Mixed messages: Ambiguous penalty information in modified restaurant menu items

Food Quality and Preference 52 (2016) 232–236

Contents lists available at ScienceDirect

Food Quality and Preference journal homepage: www.elsevier.com/locate/foodqual

Short Communication

Mixed messages: Ambiguous penalty information in modified restaurant menu items Harry T. Lawless a,⇑, Anjali A. Patel b, Nanette V. Lopez c a

Department of Food Science, Cornell University, Ithaca, NY 14853, USA Department of Research, Accents on Health, Inc. (dba Healthy Dining), San Diego, CA 92123, USA c Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA b

a r t i c l e

i n f o

Article history: Received 29 February 2016 Received in revised form 26 April 2016 Accepted 3 May 2016 Available online 6 May 2016 Keywords: Just-about-right scales Hedonic scales Acceptance testing Penalty analysis Consumer testing Restaurant menu items

a b s t r a c t Restaurant menu items from six national or regional brands were modified to reduce fat, saturated fat, sodium and total calories. Twenty-four items were tested with a current recipe, and two modifications (small and moderate reductions) for 72 total products. Approximately 100 consumers tested each product for acceptability as well as for desired levels of tastes/flavor, amounts of key ingredients and texture/consistency using just-about-right (JAR) scales. Penalty analysis was conducted to assess the effects of non-JAR ratings on acceptability scores. Situations arose where JAR ratings and penalty analyses could yield different recommendations, including large groups with low penalties and small groups with high penalties. Opposing groups with moderate to high penalties on opposite sides of the same JAR scale were also seen. Strategies for dealing with these observances are discussed. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction Obesity is a growing concern in U.S. public health policy (Ogden, Carroll, Fryar, & Flegal, 2015). Current trends in eating lifestyle include a greater reliance on food eaten or procured away from home, including restaurants (USDA, 2015). Compared to foods prepared at home, foods eaten away from home contain more calories, total fat, saturated fat and sodium (Lachat et al., 2012). These trends suggest both one source of the obesity problem, and a potential for addressing the problem by modification of restaurant menu items. Various strategies are available to address this problem, by making smaller portions available in restaurants or offering new items with healthier nutritional content. Another approach would be to make small modifications in recipe components of existing popular items, in order to reduce calories, fat and sodium. The successful adoption of such items depends upon maintaining their consumer appeal. Information presented here was part of a larger study of 24 total restaurant items from six chain restaurants that agreed to cooperate in making recipe changes aimed at reducing saturated fat, calories and/or sodium. Each chain submitted four items for modification and testing. Items were prepared according to the

⇑ Corresponding author. E-mail address: [email protected] (H.T. Lawless). http://dx.doi.org/10.1016/j.foodqual.2016.05.005 0950-3293/Ó 2016 Elsevier Ltd. All rights reserved.

standard or modified recipes in the kitchens of the actual restaurants and tests were conducted in the dining areas of those restaurants. Two levels of ingredient reduction were tested, one with a small change (e.g. 10% reduction, called ‘‘version 1”) and one with a more substantial change (e.g. 20% reduction, ‘‘version 2”). The overall goal was to identify modified versions of the products that were equal to or superior to the current version of each product. In those cases, recommendations were made for adoption of the modified version by the restaurant chain. The ingredient reductions might also produce cost savings, an additional benefit to the nutritional improvement. If the modified products were significantly lower in acceptability, the client-collaborators were cautioned about making the change. Overall, the study was done to explore modifications with an accurate assessment of potential risks to consumer opinion. Several common consumer testing tools were used to assess the viability of the modifications. Hedonic scales, Just-about-right scales (JAR), willingness-to-try and purchase intent were the main dependent variables. Information is presented here only from the JAR scales and the overall liking hedonic scale. A JAR scale usually consists of categorical ratings where the center point indicates the attribute is ‘‘just-about-right” and categories to either side indicate the attribute is too weak or too strong, relative to the consumer’s ideal (Popper & Kroll, 2005; Rothman & Parker 2009). Other variations include attributes or components of the food that have either too little or too much of the item. The ideal outcome for JAR data is

