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One type of paired preference test uses the 'target' pair of stimuli under consideration to record the measured preferences (prefer A, Prefer B, 'no preference') and a second putatively identical control pair, the 'placebo' pair (AA or BB) to indicate 'false' preferences, unrelated to the target pair, elicited by the effects of the testing situation. From the literature there is disagreement regarding whether it is important to place a placebo pair before or after its corresponding target pair, to elicit a greater proportion of 'no preference' responses. This is important, because the higher the frequency of 'no preference' responses in the placebo pair, the more powerful will be the chi-squared style analysis, which determines whether the target pair displays a significant preference or not. It has been hypothesized that placing the placebo pair after the target pair would elicit more 'no preference' responses in the placebo, because the consumers would have had a chance to experience the difference in sensory characteristics of the two stimuli in the target pair. Using a related samples design, the hypothesis was confirmed although the difference between placebos placed before and after the target was not always significant. It was hypothesized that this lack of significance was caused by greater variance among the items being assessed, making it more difficult to decide whether a putatively placebo pair, in the context of the experiment, was really 'identical'. Psychological adjustments in the consumers were discussed in terms of difference and preference tau criteria. A boost in the proportion of 'no preferences' was observed for the placebo pair built into the triadic preference test, this was explained by the experimenter gaining some control over the consumers' preference tau criteria., Competing Interests: Declaration of Competing Interest Jeremia Halim, Xiaotian Zhang and Michael O’Mahony have no conflict of interest regarding this paper, due to any financial or other external interest., (Copyright © 2020. Published by Elsevier Ltd.)

214 consumers used the verbal 9-point hedonic scale to assess 4 types of flavor coated peanuts and 4 types of flavored teas. They used the traditional ANOVA/LSD analysis to provide mean values derived from the 9-point hedonic scale along with measures of significant difference. However, these data did not provide effect sizes. They did not give direct measures of the strength of preference between the various products, which was the main interest. Accordingly, effect sizes were computed. For this, each consumer had also ranked their preferences as they made their ratings on the 9-point hedonic scale. From these, R-Index values were computed to provide the percentages of consumers, who preferred each product to every other product. These direct measures of effect size completed the analysis begun by the ANOVA analysis of the set of mean scores. Also, the measures were nonparametric and avoided issues of the validity of a parametric statistical analysis. They also avoided the problem with the traditional analysis when products in the same scale category are attributed the same scores, when they are not equally liked. Experiment 2, using 207 consumers indicated that this problem was only serious enough to reduce the power of the traditional analysis, compared with the R-Index Preference Measurement, when the number of products being tested approached a dozen say, for product optimization., (Published by Elsevier Ltd.)

This study was conducted to understand the acceptance levels of hot sauces among consumers from different culinary cultures. Two newly developed hot sauces [fermented red chili pepper with soybean-paste-based sauce(GS) and fermented red chili-pepper-based sauce(KS)] were compared with Tabasco sauce(TB) and Sriracha sauce(SR). Two separate cross-cultural home-use tests(HUTs) were conducted: pizza and cream soup were provided as food items in HUT 1, whereas grilled chicken wings and rice noodle soup were provided in HUT 2. Consumers residing in Denmark, South Korea, and US participated in each HUT (n≅100 per country). Acceptance levels and the reasons for (dis)liking particular hot sauces applied to food systems were assessed. The food items that paired well with different hot sauces when the sauces were applied freely to regular meals were also analyzed among the US and Korean subjects. When the hot-sauce samples were applied to pizza and cream soup, the preferred order of the samples exhibited a cross-cultural agreement (GS = KS > TB). In the case of grilled chicken and rice noodle soup, the acceptance rating was similar for the three types of hot sauces among Koreans, whereas the acceptance was higher for SR among the US subjects for both foodstuffs, while Danish subjects preferred GS and KS over SR. The US subjects did not like hot-sauce samples with sweet and weak spiciness, whereas the Korean and Danish subjects disliked the hot-sauce sample when it was too spicy and not sufficiently sweet. These findings indicate that the matching of particular sauces with specific food items is culture-dependent, and this needs to be considered when trying to export food products such as hot sauce to other countries., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

