culturally specific, but also the same across cultures

21

(Curtis & Biran, 2001; J. M. Tybur et al., 2013). Some elicitors are directly related to the

22

presence of pathogens, while others do not directly pose a health threat. The variability of

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disgust elicitors within and across cultures caused researchers to develop a theoretical model

24

to classify disgust elicitors above and beyond the domain of pathogen avoidance – for

25

example disgust elicitors related to moral violations (Haidt et al., 1994; Tybur, Lieberman, &

26

Griskevicius, 2009; Tybur et al., 2013). In food research, the moral domain of disgust might

27

be of importance when it comes to either the acceptance of new food technologies (Scott,

28

Inbar, & Rozin, 2016) or differentiation between appropriate and inappropriate animal-based

29

food products. Most people in Western societies would probably call it disgusting (in terms of

30

morally unacceptable and offensive) to eat cats or dogs, while in certain non-Western

31

countries these animals are part of the countries’ cuisine. Food-related moral disgust is 1

For a comprehensive introduction to the topic of disgust, please see Chapman and Anderson

(2012), Oaten, Stevenson and Case (2009), Rozin, Haidt, and McCauley (2017), Schaller and Park (2011), Tybur, Liebermann, Kurzban and DeScioli (2013).

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FOOD DISGUST SCALE 32

especially relevant with regard to culturally determined food appropriateness, though there is

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presumably a low level of variation between individuals from the general population within a

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cultural region. Therefore, the moral domain of disgust was not considered in the present

35

research. Rather, the focus was on both cues that might symbolize hazardous items and cues

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that are not pathogen-related that may evoke a non-morally based disgust reaction. For

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example, spoilage and decay of animal and non-animal food often coincide with changes in

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color, texture, smell and taste, which are then recognized as disgust elucidating cues, even

39

though they are not necessarily an indicator of pathogen presence (Martins & Pliner, 2006).

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Moreover, food contamination with human body fluids and products was identified as a

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disgust elicitors in previous research (Haidt et al., 1994; Tybur et al., 2009).

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In fact, people can vary in their sensitivity and reactivity toward such cues and thus

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experience disgust in various situations, induced by various substances. On the one hand,

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insensitivity to cues (e.g., mold on cheese) might inhibit the necessary preventive behavior,

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leading people to expose themselves to higher risks. On the other hand, oversensitivity to non-

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pathogen-related cues (e.g., black spots on a banana) and overgeneralization based on crudely

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defined cues trigger false alarms and might lead to the neglect of viable food resources.

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However, the functional and dysfunctional effects of food disgust sensitivity on eating

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behavior still need to be explored. To explain the effect of food disgust on behavior, a reliable

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and valid scale is needed. Therefore, the aim of the studies presented in the following, was to

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develop and test a tool that can measure individual differences in the reactivity to cues that

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may evoke food-related disgust.

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1.2. Measuring disgust sensitivity

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Various scales to measure disgust sensitivity have been proposed (Table 1). Haidt et

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al. (1994) established the 32-item Disgust Scale (DS), which measures disgust sensitivity in

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eight domains: food, animals, body products, sex, body envelope violations, death, hygiene,

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and magic (contact with or visual appearance of a disgust elicitor). Olatunji, Williams, et al.

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(2007) proposed a revised version of the DS (DS-R) with a reorganized item structure and a

60

reduced number of subscales (core disgust, contamination-based disgust, and animal reminder

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disgust). Other researchers have also suggested new disgust measures. For example, Tybur,

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Lieberman, and Griskevicius (2009) proposed the 21-item Three Domains Disgust Scale

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including pathogen disgust, sexual disgust, and moral disgust. Van Overveld, de Jong, Peters,

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Cavanagh, and Davey (2006) published the Disgust Propensity and Sensitivity Scale (revised

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by Fergus & Valentiner, 2009), which is supposed to measure the frequency of experiencing

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disgust and the bodily and emotional impact of experienced disgust. These disgust scales

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consist of three to eight subscales, and factor analyses of disgust sensitivity measures have

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consistently produced multidimensional factor solutions (e.g., Kleinknecht, Kleinknecht, &

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Thorndike, 1997; Olatunji, Williams, et al., 2007). Thus, researchers have suggested that

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disgust sensitivity is domain specific (e.g., Tybur et al., 2009). For a more detailed review of

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current disgust scales see Rozin et al. (2017).

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A major deficit in the disgust literature, however, is that the published scales measure

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overall disgust sensitivity or disgust sensitivity in certain domains; none of these scales is

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sensitive enough to capture individual differences in disgust sensitivity in the domain of food.

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Food-related items, if they are included in the disgust scales at all (Fergus & Valentiner,

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2009), are diffusely spread among the subscales with questionable theoretical justifications

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for the loadings and low mean factor loadings (Olatunji, Cisler, Deacon, Connolly, & Lohr,

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2007). Moreover, in various studies using the DS by Haidt et al., low alpha reliabilities below

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.40 did not allow the formation of a food-related subscale (Haidt et al., 1994; Olatunji, Cisler,

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et al., 2007; Schienle, Walter, Stark, & Vaitl, 2002; Stark, Walter, Schienle, & Vaitl, 2005;

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van Overveld, de Jong, Peters, & Schouten, 2011). In the scale by Tybur et al. (2009), the four

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included food items had low factor loadings on one single factor (pathogen disgust) and were

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not distinctive enough to provide sufficient information about the different types of food

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disgust. Likewise, in the disgust scale by Kleinknecht et al. (1997), only disgust related to

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rotting food is measured. To date, there is no extensive measure that captures disgust

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sensitivity in the domain of food.

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1.3. Food disgust2

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Rozin and Fallon (1987) suggested that food rejection can be classified into four

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types: distaste, danger, inappropriateness and disgust. In the present study, the focus was

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solely on disgust and the development of a food-specific disgust measure. The measure

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focuses on the food domain and people’s ability to detect pathogen-related cues (functional

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cues) that symbolize potentially hazardous items. It also considers sensitivity to certain cues

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that are actually not pathogen related or that do not indicate a health threat. Within the scope

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of the new scale proposed here, disgust sensitivity was defined based on the conceptualization

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by Haidt et al. (1994) and Olatunji et al. (2007). Thus, the new scale measures food disgust as

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a measure of trait disgust (i.e., a person’s emotional predisposition to be more or less easily 2

The English word “disgust/disgusted” was used to signify being sickened or grossed out in

the presented studies.

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disgusted by certain food-related cues). The focus was on potential disgust elicitors and not

99

on items that are avoided because of a special medical condition such as a food allergy or

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lactose intolerance. The new scale was also not intended to measure peoples’ reactions

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towards toxic substances or items (e.g. fly agaric or deadly nightshade) that do not show

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disgust-elucidating cues (e.g. mold, slime, or a bad smell). In this context, knowledge seems

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to be a more relevant factor than disgust sensitivity.

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After an extensive literature review on disgust and food rejection (Curtis & Biran,

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2001; Haidt et al., 1994; Martins & Pliner, 2006), we aimed to find a representative selection

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of food-specific disgust cues. We chose cues that are related to the process of decay (e.g.,

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mold), hygienic aspects of food preparation (e.g. an unclean cook), reminders of animal origin

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(e.g., blood), visible and invisible contamination (e.g., hair), and hygiene (e.g., dirty

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silverware). Different food groups were selected as carriers of these cues. Food-cue

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combinations were chosen that were familiar to most people in Western societies and that

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worked equally well in English and German language. We asked if participants perceived

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something as disgusting instead of whether they were willing to eat it. The intention to eat

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may not be a useful predictor of food disgust sensitivity because of the various reasons that

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people eat something despite being disgusted by it (e.g. social pressure and/or prevention of

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food waste).

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The development of the new Food Disgust Scale (FDS) was processed in three phases.

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In the first phase, the construct of interest, food disgust, was defined based on previously

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established theories of disgust and pathogen avoidance. Items were developed and tested.

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Item performance was analyzed, and the scale was constructed. In the second phase, the new

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scale was tested in another sample, and various construct validations were accomplished,

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including test-retest reliability. In the third phase, the scale’s incremental validity was tested

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within the scope of a study on willingness to eat.

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2. Item generation and scale construction (Study 1)

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Study 1 aimed to generate items that measure food-related disgust sensitivity and to

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identify an underlying factor structure. No assumptions were made regarding the

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dimensionality of the new scale.