H.T. Lawless et al. / Food Quality and Preference 52 (2016) 232–236

to obtain a symmetric distribution centered on the JAR point, with a small minority, possibly only 10–15% of consumers on either side of the JAR point (Lawless & Heymann, 2010). If a large proportion of consumers was on either side of the JAR point (thus having too much or too little of that attribute) a product could be modified and improved to change the JAR distribution to a more favorable outcome. Alternatively, that product could just be discontinued, or if testing a modified version, the change not made. In this restaurant study, the JAR data from modified products was compared to the standard (existing) recipe version. If the JAR distribution was improved or roughly equivalent (i.e. more symmetric and fewer non-JAR responses), then a recommendation for the adoption of the modified product was supported. If the JAR data showed an increase in non-JAR ratings, then the modified product would not be recommended. When considered alone, JAR information and hedonic information can lead to different outcomes in product optimization (Li, Hayes, & Ziegler, 2015). However, combining JAR information with hedonic scale data can provide additional valuable information. This is the basis of penalty analysis (Rothman, 2009). Penalty analysis proceeds in three steps (Schraidt, 2009). First, the sample is split into three groups: Those rating above the JAR point (i.e., the product has too much for them), those at the JAR point, and those below (the product has too little for them). Then the mean overall liking scores (OAL) are calculated for each group and compared. For those groups above or below the JAR point, the OAL means are subtracted from the OAL mean for the JAR group. This produces two mean drops, also called penalties. The mean drop is an estimate of how much consumer liking the product loses by being off the JAR point. A useful way to look at this information is to plot the mean drop and group size (proportion non-JAR) for each group in a biplot, usually with the group size on the abscissa, as shown in Fig. 1. In considering product modifications, movements in the penalty plot are also important pieces of information. Product modifications that move to the upper right are undesirable, while modifications that move to the lower left are potential improvements. As a general rule, products with a group size less than 10% and/or a penalty less than 0.75 units may be acceptable with regard to that JAR attribute.

Fig. 1. An example of a penalty plot (without products). The action line is the product of the group size (expressed as a decimal fraction of the total consumer sample) times the mean drop. This ‘‘total” penalty is a common decision criterion. Products may be rejected or re-formulated when the quantity exceeds 0.30. The problematic quadrants of the penalty plot are the zone with a large group but low penalty, and the zone with a small group with high penalty.

233

The penalty plot has zones or quadrants that suggest different courses of action. Products in the upper right quadrant, with a large group and a large mean drop indicate products with potentially serious consumer dissatisfaction. Conversely, a product positioned in the lower left (small group, small drop) causes no concern, at least as far as that attribute is considered. Products in the other two quadrants (see Fig. 1) are more problematic. Products plotted in the upper left, i.e. a small group with large drop, may indicate a strongly dissatisfied consumer segment, but there are other possibilities discussed below. Products plotting in the lower right quadrant show a large group wanting more (or less) of that attribute, but little or no apparent penalty. In this case, looking at the JAR data alone would suggest discarding this product or making a large change. However, considering the lack of any drop in overall liking, the product may be perfectly acceptable in its current form. Thus JAR information and penalty analysis would give different risk assessments and different recommendations. The purpose of this paper was to document a surprisingly large number of such instances that were observed in the restaurant recipe modification study, and to alert practitioners that making decisions on the basis of JAR information alone can be unwise.

2. Methods The goal was to conduct four tests in different regions for each restaurant with N = 25 at each location in order to obtain a total N of 100 per product. The three versions of each product were tested monadically, so the total N required for each client-collaborator was 300 consumers. Consumers were screened for being patrons of that chain, but were not required to be frequent consumers of all four items (deemed impractical). They did, however, express a willingness to try all four items if they had not consumed one or more of them previously. The actual sample sizes obtained for each product are given in the companion paper (Patel et al., submitted for publication). A total of 1838 participants participated in 83 separate taste test sessions. For each menu item, one or more ingredients were selected as ‘‘target(s)”. Each menu item was modified to give two menu item versions: 1) up to 16% less calories and up to 28% less sodium, and 2) up to 26% less calories and up to 43% sodium. The items tested included three side dishes (hummus, white beans and seafood nachos), two soups, three mayonnaise-based salads, two green salads, one pizza, three pastas/casseroles, four burgers, four sandwiches and two seafood dishes. Preparation of all items was similar to how the item would be normally be prepared and served to paying customers. Two examples of the menu items, their targeted components and reductions are given in Table 1, along with nutritional analysis information. As an example, the JAR scales for the Italian sandwich were flavor strength, aroma strength, saltiness, moistness (too moist to too dry) and amounts of mayonnaise, salami, pepperoni, ham, and cheese. The JAR scales for the blue cheese burger were flavor strength, beef flavor strength, steak sauce flavor strength, saltiness, juiciness and amounts of chipotle mayonnaise, blue cheese and onion. Nine-point hedonic (like-dislike) scales (Peryam & Girardot, 1952) were used for overall liking, liking of appearance, aroma, taste/flavor and texture/consistency. Only the data from the overall liking scales were used for penalty analysis. JAR scales were always five-point scales, and differed for each product depending upon the attributes of that item, as well as the ingredient that was reduced or modified. Typically, the JAR scales included attributes such as aroma strength, flavor strength, saltiness, spiciness, texture (thinthick) and amounts of items such as meat, cheese, sauce or dressing (rated from too little to too much). The specific scales used for each item are given in the companion paper.