One form of paired preference test protocol requires consumers to assess 2 pairs of products. One is the target pair under consideration, while the other is a putatively identical pair named the "placebo pair" which is also presented as a control. Counterintuitively, the majority of consumers report preferences when presented with the placebo pair. Their response frequencies are hypothesized to be those of consumers having "no preference" and are compared with the response frequencies elicited by a target pair, to determine whether the target pair elicits significant preferences. The primary goal of this paper was to study the robustness of 2 new so called disruptive protocols that reduced the proportion of consumers, who reported preferences when assessing a putatively identical pair of products. For this task, the tests were performed in a different language, in a different country, using different products from before. The results showed that the proportion of consumers reporting preferences for the placebo pair was reduced, confirming earlier work. Also, comparison of d' values showed a lack of significant overall differences between the placebo and target pairs, while chi-squared analyses indicated significant differences in the response frequencies. This indicated that the sample was segmented into 2 balanced groups with opposing preferences., (© 2016 Institute of Food Technologists®)

The 9-point hedonic scale has been used routinely in food science, the same way for 60 years. Now, with advances in technology, data from the scale are being used for more and more complex programs for statistical analysis and modeling. Accordingly, it is worth reconsidering the presentation protocols and the analyses associated with the scale, as well as some alternatives. How the brain generates numbers and the types of numbers it generates has relevance for the choice of measurement protocols. There are alternatives to the generally used serial monadic protocol, which can be more suitable. Traditionally, the 'words' on the 9-point hedonic scale are reassigned as 'numbers', while other '9-point hedonic scales' are purely numerical; the two are not interchangeable. Parametric statistical analysis of scaling data is examined critically and alternatives discussed. The potential of a promising alternative to scaling itself, simple ranking with a hedonic R-Index signal detection analysis, is explored in comparison with the 9-point hedonic scale., (© 2014 Society of Chemical Industry.)

Background: The present study cross-culturally investigated the drivers of liking for traditional and ethnic chicken marinades using descriptive analysis and consumer taste tests incorporating the check-all-that-apply (CATA) method. Seventy-three Koreans and 86 US consumers participated. The tested sauces comprised three tomato-based sauces, a teriyaki-based sauce and a Korean spicy seasoning-based sauce. Chicken breasts were marinated with each of the five barbecue sauces, grilled and served for evaluation. Descriptive analysis and consumer taste tests were conducted. Consumers rated the acceptance on a hedonic scale and checked the reasons for (dis)liking by the CATA method for each sauce. A general linear model, multiple factor analysis and chi-square analysis were conducted using the data., Results: The results showed that the preference orders of the samples between Koreans and US consumers were strikingly similar to each other. However, the reasons for (dis)liking the samples differed cross-culturally. The drivers of liking of two sauces sharing relatively similar sensory profiles but differing significantly in hedonic ratings were effectively delineated by reasons of (dis)liking CATA results., Conclusion: Reasons for (dis)liking CATA proved to be a powerful supporting method to understand the internal drivers of liking which can be overlooked by generic descriptive analysis., (© 2014 Society of Chemical Industry.)

The food industry is now global, so that hedonic comparisons for various products may need to be made between consumers from different countries. Using the 9-point hedonic scale, it has been shown that East Asians use a significantly smaller range of scores than Americans; they are essentially using different scales, rendering comparisons between the two sets of consumers problematic. There is a necessity for a culture-free method of hedonic numerical assessment, which is not affected by such ‘cultural range variation’. American and Chinese consumers assessed candies, toothbrushes and pens, using the regular verbal 9-point hedonic scale as well as a purely numerical version. As expected from previous studies, the Chinese consumers tended to have a significantly smaller range of responses than the American consumers, although the effect was not as strong for the purely numerical 9-point hedonic scale. This meant that Chinese consumers were often using a shorter scale than the Americans, making comparisons between the two groups problematic. However, a ranking protocol with an R-Index analysis was also used. With this protocol, the range of responses for the Chinese and Americans was the same, being equal to the number stimuli being ranked, circumventing the problem of ‘cultural range variation’. [ABSTRACT FROM AUTHOR]