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2.1. Material and methods

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2.1.1. Survey participants

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Data collection for the first study occurred in January 2015 in Switzerland. The study

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participants were recruited from an Internet panel from a commercial provider of sampling

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services (Respondi AG). Excluded were respondents who did not complete the survey and

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whose total survey duration was less than half of the median of the total survey duration (e.g.,

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Hartmann, Keller, & Siegrist, 2016), which indicates that the respondent did not seriously

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answer the questions (n = 18). Quota samples were used with the quota variables of gender

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and age. The sample consisted of 318 respondents (Table 2).

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2.1.2. Item generation

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We developed a large pool of 63 items. For example, participants were asked how

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disgusting they perceived “the mold-free part of a partially moldy tomato,” “an apple that

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dropped on the street,” or “food prepared by unknown neighbors.” As in previous research

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(Tybur et al., 2009), items were rated on a 6-point scale ranging from 1 (not disgusting at all

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[in German: überhaupt nicht eklig]) to 6 (totally disgusting [extrem eklig])3. The first set of

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items was pretested with a small sample of people of different ages and educational

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backgrounds. Ambiguous and unclear items were revised, and sources of misunderstanding

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were fixed. Face validity, which indicates to what extent the items are subjectively viewed as

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to have captured the underlying concept, was assessed by two research assistants.

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2.1.3. Item analysis and scale construction

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The 63 items were analyzed using exploratory factor analysis (principal component

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analysis [PCA] with varimax rotation) to examine the underlying factor structure. When

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selecting the number of factors, the following criteria were used: (a) all factors with

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eigenvalues greater than 1.0, (b) the point of inflection on the scree plot, and (c) a good

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interpretability of the factors. In addition, items with cross-loadings and loadings of <.40 were

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excluded (Stevens, 2012). Reliability analysis (Cronbach’s alpha) was conducted for every

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subscale. Items with item-total correlations of <.30, items that did not affect reliability when

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excluded, and items that were redundant with other higher-loading items were excluded as

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well.

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Since the option “forced response” (available for online surveys) was used, there were

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no cases of missing data. All statistical analyses were performed using the SPSS Statistics

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software package version 23 (SPSS Inc., Chicago, IL). 3

For a discussion of the translation of disgust in this context, please see the discussion section.

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2.2. Results

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Initial item analysis revealed that participants used the whole 6-point response scale

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for all the items. Thirty-two items were dropped based on the criteria described above. The

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reduced item pool was submitted to a final exploratory factor analysis. The Kaiser-Meyer-

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Olkin (KMO) measure was 0.88, and the Bartlett’s test of sphericity was χ2(465) = 5868.81, p

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< .001), which verified the sampling adequacy of the analysis and indicated that the

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correlations between items were sufficiently large for a PCA. Seven components had

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eigenvalues over Kaiser’s criterion of 1 and together explained 68.6% of the variance. Based

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on the scree plot and the interpretability of the factors, a seven-factor solution was considered

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the best model. Items of the same food type or disgust cue loaded on one factor. They were

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labeled (1) animal flesh, (2) poor hygiene, (3) human contamination, (4) mold, (5) decaying

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fruit, (6) fish, and (7) living contaminants. The subscales consisted of two to six items. The

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alpha reliabilities of the single subscales were very good, ranging from α = .75 (living

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contaminants) to α = .89 (fish and animal flesh).

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2.3. Discussion

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The first study developed a new scale that measures food disgust sensitivity. The

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analysis revealed a seven-factor solution, and the resulting subscales showed good internal

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consistencies in a sample of adults. Results of the study suggest that the structure of food

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disgust in adults may be best characterized by the nature of the stimuli, distinguished by the

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food group and the origin of the disgust-eliciting food cue. However, the factor structure of

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the FDS observed in Study 1 requires confirmation in another sample. In addition, the

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vegetable food group was not covered by the items in Study 1, and the living contaminants

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subscale consisted of only two items. Therefore, some new items were developed with regard

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to vegetables and living contaminants and included in Study 2.

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3. Scale refinement and development of a short version (Study 2)

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One of the aims of Study 2 was to test if the basic factor structure found in Study 1

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could be replicated in another sample of Swiss adults. The study also explored if the newly

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developed items considering the potentially disgust-eliciting cues of vegetables form a new

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subscale. The newly constructed scale enables differentiation between various types of food

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disgust but consists of a large number of items. Therefore, another aim of Study 2 was to

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develop a short version of the FDS. Gender and age effects were also explored.

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3.1. Material and methods

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3.1.1. Survey participants

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Data collection for the second study occurred in June 2015 in the German-speaking

205

part of Switzerland. The study participants were recruited from an Internet panel from a

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commercial provider of sampling services (Respondi AG). The exclusion criteria of Study 1

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were also applied here (n = 54). Again, quota samples were used with the quota variables of

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gender and age. The sample consisted of 527 respondents (Table 2).

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3.1.2. Scale refinement and verification

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Based on the factor structure of Study 1, the vegetable-related items were expected to

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form a new eighth factor, and the new item related to living contaminants was expected to

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load on the corresponding factor. This was tested by performing an exploratory factor analysis

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(varimax rotation) on the 36 items. The same criteria for the number of selected factors like in

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Study 1 were used. In the second step, the proposed factor model from the exploratory factor

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analysis was tested with a confirmatory factor analysis using maximum-likelihood estimation.

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Model fit was examined via the chi-square statistic, the root mean square error of

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approximation (RMSEA < 0.05), the comparative fit index (CFI > 0.90), and the normed fit

219

index (NFI > 0.90). Modification indices were checked to identify redundant items.

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3.1.3. Development of the FDS short version

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The FDS subscales identified here are considered specific manifestations of a more

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general construct (i.e., food disgust). Therefore, a composite score in the form of a short

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version of the scale was calculated (Clark & Watson, 1995). A common method for

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constructing a short-form measure is to select items with the highest item-total correlations

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and the highest face validity (Widaman, Little, Preacher, & Sawalani, 2011, pp. 52-53). This

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method was used in the present study, and appropriate items were chosen from every subscale

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of the final FDS. The short form was tested using confirmatory factor analysis with

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maximum-likelihood estimation, and the model fit criteria described above were applied.

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3.1.4. Statistical analysis

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Mean scores and Cronbach’s alphas across the subscales were calculated. Gender

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differences in food disgust sensitivity were tested using student’s t-test. Associations with age

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were assessed using correlational analysis. Data were analyzed using the SPSS Statistics

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software package version 23 (SPSS Inc., Chicago, IL) and SPSS AMOS.

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3.2. Results

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The PCA with the 31 items from Study 1 and the five new items suggested an eight-

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factor solution. Likewise, the 36-item confirmatory factor analysis indicated an adequate

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eight-factor fitting model. An examination of the modification indices suggested significant

241

redundancy between four items. The removal of the four items resulted in a 32-item good-

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2 fitting model, χ (435) = 945.37, p < .001, CFI = 0.95, RMSEA = 0.05, NFI = 0.91. Each of

243

the eight factors represent unique aspects of food disgust. The factors representing different

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types of food disgust were labeled as in Study 1: animal flesh (4 items), poor hygiene (5

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items), human contamination (4 items), mold (4 items), decaying fruit (4 items), fish (4

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items), and living contaminants (3 items). The new eighth factor identified in this study was

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named “decaying vegetables” (4 items). Each of the eight factors demonstrated good internal

248

consistency. The final model, standardized factor loadings, and Cronbach’s alphas for each

249

subscale are presented in Table 3 and Figure 1. The eight subscales were intercorrelated. The

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intercorrelations were of small or medium size and therefore acceptable. The highest

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correlation occurred between animal flesh and fish (r = .71, p < .001), which is probably

252

because both originate in or are related to animal-based food. To obtain a good model, the

253

error terms of two items (HYG4, HYG5) were allowed to be correlated. Both of these items

254

are related to body products; therefore, this correlation can be justified.

255

2 The eight-item model fit was good, χ (18) = 44.02, p = .001, CFI = 0.97, RMSEA =

256

0.05, NFI = 0.95. The standardized factor loadings varied between 0.41 and 0.68 (Figure 2).

257

Cronbach’s alpha was .77, which is acceptable for short versions (Widaman et al., 2011, p.

258

46). In addition, the error terms of the items MEAT1 and FISH4, as well as those of the items

259

FRUIT4 and VEGI1, were allowed to be correlated to improve the model fit statistics. This

260

makes sense from a theoretical point of view because the corresponding items belong to

261

similar food groups. The items of the short scale and corrected item-total correlations are

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depicted in Table 4.

