Development of a questionnaire to assay recalled liking for salt, sweet and fat

Food Quality and Preference 23 (2012) 110–124

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Development of a questionnaire to assay recalled liking for salt, sweet and fat Amélie Deglaire a,b,⇑, Caroline Méjean b, Katia Castetbon b,c, Emmanuelle Kesse-Guyot b, Christine Urbano a, Serge Hercberg b, Pascal Schlich a a

INRA, Centre des Sciences du Goût et de l’Alimentation, UMR CNRS 6265/INRA 1324/Université de Bourgogne, F-21000 Dijon, France Unité de Recherche en Epidémiologie Nutritionnelle, UMR INRA 1125/INSERM 557/CNAM/Université Paris 13, F-93000 Bobigny, France c Unité de Surveillance et d’Epidémiologie Nutritionnelle, Institut de Veille Sanitaire/Université Paris 13, F-93000 Bobigny, France b

a r t i c l e

i n f o

Article history: Received 27 December 2010 Received in revised form 23 August 2011 Accepted 24 August 2011 Available online 30 August 2011 Keywords: Liking Preference Fat Sweet Salt Questionnaire

a b s t r a c t Liking for a sweet, salty or fatty diet may induce overconsumption of simple carbohydrates, sodium or lipids. Measuring overall liking of the corresponding sensory sensations contributes to understanding the determinants of dietary behaviours. However, no standardized validated questionnaire assaying these sensations is currently available. In the present study, we developed a web-based questionnaire, ‘‘PrefQuest’’, which measures recalled liking for the following four sensations: salt, fat and salt, sweet, fat and sweet. PrefQuest included four types of items: (1) liking for sweet, fatty-sweet and fatty-salty foods, (2) preferences in the level of seasoning by adding salt, sweeteners, or fat, (3) preferences for types of dishes in a restaurant menu and (4) overall questions about sweet-, salt- and fat-related behaviours. A development study (n = 198) demonstrated that PrefQuest was feasible, well-perceived, only lasted about 20 min and that items were repeatable (overall mean intra-class correlation coefficient: 0.77, SD 0.08). Construct validity was then evaluated on a larger population sample (n = 47 803). The underlying structure within each of the four sensations was determined by exploratory factor analysis and then internally validated by confirmatory factor analysis. After a selection of the most relevant items, the salt, fat-and-salt, sweet, and fat-and-sweet scales exhibited a theoretically meaningful factor structure, unidimensional for the salt scale and with interrelated sub-dimensions for the sweet, fat-and-salt, and fat-and-sweet scales. Based on the fat-and-sweet and fat-and-salt scales, a fat model was also unveiled. For each factor, internal consistency as well as convergent and divergent validities were demonstrated. Overall, PrefQuest is an internally valid and original tool that can be used to assay recalled liking for sweet, salt, and fat considered altogether or separately as fat and salt or fat and sweet, and can be applied to large population surveys. Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction High intakes of sodium, sugars and lipids have been linked to the development of chronic diseases such as obesity, diabetes and cardiovascular diseases (WHO/FAO, 2003). Public health policies, e.g. in France, have therefore been implemented to provide nutritional recommendations which include limitations in foods containing too high amounts of fat, salt or sugar (Hercberg, ChatYung, & Chauliac, 2008). But these nutrients also play an important role in the sensory perception of many foods and greatly contribute to food hedonics (Mela & Sacchetti, 1991), which can partly explain why consumption is often over the recommended limits (Castetbon et al., 2009).

Abbreviations: EFA, exploratory factor analysis; CFA, confirmatory factor analysis; ICC, intra-class correlation coefficient. ⇑ Corresponding author at: INRA, Centre des Sciences du Goût et de l’Alimentation, UMR CNRS 6265/INRA 1324/Université de Bourgogne, F-21000 Dijon, France. E-mail address: [email protected] (A. Deglaire). 0950-3293/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodqual.2011.08.006

An assessment of dietary intakes showed that some population sub-groups seem to specifically like salty, sweet and/or fatty foods (Mejean, Macouillard, Castetbon, Kesse-Guyot, & Hercberg, 2010). Understanding how liking for such sensations contributes to food choices and behaviours may help to define public health actions aimed at improving nutritional behaviours. Thus, it would be of interest to study the extent of the relationship between dietary consumption of these nutrients and liking for the corresponding sensations in the general population. While nutritional intake can be assessed using validated and standardized tools (e.g. food frequency questionnaire, repeated 24 h recalls and records) (Willett, 1998), there is no such questionnaire assaying liking for sweet, salt and fat sensations. So far most studies have evaluated liking based on a single type of question, such as favourite levels of fat (Ledikwe et al., 2007), sugar (Mennella, Pepino, & Reed, 2005) or salt (Leshem, 1998) added to foods. However, such preference may not be similar to liking for food already containing fat, sugar or salt. Multiple types of measurements should provide a better understanding of overall liking, i.e.

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liking primarily derived from the sensation independently of the food product. Other studies have assessed salt or sweet liking based on liking for foods that were salty and fatty-salty (Beauchamp, Bertino, Burke, & Engelman, 1990; Stein et al., 1996) or sweet and fattysweet (Duffy & Bartoshuk, 2000; Keskitalo et al., 2007; Lahteenmaki & Tuorila, 1995). However, the presence of fat is misleading, so it would be more appropriate to consider the sweet or the salty tastes independently of the fat sensation as much as possible. Similarly, liking for fat may be better assessed when the fat-and-sweet or fat-and-salt sensations are considered separately, as previously undertaken (Keskitalo et al., 2008). Overall, there is to date no questionnaire that assays liking for the sensations of sweet, fat and sweet, salt, and fat and salt by combining multiple types of measurements. The present study aims at developing a single web-based questionnaire, PrefQuest, composed of four scales assaying recalled liking for the sensory sensations of sweet, salt, fat and sweet, and fat and salt. The choice of the various types of items was made with experts’ advice. As suggested previously for questionnaire development (DeVellis, 2003; Jensen, 2003), feasibility, repeatability and internal validity of PrefQuest were evaluated. Internal validity of the scales was determined using the statistical approach of the exploratory and confirmatory factor analyses, as previously undertaken (Jover, Montes, & Fuentes, 2004). This allowed the scales to be purified, that is to select the most relevant items. 2. Method 2.1. Development study PrefQuest (cf. Appendix) was developed by a team of experts involved in a multidisciplinary project, composed of one sensometrician, two sensorialists and four nutritional epidemiologists with experience in developing web-based questionnaires aimed at the general population. These experts are also the authors of the present article. PrefQuest was reviewed by two dieticians. Four pretests, each including 54–62 participants, were conducted to test and select the most relevant questions (items). This preliminary process resulted in a questionnaire including 146 items. 2.1.1. Questionnaire items The items were scattered into four scales related to liking for sweet (36 items), fat and sweet (39 items), salt (17 items), and fat and salt (55 items). They were presented in four successive sections, each based on a question type as described hereafter. Section 1: liking for foods. The subjects were asked to rate their liking for a given food on a 9-point scale as shown in Fig. 1A. If the subject had never tasted the food in question, he/she could choose ‘‘I have never tasted’’ as an answer (Fig. 1A). Food items were selected within usual French food groups based on their high nutritional content in sugars, sugars and lipids, or sodium chloride and lipids. The contents of each nutrient of interest belonged to the 3rd and 4th quartiles of a given food group. Our nutrient standards were sugars P 10 g/100 g and lipids 6 5 g/100 g for sweet foods, sugars P 10 g/100 g and lipids P 15 g/100 g for sweet-fatty foods, and Na P 400 mg/100 g and lipids P 15 g/100 g for salty-fatty foods. Nutritional data were obtained from a published French food composition table (Hercberg, 2005). Pre-tests allowed us to select foods that were the most discriminant (SD P 1.5 out of a rating 1–9 range) or had been tasted by more than 85% of participants. Some exceptions were made for foods that looked relevant to experts, like tarama or hollandaise sauce (fatty-salty foods). The final food list contained 36 salty-fatty foods, 20 sweet foods, and 22 sweet-fatty foods. No food was listed in the salt scale as foods with a high content of salt together with a