234

H.T. Lawless et al. / Food Quality and Preference 52 (2016) 232–236

Table 1 Sample menu items with ingredient reductions and nutritional content. Menu items

Ingredient reductions

Italian sandwich

Salami; Pepperoni; Ham; Cheese; Mayonnaise 5 slices; 3 slices; 2.0 oz; 2 slices; 1.0 oz 4 slices; 3 slices; 1.75 oz; 2 slices; 0.5 oz 3 slices; 2 slices; 1.5 oz; 1 slices; 0.5 oz

C V1 V2 Beef burger with blue cheese C V1 V2

Blue Cheese Crumbles; Chipotle Mayonnaise; Fried onions 1.5 oz; 1 oz total; 1.5 oz 1.33 oz; 5 tsp total; 1.33 oz 1.0 oz; 5.0 tsp; 1.0 oz

% Calorie reductions

% Sodium reductions

Energy (kcal)

Total fat (g)

Calories from fat

Saturated fat (g)

Cholesterol (mg)

Sodium (mg)

920

57

520

16

105

2370

15.22%

9.28%

780

44

400

14

90

2150

22.83%

21.52%

710

39

350

12

75

1860

5.80% 15.22%

6.07% 14.95%

1380 1300 1170

85 79 70

760 710 630

24 22 20

140 135 125

2140 2010 1820

The nutrition analysis for the sandwich was provided by the restaurant. The burger was analyzed by a registered dietitian using Genesis R&D, 9.14.41 database structure version 9.8.2 June 2015. C = current menu item. V1 = slightly reduced calorie, fat, saturated fat and/or sodium version. V2 = moderately reduced calorie, fat, saturated fat and/or sodium version. oz = Ounces; (v) = volume; fl = fluid; Tbsp = tablespoon; tsp = teaspoon.

Three effects or zones in the penalty plot were of primary interest in this paper. The first was the combination of a large group but low penalty. For purposes of comparison, this was defined as penalty less than 1.0 but a group size greater than or equal to 20% of the total consumer sample. The second zone of interest was a high penalty occurring in a small group, defined as a mean drop greater than or equal to 1.5 but a group size less than or equal to 15%. For this zone, group size less than 5% was not considered as too few consumers were contributing to that count. Third, a situation with ‘‘opposing opinions” was defined as having both groups greater than or equal to 10% and a mean drop greater than 1.0. For purposes of comparison, these three events can be compared to situations in the danger zone, operationally defined as group size greater than 15%, mean drop greater than 0.75 and the product of the group size (as a decimal fraction) multiplied by the mean drop that must be greater than 0.3. This value is an action standard in some companies, and is sometimes referred to as ‘‘total penalty” although it is in fact the product of two numbers. This value or limit produces a hyperbola cutting through the penalty plot from upper left to lower right as shown in Fig. 1. Occurrences in the danger zone were tabulated for comparison purposes, although it should be noted that most of them occurred near the total penalty action line, and relative few in the far upper right corner of the plot. The practical boundaries of a penalty plot were delimited as follows: values of the mean drop between zero and 3.0 and values of the group size less than 60%. Data falling outside these levels are rarely seen in practice, and almost never occurred in the current study. Accepting these limits as defining the total area of the penalty plot, and combined with the effect limits described above, 51% of the total plot was in the danger zone, 23% for the occurrences of large group, small penalty, 12% for the occurrences of small group, but high penalty, and about 45% of the total area possible for ‘‘opposing opinions”. These percentages are provided to suggest a baseline for comparison, if observations were distributed randomly. For example, with random observations, we might expect 23% of the JAR scales to fall into the large group, small penalty area. 3. Results In the main study, 20 of the 24 menu items had one or more modified versions that were as good as or better than the current item. Ratings indicating that a modified product was ‘‘as good or