263

Women scored significantly higher on all subscales of the FDS except for decaying

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fruit and human contamination (Table 5). A larger difference between genders was observed

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FOOD DISGUST SCALE 265

for the subscales of poor hygiene and animal flesh, while smaller differences were noted for

266

the subscales of mold and decaying vegetables. Significant gender differences were also

267

observed for the short version of the scale. With regard to age, small positive correlations

268

were found with the FDS subscales of poor hygiene (r = .17, p < .001), decaying vegetables (r

269

= .12, p < .01), and human contamination (r = .16, p < .001). Animal flesh was negatively

270

correlated with age (r = -.17, p < .001). The other four subscales and the short FDS were not

271

significantly correlated with age (p > .01).

272 273

3.3. Discussion

274

Results of Study 2 show that an eight-factor model fit the data very well. The new

275

scale allows for the measurement of a disgust experience induced by cues related to the

276

process of food decay and thus induced by specific characteristics of aging foods, such as

277

black spots on a banana or wrinkled radishes. Moreover, the poor hygiene and human

278

contamination subscales cover two distinct sources of disgust caused by other familiar or

279

unfamiliar persons. While the human contamination subscale assesses whether people are

280

disgusted by certain socially accepted behaviors such as sharing a glass with a friend, the poor

281

hygiene subscale includes food preparation and consumption behaviors that are not socially

282

accepted and generally considered unhygienic (e.g., another person’s hair in one’s food).

283

The animal flesh subscale is based on items that are reminders of the animal nature of

284

meat. People seem to vary in their sensitivity to disgust over cues that reflect the origin of

285

meat (e.g., seeing a whole pig en brochette) and the associated bloody slaughter of animals

286

(e.g., seeing raw meat). Previous researchers identified these associations as potential disgust

287

elicitors because they are reminders of death and eating living creatures (Rozin, Haidt, &

288

McCauley, 2000). However, the animal flesh subscale of the FDS should not be confused

289

with the animal reminder subscale of the DS-R. The latter was described as reflecting “the

290

aversion of stimuli that serve as reminders of the animal origins of humans” (Olatunji,

291

Williams, et al., 2007, p. 285). Thus, the animal reminder subscale of the DS-R focuses on

292

humans and reminders of human mortality (e.g., seeing a man with his intestines exposed

293

after an accident), while the animal flesh subscale focuses on meat in the context of food and

294

eating. The fish subscale is based on stimuli (e.g., the smell or texture of fish) that are related

295

to characteristics of the food as well as reminders of the animal nature of the food (e.g.,

296

having a whole fish with its head on one’s plate). In addition, both meat and fish, especially

297

when they are raw, are often contaminated with pathogens that drastically increase in number

298

during the ageing process. This high pathogen load often leads to changes in the appearance

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FOOD DISGUST SCALE 299

of the food and can cause a number of food-borne illnesses. This might be another aspect that

300

influences peoples’ disgust experience.

301

Some items of the FDS contain information about the texture of a food (e.g., “eating

302

limper salad,” “wrinkled radishes,” or “the texture of certain kinds of fish in the mouth”).

303

Other items give hints about the changed color of the food, indicating decaying food (e.g.,

304

“brown spots on a banana” or “brown flesh of an avocado”). Both texture and color are

305

characteristics that typically change when food gets older, even though such changes are not

306

necessarily an indication of expiration or inedibility. Some people are more sensitive to these

307

kinds of cues than others, as manifested in the varying scores on the subscales. It is

308

noteworthy to mention that, based on the statistical analysis, two separate factors (or

309

subscales) for fruit and vegetables were identified. They were rather similar and highly inter-

310

correlated (r = .69), because they were both related to the process of decay and accompanying

311

color and texture changes. However, the somehow comparable cues might be perceived

312

differently when changes in color (e.g. black spots on a banana) are associated with changes

313

in taste (e.g., increased sweetness).

314

Another aim of Study 2 was to develop a short version of the FDS. The eight-item

315

short version enables researchers to measure food disgust in a time-efficient way. Applied

316

research in particular should use the shorter version when a detailed assessment of food

317

disgust types is not relevant. Applications in the domain of food could be associations with

318

food waste behavior, kitchen hygienic behavior, or the prevalence of food-borne diseases.

319

Although short scales have poorer psychometric properties (e.g., lower Cronbach’s alpha than

320

longer forms), overall results of the present study suggest that the short form of the FDS is

321

adequate for many research purposes.

322

Previous studies have shown that women show more disgust sensitivity than men in

323

various domains such as hygiene, contamination-based disgust, and animal reminder disgust

324

(Haidt et al., 1994; Olatunji, Williams, et al., 2007; Petrowski et al., 2010). The same was

325

observed in the domain of food. Women scored significantly higher on almost all subscales

326

and the short FDS. Only the two subscales of human contamination and decaying fruit

327

showed no gender effects. In line with previous results (Petrowski et al., 2010), the

328

associations with age were weak in the present study. Although people might experience

329

changes in disgust sensitivity after certain events (e.g., transition to motherhood) or

330

physiological circumstances (e.g., illness), food disgust sensitivity might be rather stable over

331

the adult life course. Nevertheless, further research has to confirm this cross-sectional

332

observation.

12

FOOD DISGUST SCALE 333 334 335

4. Validity testing (Study 3)

336

Convergent and discriminant validation was performed for both the short and the long

337

version of the FDS. Convergent validity was tested in relation to overall disgust sensitivity

338

and germ aversion, which describes an individual’s tendency to be frightened of germs

339

(Duncan, Schaller, & Park, 2009). Germ aversion was expected to be positively associated

340

with the FDS. The discriminant validity of the FDS was tested in relation to food neophobia

341

and picky eating. Food neophobia is an established psychological construct that describes a

342

person’s tendency to reject or avoid eating unfamiliar food or food from other cultures. Food

343

neophobia has an impact on people’s diet quality (Siegrist, Hartmann, & Keller, 2013) and

344

can hinder people from accepting new food sources such as insects (Hartmann, Shi, Giusto, &

345

Siegrist, 2015). Picky eating encompasses the rejection of familiar food and can be present in

346

adults (Dovey, Staples, Gibson, & Halford, 2008; Kauer, Pelchat, Rozin, & Zickgraf, 2015).

347

Food disgust was expected to share some common variance with food neophobia and picky

348

eating because there is empirical evidence that disgust is an underlying aspect of both

349

constructs (Al-Shawaf, Lewis, Alley, & Buss, 2015; Kauer et al., 2015).

350

Another validity-supporting relationship that was tested in this study was the

351

association between food disgust sensitivity and the occurrence of food-borne diseases.

352

Again, a function of disgust is to prevent people from the ingestion of potentially pathogenic

353

substances or spoiled food. In other words, disgust may reduce the likelihood of food

354

poisoning that is a direct consequence of eating spoiled or contaminated food. Following this

355

line of thinking, it is reasonable to assume that people with low food disgust sensitivity are

356

more likely to expose themselves to food risks or that people who have experienced several

357

food-borne diseases are more disgust sensitive. Thus, we expected a positive association

358

between food disgust sensitivity and an individual’s experience of food-borne diseases in the

359

last five years.

360 361

4.1. Material and methods

362 363 364

4.1.1. Participants The study used the same sample of Swiss adults as in Study 2 (see Section 3.1.1).

365 366

4.1.2. Measures

13

FOOD DISGUST SCALE 367

The study used the DS-R (Haidt et al., 1994; revised by Olatunji, Williams, et al.,

368

2007). The DS-R consists of 25 items that are supposed to measure disgust sensitivity in three

369

domains: core disgust, animal reminder disgust, and contamination-based disgust. Sample

370

items are “It bothers me to hear someone clear a throat full of mucus” and “Even if I were

371

hungry, I would not drink a bowl of my favorite soup if it had been stirred by a used but

372

thoroughly washed flyswatter.” Since no German version is available for the DS-R, all items

373

had to be translated into German by the first author. The second author and another member

374

of the research group reviewed the translation. Individual sum scores were calculated

375

according to the scoring guide from Olatunji, Williams, et al. (2007), with higher scores

376

indicating higher disgust sensitivity. The following mean values were observed: M = 7.43 (SD

377

= 2.33, possible range = 0–12) for core disgust, M = 4.18 (SD = 2.04, possible range = 0–8,)

378

for animal reminder disgust, M = 1.86 (SD = 1.24, possible range = 0–5) for contamination-

379

based disgust, and M = 13.48 (SD = 4.72, possible range = 0–25) for the whole DS-R. The

380

alpha reliabilities of the DS-R subscales were .69 (core), .75 (animal reminder), and .58

381

(contamination-based). The alpha reliability for the whole scale was 0.82. Mean values were

382

comparable but alpha reliabilities were lower compared with those observed in the original

383

article by Olatunji, Williams, et al. (2007).