Consumers were tested for their ability to detect the presence of KCl added to orange juice. They were also tested for their preferences for either the plain orange juice or the KCl-spiked orange juice. It was found that for lower concentrations of KCl added to the orange juice, consumers could detect its presence without it eliciting a preference. As the KCl concentrations increased, the proportion of consumers reporting a preference increased. Some of the preferences were for the KCl-spiked orange juice. At first, consumers reported that despite their reported preferences, they would be willing to consume the nonpreferred juice. At higher concentrations, preferences migrated to the plain orange juice. Here, consumers began to report that they would not consume the KCl-spiked orange juice; they would reject it. For the preference tests, three approaches were investigated but all three demonstrated that when the presence of KCl in the juice could be detected, it did not necessarily elicit a preference. Practical Applications For evaluating new or changed versions of products in the market, difference testing has become more and more important. However, a common practice in the interpretation of these tests is to assume that if consumers can detect the difference between the original and changed product, then the changed product will be rejected in the marketplace. Yet, this strategy could be overcautious, especially if the changed produced a valuable cost reduction or a nutritional improvement. The present study confirms that when a change in the product is detected by consumers, it does not necessarily elicit a preference. This has consequences for marketing decisions. It is recommended in these situations that difference tests be accompanied by preference tests. Difference tests alone can sometimes only provide half the required information. [ABSTRACT FROM AUTHOR]

To gain insight into the proportion of consumers who respond to extraneous factors in the paired preference testing situation, rather than the sensory properties of products being assessed, 'placebo' preference tests with putatively identical products are used. One use is as a tool to select consumers who ignore extraneous factors and are thus suitable for use in preference tests, where experimenters wish to have confidence that consumers are ignoring extraneous factors and responding only to the sensory attributes of the products being tested. Yet, selecting such consumers tends often to reduce the sample size to approximately 20-35% of its original, which is unacceptably low. The protocol used in this study employed unusual instructions and questions to reverse the hidden demand characteristics of the test, so that the sample size was only reduced to 80-90% of its original. Practical Applications Paired preference tests are part of the battery of tests used for measuring food acceptance. Although, when used casually, the test is simple; when used for a formal assessment of acceptance, there are problems that must be addressed. One such problem is the tendency of consumers to respond with preferences, even when the stimuli are putatively identical. Such a bias has the potential to produce an overestimation of preferences, when products are being assessed by paired preference protocols. Accordingly, research into this phenomenon is worthwhile, so that more valid and reliable tests can be developed. [ABSTRACT FROM AUTHOR]

Besides responses to the target pair of stimuli being tested in a paired preference test, comparison with responses to a putatively identical "placebo" pair can be used for a test of significance, to determine whether the response frequencies elicited by the target pair are different from those that would be obtained had there been no preference. Yet, preference responses elicited by the placebo pair can also be used as a way of identifying consumers who bias avoided the "No preference" option and thus generated biased response. Such consumers can then be eliminated. Yet, using this approach greatly reduces the sample size. However, using some response options associated with purchase intent rather than liking can reduce the number of consumers that are eliminated. This was confirmed for potato chip, milk and orange juice stimuli. From psychology, the concepts of operational preference and types of response bias appropriate to preference testing were introduced and discussed. PRACTICAL APPLICATIONS: The measurement of preference and acceptance of foods is important for product development and decisions regarding the launching of new products into the market. The paired preference test has some issues associated with its design and analysis, and these must be considered if preference tests are to give valid and reliable data. One of the problems is that the majority of consumers tend to report preferences even when the stimuli are putatively identical; here, a "No preference" response would seem more appropriate. Such consumers would seem to be reacting to the extraneous conditions of the preference test rather than the sensory characteristics of the putatively identical stimuli under consideration. Such responses could be termed "biased response." Yet, the consumers' responses to the target (different) stimuli under consideration could be trusted more if the consumers had shown that they did not have this tendency to give "biased responses." They would show this by selecting a "No preference" response to putatively identical stimuli. This paper shows that the tendency to give "biased response" can be reduced by manipulating the wording of the preference question. This does not furnish a complete solution to the problem but it indicates one possible step toward solution and adds valuable information to the body of knowledge that is being built with a view avoiding the difficulties associated with preference testing. [ABSTRACT FROM AUTHOR]