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low content of fat, e.g. soy sauce, are unusual in the French food repertory. Section 2: preferred level of seasoning. The subjects were asked to give their preferred level of seasoning for a given food on a 6-point scale, as shown in Fig. 1B. The scale points 0, 2 and 4 were illustrated by a picture of the same food with grading levels of salt for the salt scale (nine items), mayonnaise, butter, cream or grated cheese for the fat-and-salt scale (13 items), sugar or jam for the sweet scale (nine items), and whipped cream, chocolate spread, or butter for the fat-and-sweet scale (10 items). The seasoning level shown in the pictures was based on the quantity of seasoning available in an individual sachet from institutional catering, with point 0 corresponding to no seasoning at all, point 2 to a quarter of a sachet and point 4 to half a sachet. This grading level was selected through a pre-test (n = 54 subjects), which demonstrated that it allowed ratings to be spread out and high ratings to be ticked. Seasonings were associated with common foods that represented the various food categories as much as possible. If the subject did not like the food in question, he/she could choose ‘‘I do not like [this food]’’ as an answer (Fig. 1B). Eight distracting items related to other seasonings, e.g. lemon, ketchup, pepper, were randomly included. The effect of the illustrative picture was assessed in the first administration of PrefQuest by including 12 duplicate questions (3 in each scale) asking about the preferred level of seasoning but with no picture, with a 5-point scale labelled at each anchor, and with the additional ‘‘I do not like [this food]’’ (Fig. 1C). Section 3: preferred dishes in a menu. The subjects were told that a new restaurant was about to open in their neighbourhood and that, beforehand, a survey was organized about the food preferences of the future clientele. For each type of dish usually proposed in a restaurant, a list of 6 or 8 dishes or drinks was established (Fig. 1D) based on an equal number of foods or drinks that were more or less salty (cocktail snacks, meat), fatty and salty (meat, side dishes, Italian food), sweet (appetizer drinks, desserts, soft drinks), and fatty and sweet (desserts, snack desserts, hot drinks). Dishes were chosen so as to contain ingredients that were typically salty or non-salty, sweet or non-sweet, fatty and salty or non-fattyand-salty, fatty and sweet or non-fatty-and-sweet. These ingredients followed the nutrient standards described in section 1, except for sweet appetizer drinks with sugars P 5 g/100 g. Salty ingredients contained Na P 1200 mg/100 g and lipids 6 10 g/100 g. These ingredients were stated in the dish names (Fig 1D). The subjects had to choose the dishes they found the most tempting within each list, with maximum numbers of 3 out of an 8-dish list or 2 out of a 6-dish list. They could answer that none of the dishes attracted them. Section 4: sweet-, salt- or fat-related dietary behaviour questions. In that section there were 6 items for the salt scale, 3 for the fatand-salt scale, 4 for the sweet scale, and 4 for the fat-and-sweet scale. An example is given in Fig. 1E. Depending on the item, a 5-point frequency scale or a 9-point scale was used. If applicable, an additional point saying ‘‘I have never tasted’’ or ‘‘I do not like [this food]’’ was included. Socio-demographic and anthropometric data were collected. In the first administration, the subjects were also asked about the questionnaire feasibility, i.e. whether the overall questionnaire was difficult, annoying or too long. Additionally, as sections 2 and 3 were potentially the trickiest ones, the subjects were asked about the difficulties encountered in these sections. 2.1.2. Questionnaire administration and participants PrefQuest was launched on the web by the ‘‘Biosystèmes’’ company using the FIZZ software (Biosystèmes, Couternon, France). The questionnaire was administered twice with a mean test–retest interval of 24 days (SD 3). Once a participant got connected to the

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Fig. 1. Extract of the PrefQuest questionnaire, measuring overall liking for sweet, fat and sweet, salt and fat and salt, through four sections (each corresponding to one type of question). aInstructions as presented in the validity study.

Fig. 2. Overview of the statistical analyses (






) performed on each scale of PrefQuest, and based on data from the whole sample (—) or from subsamples (–

–).

server hosting PrefQuest, he/she had to fill it at once. The time elapsed between the start and the end of the connexion on the PrefQuest web site was monitored for each participant. People solicited to fill in PrefQuest were subjects enrolled in a study within our laboratory (not detailed here), co-workers, and family and friend circles of the authors. 2.1.3. Statistical analysis An overview of the analyses performed in the development study is given in Fig. 2. Test–retest reliability (repeatability) was assessed by computing the intra-class correlation coefficient (ICC) for absolute agreement over single measurements [ICC(A,1) or ICC(2,1)] for each item. This ICC was derived from a two-way

mixed model with the random effect of participant, considered as target, and the fixed effect of repetition (test or retest), considered as rater. It was computed as the ratio of the variance of participant over the sum of the variances of participant, repetition and error (McGraw & Wong, 1996; Shrout & Fleiss, 1979). ICCs were averaged for each sensation. The picture effect for duplicated items in section 2 was examined by computing the ICC for consistency over single measurements [ICC(C,1) or ICC(3,1)] for each duplicated item. This ICC was derived from a two-way analysis of variance with the random effect of participant (target) and the fixed effect of picture (rater), i.e. item with or without pictures. It was computed as the ratio of the variance of participant over the sum of the variances for participant and error (McGraw & Wong, 1996;

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Shrout & Fleiss, 1979). ICCs were averaged over the duplicated items. ICCs were calculated using the SAS macro intracc developed by Hamer (2000). The closer ICCs are to 1, the better they are. Statistical analyses were performed with SAS software (Release 9.1, SAS institute Inc., Cary, NC). 2.2. Validity study 2.2.1. Questionnaire items The PrefQuest questionnaire was similar to that described in the development study, with slight modifications. In section 1, five fatty-salty foods were deleted: tarama and hollandaise sauce were removed as the development study confirmed that less than 85% of participants had previously tasted these foods; the other three were not discriminant enough or were too redundant with the other items. In section 2, there were no more duplicated items. The items of this section were presented either with pictures or without pictures, as illustrated in Fig. 1B and C. In section 3, the instructions were modified. The subjects were asked to choose a free number of their preferred dishes in the list with a maximum of 4 in an 8-dish list (Fig. 1D) or 3 for a 6-dish list. The number of items per scale and per section is detailed in Table 1. 2.2.2. Questionnaire administration and participants PrefQuest was administered through the web-based ‘‘NutrinetSanté study’’ (2010) and was available for four months. The study, launched in France in May 2009 for 5 years, is a prospective observational cohort study on volunteers aged over 18 years. It is designed to investigate the relationships between nutrition and health as well as the determinants of dietary behaviours. Its design, methods and rationale have been described elsewhere (Hercberg et al., 2010). All procedures were approved by the Institutional Review Board of the French Institute for Health and Medical Research (IRB INSERM, no. 0000388FWA00005831) and by the French Data Protection Authority (Commission Nationale Informatique et Libertés, nos. 908450 and 909216). All participants gave their electronic informed consent to participate to the study according to the principles of the Declaration of Helsinki. 2.2.3. Statistical analysis 2.2.3.1. Data preparation and screening. (1) Items from sections 1, 2 and 4. Data were linearly transformed into values ranging from 0 to 10. For each scale, answers to the additional point labelled ‘‘I have never tasted’’ or ‘‘I do not like [this food]’’ were estimated

by adding the mean of all the ratings of a given subject to the mean of the ratings of a given item, and then subtracting the mean of the entire sensation. Participants who answered ‘‘I have never tasted’’ or ‘‘I do not like [this food]’’ to more than 25% of items within a sensation were not considered for further analysis. (2) Items from section 3. For each dish list, a rating was calculated by dividing the number of dishes considered as salty, fattysalty, sweet, or fatty-sweet, by the total number of dishes selected in the list. If a participant answered that none of the dishes attracted her or him, a rating of 0 was attributed. (3) Normality. Univariate normality was assessed by determining the skewness and kurtosis values for the rating distribution of each item. The items for which normality was strongly violated, that is with coefficients higher than 2 for skewness and 7 for kurtosis in absolute value (Curran, West, & Finch, 1996; Kline, 2005), were not considered for further analysis. Multivariate normality was assessed by determining the Mardia’s (1970) multivariate kurtosis, which should be close to 0 when multivariate normality is verified. (4) Sampling. The dataset was randomly sampled with control for age class and gender to obtain two datasets, a training dataset and a test dataset, each including 5620 subjects with 50% of women and 50% of men, and 20% of subjects in each of the following age classes: 18–29, 30–39, 40–49, 50–59, 60–75 years. This distribution was close to that observed in the French population aged between 18 and 75 years (INSEE, 2010).