384

Food neophobia was measured with the validated German version (Siegrist et al.,

385

2013) of the scale by Pliner and Hobden (1992). A sample item is “I am very particular about

386

the foods I will eat.” As in the original scale, participants answered on a 7-point response

387

scale ranging from -3 (do not agree at all) to 3 (totally agree). The extreme categories were

388

verbally anchored, and the other categories were only numerically anchored. Cronbach’s

389

alpha was good (α = .87). Mean food neophobia scores were calculated by averaging all 10

390

items (M = 2.96, SD = 1.14).

391

Pickiness was measured with four items (i.e., “I consider myself to be a picky eater,”

392

“I have been called a picky eater,” “I think that many foods are disgusting,” and “I find many

393

foods distasteful”) from the food and eating questionnaire by Raudenbush, Van der Klaaus,

394

and Frank (1995). All items were translated into German by the first author and reviewed by

395

the second author and another member of the research group. Answers were given on a 5-

396

point scale ranging from 1 (totally agree) to 5 (do not agree at all). Scale scores represent the

397

average of the four items (M = 2.34, SD = 0.99, Cronbach’s α = .85).

398

Germ aversion was measured with the germ aversion subscale of the Perceived

399

Vulnerability to Disease Scale by Duncan et al. (2009). Sample items are “It really bothers me

400

when people sneeze without covering their mouths” and “I do not like to write with a pencil

14

FOOD DISGUST SCALE 401

someone else has obviously chewed on.” All items were translated into German by the first

402

author and reviewed by the second author and another member of the research group.

403

Respondents answered on a 7-point response scale ranging from 1 (totally agree) to 7 (do not

404

agree at all). The scale scores represent the average of six items 4 (M = 3.97, SD = 1.01,

405

Cronbach’s α = .51).

406

Participants were asked to indicate how many food-borne diseases (with and without

407

medical diagnosis) they suffered from within the last five years. Typical symptoms of such

408

diseases are diarrhea, stomach cramps, nausea, sickness, vomiting, and fever. The response

409

scale varied from never to more than seven. Most participants (62.2%) indicated that they did

410

not have a food-borne disease within the last five years, 15.7% had one, and 16.1% had two

411

or more. “Do not know” responses were coded as missing (n = 31 or 5.9%).

412 413

4.1.3. Statistical analysis

414

Correlational analyses were used to examine the validity of the FDS scores in relation

415

to the overall disgust measure (DS-R), food neophobia, pickiness, germ aversion, and

416

occurrence of food-borne diseases. The eight subscales of the FDS and the short version of

417

the FDS were tested. Data were analyzed using the SPSS Statistics software package version

418

23 (SPSS Inc., Chicago, IL).

419 420

4.2. Results

421

All FDS subscales were significantly correlated with the DS-R and its subscales, but

422

the strength of the relationships differed between the various subscales (Table 6). For

423

example, the human contamination subscale of the FDS correlated highly with the

424

contamination subscale of the DS-R (r = .46, p < .001), while lower correlations were

425

observed for the other two DS-R subscales (core, animal-reminder). High correlations were

426

also observed between the DS-R core subscale and the FDS subscales of poor hygiene, mold,

427

and living contaminants (r = .43–.53, all p < .001), while lower correlations were found for

428

decaying fruit, fish, animal flesh, and human contamination (r = .28–.31, all p < .001).

429

As depicted in Table 6, the short FDS moderately correlated with food neophobia and

430

pickiness (r = .37 and r = .35, respectively, p < .001). The fish and animal flesh subscales had

431

the highest correlation with food neophobia (r = .42 and r = .32, p < .001), while the poor 4

One item was considered out of date and was not included in the scale (“I avoid using public telephones

because of the risk that I may catch something from the previous user.”). Unfortunately, another item got lost in the programming of the survey (“I prefer to wash my hands pretty soon after shaking someone’s hand.”).

15

FOOD DISGUST SCALE 432

hygiene subscale was not correlated with food neophobia, indicating discriminant validity. A

433

similar pattern of correlations was observed for pickiness.

434

The short FDS and germ aversion were moderately correlated (r = .45, p < .001; Table

435

6). The highest correlations between germ aversion and the FDS were observed for the

436

subscales of poor hygiene (r = .41, p < .001), human contamination (r = .50, p < .001), and

437

living contaminants (r = .38, p < .001). Except for fish (r = .20, p < .001) and animal flesh (r

438

= .22, p < .001), which had relatively lower correlations with germ aversion, the remaining

439

subscales were related to the process of decay and thus reasonably associated with germ

440

aversion. Nevertheless, the effect sizes differed, which again shows convergent and

441

discriminant validity.

442

Correlational analysis revealed that the short FDS and the number of food-borne

443

diseases in the last five years were positively albeit weakly correlated (r = .13, p < .01). No

444

significant correlation was observed for the DS-R and the number of food-borne diseases

445

experienced in the last five years (r = .07, p = .11).

446 447

4.3. Discussion

448

The results of Study 3 provide support for the convergent and discriminant validity of

449

the FDS. The FDS subscales are more or less strongly and significantly correlated with the

450

DS-R subscales. The magnitude and direction of these criterion-related correlations are

451

consistent with theoretical considerations and the item contents of the two scales. The core

452

subscale of the DS-R is a mix of items including eating culturally inappropriate food (e.g.,

453

monkey meat and vanilla ice cream with ketchup), contact with insects and animals (e.g.,

454

seeing a rat on one’s path, seeing a cockroach in someone else’s house, and stepping on an

455

earthworm), consuming spoiled food, and hygienic aspects (e.g., a friend who changes

456

underwear only once a week). Thus, the observed pattern of correlation supports the

457

convergent and discriminant validity of the FDS, with the highest correlations observed for

458

the subscales of poor hygiene, mold, decaying vegetables, and living contaminants, and the

459

lowest correlations observed for fish and animal flesh. Of course, the DS-R is not tailored for

460

application in the food domain, and the pattern of correlations highlights that the established

461

disgust measure, DS-R, does not assess the full range of existing food-related disgust types.

462

The correlational pattern between the FDS subscales on the one hand and pickiness

463

and food neophobia on the other hand suggest that both selective eating behaviors are related

464

to food disgust sensitivity. In particular, people who tend to be easily disgusted by certain

465

food-related offensive stimuli tend to be more reluctant to eat unfamiliar food and food from

16

FOOD DISGUST SCALE 466

other cultures. Even though food disgust sensitivity is one influential factor in food neophobia

467

(Al-Shawaf et al., 2015), the effect size of the relationship confirms that they are different

468

psychological constructs. All food groups covered in the FDS are familiar to most people in

469

Western cultures, which is an important factor that discriminates between both constructs.

470

One of the first studies that empirically showed an association between food neophobia and

471

disgust utilized the Three-Domains Disgust Scale by Tybur et al. (2009). A correlation of .23

472

was found for pathogen disgust and food neophobia, while in the present study, a stronger

473

relationship (r = .37) was observed for food neophobia and food-related disgust sensitivity. In

474

addition, observed correlational associations differed by food disgust type, with the highest

475

correlations observed for fish and animal flesh and no or low correlations observed for poor

476

hygiene and living contaminants. Altogether, the results show that (1) unlike the food

477

neophobia scale or pickiness, the FDS is not a measure of reluctance to eat certain foods; (2)

478

the link between food neophobia and disgust sensitivity is slightly stronger than previously

479

assumed; and (3) not all food disgust types are equally strongly related to food neophobia and

480

pickiness.

481

The results of the present study suggest that food disgust sensitivity is positively

482

linked to the individual’s experiences of food-borne diseases in the last five years. However,

483

the effect size is small, and multiple factors surrounding food preparation and consumption

484

influence whether a person is exposed to contaminated food in the first place and whether the

485

person will eat the food and become ill. Some population subgroups such as children or older

486

people are more susceptible to becoming ill, often because of reduced immune function or

487

unfavorable physiological conditions (Gerba, Rose, & Hass, 1996). People who are low in

488

food disgust sensitivity and rather insensitive to pathogen-related food cues seem to be

489

another subgroup at increased risk. In the present study, the severity of the illness symptoms

490

was not assessed, and people who experienced severe symptoms might be more likely to

491

remember these events. Thus, the relationship between food disgust and experience of food-

492

borne diseases might have been underestimated in this investigation. Moreover, we do not

493

know the direction of the association; longitudinal studies need to further investigate whether

494

food-borne diseases are a cause or a consequence of varying levels of food disgust sensitivity.