Abstract: Paired preference tests of liking require consumers to specify which of two foods are preferred or whether there is no preference. For ‘Buying’ preference tests, consumers specify which of two foods they are more likely to buy or whether they might buy either or neither. For ‘Choosing’ preference tests, consumers specify which of two foods they are more likely to choose when offered both, or whether they might choose both or neither. The first test is designed to predict greater degrees of liking, the second to predict buying behavior and the third to predict choice behavior. Yet, test results may not necessarily predict ‘real life’ behavior once consumers have left the testing situation; the ‘test’ preferences may not be ‘operational’. To validate the predictive ability of such tests, consumer behavior should be monitored for several months. Another approach is to allow consumers to take away some of the foods used in the test and observe what they take away. Consumers of potato chips were required to taste chips whose flavor and appearance were obviously different. A first group (N =107) were given ‘Liking’, ‘Buying’ and ‘Choosing’ preference tests for two palatable types of chip. After the test, out of sight of the experimenter, consumers were presented with two rows of plain plastic ‘snack’ bags, filled with the chips used in the test. They could take away either two bags of the same type of chip (a ‘Take Away’ preference) or one of each type (no ‘Take Away’ preference) or take neither (rejection). They were also subtly questioned regarding whether they were going to consume the chips themselves or were going to share them with friends. A second group (N =103) repeated the experiment. However, for these consumers, one type of chip was unpalatable. In general, ‘Buying’ preferences corresponded more to ‘Take Away’ preferences while ‘Liking’ and ‘Choosing’ preferences showed little correspondence. For the palatable chips, the preferences were spread more evenly than for the case where one type of chip was undesirable. In this latter case, approximately 50% of consumers were consistent over all tests in choosing the palatable chip. This might suggest operational preferences. ‘Take Away’ preferences suggested low predictability for paired preference tests of liking, choosing and buying. ‘Take Away’ preferences are not a substitute for monitoring consumer behavior for several months but they are a step in the right direction. [Copyright &y& Elsevier]

Ecuadorian consumers performed paired preference tests between sunflower rye bread and artisan wholegrain bread enriched with omega-3 fatty acids. Preferences for each were split fairly evenly. Further difference tests suggested that these preferences were elicited by visual rather than flavor/texture cues. The preference test included a 'placebo' pair of 'identical' stimuli to assess statistically whether the responses to the two test stimuli were merely because of response biases operating in the test situation and not differences in their sensory attributes. The concept of an 'operational preference' was introduced to understand some of the ambiguities involved in the definition of preference. PRACTICAL APPLICATIONS The measurement of preference and acceptance of foods is important for product development and decisions regarding the launching of new products on to the market. The paired preference test has several problems associated with its design and analysis, and these are worthy of investigation. This article uses a practical example to illustrate some procedures developed as solutions to these challenges. Solutions to the problems involved in preference testing are essential so that the food industry can obtain trustworthy data. [ABSTRACT FROM AUTHOR]

Besides responses to the different stimuli being tested in a paired preference test, responses to identical “placebo” stimuli can be used as a screening tool to identify biased consumers. Those consumers who give preference responses to identical stimuli can be assumed to be biased. Accordingly, only the data from unbiased consumers need to be considered for the different stimuli. The problem with this procedure is that the sample size is reduced. The goal of the present research was to see whether using options associated with purchase intent, elicited a greater number of “No Preference” responses to identical “placebo” stimuli. It was found that they did. The increase was large when the preference options implied exclusivity. In conditions where the preference strength options were not so strong, the frequency of “No Preference” responses dropped accordingly. PRACTICAL APPLICATIONS A problem with paired preference testing is the tendency of consumers to give false preferences, which produces the seriously misleading overestimation of the proportion of consumers who have preferences for one or other of the products being assessed. The “placebo” condition is an important control for alleviating this problem. The statistical analysis can be improved by finding a protocol that maximizes the proportion of “No Preference” responses in the placebo condition. The key finding here is that using purchase intent questions rather than preference questions may possibly provide a way of achieving this aim. [ABSTRACT FROM AUTHOR]