2.2.3.2. Construct validity. An overview of the analyses that were performed is given in Fig. 2. Each scale was first analysed separately before running an overall analysis. (1) Exploratory factor analysis. As suggested by Jover et al. (2004), an exploratory factor analysis (EFA) was run to determine the smallest number (and the nature) of the underlying dimensions (factors) within each scale before subjecting these hypotheses to a confirmatory factor analysis (CFA). EFA was performed on the training dataset using squared multiple correlations as prior communality estimates. Factors were extracted based on the maximum likelihood method. As factors were expected to correlate, an oblique rotation (PROMAX) was applied (Brown, 2006). The number of factors to be extracted was determined based on the scree test (Cattell, 1966), the proportion of variance explained by a factor (at least 5–10%) and the interpretability criterion (Hatcher, 1994). Items that greatly contributed to a given factor were selected based on their salient loading (>0.30) onto this

Table 1 Number of PrefQuest items per sensation and per type of questions. Sensation

Question type Liking for foods

Items listed in PrefQuest Salt 0 Fat and salt 31 Sweet 20 Fat and sweet 22 Total 73

Total Preferred level of seasoning

a

Related dietary behaviour

With pictures

Without pictures

5 9 5 6 25

4 4a 4 4a 16

2 3 3 3 11

6 3 4 4 17

17 50b 36 39 142

4 3 4 2 13

0 0 1 0 1

3 1 3 2 9

11 31 21 20 83

Items selected after confirmatory factor analysis Salt 0 4 Fat and salt 20 7 Sweet 9 4 Fat and sweet 12 4 Total 41 19 b

Preferred dishes in menu

The item ‘‘butter + bread’’ is counted in both fat-salt and fat-sweet sensations. The five items removed for the validity study are not counted here.

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factor, as given by the rotated factor pattern. If an item presented a loading close to being salient (>0.25) and looked relevant for the scale, it was retained. If an item presented salient or close-to-salient (>0.25) loadings on two or more factors, it was not retained, except if the loading difference was higher than 0.20. (2) Confirmatory factor analysis. A measurement model based on first-order factors with their salient items as indicators was set up for each scale; intercorrelations among all factors were allowed. Metrics of the factors (unobserved variables with no defined metrics) were given by fixing their variances to 1.0. Data adjustment to the given model was evaluated on the test dataset (cross-validation approach) by CFA, based on the variance/covariance matrix. The robust maximum likelihood estimator (Bentler & Dijkstra, 1985; Satorra & Bentler, 1994) was used to address normality and to provide robust standard errors, corrected v2 (Satorra–Bentler v2) and corrected goodness-of-fit indices when based on v2. The models were revised based on the matrix residuals. The items with the largest residuals, i.e. items for which the fitted model could not predict their values adequately, were deleted. Model revision stopped when the given model fitted reasonably well with the data, as suggested by the goodness-of-fit indices. Although reported as such, the v2 statistic was not considered as a goodness-of-fit index, as it depends on sample size and is known to be problematic with large sample sizes (Byrne, 2009; Cheung & Rensvold, 2002). Therefore alternative goodness-of-fit indices were examined (Cheung & Rensvold, 2002). As an absolute fit index, the standardized root mean square residual has been reported, with values less than 0.10 generally considered as favourable. The root mean square error of approximation (Steiger & Lind, 1980) was chosen as a fit index adjusting for model parsimony; values less than 0.05 indicate close approximate fit, values between 0.05 and 0.08 suggest a reasonable error of approximation and values higher than 0.10 suggest poor fit (Kline, 2005). As incremental fit indices, the comparative fit index (Bentler, 1989) and the Tucker–Lewis index, also known as the non-normed fit index (Bentler & Bonett, 1980), have been reported. For both indices, values higher than 0.90 indicate a reasonably good fit of the model (Hu & Bentler, 1999; Kline, 2005). CFA made it possible to determine the psychometric properties of each scale. Within each final model, internal consistency was evaluated for each factor by calculating its Cronbach’s alpha coefficient (Cronbach, 1951) and its composite reliability (Hatcher, 1994), which should both be higher than 0.70 (Hatcher, 1994; Nunnally, 1978). Convergent validity was assessed by reviewing the t tests for the factor loadings that should all be significant to support the convergent validity of the items (Anderson & Gerbing, 1988). Discriminant validity was evaluated by examining the confidence interval of factor intercorrelations, which should not include 1 (Anderson & Gerbing, 1988). Higher-order factor analyses were performed on scales having two or more interrelated first-order factors in order to assess whether these factors were influenced by a broader dimension. This provides a parsimonious conceptual account for the interrelationships of the first-order factors. A model based on the structure of the first-order model (as analysed above) was tested for each scale, with first-order factors used as indicators for second-order factors. A further model based on a single third-order factor with second-order factors used as indicators was assessed. If there were only two second-order factor indicators for the third-order factor, their unstandardized loadings were set to 1.0 for identification purposes. Data adjustment to these models was examined using the fit indices described above. To evaluate intercorrelations among first-order factors, independently of their scale of origin, a single model based on all PrefQuest first-order factors was subjected to CFA. All factors were freely intercorrelated.

Statistical analyses were performed with SAS software (Release 9.1, SAS institute Inc., Cary, NC), except for confirmatory factor analysis with robust maximum likelihood estimator, which was performed in the R software environment (R Development Core Team, 2010) with the LAVAAN package (Rosseel, 2010). 3. Results 3.1. Development study 3.1.1. Participants PrefQuest was first filled in by 198 French participants (55% women; 45% men), whose average age was 39.4 years (SD 12.3; 18–67 years range) and average body mass index was 24.1 kg/m2 (SD 4.0). Repeatability was assessed on 74% of these participants (n = 145). This subsample was very similar to the whole sample in terms of age, gender and body mass index. 3.1.2. Feasibility Participants filled in the entire questionnaire (socio-demographic, feasibility and duplicated items, and PrefQuest items) within a mean time of 30.9 min (SD 9.5) at the first administration. The completion time for PrefQuest items only lasted an average 23.5 min (SD 7.7) at the first administration and 20.3 min (SD 5.7) at the second administration. A few participants (3–4%) did not answer the feasibility questions. For 95% of the participants, the questionnaire was not difficult; only 1% found it difficult. For 90% of the participants, the questionnaire was not annoying; only 6% found it annoying. Finally, 74% thought the questionnaire was not too long but 22% found it too long. Regarding the items based on the preferred level of seasoning illustrated with pictures (section 2), 90% of the participants found it easy to answer the questions. Only 7% found it difficult, with 2% who found the pictures not easy to understand and 2% who found it difficult to distinguish between their preferred and habitual levels of seasoning. Regarding section 3 (preferred dishes in a menu), 77% of participants found it easy to select their 3 preferred dishes in the lists, but 20% found this difficult because they were attracted by only 2 dishes (11% of participants) or by more than 3 dishes (6% of participants). Only 1% of participants declared that they were attracted by only one dish. None of them were attracted by no dish at all. 3.1.3. Repeatability ICC(A,1) was P 0.70 for each sensation (Table 2). This means that the participant effect explained more than 70% of the rating variance, whereas the repetition effect was responsible for less than 30% of the rating variance. Thus, the test and retest participant rank orders were similar. Additionally, ICC(A,1) was equal to ICC(C,1) for each item, which indicates that there was no absolute difference between test and retest rating means. Overall, this shows that there was a good absolute agreement between the test and retest data of PrefQuest. Two items of the salt scale had an ICC lower than 0.50, so they were not further considered. Subsequently, the average ICC for this scale was 0.75 (SD 0.07). The average ICC for section 3 (menu items) was the lowest compared with those of the other sections (0.61 vs. 0.75–0.79). 3.1.4. Duplicated items (section 2: preferred level of seasoning) A strong correlation and a high consistency between items with or without pictures were found with a mean correlation coefficient of 0.83 (SD 0.06; range 0.75–0.91) and a mean ICC(C,1) of 0.79 (SD 0.06; range 0.71–0.88). Because none of these item types was better than the other, both were included in PrefQuest, as administered in the validity study.

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A. Deglaire et al. / Food Quality and Preference 23 (2012) 110–124 Table 2 Meana intraclass correlation coefficient [ICC(A,l)b] of each PrefQuest scale (development study). Scale

Intraclass correlation coefficient

Salt sensation Fat and salt sensation Sweet sensation Fat and sweet sensation a b

Number of items

Mean

Standard deviation

Minimum

Maximum

0.70 0.75 0.75 0.78

0.16 0.07 0.09 0.09

0.21 0.58 0.54 0.58

0.84 0.87 0.92 0.91

17 50 36 39

ICC(A,1) was calculated for each item and averaged over the items of a same scale. ICC(A,1) was equal to ICC(C,1) for each item.

Table 3 Confirmatory factor analysisa of the first-order model for liking for salt: parameter estimates, internal consistency and overall goodness-of-fit indices. Factor: F1, salt. Parameter

F1 F1 F1 F1 F1 F1 F1 F1 F1 F1 F1

? ? ? ? ? ? ? ? ? ? ?