495

It must be noted that no statistically significant association was found between the DS-R and

496

the number of food-borne diseases experienced by the individual. Hence, the application of

497

the FDS in the food domain has the potential to outperform previous non-food-specific

498

disgust measures such as the DS-R in the prediction of food behavior and its consequences.

499

17

FOOD DISGUST SCALE 500 501 502 503

5. Test-retest reliability (Study 4) The aim of Study 4 was to evaluate the short-term stability of the FDS scores by examining the two-week test-retest reliability.

504 505

5.1. Material and methods

506 507

5.1.1. Survey participants

508

Participants from an Internet panel completed the online version of the FDS twice, two

509

weeks apart. The Internet panel is composed of individuals from the general population who

510

voluntarily participate in online studies from time to time. The first survey had 224

511

participants, and the second survey had 236 participants. The data cleaning procedure

512

included the deletion of participants whose demographic characteristics (i.e., gender, age , and

513

educational level) differed between the first and second survey. Data from both surveys were

514

matched using an anonymized personalized code. The final sample consisted of 170 persons

515

(Table 2).

516 517

5.2. Results and discussion

518

Cronbach’s alphas were calculated and Pearson correlations were computed for the

519

eight subscales and the 8-item short version. Cronbach’s alphas varied between .79 and .93

520

for the subscales, and Cronbach’s alpha was .77 for the short FDS (Table 7). These values

521

were similar to those observed in Study 2, which suggests that the Cronbach’s alphas of the

522

FDS scale are stable across different study samples. Test-retest correlations for the subscales

523

varied between .75 and .90; for the FDS short scale, the correlation was .86, which indicates

524

good test-retest reliability. Thus, the scale consistently reflects food disgust sensitivity over a

525

period of two weeks.

526 527 528

6. Incremental validity (Study 5)

529

Having provided support for the convergent and discriminant validity of the FDS

530

scale, it is still unclear whether the FDS is incrementally valid and has predictive potential for

531

behavioral responses to certain foods. The aim of Study 5 was to test whether consumers’

532

willingness to eat unfamiliar, novel food (i.e., products made from insects) varies as a

533

function of food disgust sensitivity.

18

FOOD DISGUST SCALE 534

In most Western societies including Switzerland, insects are viewed as non-food,

535

dirty, health risks, and food contaminants (Kellert, 1993; Looy, Dunkel, & Wood, 2014) and

536

might even be indicators of low hygienic standards. In addition, they are of animal origin and

537

therefore processed insect-based products might have animal reminders that provoke disgust.

538

Hence, three out of the eight FDS subscales were predicted to be highly relevant to the

539

willingness to eat insect-based products (i.e., living contaminants, animal flesh, and poor

540

hygiene). Individuals who score high on these subscales are probably more reluctant to try

541

insects or insect-based meat substitutes than those who score lower on these subscales. In

542

addition, previous studies showed that overall disgust sensitivity (Ruby, Rozin, & Chan,

543

2015) and food neophobia (e.g., Hartmann et al., 2015; Verbeke, 2015) were important

544

constructs in predicting the willingness to eat insects. It was hypothesized that FDS scores

545

predict additional variance in the willingness to eat insects over and above food neophobia.

546

This was tested with the following experiment.

547 548

6.1. Material and methods

549 550

6.1.1. Participants

551

This study was part of another project about the willingness to eat insects (Hartmann

552

& Siegrist, 2016). The study took place in the German-speaking part of Switzerland at the

553

research facilities of ETH Zurich. A convenience sample was recruited through web-based

554

advertisements, flyers in supermarkets, and an Internet panel of people who agreed to

555

regularly participate in experiments and surveys. From 107 recruited individuals from the

556

general population, 18 vegans and vegetarians were excluded in addition to three other people

557

for reasons previously reported (for details see Hartmann & Siegrist, 2016). The original

558

study sample consisted of a control group and an experimental group. For the present study,

559

only data from the control group were analyzed (N = 43; Table 2).

560 561

6.1.2. Measures

562

Participants answered a computer-administered version of the FDS. Cronbach’s alphas

563

were calculated for the three relevant subscales and the FDS short version: .77 for animal

564

flesh, .88 for poor hygiene, .89 for living contaminants, and .78 for the FDS short scale. These

565

values are similar to those observed in Study 2. Mean scores were as follows: M = 3.35 (SD =

566

0.93) for the FDS short scale, M = 2.51 (SD = 1.1) for animal flesh, M = 4.99 (SD = 0.93) for

19

FOOD DISGUST SCALE 567

poor hygiene, and M = 4.53 (SD = 1.31) for living contaminants. These mean scores were

568

comparable to those observed in Study 2.

569

Food neophobia was assessed with the German version (Siegrist et al., 2013) of the

570

food neophobia scale by Pliner and Hobden (1992). For a more detailed description of the

571

scale, see section 4.1.2. Cronbach’s alpha for food neophobia (α = 0.84) was good. The mean

572

score for food neophobia was M = 2.40 (SD = 0.84).

573

Participants were asked whether they were willing to eat insect-based products. These

574

products are made with ground insects so that the insect origin is no longer visible. Following

575

the procedure previously described (Hartmann et al., 2015; Hartmann & Siegrist, 2016),

576

participants were informed about the following ideas regarding insects: Insects are a good

577

source of high-value protein, their production requires little space, and their feed conversion

578

rate is efficient. Therefore, eating insects can provide benefits in terms of sustainability. After

579

receiving this information, the participants indicated their willingness to eat three different

580

kinds of products made with processed insects: cookies made with cricket flour, a drink made

581

with silkworm protein, and a burger made from buffalo worms. The cookies and the burger

582

were displayed in a picture. All three items were answered on a 10-point scale ranging from 1

583

(do not agree at all) to 10 (totally agree). The extreme categories were verbally anchored; the

584

other categories were only numerically anchored. A mean value for willingness to eat across

585

all three products was calculated. Cronbach’s alpha for willingness to eat (α = 0.90) was

586

excellent, and the mean score (M = 6.52, SD = 2.69) was slightly above the midpoint of the

587

scale.

588 589

6.1.3. Statistical analysis

590

In the first hierarchical regression, the three FDS subscales of animal flesh, poor

591

hygiene, and living contaminants were included as independent variables to estimate their

592

predictive validity on willingness to eat. Two additional hierarchical linear regressions were

593

conducted to determine whether the FDS short scale and the relevant FDS subscales

594

accounted for unique variance in willingness to eat above and beyond the variance explained

595

by food neophobia.

596 597

6.2. Results

598

The predictive validity of the FDS subscales of animal flesh, poor hygiene, and living

599

contaminants on the willingness to eat insect-based products was examined using linear

600

regression analysis (Table 8). The final model explained 46% of the variance in willingness to

20

FOOD DISGUST SCALE 601

eat, F(3, 39) = 10.92, p < .001. The subscales of animal flesh (ß = -0.29, p < .05) and poor

602

hygiene (ß = -0.45, p < .001) were statistically significant predictors for willingness to eat

603

insect-based products. A second regression analysis was conducted with the significant

604

predictors from the first regression (i.e., animal flesh and poor hygiene) as well as food

605

neophobia as predictors of the willingness to eat insects. The final model explained 50% of

606

the variance in willingness to eat insect-based products, F(3, 39) = 13.07, p < .001. The poor

607

hygiene subscale turned out to be the strongest predictor (ß = -0.38, p < .01), which helped

608

explain slightly more variance than food neophobia (ß = -0.29, p < .05). The animal flesh

609

subscale was also a significant predictor (ß = -0.26, p < .05).

610

The linear regression model with the FDS short scale and food neophobia as

611

predictors and willingness to eat insect-based products as the dependent variable was

612

significant and explained 37% of the variance, F(2, 39) = 11.68, p < .001. Both the short scale

613

(ß = -0.34, p < .05) and food neophobia (ß = -0.35, p < .05) were significant predictors.