better” led to a recommendation for adoption of the modified version. This recommendation was based on equal or better acceptance scores, no significant increase in alienation (defined as the frequency of scores on the negative end of the hedonic scale), and no increase in skew (i.e. non-JAR ratings) in the JAR distributions. Overall, the results show that modest reductions in sodium, calories and/or saturated fat can often be made successfully without risking consumer dissatisfaction, although not in all cases. Fig. 2 shows the main results for the areas of 1) large group, small penalty, 2) the danger zone and 3) small group, large penalty. The three areas subsume 23%, 51% and 12% of the penalty plot, as described above. The most frequent JAR scales are listed in bold italics. Frequencies of the five categories clearly differed for the three groups (v2(8) = 110.5, p < 0.01). The most common danger zone non-JAR scales were those indicating weak flavor. The large group, small penalty had a surprisingly large number of non-JAR responses for ‘‘too little” of some key ingredient such as meat,

Fig. 2. Frequency counts of the different JAR-scale categories for three areas, the danger zone (total penalty P 0.3, Mean drop P 0.75, N P 15%), the large group with small penalty (N P 20%, mean drop < 0.1) and the small group with high penalty (N < 15%, mean drop P 1.5). The size of each ellipse is roughly proportional to the percentage of the total area subsumed by each type of occurrence. Further details are given in Table 2. The most frequent JAR-scale category is listed first in bold italics for each area.

235

H.T. Lawless et al. / Food Quality and Preference 52 (2016) 232–236 Table 2 Penalty plot problem areas and frequency of observations. Observation

Taste/flavor Too weak

Amount

Texture/consistency

Too strong

Too little

Too much

(N/A)

Large group, low penalty (mean drop < 1.0) N P 20% Spicy: 15 Other: 29 Total:44 N P 30% Spicy: 6 Other: 8 Total: 14

Salty:15 Other: 5 Total: 20 Salty:7 Other: 2 Total: 9

Sauce/dressing/mayo: 33 Meat/cheese: 43 Total: 76 Sauce/dressing/mayo: 18 Meat/cheese: 30 Total: 48

Sauce/dressing/mayo: 16 Other: 4 Total: 20 Sauce/dressing/mayo: 9 Other: 1 Total: 10

10

Small group (N < 15%), high penalty Mean Drop P 1.5 Mean Drop P 2.0

41 21

0 0

6 1

16 6

22 12

‘‘Opposing opinions” (both mean drop P 1.0) Both N P 10% 36 Both N P 15% 13 ‘‘Danger zone” Total penalty P 0.30, mean drop P 0.75, N P 15% Total number:JAR scales Total number: JAR segments

67

264 528

3 0 39

3 0

18

15

147 294

cheese or dressing. Note that this scale was nonexistent for the small group, high penalty area. The most frequent non-JAR scales for small group, high penalty were those indicating that taste or flavor was judged to be too strong. Table 2 shows greater detail of the frequency counts of observations in the zones of interest, and includes counts of some values higher than the limits in Fig. 2 such as group size greater than 30% (with a small penalty), mean drop greater than 2.0 (with a small group), and both groups larger than 15% for the ‘‘opposing opinions” situation. The danger zone occurrences are also tabulated to provide a baseline for comparison, along with the total number of JAR scales, and total number of JAR segments (JAR scales times 2). Total segment counts are shown because there are two opportunities, one on each side of the just-about-right point, for non-JAR judgments. The counts are further broken into categories including tastes/ flavor that were judged as too strong or too weak, and amounts of ingredients (rated as too much or too little). Texture/consistency scales were not broken out this way because they were true bipolar scales (e.g., thin-thick, moist-dry, soft-firm) and it would be somewhat arbitrary to decide one end of the scale was representing more or less of something. Counts for taste/flavor and amount were broken down further to illustrate commonly occurring categories such as” too little meat/cheese” or too much ‘‘sau ce/dressing/mayo”. In the following comparisons, chi-square tests were used to compare frequencies extracted into 2  2 tables, and z-score tests on proportions to compare two frequencies (Kanji, 1993). All comparisons were significant at p < 0.05 unless noted otherwise. Each ‘‘occurrence” is one JAR segment (half-scale) for one product. Considering the large group, low penalty situation, taste and flavor occurrences were less frequent than danger zone events (Z = 5.59). That is, there were 64 (44 + 20) scales with N P 20%. For comparison, there were 106 total (67 + 39), danger zone observations for taste/flavor problems. However, the large group, low penalty occurrence was larger than one would expect considering the larger relative size of the danger zone area. Given areas of 23% vs. 51% of the total plot, one might expect 121/528 large group, small penalty occurrences and 269/528 danger zone occurrences if all scales were counted and results were distributed randomly. Using these as expected proportions, the large group/small penalty was greater than the danger zone observations ((64/121 = 53% vs. 106/269 = 39%, Z = 2.50).