S2Qb: Salt + soup S2Q: Salt + pasta S2P: Salt + chicken S2Q: Salt + steak S2P: Salt + mashed potatoes S2P: Salt + green beans S2Q: Salt + fries S2P: Salt + egg S4: To salt its own dish before any tasting S4: To bring the salt for a picnic S4: Salt in cooking water for pasta

Standardized factor loadingc

Factor internal consistency Cronbach’s alpha

Composite reliability

0.807 0.796 0.798 0.793 0.777 0.759 0.687 0.622 0.491 0.398 0.332

0.894

0.912

Fit indices Satorra–Bentler v2 = 2194; degree of freedom = 54; comparative fit index = 0.92; Tucker–Lewis index = 0.90; root mean square error of approximation = 0.08; standardized mean square residual = 0.04 a

Robust maximum likelihood estimation. Item prefixes indicate the section and the question type with S1 being section 1 (liking for foods), S2 section 2 (preferred level of seasoning) with picture (S2P) or not (S2Q), S3 section 3 (preferred dishes in menu) and S4 section 4 (salt-related dietary behaviour questions). c Values statistically significant at p < 0.001. b

3.2. Validity study PrefQuest was filled in by 47 803 Nutrinet participants; 77% were women and 23% were men. The mean age was 44.6 years (SD 14.5; 18–75 years range for nearly all participants) and the mean body mass index was 24.0 kg/m2 (SD 4.9). The data from one participant were excluded, as 23% of the data went missing due to a technical problem. There were 1.4% of participants who were removed from the dataset because they answered ‘‘I have never tasted’’ or ‘‘I do not like [this food]’’ to more than 25% of the items of a scale. Five items, which exhibited a distribution strongly deviating from normality, were excluded from the present analysis. Multivariate normality was not verified for any of the scales, with Mardia’s multivariate kurtosis values of 32, 253, 106 and 160 for the salt, fat-and-salt, sweet, and fat-and-sweet scales, respectively. 3.2.1. Salt scale According to EFA, a single factor solution appeared as the most satisfactory. A model based on this factor and on the selected items was subjected to CFA. After removing items with large residuals in the latter analysis, the goodness-of-fit indices demonstrated an adequate fit of the final model to the observed data (Table 3). Convergent validity and internal consistency were demonstrated for factor F1 (Table 3). As the salt scale had a single factor, no higher-order CFA was run. 3.2.2. Sweet scale A four-factor solution was revealed by EFA. These factors were based on foods, with sweetened foods and drinks, or naturally sweet foods, and on added sugar or added sweetener. In CFA, these

two factors were reorganized to be more homogeneous and were related to sugar added to hot drinks (F4), foods (F6), or jam (F5). After removing items with large residuals in CFA, the goodnessof-fit indices demonstrated an adequate fit of the final model to the data (Table 4). Internal consistency, convergent and discriminant validities were verified for each factor (Table 4). Factor pairs F2 F6 and F4 F6 were the most highly correlated. F3 was not correlated to F4 and F6 but was moderately correlated to F5 and to a lesser extent to F2 (Table 4). A model with the second-order factors of sweet foods (F21), added sugar (F22) and natural sweetness (F23), each, respectively, based on F2 F6, F4 F6 and F3 F5, was subjected to CFA. Fit indices of this model (Table 5A) were slightly lower than those of the first-order model, but still reached or nearly reached the acceptable level. Convergent and discriminant validities were verified for F21, F22 and F23. Factor F21 was strongly correlated with F22 and F23, but F22 and F23 were only slightly intercorrelated (Table 5A). When a third-order model (F31) based on F21, F22 and F23 was subjected to CFA, a similar fit to that of the second-order model was found (Table 5A). 3.2.3. Fat-and-salt scale According to EFA, a five-factor solution appeared as the most satisfactory. These factors were either fatty-salty foods, such as meats (F7), fatty foods (F9), cheesy foods (F10) and savoury snacks (F11), or added fat (F8). A model based on these factors and their selected items (Table 6) was subjected to CFA. After removing items with large residuals in the latter analysis, fit indices showed an adequate fit of the final model to the data (Table 6). Internal consistency, convergent and discriminant validities were demonstrated for each factor (Table 6). Factors based on fatty-salty foods (F7, F9, F10 and F11) were the most intercorrelated ones (Table 6).

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Table 4 Confirmatory factor analysisa of the first-order model of liking for sweet: parameter estimates, internal consistency and overall goodness-of-fit indices. Factors: F2, sweetened foods; F3, naturally sweet foods; F4, added sugar to hot drinks; F5, added jam; F6, added sugar to foods. Parameter

Estimateb

Standardized factor loadings F2 ? S1c: Fruit biscuit F2 ?S1: Candies F2 ? S1: Cream pudding F2 ?S1: Fruit nectar F2 ? S1: Floating island F2 ? S3: Soft drinks F3 ? S1: Honey F3 ? S1: Gingerbread F3 ? S1: Sweet dried fruits F3 ? S1: Chestnut spread F4 ? S4: To be upset to get a hot drink without sugar F4 ? S2Q: Sugar + coffee F4 ? S2Q: Sugar + tea F5 ? S2Q: Jam + crepe F5 ? S2P: Jam + bread F5 ? S2P: Jam + yogurt F5 ? S4: To eat some jam with a spoon F6 ? S2P: Sugar + yogurt F6 ? S2Q: Sugar + crepe F6 ? S2P: Sugar + strawberries F6 ? S4: To be upset to get some white cheese not sweet

0.780 0.692 0.627 0.493 0.448 0.431 0.702 0.658 0.573 0.525 0.851 0.791 0.725 0.715 0.660 0.556 0.420 0.726 0.659 0.658 0.441

Factor correlations F2 M F3 F2 M F4 F2 M F5 F2 M F6 F3 M F4 F3 M F5 F3 M F6 F4 M F5 F4 M F6 F5 M F6

0.290 0.350 0.402 0.517 0.085 0.444 0.023d 0.164 0.660 0.478

Factor internal consistency

Confidence interval (95%)

Cronbach’s alpha

Composite reliability

Lower bound

0.748

0.851

0.710

0.798

0.828

0.749

0.675

0.794

0.712

0.795

0.255 0.320 0.371 0.490 0.119 0.412 0.060 0.128 0.634 0.445

Upper Bound

0.325 0.379 0.433 0.545 0.052 0.476 0.014 0.199 0.685 0.512

Fit indices Satorra–Bentler v2 = 3110; degree of freedom = 179; comparative fit index = 0.91; Tucker–Lewis index = 0.89; root mean square error of approximation = 0.05; standardized mean square residual = 0.05 a

Robust maximum likelihood estimation. Values statistically significant at p < 0.001, except if noted. c Item prefixes indicate the section and the question type with S1 being section 1 (liking for foods), S2 section 2 (preferred level of seasoning) with picture (S2P) or not (S2Q), S3: section 3 (preferred dishes in menu) and S4 section 4 (sweet-related dietary behaviour questions). d Not significant (p-value = 0.22). b

A model, based on F8 and on a second-order factor F24 (fatty-salty foods) with F7, F9, F10 and F11 as indicators, was subjected to CFA (Table 5B). The fit indices showed a similar adequate fit to that of the first-order model. Convergent validity was verified for F24. F24 and F8, which were moderately correlated, served as indicators for the single higher-order factor F32 (Table 5B). This model fitted the data as well as the first- and second-order models.

3.2.4. Fat-and-sweet scale According to EFA, a four-factor solution appeared as the most satisfactory. These factors were based either on fatty-sweet foods, i.e. pastries and desserts (F12), chocolate spread (F14) and chocolate desserts (F15), or on added whipped cream (F13). A model based on these factors and their selected items was subjected to CFA. After removal of items with large residuals in the latter analysis, fit indices showed an adequate fit of the final model to the data (Table 7) with factors having a similar meaning to that revealed in EFA. Internal consistency, convergent and discriminant validities were verified for each factor (Table 7). The factors based on fatty-sweet foods (F12, F14 and F15) were the most intercorrelated. A model based on F2 and on a second-order factor F25 (fattysweet foods) based on F12, F14, and F15 was subjected to CFA (Table 5.C). The fit indices showed a similar adequate fit to that

of the first-order model. Convergent validity and discriminant validity were verified for factors F25 and F13. These factors, being moderately correlated, served as indicators for a higher-order factor F33 (Table 5C). This model presented similar fit indices to those of the first and second-order models.