614 615

6.3. Discussion

616

Results of Study 5 support the predictive validity of two FDS subscales, animal flesh

617

and poor hygiene, and highlight that the FDS explains additional variance over and above

618

food neophobia. Consistent with previous results (e.g., Hartmann et al., 2015; Verbeke, 2015),

619

food neophobia was an important construct in the prediction of consumers’ acceptance of

620

novel food such as insects. Further analyses shows that another driver of the rejection of

621

insects as food is disgust sensitivity related to poor hygienic food behavior and reminders of

622

the animal nature of the food. People who scored high on the subscales of animal flesh and

623

poor hygiene reported lower willingness to eat insects. Surprisingly, the living contaminants

624

subscale was not a significant predictor in the regression analysis. People who are sensitive to

625

disgust toward living food contaminants (e.g., a worm in an apple) are not necessarily

626

reluctant to try insects as food. Of course, living contaminants are not supposed to be in food,

627

while insect-based products are conceptualized as food.

628

Results further indicate that the FDS short scale explains additional variance in

629

willingness to eat insects over and above food neophobia. The comparable lower variance that

630

can be explained by the FDS short scale can be ascribed to the multidimensional nature of the

631

FDS scale. Overall, the results suggest not only the incremental validity but also the

632

convergent validity of the newly developed construct.

633 634

21

FOOD DISGUST SCALE 635

7. General discussion

636

The presented studies examine the psychometric properties of the newly developed

637

FDS, which offers researchers interested in the functional and dysfunctional effects of food

638

disgust on food behavior a reliable and valid self-report measure for a construct that has not

639

received much attention in previous food research. The FDS is a trait measure that assesses an

640

individual’s emotional disposition to react with disgust to certain food-related stimuli. It is a

641

self-report measure for adults, which can be applied as an 8-item short version or a

642

comprehensive 32-item long version. The long version contains eight subscales that capture

643

different types of food disgust sensitivity. Each subscale consists of three to five items. Eight

644

distinct factors of food disgust sensitivity can be measured (i.e., animal flesh, human

645

contamination, poor hygiene, decaying fruit, decaying vegetables, mold, fish, and living

646

contaminants).

647

The eight-factor solution for the FDS was confirmed with both exploratory and

648

confirmatory factor analysis. An eight-factor model has important implications for the study

649

of food disgust sensitivity because the examination of profile scores on the FDS subscales and

650

may offer greater predictive validity than an overall measure of food disgust, as highlighted in

651

Study 5. Across the four samples used in the present investigation, Cronbach’s alphas for the

652

eight subscales were good or very good, as well as rather stable. The slightly lower reliability

653

of the FDS short version may be partly accounted for by the multidimensionality of food

654

disgust. Nevertheless, the findings revealed that the short version is a reliable measure and

655

suitable for some applications. Results of the test-retest reliability analysis confirmed that the

656

FDS scale can be considered temporarily stable over two weeks. Construct validity was also

657

supported: The FDS was related to measures of overall disgust sensitivity and the number of

658

food-borne diseases experienced by the individual in the last five years. Evidence was also

659

presented for the incremental validity of FDS scores predicting willingness to eat unfamiliar

660

food (i.e., insects) over and above food neophobia.

661

The FDS, like other disgust scales such as the TDDS (Tybur et al., 2009), use a

662

disgust-rating format. According to the Oxford Dictionary of current English, the term

663

‘disgust’ can be used to indicate a strong feeling of dislike, disapproval of somebody, or

664

something that one feel is unacceptable (Turnbull et al., 2010) and it is also used to indicate

665

anger. Therefore, while disgust can be used to express that something has an extremely

666

unpleasant smell or taste, it can also be used to indicate unpleasant personal habits and people

667

who have them. In German, the word disgusting (eklig) is used to indicate that something is

668

unpleasant and revolting or to indicate that something is morally reprehensible. It is, however,

22

FOOD DISGUST SCALE 669

not used to indicate anger. The FDS was developed in German and the meaning of disgust

670

might be broader in the English language than in German. Therefore, researchers who are

671

interested in using the FDS in an English-speaking context should consider to substitute the

672

word disgust by grossed out or alternatively to further specify to study participants the

673

intended meaning of disgust.

674

Researchers have stated that the English term ‘disgust’ literally means ‘bad’ or ‘bad

675

taste’ (Rozin et al., 2017). To rule out the alternative explanation that participants in our

676

studies had ‘liking’ in mind instead of ‘disgusting’ (in terms of being grossed out), the

677

following aspects were considered in the development of the scale. We focused on foods that

678

are commonly consumed in Western societies and are part of a traditional Western diet (e.g.,

679

meat, fish, bread, cheese, marmalade, tomato, and apples). Therefore, the likelihood that a

680

significant number of participants did not like a majority of the foods was rather low.

681

Moreover, for many food items in the scale it would not make sense for a participant to

682

indicate liking. Notably, ‘liking’ simply does not apply to any of the items in the poor

683

hygiene, human contamination, and living contaminants subscales. ‘Liking’ would not apply,

684

for example, to item 1 (‘To put animal cartilage into my mouth’), item 4 (‘To see a whole pig

685

en brochette’), item 22 (‘To have a whole fish with its head on the plate’), and item 24 (‘The

686

smell in a fish shop or in fish sections with fresh fish”). Indicating disgust here is definitely

687

not a question of liking or not liking the taste of e.g. cartilage (which is tasteless) or of a fish

688

with its head on the plate (which tastes like a fish without its head on the plate). In addition, if

689

we would have measured liking, we would have expected weaker correlations between the

690

FDS and the overall disgust measure DS-R (FDS short r = .59). Nevertheless, we would also

691

expect a correlation between disgust and liking of some foods, since disgust influences

692

people’s food choices and thus their food preferences.

693

The various food-cues combinations that were tested in the first study and those

694

incorporated into the FDS might not have been exclusive, and other cues might have also

695

been suitable. However, we expected a high correlation between reactivity towards the cues

696

tested here and other cues in the food-domain.

697 698

7.1. A new line of research

699

The scale was developed and tested among Swiss adults. The question remains as to

700

how the scale will perform in samples outside Switzerland. In general, the associations

701

between food disgust sensitivity and other attitudinal as well as behavioral variables of food

702

behavior need to be explored in further research. Depending on the region of the world,

23

FOOD DISGUST SCALE 703

people’s attitudes toward certain foods can differ substantially. Therefore, the scale should be

704

tested and used in a multinational context. In addition, experiences, illness-mediated food

705

aversions, parental and group influences, culture, and genetic predisposition are all factors

706

that might determine whether a person is more or less sensitive to disgust. However, how

707

people’s disgust sensitivity evolved over their lifetime was not part of this investigation;

708

further research might be necessary to identify promoters of increasing disgust sensitivity.

709

Food disgust sensitivity might be a barrier to the acceptance of new food sources and

710

technologies. This is a problem because new technologies are important for developing

711

innovations and consequently for economic growth. Informing people about the benefits of

712

certain foods, new food sources, or food technologies might be insufficient when some food

713

characteristics are considered disgusting and hazardous, even though such perceptions might

714

be “irrational” from a scientific point of view. The new psychometric tool enables the

715

identification of consumer groups who are likely to reject novel food sources and

716

technologies. It will also help classify the type of food disgust that is elicited by novel food

717

products. Laypeople’s risk perceptions in the food domain are not only relevant when it

718

comes to the acceptance of new food technologies and sources, but also determine food

719

safety–related behaviors or public acceptance of governmental resource allocations in food

720

safety controls, among others. Understanding the psychological factors and individual

721

differences such as food disgust sensitivity that drive food risk perceptions is therefore

722

important if effective food safety policy and risk communication are to be developed. Thus,

723

the new FDS scale enables a new line of research that will supply further insights into aspects

724

of dietary selectivity as well as the rejection and acceptance of food products.

725 726 727

8. Conclusion

728

The rejection of substances based on disgust prevents consumers from ingesting

729

pathogenic substances and contracting food-borne diseases or infections. Thus, it is a useful

730

preventive behavior; but under some circumstances, it can be counterproductive. In this study,

731

the FDS scale was developed and psychometrically tested within various consumer samples.

732

The study provided empirical evidence that food disgust sensitivity is related to popular

733

constructs of food behavior such as food neophobia; thus, food disgust sensitivity could be a

734

barrier to dietary quality. Food disgust might also hinder consumers’ acceptance of novel food

735

sources and technologies and might further determine laypeople’s food risk perceptions. The

24

FOOD DISGUST SCALE 736

quantification of people’s food disgust sensitivity offers further insights into the drivers of

737

food acceptance and rejection as well as consumers’ responses related to food safety.

738 739

Acknowledgment

740

We would like to thank Aisha Egolf for her support in conducting Study 3.

741

Funding sources

742

This research was supported by the Swiss National Science Foundation (project number

743

100014_165630).

744

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745

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847

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848

for Psychologists (pp. 39-61). Washington DC: American Psychological Association.