5

25

102 204

An even wider discrepancy was noted for the scales dealing with the amount of an ingredient, notably that occurrences of ‘‘too little” were quite frequent (76 events out of 147 total halfscales or 52%) compared to only 18 out of 147 (12%) for danger zone observations, even though the total area of the danger zone in the plot was twice as big. ‘‘Too much” judgments were less frequent, but the combined frequencies compared to danger zone events were still significantly higher (v2(2) = 7.53). The most frequent events for the large group, low penalty situation were ratings indicating a desire for more meat and/or cheese in the product, and second was a desire for more sauce, dressing or mayonnaise. The next problem area concerns a small group with high penalty (upper left quadrant). The occurrences of a small group with high penalty were less frequent than the large group, low penalty discussed above, and almost all concentrated in the taste/flavor category as opposed to ‘‘amount of X” scales (Z = 4.54). ‘‘Too strong” judgments were more common than ‘‘too weak” by a two to one margin. That trend ran counter to the danger zone complaints in which ‘‘too weak” situations were more common than ‘‘too strong”, a significant reversal of the pattern (v2(2) = 12.68). The ‘‘opposing opinions” situation was concentrated in the taste/flavor scales (36 out of 42 total opposing opinions). These problematic results were less frequent than the small group, low penalty with only 36/264 (13.6%) showing mean drops greater than one and 13/264 (5%) showing mean drops greater than 1.5.

4. Discussion One unexpected finding in the menu-item study was a surprising number of large-group/low-penalty results. This, of course, is a quadrant of the penalty plot where simply using the JAR distribution would lead to a different recommendation than considering both the JAR distribution and the penalty (or lack thereof). The JAR distribution, if considered alone, would suggest product improvements were necessary. There were two main categories where this effect occurred. One was an apparent desire for more dressing, sauce or condiment like mayonnaise. The second category was an apparent desire for a greater amount of meat or cheese. We dubbed this ‘‘lack of amount” JAR rating (with low

236

H.T. Lawless et al. / Food Quality and Preference 52 (2016) 232–236

penalty) a ‘‘bacon effect”.1 Popper and Kroll (2005) had also commented on the tendency to rate highly desirable attributes as ‘‘not enough of X” although they considered it simply a form of response bias. Note that the protein-rich meat and cheese components are often some of the more expensive parts of the recipe, and so taking into account the lack of penalty can have important cost implications for the restaurant chain. Another way to look at the penalty (or lack thereof) is that there is little or nothing to be gained by increasing the desired item in terms of improvement to the overall hedonic score. Somewhat more problematic is the result where there is a small group with a high penalty (large mean drop). If resources permit, it could be wise to replicate the item in a test with a different consumer group to see if the effect is real or just a peculiarity of the current consumer sample. Because the group is small, it is worthwhile to check the frequency of usage/purchase by the offended minority. If they are infrequent users, then no action need be taken. Second, a close look at the actual data may reveal that the high penalty was caused by low overall acceptance ratings by just a few outliers, a form of statistical leverage. Leverage from a very few outliers would also suggest little cause for concern. Third, it is also possible that the penalty is a result of a halo (or in this case, horns) effect. This could occur when the consumers are annoyed or dissatisfied with one particular attribute, but they penalize the product on all scales. Examination of verbatim opinions from open-ended questions (e.g. ‘‘In your own words, what did you dislike about the product?”) could be informative for detecting a halo/ horns effect. Penalty analysis can also provide insights into two important phenomena in consumer testing, namely segmentation and socalled ‘‘drivers of liking”. With regard to segmentation, it is sometimes seen that for a given product, there may be opposing groups. One group may desire more of a given attribute (more pulp in orange juice for example) and another group preferring less. For our menu items, this was sometimes seen with polarizing ingredients such as blue cheese, which some people like a lot of, but others prefer little, if any. Although such opposing JAR opinions are not conclusive evidence of segmentation, they may confirm or support hypotheses derived from other analyses or other studies. Another type of insight can be derived from attributes that show high penalties. Such attributes, for which there is a high degree of consumer sensitivity to non-optimal levels, may be attributes that are critical in determining consumer satisfaction. Such characteristics are referred to as ‘‘drivers of liking”, although for penalty analysis, a better term might be ‘‘drivers of disliking”. As with segmentation, this kind of evidence is not airtight. It simply means that for these products, as formulated for this given study, variation in some intensity-related attributes had a large effect on liking scores. It is always possible that other variables, not studied in the current products, might also be influential. Along these lines, several researchers have used penalty-lift analysis for check-all-that-apply (CATA) data (Meyners, Castura, & Carr, 2013; Williams, Carr & Popper, 2011). Attributes that have lift have a positive mean liking difference when checked as opposed to when they are not checked. Such attributes could be drivers of liking, although they may simply be correlates of a true driver. Conversely, if an attribute is not checked, and the product has a