3.2.5. Overall analysis When all first-order factors (F1–F15) were subjected to CFA, goodness-of-fit indices were close to, but did not reach the acceptable level (Table 8). This was likely due to the absence of correlation (p > 0.05) for F3 (naturally sweetened foods) with factors F6, F8, F9 and F13. Nevertheless, convergent validity and discriminant validity were verified for each factor. This overall CFA resulted in similar standardized loadings (data not shown) and similar correlations among factors from the same sensation (salt, sweet, fat and salt, fat and sweet; Table 8) to those obtained with CFAs run separately for each sensation. Intercorrelations (0.6–0.8) among factors from different sensations were revealed, especially among sweetened foods (F2), pastries and desserts (F12), fatty foods (F9) and savoury snacks (F11). Modest intercorrelations (60.4) were found among added salt (F1), added sugar (F4, F6), added jam (F5), added dairy fat (F8), and added whipped cream (F13). Sugar added to foods (F6) stood apart, with a 0.6 correlation to sugar

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Table 5 Confirmatory factor analysesa of second-order and third-order models for liking for sweet (A), fat and salt (B) and fat and sweet (C): parameter estimates and overall goodness-offit indices. Second-order factors: F21, sweet foods; F22, added sugar; F23, natural sweetness; F24, fatty-salty foods; F25, fatty-sweet foods. Third-order factors: F31, sweet sensation; F32, fat and salt sensation; F33, fat and sweet sensation. Each model was subjected separately to confirmatory factor analysis. Parameter

Estimateb

Confidence interval (95%) Lower bound

Upper bound

A. Sweet Second-order model Standardized factor loadings F21 ? F2 (sweetened foods) 0.707c F21 ? F6 (added sugar to foods) 0.347 F22 ? F4 (added sugar to hot drinks) 0.707c F22 ? F6 (added sugar to foods) 0.672 F23 ? F3 (naturally sweet foods) 0.515 F23 ? F5 (added jam) 0.707c Factor correlations F21 M F22 0.650 0.593 0.707 F21 M F23 0.823 0.770 0.876 F22 M F23 0.206 0.145 0.267 Fit indices: Satorra–Bentler v2 = 3644; degree of freedom = 183; comparative fit index = 0.89; Tucker–Lewis index = 0.87; root mean square error of approximation = 0.06; standardized mean square residual = 0.06 Third-order model Standardized factor loadings F31 ? F21 0.877 F31 ? F22 0.522 F31 ? F23 0.675 2 Fit indices: Satorra–Bentler v = 3668 degree of freedom = 183; comparative fit index = 0.89; Tucker–Lewis index = 0.87; root mean square error of approximation = 0.06; standardized mean square residual = 0.06 B. Fat and salt Second-order model Standardized factor loadings F24 ? F7 (meats) 0.775 F24 ? F9 (fatty foods) 0.782 F24 ? F10 (cheesy foods) 0.710 F24 ? F11 (savoury snacks) 0.780 Factor correlations F24 M F8 (added dairy fat) 0.432 0.407 0.457 Fit indices: Satorra–Bentler v2 = 6616; degree of freedom = 429; comparative fit index = 0.91; Tucker–Lewis index = 0.90; root mean square error of approximation = 0.05; standardized mean square residual = 0.05 Third-order model Standardized factor loadings F32 ? F24 0.707c F32 ? F8 0.707c Fit indices: Satorra–Bentler v2 = 6628; degree of freedom = 430; comparative fit index = 0.91; Tucker–Lewis index = 0.90; root mean square error of approximation = 0.05; standardized mean square residual = 0.05 C. Fat and sweet Second-order model Standardized factor loadings F25 ? F12 (pastries and desserts) 0.817 F25 ? F14 (chocolate spread) 0.633 F25 ? F15 (chocolate dessert) 0.650 Factor correlation F25 M F13 (added whipped cream) 0.421 0.394 0.448 Fit indices: Satorra–Bentler v2 = 5102; degree of freedom = 166; comparative fit index = 0.91; Tucker–Lewis index = 0.89; root mean square error of approximation = 0.07; standardized mean square residual = 0.06 Third-order model Standardized factor loadings F33 ? F25 0.707c F33 ? F13 0.707c Fit indices: Satorra–Bentler v2 = 5120; degree of freedom = 167; comparative fit index = 0.91; Tucker–Lewis index = 0.89; root mean square error of approximation = 0.07; standardized mean square residual = 0.06 a b c

Robust maximum likelihood estimation. Values statistically significant at p < 0.001. Unstandardized loading fixed to 1.0 for identification purposes.

added to hot drinks (F4) but also to added dairy fat (F8). Finally, there were some intercorrelations among factors based on fattysalty and fatty-sweet foods (0.3–0.6), on the one hand, and for added dairy fat and added whipped cream (0.4), on the other hand. Consequently, a model for liking for fat based on the second-order fatty-salty foods (F24), fatty-sweet foods (F25) and added fat (F26) was subjected to CFA (Table 9). Due to the high correlation between fatty-salty and fatty-sweet foods, these factors were grouped into a third-order factor (F34), which, in turn, formed a

single fourth-order factor with added fat (F26), related to the fat sensation (F41). These second, third and fourth-order models exhibited similar fit indices, which were very close to the acceptable level. Convergent validity and discriminant validity was verified for each factor. Overall, 59% of the items initially included in PrefQuest were selected after CFA (Table 1). When ICC(A,1) was averaged over these selected items (development study), ICC remained similar or was slightly improved (data not shown).

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Table 6 Confirmatory factor analysisa of the first-order model of liking for fat and salt: parameter estimates, internal consistency and overall goodness-of-fit indices. Factors: F7, meats; F8, added dairy fat: F9, fatty foods: F10, cheesy foods: F11, savoury snacks. Parameter

Estimateb

Standardized factor loadings F7 ? Slc: Dried sausage F7 ? S1: Chipolata sausage F7 ? S1: Pâté F7 ? S1: Morteau sausage F7 ? S1: Rillettes F7 ? S1: Chorizo F7 ? S1: Salami F7 ? S1: Duck or goose meat conserve F8 ? S2Q: Butter + pasta F8 ? S2P: Butter + green beans F8 ? S2Q: Butter + mashed potatoes F8 ? S2P: Butter + dried sausage on F8 ? S2P: Butter + radishes F8 ? S2P: Butter + bread F8 ? S2P: Butter + steak F8 ? S4: To be upset to get a butter-jam F8 ? S2Q: Cream + soup F8 ? S2P: Cream + salmon F8 ? S2P: Butter + cheese F9 ? S1: Nuggets F9 ? S1: Burgers F9 ? S1: Meat or cheese pies F9 ? S1: Kebab F10 ? S1: Tartiflette F10 ? S1: Cheese fondue F10 ? S1: Roquefort sauce F10 ? S1: Caprice cheese F10 ? S1: Cantal cheese F11 ? S1: Chips F11 ? S1: Savoury biscuits F11 ? S1: Peanuts

0.795 0.777 0.739 0.732 0.725 0.673 0.661 0.550 0.710 0.693 0.681 0.628 0.603 0.580 0.559 0.516 0.496 0.492 0.484 0.808 0.807 0.717 0.624 0.838 0.790 0.509 0.495 0.464 0.813 0.803 0.734

Factor correlations F7 M F8 F7 M F9 F7 M F10 F7 M F11 F8 M F9 F8 M F10 F8 M F11 F9 M F10 F9 M F11 Fl0 M Fll

0.362 0.585 0.580 0.594 0.289 0.345 0.327 0.548 0.654 0.514

Factor internal consistency

Confidence interval (95%)

Cronbach’s alpha

Composite reliability

Lower bound

Upper bound

0.888

0.888

0.852

0.915

0.822

0.798

0.764

0.824

0.826

0.750

0.334 0.564 0.554 0.568 0.260 0.317 0.299 0.520 0.633 0.486

0.389 0.606 0.605 0.619 0.319 0.373 0.355 0.576 0.676 0.541

Fit indices Satorra–Bentler v2 = 6514; degree of freedom = 424; comparative fit index = 0.91; Tucker–Lewis index = 0.9; root mean square error of approximation = 0.05; standardized mean square residual = 0.05 a