849 850

28

FOOD DISGUST SCALE 851

Table 1. Overview of popular scales to measure disgust sensitivity and their limitations

852

regarding food disgust Author

Scale

Domains

Limitations

Haidt et al.,

Disgust Scale (DS)

Food, body products,

Food subscale not

sex, death, body

internally consistent

envelope violations,

across studies, alpha

animal, hygiene, magic

reliabilities of the food

1994

items below .40 Olatunji et al.,

Disgust Scale–

Core, animal-reminder,

No food subscale, 7 food

2007

revised (DS-R)

contamination

items (rotting, contamination) diffusely spread among subscales

Schienle et al.,

Fragebogen zur

Death, body secretions,

No food subscale, 12 food

2002 [revised

Erfassung der

spoilage, hygiene, oral

items diffusely spread

German

Ekelempfindlichkeit defense

among subscales with

version of the

(FEE)

mostly low factor loadings

DS]

[Questionnaire for

(on average .40)

the assessment of Disgust sensitivity] Petrowski et

Questionnaire for

Core, animal-reminder,

No food subscale, 12 food

al, 2010

the assessment of

contamination

items diffusely spread

[revised

Disgust sensitivity

among subscales with

English

(QADS)

mostly low factor loadings

version of the

(on average .45)

FEE] Kleinknecht et

Disgust Emotion

Animals, injections,

al., 1996

Scale (DES)

blood draws, mutilation,

Focus on rotting food

death, rotting food, smells Tybur et al.,

Three Domain

Pathogen, sexual, moral

No food subscale, 4 food

2009

Disgust Scale

items included (rotting,

(TDDS)

contamination) with low loadings on pathogen disgust subscale

29

FOOD DISGUST SCALE Van Overveld

Disgust propensity

et al., 2006

and disgust

(revised van

sensitivity scale

Overveld,

(DPSS)

Jong, & Peters, 2010)

Disgust propensity (the tendency to react with the emotion of disgust), disgust sensitivity (tendency to experience disgust as something ‘horrid’)

No food items

853

30

FOOD DISGUST SCALE 854

Table 2. Overview of the study samples Study 1:

Study 2 + 3: Scale

Study 5:

Scale

refinement &

Study 4:

Incremental

development

validation

Test-Retest

validity

N

318

527

170

43

Men [%]

49.4

48.8

61.0

55.8

Age [Mean (sd)]

49.50 (16.22)

44.89 (14.12)

62.10 (12.36)

34.98 (11.09)

20-39 years [%]

32.1

38.5

69.8

40-64 years [%]

45.0

52.2

27.9

65-79 years [%]

22.3

9.3

2.3

80+ years [%]

0.6

0

0

Highest level of education [%] Primary, lower secondary school

5.7

6.5

1.2

0

49.3

56.4

32.9

48.8

32.3

17.6

25.9

7.0

and above

11.6

9.9

38.3

41.8

Missing

1.1

9.7

1.7

2.3

Secondary school, vocational education, senior high school Higher vocational education College, university

31

FOOD DISGUST SCALE 855

Table 3. Final 32-item Food Disgust Scale with eight subscales measuring different types of

856

food disgust (data from Study 2, N = 527). Standardized Label

Item

loadings (CFA)

Animal flesh (α = .79) 1

MEAT1a

To put animal cartilage into my mouth

.55

2

MEAT2

To see raw meat

.77

3

MEAT3

To eat a steak that is still bloody insideb

.76

4

MEAT4

To see a whole pig en brochette

.76

Poor hygiene (α = .87) 5

HYG1 a

To eat with dirty silverware in a restaurant

.74

6

HYG2

A meal prepared by a cook who has greasy hair and dirty

.90

fingernailsb 7

HYG3

If the cook in a restaurant has an open cut

.82

8

HYG4

If people blow their nose before they serve my meal

.63

9

HYG5

Another person’s hair in my soup

b

.65

Human contamination (α = .85) 10 HUCO1 a

Food donated from a neighbor whom I barely know

.52

11 HUCO2

If a friend bites into my bread

.88

12 HUCO3

To drink from the same drinking glass a friend has already

.91

drunk fromc 13 HUCO4

If friends or acquaintance have touched my food

.76

To eat the mold-free part of a moldy tomato

.83

To eat bread from which mold was cut away

.84

To eat hard cheese from which mold was cut off

.84

To eat marmalade from which mold was removed from the

.82

Mold (α = .90) 14 MOLD1 15 MOLD2 16 MOLD3

a

17 MOLD4

surface Decaying fruit (α = .87) 18 FRUIT1

To eat overripe fruits

.69

19 FRUIT2

To eat a banana that has black spots

.79

To eat fruits (e.g., apple and peach) with pressure marks

.89

To eat apple slices that turned brown when exposed to air

.82

20 FRUIT3 21 FRUIT4

a

32

FOOD DISGUST SCALE Fish (α = .87) 22 FISH1

To have a whole fish with its head on the plate

.78

23 FISH2

To eat raw fish like sushi

.78

24 FISH3

The smell in a fish shop or in fish sections with fresh fish

.76

25 FISH4 a

The texture of some kinds of fish in the mouth

.87

Decaying vegetables (α = .89) 26 VEGI1a

To eat brown-colored avocado pulp

.70

27 VEGI2

To eat an overripe cucumber that can already be bent

.85

28 VEGI3

To eat shrunken radishes

.90

29 VEGI4

To eat salad that is not crispy anymore

.82

Living contaminants (α = .90)

857 858 859 860 861 862

30 LCON1

There is a maggot in the cherry that I wanted to eat

.85

31 LCON2a

There is a little snail in the salad that I wanted to eat

.85

32 LCON3

There is a worm in my apple

.92

Note. Instruction for participants: “Please indicate how disgusting you perceive the following products or situations to be.” Response scale varied from 1 (not disgusting at all) to 6 (extremely disgusting). a

Items are included in the 8-item short version of the FDS (α = .77). These are item number 1, 5, 10, 16, 21, 25, 26 and 31.

b

Item adapted from Schienle, Walter, Stark, and Vaitl (2002).

c

Item adapted from Olatunji, Williams, et al. (2007).

CFA: Confirmatory factor analysis.

33

FOOD DISGUST SCALE 863

Table 4. Eight-item short version of the Food Disgust scale (FDS short) and corrected item-

864

total correlations (data from study 2, N = 527) Corrected Label

Item

item-total correlations

865

MEAT1

To put animal cartilage into my mouth

.48

HYG1

To eat with dirty silverware in a restaurant

.42

HUCON1

Food donated from a neighbor whom I barely know

.37

MOLD3

To eat hard cheese from which mold was cut off

.53

FRUIT4

To eat apple slices that turned brown when exposed to air

.43

FISH4

The texture of some kinds of fish in the mouth

.43

VEGI1

To eat brown-colored avocado pulp

.51

LCON2

There is a little snail in the salad that I wanted to eat

.58

Note: α = .78

866

34

FOOD DISGUST SCALE 867

Table 5. Mean values and standard deviations for the eight subscales and the short version of

868

the Food Disgust Scale (data from Study 2). All

Women

Men

(N=527)

(n = 269)

(n = 258)

M

SD

M

SD

M

SD

MEAT

2.75

1.30

3.15

1.30

2.34

1.16

7.50**

HYG

5.22

0.87

5.44

0.69

4.98

0.97

6.25**

HUCON

2.94

1.19

2.97

1.20

2.90

1.18

0.77

MOULD

4.24

1.49

4.43

1.42

4.03

1.53

3.08*

FRUIT

2.65

1.18

2.67

1.20

2.62

1.15

0.47

FISH

3.22

1.51

3.50

1.54

2.92

1.42

4.43**

VEGI

3.32

1.27

3.50

1.26

3.14

1.24

3.33**

LCON

4.83

1.30

5.08

1.17

4.57

1.37

4.58**

3.76

0.93

3.97

0.88

3.53

0.94

5.69**

t-test for gender

FDS subscales

FDS short (8 items) 869 870 871

Note. FDS subscales: MEAT = Animal flesh, HYG = Poor Hygiene, HUCON = Human contamination, MOLD = Mold, FRUIT = Fruit in decay, FISH = Fish, VEGI = Vegetables in decay, LCON = Living contaminants; Possible scoring range 1 (not disgusting at all) to 6 (very disgusting); **p ≤ .001; *p ≤ .01.