1 The name ‘‘bacon effect” derived from the opinions of first author’s acquaintances in the culinary professions, many of whom believe that a recipe, burger or sandwich item ‘‘can never have too much bacon”.

higher liking score when it is checked, that attribute has resulted in a potential penalty, and could be a driver of disliking when it is missing. Application of penalty analysis for CATA data can also be done when ideal products are evaluated on the same list of attributes, as discussed by Meyners et al. (2013). 5. Conclusions Modifications of successful restaurant menu items can be made to reduce calories, fat, saturated fat and sodium, often with little or no risk of consumer dissatisfaction, if the modifications are small. In consumer testing, data from just-about-scales and from hedonic scores may suggest different courses of action. Results from justabout right scales should be combined with penalty analysis to insure that correct decisions and recommendations are made about the modified products. Acknowledgements Research reported here was supported by the Small Business Innovative Research (SBIR) Program, National Cancer Institute of the National Institutes of Health under Award Number R44CA150528. The funding source played no part in the design, conduct of the study, preparation and submission of the article. The authors would like to thank Dr. Esther Hill, Anita JonesMueller, Erica Bohm, Mariana Beleche, Nancy Snyder, Marlene Dinklage and the Participating Restaurants’ Personnel for their assistance in designing and conducting the study. The authors would also like to thank all of the Culinary Dietitians at Healthy Dining for conducting the menu items’ nutrition analysis. References Kanji, G. K. (1993). 100 statistical tests.London, UK: Sage Publications LTD. Lachat, C., Nago, E., Verstraeten, R., Roberfroid, D., Van Camp, J., & Kolsteren, P. (2012). Eating out of home and its association with dietary intake: a systematic review of the evidence. Obesity Reviews, 13, 329–346. Lawless, H. T., & Heymann, H. (2010). Sensory evaluation of food: Principles and practices (2nd ed.). New York: Springer Science+Business. Li, B., Hayes, J. E., & Ziegler, G. R. (2015). Maximizing overall liking results in a superior product to minimizing deviations from ideal ratings: An optimization case study with coffee-flavored milk. Food Quality and Preference, 42, 27–36. Meyners, M., Castura, J. C., & Carr, B. T. (2013). Existing and new approaches for the analysis of CATA data. Food Quality and Preference, 30, 309–319. Ogden, C. L., Carroll, M. D., Fryar, C. D., & Flegal, K. M. (2015). Prevalence of obesity among adults and youth: United States, 2011–2014. NCHS Data Brief, 219, 1–8. Patel, A. A., Lopez, N. V., Lawless, H. T., Njike, V., Beleche, M. & Katz, D. (submitted for publication). Reducing calories, fat, saturated fat and sodium in restaurant meals: Effect on consumer acceptance. Obesity. Peryam, D. R., & Girardot, N. F. (1952). Advanced taste-test method. Food Engineering, 24(58–61), 194. Popper, R., & Kroll, D. R. (2005). Just-about-right scales in consumer research. Chemosense, 7(3), 1–6. Rothman, L., & Parker, M. J. (2009). Just-About-Right scales: Design, usage, benefits, and risks. ASTM Manual MNL63. Conshohocken, PA: ASTM International. Schraidt, M. (2009). Penalty analysis or mean drop analysis. Appendix L. In L. Rothman & M. J. Parker (Eds.). Just-about-right scales: Design, usage, benefits, and risks. ASTM Manual MNL63 (pp. 40–47). Conshohocken, PA: ASTM International. U.S. Department of Agriculture. (2015). Interactive chart: Food expenditures. Accessed 12.15.15. Willliams, A., Carr, B. t., & Popper, R. (2011). Exploring analysis options for checkall-that-apply (CATA) data. In 9th Rose-Marie Pangborn sensory science symposium. Toronto, ON, Canada.