Robust maximum likelihood estimation. Values statistically significant at p < 0.001. Item prefixes indicate the section and the question type with S1 being section 1 (liking for foods), S2 section 2 (preferred level of seasoning) with picture (S2P) or not (S2Q), S3 section 3 (preferred dishes in menu) and S4 section 4 (fat-and-salt-related dietary behaviour questions). b

c

4. Discussion The present study demonstrates a good feasibility, reliability (repeatability and internal consistency) and internal validity of the PrefQuest questionnaire for measuring liking for salt, sweet, and fat considered altogether or separately as fat and salt or fat and sweet. This questionnaire approach provides a score of recalled liking mainly based on questions about foods and about the preferred level of seasoning in foods, unlike a sensory test that would give a direct measure of liking for salt, sweet and fat in foods that are actually tasted. 4.1. Feasibility and repeatability Most participants found PrefQuest short, easy and entertaining. The items illustrated by pictures and those based on the menu

probably contributed to this entertaining feeling. Some participants (1 out of 5) found PrefQuest long to fill in; this was likely due to the socio-demographic, feasibility and duplicated items, as they added 7 min to PrefQuest itself. These items were included in the first administration only. On each scale, all items exhibited a rather good repeatability, with an average ICC higher than 0.7. As reported by Weir (2005), there is no real consensus as to the range of values for a good ICC; however, an ICC below 0.5 indicates that the item has a variance mostly explained by repetition and error variance and is thus unreliable. Such items were not included in further analyses. Over the entire PrefQuest, the menu items (section 3) had on average the lowest repeatability. This could arise from the difficulty to choose a fixed number (3) of dishes in the list, as reported by 20% of participants in the development study. Instructions for that section were consequently modified for the validity study:

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Table 7 Confirmatory factor analysisa of the first-order model of liking for fat and sweet: parameter estimates, internal consistency and overall goodness-of-fit indices. Factors: F12, pastries and desserts; F13, added whipped cream; F14, chocolate spread; F15, chocolate dessert. Parameter

Estimateb

Standardized factor loadings F12 ? Slc: Chocolate croissant F12 ? S1: Croissant F12 ? S1: Apple turnover F12 ? S1: Doughnut F12 ? S1: Shortbread F12 ? S1: Wafer F12 ? S1: Paris-brest F12 ? SL: Crème brûlée F13 ? S2P: Whipped cream + ice cream F13 ? S2Q: Whipped cream + strawberry F13 ? S4: To ask for whipped cream when buying an ice F13 ? S2P: Whipped cream + crepe F13 ? S2P: Whipped cream + brownie F14 ? S2Q: Chocolate spread + crepe F14 ? S1: Chocolate spread F14 ? S2P: Chocolate spread + bread F14 ? S4: To eat some chocolate spread with a spoon F15 ? S1: Chocolate cake F15 ? S1: Brownie F15 ? S1: Chocolate mousse

0.849 0.835 0.665 0.654 0.536 0.528 0.469 0.433 0.854 0.811 0.787 0.761 0.635 0.851 0.834 0.816 0.654 0.925 0.795 0.764

Factor correlations F12 M F13 F12 M F14 F12 M F15 F13 M F14 F13 M F15 F14 M F15

0.363 0.495 0.542 0.309 0.199 0.429

Factor internal consistency

Confidence interval (95%)

Cronbach’s alpha

Composite reliability

Lower bound

Upper bound

0.842

0.883

0.879

0.832

0.868

0.798

0.86

0.749

0.337 0.473 0.516 0.280 0.171 0.406

0.389 0.516 0.567 0.339 0.227 0.453

Fit indices Satorra–Bentler v2 = 5025 degree of freedom = 164; comparative fit index = 0.91; Tucker–Lewis index = 0.89; root mean square error of approximation = 0.07; standardized mean square residual = 0.06 a

Robust maximum likelihood estimation. Values statistically significant at p < 0.001, except if noted. Item prefixes indicate the section and the question type with S1 being section 1 (liking for foods), S2 section 2 (preferred level of seasoning) with picture (S2P) or not (S2Q), S3 section 3 (preferred dishes in menu) and S4 section 4 (fat-and-sweet-related dietary behaviour questions). b

c

participants were allowed to choose a free number of dishes within a maximum of 4. The repeatability of these items could not be further re-evaluated. However, loadings of menu items from the fatand-sweet and fat-and-salt scales, which were salient in the EFA of the development study, became non-salient in the EFA of the validity study (results not shown). This may result from the instruction modifications, although a population effect cannot be ruled out. On the contrary, loadings of menu items from the salt and sweet scales (except for soft drinks) remained non-salient in both the EFAs of the development and validity studies. This may suggest that the listed dishes were not representative of the sensation, as discussed below. 4.2. Validity Due to violation of multivariate normality in the present data, the robust maximum likelihood estimator, also known as the Satorra–Bentler approach (Bentler & Dijkstra, 1985; Satorra & Bentler, 1994), was employed for CFA. It is assumed to be a well-behaved estimator across different levels of non-normality, model complexity and sample size (Brown, 2006). Such correction is not available in EFA, but for it to be consistent with CFA, the maximum likelihood method was also chosen for EFA. In the latter analysis, only factor loadings were examined, because they are stable against violation of normality, as demonstrated in CFA (Byrne, 2009), unlike standard errors and fit statistics that were consequently not considered for EFA.

Factorial analyses were performed on the validity study as the number of required observations per item for a stable factorial pattern (5–10) (Hatcher, 1994; Kline, 2005) was largely met for each scale, unlike in the development study, with ratios of 4–6 for the sweet, fat-and-sweet, and fat-and-salt scales. When factorial analyses were applied to the development study (results not shown), the factors were similar to those found in the validity study, except for some items (22–24%) from the sweet, fat-and-sweet, and fat-andsalt scales which exhibited different loadings. This degree of factorial instability is likely due to the limited number of observations, thus supporting the need for larger samples. EFA was first performed separately on the four scales, which were developed independently. When an overall EFA was applied to the entire PrefQuest in the validity study, the resulting factorial structure was in line with that obtained from the separate EFAs (results not shown). 4.2.1. Reliability and internal validity of factors PrefQuest internal validation was based on CFA, as previously undertaken (Eertmans, Victoir, Notelaers, Vansant, & Van den Bergh, 2006; Jover et al., 2004; Nijs, Franken, & Muris, 2007; Thompson et al., 2009). Our results demonstrated that the factors of each scale had good psychometric properties. In particular, our factors had a good internal consistency and were valid in terms of content, thanks to the preliminary consulting of experts, and in terms of construct, with factors being convergent (factor items measuring the same construct) and discriminant (factors being

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Table 8 Overall confirmatory factor analysisa of a first-order model including all the first-order factors of liking for salt, sweet, fat and sweet and fat and salt: factor correlationsb and overall fit indices. Factors

Factors F2 Sweetened foods

F1 F2 F3

F4

F5 F6

F7 F8 F9 F10 F11 F12

F13

F14

Salt Sweetened foods Naturally sweet foods Added sugar to hot drinks Added jam Added sugar to foods Meats Added dairy fat Fatty foods Cheesy foods Savoury snacks Pastries and desserts Added whipped cream Nutella

0.13

F3 Naturally sweet foods 0.05 0.29

F4 Added sugar to hot drinks

F5 Added jam

F6 Added sugar to foods

F7 Meats

F8 Added dairy fat

F9 Fatty foods

F10 Cheesy foods

F11 Savoury snacks

F12 Pastries and desserts

F13 Added whipped cream

F14 Nutella

F15 Chocolate dessert

0.26 0.36

0.14 0.41

0.44 0.52

0.18 0.50

0.43 0.33

0.12 0.73

0.09 0.53

0.22 0.68

0.14 0.81

0.12 0.38

0.08 0.58

0.04 0.49

0.09

0.44

0.02c

0.15

0.00c

0.03c

0.25

0.05

0.27

0.02c

0.05

0.28

0.16

0.65

0.22

0.34

0.30

0.14

0.29

0.26

0.20

0.21

0.08

0.48

0.22 0.32

0.33 0.59

0.17 0.42

0.26 0.28

0.18 0.41

0.33 0.42

0.21 0.38

0.34 0.43

0.24 0.20

0.36

0.58 0.29

0.58 0.35

0.59 0.33

0.58 0.39

0.24 0.40

0.27 0.27

0.28 0.17

0.55

0.65 0.51

0.63 0.58

0.33 0.30

0.56 0.40

0.36 0.38

0.62

0.28

0.44

0.33

0.37

0.50

0.55

0.31

0.20

0.43

Fit indices Satorra–Bentler v2 = 30 142; degree of freedom = 3215; comparative fit index = 0.87; Tucker–Lewis index = 0.86; root mean square error of approximation = 0.04; standardized mean square residual = 0.05 a b c

Robust maximum likelihood estimation. Values statistically significant at p < 0.001, except if noted. Values not statistically significant at p > 0.05.

