Communal and Personal Identity

Ennis, Mark William; Hamman, Jaco J.; Spitzer, Anais N.; Waldron, David; Spaeth, M. J. Drake; Thralls, Chad; O'Sullivan, Trish; Chan, Wing-Shing; Halligan, Fredrica R.; Helsel, Philip Browning; Hui, C. Harry; Ng, Eddie C. W.; Tai, M. Hannah; Waldron, Sharn; Fontelieu, Sukey; Larson, Paul; Popovsky, Mark; Teitelbaum, Stefanie; Webb, Hillary S.; Haule, John Ryan; Killinger, John Eric; Stephenson, Craig; Braden, Dianne; Stebbins, Morgan; Pettis, Jeffrey B.; Loewenthal, Kate M.; Charet, F. X.; Moir-Bussy, Ann; Beit‐Hallahmi, Benjamin; Köse, Ali̇; Blackhirst, Rod; Denend, Jessica Van; Sanderson, Krystyna; Grzymała-Moszczyńska, Halina; Leeming, David A.; Mills, Alice; Loue, Sana

doi:10.1007/978-0-387-71802-6_120

Cited by 5 publications

(6 citation statements)

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“…It has been noted that when placebo pairs are presented, the number of consumers choosing the "No Preference" option is low (Marchisano et al 2003;Alfaro-Rodríguez et al 2005;Angulo and O'Mahony 2005;Chapman and Lawless 2005;Ennis 2005;Chapman et al 2006;Alfaro-Rodríguez et al 2007;Kim et al 2008;Villegas-Ruiz et al 2008). Kim et al (2008) discussed reasons for this.…”

Section: Discussionmentioning

confidence: 99%

“…Rather than discarding or altering the data, it would be preferable to use a multinomial analysis for multinomial data. Chi-squared is a simple multinomial test, and use of this analysis has been investigated (Foley et al 2003;Marchisano et al 2003;Alfaro-Rodríguez et al 2005;Angulo and O'Mahony 2005;Alfaro-Rodríguez et al 2007;Ennis 2005). The problem with the expected frequencies for such an analysis is that they should represent responses obtained when there is no preference.…”

Section: Introductionmentioning

confidence: 99%

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Paired Preference Tests: “50:50” and “Alternating” No Preferences

Alfaro-Rodriguez¹,

Angulo²,

O’Mahony³

2008

Journal of Sensory Studies

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Paired Preference Tests: “50:50” and “Alternating” No Preferences

Alfaro-Rodriguez¹,

Angulo²,

O’Mahony³

2008

Journal of Sensory Studies

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“…There is also a long version, where 2 pairs are presented, 1 the same and the other different, although the judge is unaware of this. Bi (2006) indicated that the short version is statistically less powerful than the triangle or duo-trio methods, while Ennis (2004) indicated that the long version is more so. Rousseau and others (1998) using vanilla flavored yogurts, confirmed this.…”

Section: Hits False Alarms and Constructing An Roc Curvementioning

confidence: 99%

A Transfer of Technology from Engineering: Use of ROC Curves from Signal Detection Theory to Investigate Information Processing in the Brain during Sensory Difference Testing

Wichchukit¹,

O’Mahony²

2010

Journal of Food Science

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This article reviews a beneficial effect of technology transfer from Electrical Engineering to Food Sensory Science. Specifically, it reviews the recent adoption in Food Sensory Science of the receiver operating characteristic (ROC) curve, a tool that is incorporated in the theory of signal detection. Its use allows the information processing that takes place in the brain during sensory difference testing to be studied and understood. The review deals with how Signal Detection Theory, also called Thurstonian modeling, led to the adoption of a more sophisticated way of analyzing the data from sensory difference tests, by introducing the signal-to-noise ratio, d′, as a fundamental measure of perceived small sensory differences. Generally, the method of computation of d′ is a simple matter for some of the better known difference tests like the triangle, duo–trio and 2-AFC. However, there are occasions when these tests are not appropriate and other tests like the same–different and the A Not–A test are more suitable. Yet, for these, it is necessary to understand how the brain processes information during the test before d′ can be computed. It is for this task that the ROC curve has a particular use.

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“…However, these methods require large sample sizes to maintain reasonable power (Ennis 1990(Ennis , 1993 and so more powerful alternatives would be desirable. One possible alternative is the same-different test which can be more powerful than the duo-trio or triangle methods (Ennis 2004). Because it is a suitable alternative for consumer difference testing and has already been used in several studies (Frandsen et al 2007;Ishii et al 2007), it is important to understand the variables associated with the test so that it can be used effectively.…”

Section: Sensory Difference Tests: the Same-different Testmentioning

confidence: 99%

Decision Strategies Determined From the Shape of the Same–different Roc Curve: What Are the Effects of Incorrect Assumptions?

Hautus

O’Mahony

Lee

2008

Journal of Sensory Studies

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The literature includes many studies that use the shape of the receiver operating characteristic (ROC) curve derived from the same–different test to ascertain the decision strategy used by the judge: a symmetrical ROC curve indicates a β ‐decision strategy, and an asymmetrical ROC curve indicates a τ ‐decision strategy. These studies have not considered the effect that breaches of the assumptions that underlie the standard same–different model would have on inferences drawn from the shape of the ROC curve. Three such assumptions are investigated: the equal‐variance assumption; the assumption of no stimulus order effects; and the assumption that the discriminability of the two stimuli is the same when presented first or second in the test sequence. Simulations indicate that the shape of the ROC curve is very tolerant of the breach of these assumptions. Consequently, inferences about the decision strategy used by a judge are fairly robust, and confidence in published results is upheld. PRACTICAL APPLICATIONS The same–different test is frequently used to determine the decision strategy adopted by judges. There are several assumptions made about the same–different test that may, if they were breached, undermine this test's ability to indicate the judge's decision strategy. The simulations conducted in this research indicate that the shape of the same–different ROC curve is very robust to breaches of these assumptions. Consequently, food scientists can maintain their confidence in the inferences made in the research already published on this topic, and in their own future research and development of sensory testing methods.

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Communal and Personal Identity

Cited by 5 publications

References 0 publications

Paired Preference Tests: “50:50” and “Alternating” No Preferences

Paired Preference Tests: “50:50” and “Alternating” No Preferences

A Transfer of Technology from Engineering: Use of ROC Curves from Signal Detection Theory to Investigate Information Processing in the Brain during Sensory Difference Testing

Decision Strategies Determined From the Shape of the Same–different Roc Curve: What Are the Effects of Incorrect Assumptions?

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