Because the concept of “No Preference” is ambiguous, two types of “No Preference” are defined: a “50:50” No Preference, defined operationally as consumers presented with both products having a 50% chance of choosing either, and an “Alternating” No Preference, where the probability is not 50%. The consumer would be more likely to choose one of the products. However, later, he or she might choose the other product. To investigate this, paired preference tests were performed with and without the “No Preference” option for various products. No discernible trend was noted for consumers who had chosen the “No Preference” option and distributed their votes among the preference options. These consumers and those who changed their preferences were classified according to the “50:50” No Preference and “Alternating” No Preference dichotomy accordingly. PRACTICAL APPLICATIONS Besides the tendency of consumers to report false preferences, paired preference tests have another problem: the concept of “No Preference” is ambiguous. It could mean that consumers liked both products equally and so would always have a 50% chance of choosing either. It could also mean that the probability is not 50% on any one occasion but varies so that one product is chosen on one occasion and the other product on another occasion. To avoid confusing the consumer so as to obtain more valid data, it is important to specify these two meanings of “No Preference.” This study is a first step in that direction and should help consumers understand better the instructions for preference tests. [ABSTRACT FROM AUTHOR]

In the “placebo” control condition for paired preference testing, “identical” stimuli are presented to consumers to determine the frequency of preference and “no preference” responses induced by the hidden demand characteristics of the testing condition. As a control for bias, induced by such hidden demand characteristics, these frequencies can be compared with the actual preference frequencies of the nonidentical test stimuli to be assessed for preference. It was hypothesized that the introduction of graded preference response options might reduce the frequency of “no preference” responses in the placebo condition. Using identical yogurt stimuli with related-sample (single-group) and independent-sample (multigroup) designs, this hypothesis was not confirmed. PRACTICAL APPLICATIONS The “placebo” condition in paired preference testing provides a way to control the tendency of consumers to give false preference. The statistical analysis can be improved by finding a protocol that maximizes the proportion of “no preference” responses in the placebo condition. This can be done by increasing the number of response options that imply no preference. Yet, there is sometimes a desire to increase the number of preference options by using graded responses for preference strength. Does this alter the dynamics of the ever-important placebo condition by reducing the number of “no preference” responses? This project found that any effect was minimal. [ABSTRACT FROM AUTHOR]

Three groups, each comprising 200 consumers, performed paired preference tests between two flavors of potato chips. For one group, a “No Preference” option was allowed, while for the second group, it was not. For a third group, a “No Preference” option was allowed while the preference responses were differentiated as “Strong Preference” and “Weak Preference.” For all three groups, d′ values representing overall strength of preference did not differ significantly. [ABSTRACT FROM AUTHOR]

A triadic preference test was developed as an alternative to the paired preference test. The three stimuli in the test comprised a putatively identical placebo pair and a different stimulus. This was in contrast to the regular paired preference test that utilizes a placebo pair. Such a test requires the presentation of two pairs of stimuli: a putatively identical placebo pair and a test pair. The triadic preference test only requires one triad. With the regular test, the majority of consumers respond to the placebo pair with a preference response. It is generally assumed that these consumers are responding to extraneous factors: those factors that elicit a preference response that are different from the sensory attributes of the food under assessment. As an attempt to minimize the possibility of responses to extraneous factors when assessing the test pair, it has been suggested to only use those consumers who chose the ‘No Preference’ option for the placebo pair. However, this form of ‘screening’ is not viable because the resulting ‘screened’ sample size is greatly reduced to approximately one third. However, in the present study, with the triadic preference test, the resulting ‘screened’ sample size ranged 76.5–94% of the total. Thus, this form of ‘screening’ against consumers who demonstrated response to extraneous factors for the placebo pair, was now feasible. [ABSTRACT FROM AUTHOR]