distinct constructs within a given scale) (Anderson & Gerbing, 1988). The last facet of validity, related to a criterion (DeVellis, 2003), was not evaluated here, as there is no existing external standard for PrefQuest. Ledikwe et al. (2007) validated their questionnaire by comparing fat liking and fat intake, but this does not validate it in terms of sensory preferences. Hedonic tests in sensory evaluation would be a good, though partial, point of comparison (Deglaire, Cordelle, & Schlich, 2011). Previous studies (Keskitalo et al., 2007; Leshem, 1998; Mennella et al., 2005) have reported that sweet or salt liking measured by sensory evaluation or by questionnaires were correlated, but to a rather moderate extent. Some differences between sensory and declarative tools are to be expected, as they have a different basis which is either direct liking based on the actual tasting of a food sample or, usually, recalled liking based on the mental image of a food (Cardello & Maller, 1982). This favours the inclusion of other questions like the preferred level of seasoning, which is closer to what is undertaken in sensory tests. Nevertheless, declarative and sensory tools probably represent different aspects of liking and are likely to be complementary rather than similar. The extent of the relationship between PrefQuest and sensory tests is currently investigated within our laboratory. Finally, the criterion validity of PrefQuest could be further explored by comparing the administration of the reduced version of PrefQuest, which contains about 60% of the items initially included in PrefQuest, to that of the entire PrefQuest,

as suggested previously (International Epidemiology Association, 1998). This will be undertaken in a future study. 4.2.2. Factor interpretation The scale for assaying liking for salt was demonstrated to rely on a unique dimension, based mostly on the preferred level of salt added to various foods and with a few items related to dietary behaviour. It was not possible to measure liking for intrinsic-tofood salt, as common salty foods, but not fatty foods, are very scarce, especially in Western countries. An attempt was made by listing some dishes containing salty ingredients (e.g. dried ham) in the menu items (section 3). These items were, however, not related to the other scale items, as shown by the EFA. This may partly result from the small number of menu items (two), due to the difficulty in finding typically salty or non-salty dishes. In addition, these menu items had a low consistency between test and retest (ICC: 0.40–0.53), indicating that subjects chose dishes from a different category (salty or not) from one administration to the other. This suggests that choices were not related to the salty sensation, probably because dishes were not perceived as salty or non-salty, as salt may always be added afterwards to a dish. The specificity of the salt factor is demonstrated by the overall CFA, where it differed from all the other PrefQuest factors. It was only modestly correlated to added fat and salt and to added sugar (0.4), which may indicate that there is a common basis for these ingredients when

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Table 9 Confirmatory factor analysesa of the second-order and third-order models for liking for fat: parameter estimates, factor correlations and overall goodness-of-fit indices. Secondorder factors: F24, fatty-sweet foods; F25, fatty-salty foods; F26, added fat. Third-order factor: F34, overall fatty foods. Fourth-order factor: 41, fat sensation. Parameter

Estimateb

Confidence interval (95%) Lower bound

Second-order model Standardized factor loadings F24 ? F7 (meats) 0.735 F24 ?F9 (fatty foods) 0.814 F24 ? F10 (cheesy foods) 0.720 F24 ? F1 1 (savoury snacks) 0.782 F25 ? F12 (pastries and desserts) 0.895 F25 ? F14 (chocolate spread) 0.610 F25 ? F15 (chocolate dessert) 0.574 F26 ? F8 (added dairy fat) 0.651 F26 ? F13 (added whipped cream) 0.612 Factor correlations F24 M F25 0.865 0.845 F24 M F26 0.643 0.607 F25 M F26 0.659 0.624 Fit indices 2 Satorra–Bentler v = 15 368; degree of freedom = 1212; comparative fit index = 0.89; Tucker–Lewis index = 0.88; root mean square standardized mean square residual = 0.05

Upper bound

0.884 0.678 0.694 error of approximation = 0.05;

Third-order model Standardized factor loadings F34 ? F24 0.919 F34 ? F25 0.941 Factor correlation F34 M F26 0.700 0.667 0.733 Fit indices Satorra–Bentler v2 = 15 368; degree of freedom = 1212; comparative fit index = 0.89; Tucker–Lewis index = 0.88; root mean square error of approximation = 0.05; standardized mean square residual = 0.05 Fourth-order model Standardized factor loadings F41 ? F26 0.707c F41 ? F34 0.707c Fit indices Satorra–Bentler v2 = 15 508; degree of freedom = 1213; comparative fit index = 0.89; Tucker–Lewis index = 0.88; root mean square error of approximation = 0.05; standardized mean square residual = 0.05 a b c

Robust maximum likelihood estimation. Values statistically significant at p < 0.001. Unstandardized loading fixed to 1.0 for identification purposes.

adding habits are taken into account. Conversely, the salt factor had virtually no relationship with added whipped cream, added jam or added sugar to hot drinks or with factors related to fattysalty foods. This contrasts with previous studies (Beauchamp et al., 1990; Stein et al., 1996) that measured liking for salt through a list of fatty-salty foods such as potato chips, olives, hamburger, pizza. Whether the present absence of relationship between liking for salt and liking for fatty-salty foods is due to the question type, i.e. liking for salt added to foods vs. liking for (fatty-)salty foods, or whether the fat sensation has a confounding effect with the salt sensation remains unknown. Nevertheless, the present scale is assumed to provide an estimate of overall liking for salt, independently of fat. The scale measuring liking for sweet was shown to be composed of five sub-dimensions, which were mainly based on liking for foods (section 1) and the preferred level of seasoning (section 2), and included a few items related to dietary behaviour (section 4) and only one menu item, ‘‘soft drinks’’. The other menu items were not related to the rest of the scale, probably due to the limited number of similar items or to the fact that the listed foods or drinks were not representative of the sweet sensation, as discussed for the salty menu items. The ‘‘naturally sweet foods’’ factor was not correlated to those related to ‘‘added sugar’’ to hot drinks or foods, maybe because of the healthy image conveyed by foods that are naturally sweet (e.g. honey or sweet dried fruits) unlike that of

adding refined sugar to foods. The ‘‘naturally sweet foods’’ factor was moderately correlated to the ‘‘added jam’’ factor. Both factors appeared to be influenced by a common second-order factor, assumed to be the natural sweetness aspect. There were two more second-order factors related to ‘‘added sugar’’ to foods and to hot drinks and to ‘‘sweet foods’’ based on sweetened foods and on added sugar to foods. Previous studies that claimed to measure sweet liking determined only the preferred levels of sugar added to beverages and cereals (Mennella et al., 2005), or listed some foods that were sweet and fatty-sweet (Duffy & Bartoshuk, 2000; Keskitalo et al., 2007; Lahteenmaki & Tuorila, 1995). In the present study, sweetened foods (e.g. candies, fruit nectar, soft drinks) and pastries and desserts (e.g. paris-brest, crème brûlée, chocolate croissant) were strongly correlated. As reported by Drewnowski and Schwartz (1990), this may be due to the masking effect of sugar on fat perception, resulting in the perception of high-fat desserts as sugar-rich foods. However, sweetened foods, pastries and desserts, fatty foods (e.g. nuggets, burgers, kebab) and savoury snacks (e.g. chips, peanuts) were also intercorrelated, but to a somewhat lesser extent (0.6–0.7). This relationship is probably related to the unhealthy image conveyed by these foods. This indicates that liking for sweetened foods was driven not only by the sweet sensation but also by the image of the food in the subjects’ minds. The other factors of the sweet scale were, however, more correlated with one another than with factors from another