35

FOOD DISGUST SCALE Table 6. Associations between the Food Disgust Scale and measures of validity (Study 3, N = 527) MEAT

HYG

HUCON

MOLD

FRUIT

FISH

VEGI

LCON

FDS

DS-R

Core

Animal

Con.

short

Food

Picki.

Germ aversion

Neo

FDS subscales MEAT HYG HUCON MOLD FRUIT FISH VEGI LCON FDS short DS-R Core Animal Con. Food Neo

Pickiness

1.00

.22**

.29**

.27**

.27**

.63**

.28**

.32**

.58**

.35**

.30**

.32**

.25**

.32**

.33**

.22**

1.00

.33**

.39**

.23**

.26**

.41**

.46**

.55**

.45**

.47**

.28**

.37**

0.07

.11**

.41**

1.00

.27**

.31**

.24**

.36**

.28**

.49**

.38**

.31**

.24**

.46**

.21**

.25**

.50**

1.00

.38**

.31**

.54**

.54**

.70**

.44**

.43**

.32**

.33**

.17**

.19**

.31**

1.00

.24**

.63**

.35**

.58**

.38**

.35**

.28**

.33**

.25**

.20**

.29**

1.00

.32**

.37**

.64**

.33**

.28**

.30**

.24**

.42**

.30**

.20**

1.00

.47**

.73**

.47**

.43**

.33**

.44**

.22**

.25**

.35**

1.00

.70**

.50**

.51**

.36**

.34**

.21**

.16**

.38**

1.00

.59**

.56**

.44**

.48**

.37**

.35**

.45**

1.00

.90**

.84**

.74**

.21**

.18**

.52**

1.00

.59**

.57**

.14**

.13**

.47**

1.00

.46**

.19**

.14**

.37**

1.00

.24**

.23**

.50**

1.00

.51**

.18**

1.00

.18**

Germ aversion

1.00

Note. FDS subscales: MEAT = Animal flesh, HYG = Poor Hygiene, HUCON = Human contamination, MOLD = Mold, FRUIT = Fruit in decay, FISH = Fish, VEGI = Vegetables in decay, LCON = Living contaminants; DS-R = Disgust Scale-revised (Olatunji et al., 2007); Con. = contamination; Food Neo = food neophobia (Pliner & Hobden, 1992); *p < .001.

36

FOOD DISGUST SCALE Table 7. Results of the two-weeks test-retest reliability analysis for the 32-item long and the 8-item short version of the Food Disgust Scale (Study 4, N=170). Cronbach’s

Pearson

Alpha at T1

correlation

MEAT

.79

.86

HYG

.86

.75

HUCON

.87

.82

MOLD

.88

.83

FRUIT

.86

.81

FISH

.85

.90

VEGI

.92

.79

LCON

.93

.78

.77

.86

FDS subscales

FDS short (8 items)

Note. FDS subscales: MEAT = Animal flesh, HYG = Poor Hygiene, HUCON = Human contamination, MOLD = Mold, FRUIT = Fruit in decay, FISH = Fish, VEGI = Vegetables in decay, LCON = Living contaminants.

37

FOOD DISGUST SCALE Table 8. Results of three separate stepwise regressions predicting willingness to eat products made with processed insects (Study 5, N = 43).

Predictors

Step 1

Step 2

Step 3

Beta ß

Beta ß

Beta ß

Regression 1 FDS_MEAT

-.50*** -.34*

FDS_HYG

-.29*

-.47*** -.45***

FDS_LCON ∆R2 .25***

-.15 .19***

.02

Regression 2 Food neophobia -.53*** -.40*** -.29* FDS_MEAT

-.34*** -.26*

FDS_HYG

-.38** ∆R2 .29***

.10*

.12**

Regression 3 Food neophobia -.53*** -.35* FDS short

-.34* ∆R2 .29***

.08*

Note. FDS: Food Disgust scale; FDS_MEAT: Animal flesh subscale of the FDS, FDS_HYG: Poor hygiene subscale of the FDS; FDS_LCON: Living contaminants subscale of the FDS; FDS short: 8-item short version of the FDS; R2 = 0.46 (regression 1), R2 = 0.50 (regression 2), R2 = 0.37 (regression 3); ***p ≤ .001, **p ≤ .01, * p ≤ .05.

38

FOOD DISGUST SCALE

Figure 1. The final measurement model for the Food Disgust scale (long format). The 322 item good fitting model, χ (435) = 945.37, p < .001, CFI= .95, RMSEA = .05, NFI = .91, is

based on eight subscales.

39

FOOD DISGUST SCALE

Figure 2. The final model for the Food Disgust short scale (FDS short). The good fitting 2 model (χ (18) = 44.02, p=.001, CFI= .97, RMSEA = .05, NFI = .95) is based on 8 items from

the 32-item version of the FDS.

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FOOD DISGUST SCALE Supplementary File: German version of the Food Disgust Scale (FDS) Label Tierfleisch 1 MEAT1a 2 MEAT2 3 MEAT3 4 MEAT4

Item

Überhaupt nicht eklig 1 2

3

4

Extrem eklig 5 6

Einen Tierknorpel in den Mund nehmen Der Anblick von rohem Fleisch Ein innen noch blutiges Steak essenb Den Anblick von einem ganzen Schwein am Spiess

Schlechte Hygiene 5 HYG1 a Die Vorstellung mit unsauberen Besteck in einem Restaurant zu essen 6 HYG2 Das zubereitete Essen von einem Koch, der fettige Haare und dreckige Fingernägel hatb 7 HYG3 Wenn der Koch in einem Restaurant eine unverbundene Schnittwunde am Finger hat 8 HYG4 Wenn sich Leute die Nase putzen, bevor sie mir Essen servieren 9 HYG5 Ein fremdes Haar in der Suppeb Menschliche Kontamination 10 HUCON1 a Das Essen, welches mir Nachbarn geschenkt haben, die ich kaum kenne 11 HUCON2 Wenn eine Freundin oder ein Freund von meinem Brot abbeisst 12 HUCON3 Die Vorstellung, aus demselben Glas zu trinken, aus dem schon ein Freund oder eine Freundin getrunken hatc 13 HUCON4 Wenn Freunde oder Bekannte mein Essen angefasst haben Schimmel 14 MOLD1 15 MOLD2 16 MOLD3 a 17 MOLD4

Von einer angeschimmelten Tomate den Teil ohne Schimmel essen Brot essen, von welchem Schimmel weggeschnitten wurde Hartkäse essen, von welchem zuvor Schimmel weggeschnitten wurde Konfitüre essen, die an der Oberfläche Schimmel hatte, der entfernt wurde

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FOOD DISGUST SCALE

Früchte im Verfallprozess Überreife Früchte essen 18 FRUIT1 19 FRUIT2 Eine Banane mit schwarzen Stellen essen 20 FRUIT3 Früchte (z.B. Apfel, Pfirsich) mit Druckstellen essen 21 FRUIT4 a Apfelstücke, die sich an der Luft verfärbt haben essen Fisch 22 FISH1 23 FISH2 24 FISH3 25 FISH4 a

Einen ganzen Fisch mit Kopf auf meinem Teller zu haben Die Vorstellung rohen Fisch wie Sushi zu essen Der Geruch in einem Fischladen oder einer Abteilung mit frischem Fisch Die Konsistenz einiger Fischarten im Mund

Gemüse im Verfallprozess 26 VEGI1 a Braunverfärbtes Fruchtfleisch von einer Avocado essen 27 VEGI2 Eine ältere Gurke essen, die sich bereits biegen lässt 28 VEGI3 Schrumpelige Radieschen essen 29 VEGI4 Salat essen, der nicht mehr knackig ist Lebende Kontaminanten 30 LCON1 In einer Kirsche, die ich gerade essen möchte, ist eine Made 31 LCON2 a Eine kleine Schnecke in meinem Salat, den ich gerade esse 32 LCON3 Ein Wurm in einem Apfel Note. Instruction for study participants: “Bitte geben Sie an, wie eklig Sie die folgenden Situationen oder Produkte finden.“ a

Items are included in the 8-item short version of the FDS.

b c

Item adapted from Schienle, Walter, Stark, and Vaitl (2002).

Item adapted from Olatunji, Williams, et al. (2007).

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FOOD DISGUST SCALE Highlights

•

The psychometrically sound Food Disgust Scale (FDS) was developed.

•

The FDS is a multidimensional measure of different types of food disgust.

•

The FDS is associated with food neophobia, picky eating, and germ aversion.

•

FDS scores and the number of food-borne illnesses are positively correlated.

•

The FDS incrementally predicts willingness to eat insects over and above food neophobia.

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