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sensation, thus demonstrating that they share a common and specific basis assumed to be the sweet sensation. Overall, it seems important to combine different aspects related to the sweet sensation in order to estimate an overall liking for sweet independently of fat. The overall liking score may not reflect perfectly all the aspects of liking for sweet, as suggested by the fit indices of the third-order model. However, such a score is a useful synthetic score that could be compared, for instance, with sugar intake. Sub-dimension scores should also be calculated in order to evaluate their relative influence on sweet liking among the population. Fat is rarely experienced alone, except in some fatty ingredients (e.g. butter), but is usually encountered within either a sweet or a salty matrix. While most of the studies carried out so far have assessed liking for fat by listing both fatty-salty and fatty-sweet foods (Drewnowski, Hann, Henderson, & Gorenflo, 2000; Duffy et al., 2007; Lahteenmaki & Tuorila, 1995). Keskitalo et al. (2008) highlighted two separate factors, based on either sweet-fatty or salty-fatty foods. Liking for these foods was correlated only to a moderate extent (r = 0.38). Thus, PrefQuest was first developed to measure liking for fat and sweet or fat and salt separately. The scale measuring liking for fat and salt was composed of five subdimensions. The fat-and-salt sensation was either intrinsic to foods (‘‘meats’’, ‘‘fatty foods’’, ‘‘cheesy foods’’ and ‘‘savoury snacks’’) or extrinsic to foods (‘‘added dairy fat’’). Such a distinction had never been made in previous questionnaires. The foods listed in this scale were included in larger numbers than in the other PrefQuest scales in order to represent the wide array of commonly eaten fatty-salty foods. The factors were also based on the preferred levels of fat, mostly butter or cream, added to salty foods. There was no item about adding oil; this may be a limitation for this scale but our experts considered that adding oil was not a usual behaviour in the French population during meals. In the final scale, no menu item was retained, maybe resulting from the instruction modifications, as discussed above. The intrinsic and extrinsic fat-and-salt likings appeared to be influenced by a single higher-order factor, assumed to be overall liking for the fat-and-salt sensation. A synthetic score can be calculated to estimate this overall liking, but scores on lower-order factors are still interesting for deeper analysis. A similar structure was found for the scale measuring liking for fat and sweet, based on intrinsic (‘‘pastries and desserts’’, ‘‘chocolate spread’’, ‘‘chocolate dessert’’) and extrinsic (‘‘added whipped cream’’) factors. These factors were based on liking for foods and on preferred levels of seasoning, with only two items related to dietary behaviour. Items based on added butter (‘‘butter plus bread’’ and ‘‘butter plus bread and jam’’) were not related to the other items of the scale, likely due to the limited number of similar items. There are, however, a few sweet foods or ingredients to which butter is added. The score of overall liking for fat and sweet can be calculated to synthesise the different aspects of that liking. Some strong intercorrelations were shown between fatty-salty and fatty-sweet foods in the overall CFA, and, to a lesser extent, between added dairy fat and added whipped cream. This relationship was likely due to the fat sensation. It was confirmed by testing a fat model based on overall fatty foods and on added fat. Fit indices were very close to the acceptable level but were slightly lower than those obtained with separate models for fat and sweet and fat and salt. These data may be more accurately represented by the separate models; however, the fat model is interesting to determine an overall liking for fat, which could be directly compared to dietary fat intake. Unlike the sweet and salt tastes, fat is a complex sensory sensation rarely recognized as so. It is rather sensed through the sensory properties imparted by fat to food products, particularly viscosity and texture (e.g. creaminess, tenderness and moistness), as supported by recent neuroscience studies (Kadohisa, Verhagen, & Rolls, 2005; Rolls, 2008; Verhagen, Kadohisa, & Rolls, 2004). How-

ever, the possible existence of a gustatory cue devoted to fat has emerged recently (Khan & Besnard, 2008; Laugerette et al., 2005; Schiffman, Graham, Sattely-Miller, & Warwick, 1998). Fat perception seems to depend on the food matrix. Mela (1990) showed that a gradual increase in fat content from 0% to 30% was well perceived in mashed potatoes and tuna but not in vanilla pudding and falafel. Interestingly, an inverted U-shape between hedonics and fat level was observed when the perceived level, rather than the actual content, was reported and when data from eight products were combined (Mela, 1990). Thus, it seems more appropriate to assess fat liking over a wide array of food matrixes (Mattes, 1993), as undertaken in PrefQuest. Whether the overall fat liking as obtained by PrefQuest is comparable to that obtained by sensory tests is currently under investigation. The degree of intercorrelation between fatty-salty and fattysweet foods was much higher than that reported by Keskitalo et al. (2008). The present factors, based on 15–20 items each, were possibly more representative than Keskitalo et al.’s (2008), which had each 5–6 foods only. In addition, Keskitalo et al. (2008) uncovered these factors through a factor analysis followed by an orthogonal rotation, which aims at maximizing factor independency. On the contrary, our analysis was based on a factor analysis followed by an oblique rotation allowing intercorrelations among factors. The foods listed in PrefQuest were selected as representative of the salt, fat-and-salt, sweet, and fat-and-sweet sensations. This hypothesis may not be verified for all foods, as they all contain other flavours or sensations possibly interacting with the sensation of interest by enhancing or masking it (Lawless & Heymann, 1998). Nevertheless, although the subject may not be aware of the reason/ sensation why he/she likes the food, the selected items are assumed to somehow contribute to the overall liking of the sensation of interest.

4.3. Potential uses of PrefQuest PrefQuest is of interest to better grasp the impact of food sensory attributes, particularly the hedonic component, on food intake among the general population. Unlike sensory tests that are technically and economically demanding and are thus applicable to a limited number of subjects, PrefQuest can be employed in large epidemiological studies as a proxy of measurement for sensory tests. The extent of the relationship between these two tools is currently under study, and will be the object of another publication. The overall liking score for a sensation (salt, sweet, fat) can be compared to the dietary intake of the corresponding nutrient, as assessed by traditional methods, e.g. 24 h recalls, food frequency questionnaires. Sub-factor scores can help to understand the determinants of such dietary behaviours further, possibly resulting in more precise reflexion or even recommendations in terms of public health. Liking scores for a first-order factor can be calculated by averaging the ratings of the compounding items, as previously undertaken (Duffy & Bartoshuk, 2000; Keskitalo et al., 2007; Lahteenmaki & Tuorila, 1995). Scores on higher-order factors can then be determined by averaging the scores of the compounding lowerorder factors. Each scale in PrefQuest may be used independently, measuring either liking for salt, for sweet, for fat and sweet or for fat and salt. By combining the last two scales, overall liking for fat may also be estimated. PrefQuest has been developed for the French population. Some items are thus specific to the French food culture, especially in the factors based on meats, cheesy foods, and pastries and desserts. Some adaptations may be needed before using PrefQuest in another food culture. Nevertheless, PrefQuest can serve as a basis for researchers from other countries to measure likings for salt, sweet and/or fat by means of a questionnaire.

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5. Conclusion and perspectives PrefQuest, is an original, easy, entertaining questionnaire, it is not too long to administer, and it combines four scales measuring recalled likings for salt, sweet, fat-sweet and fat-salt. PrefQuest can be applied via the web to a large population sample, and thus can compare with other surveys, especially nutritional assessments. More precisely, the synthetic overall liking scores can be compared with the dietary intake of the corresponding nutrients, thus allowing us to better understand how liking contributes to food choices and behaviours. PreQuest may also be used to sub-divide and better characterize a population. Within each PrefQuest scale, a theoretically meaningful factor structure was demonstrated. While liking for salt was unidimensional, liking for sweet, fat with or without sweet or salt, were composed of more or less interrelated sub-dimensions. PrefQuest is the first internally validated questionnaire that proposes a liking score to be calculated based on various types of items such as liking for foods, preferred seasoning level and a few items related to dietary behaviours. This contributes to cover the various aspects of liking and thus to better estimate overall, i.e. product-independent, likings. Comparison with sensory tests is currently undertaken to validate this questionnaire externally. Financial support The project was carried out with the financial support of the French National Agency of Research within the ALIA 2008 Program (EpiPref Project, ANR-08-ALIA-06), the Human Food (AlimH) Department of INRA and a postdoctoral scholarship from the Research Ministry. It was also supported by the Taste-NutritionHealth Competitive Cluster (Vitagora). Acknowledgements We wish to thank Sylvie Issanchou for thoughtful discussion and ideas. We also wish to thank Marie-Pierre Cabrillana and Sylvie Dubreuil for their dietetic advice, and Gwenaël Monot and Aurélie Malon for the web design and administration of PrefQuest. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.foodqual.2011.08.006. References Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. Beauchamp, G. K., Bertino, M., Burke, D., & Engelman, K. (1990). Experimental sodium depletion and salt taste in normal human volunteers. American Journal of Clinical Nutrition, 51(5), 881–889. Bentler, P. M. (1989). EQS. Structural equations, program manual. Program Version 3.0. Los Angeles: BMDP Statistical Software. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606. Bentler, P. M., & Dijkstra, T. (1985). Efficient estimation via linearization in structural models. In P. R. Krishnaiah (Ed.). Multivariate analysis VI (pp. 9–42). Amsterdam: North-Holland. Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: The Guilford Press. Byrne, B. M. (2009). Structural equation modeling with amos. Basic concepts, applications, and programming (2nd ed.). New York, NY: Routledge Taylor & Francis Group. Cardello, A. V., & Maller, O. (1982). Relationships between food preferences and food acceptance ratings. Journal of Food Science, 47(5), 1553–1557. Castetbon, K., Vernay, M., Malon, A., Salanave, B., Deschamps, V., Roudier, C., et al. (2009). Dietary intake, physical activity and nutritional status in adults: The French nutrition and health survey (ENNS, 2006–2007). British Journal of Nutrition, 102(5), 733–743.

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