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Kim, I.A.,Hopkinson, A.,van Hout, D.,Lee, H.S. Longman Scientific Technical ; Elsevier Science Pu 2017 Food quality and preference Vol.56 No.1
<P>Due to its simplicity and efficiency for evaluating product attributes, check-all-that-apply (CATA) questions have been widely used. Yet, CATA questions have been reported to lack discriminability of subtle product differences and suffer from the problems of response bias from satisficing and acquiescence behaviors. In the present paper, a novel two-step rating-based 'double-faced applicability' test was developed as an extended response format of CATA, to improve its sensitivity for product discrimination and for stabilizing subjects' evaluative criteria. In the 'double-faced applicability' test, each attribute was 'double-faced', meaning that two descriptors (a pair of semantic-differential descriptors) are separately presented in the questionnaires representing both sides of each attribute. For performing the two-step rating on each attribute in the questionnaire, subjects are instructed to first respond 'Yes (does apply)' or 'No (does not apply)' and then to answer a 3-point sureness rating (how sure they were about their Yes or No response). The performance of the two-step ratings in this new method was compared to a simple one-step applicability rating test method as well as the forced-choice Yes/No questions without the sureness rating in terms of sensitivity in sample discrimination. The results showed that the 'double-faced applicability' test provided better product discrimination and showed the potential to reduce acquiescence response bias when using existing variants of CATA response formats. (C) 2016 Elsevier Ltd. All rights reserved.</P>
Kim, I.A.,Hopkinson, A.,van Hout, D.,Lee, H.S. Longman Scientific Technical ; Elsevier Science Pu 2017 Food quality and preference Vol.59 No.-
<P>To measure consumers' product usage experience throughout the various product usage stages, a novel two-step rating-based 'double-faced applicability' test has recently been proposed by Kim et al. (2017). In this method, a 'two-step' rating (forced-choice Yes/No questions followed by 3-point sureness ratings) and 'double-faced' descriptors (a pair of semantic-differential descriptors) are used for each attribute to improve the product discriminability by reducing consumers' response bias and variations. In this paper, we introduce a novel measure that can be computed from the data from the 'double-faced applicability' test to provide a new way to generate affective product usage experience profiles. The novel measure was a nonparametric estimate of affect magnitude, named as d-prime affect magnitude (d'A), computed by considering the response ratio of positivity to negativity as the ratio of signal to noise in the context of Signal Detection Theory (SDT). The advantage of using this new measure d'A was that it meaningfully reflected the consumers' affective product usage experience for each product independently (and how this affect valence changed through a usage process), yet it can still be used to compare between products. The practical application of using d'A was demonstrated in comparison to the more conventional SDT measure d'. (C) 2017 Elsevier Ltd. All rights reserved.</P>
Wallace Stephen J.,Murphy Michael P.,Schiffman Corey J.,Hopkinson William J.,Brown Nicholas M. 대한슬관절학회 2020 대한슬관절학회지 Vol.32 No.-
Preoperative radiographic templating for total knee arthroplasty (TKA) has been shown to be inaccurate. Patient demographic data, such as gender, height, weight, age, and race, may be more predictive of implanted component size in TKA.A multivariate linear regression model was designed to predict implanted femoral and tibial component size using demographic data along a consecutive series of 201 patients undergoing index TKA. Traditional, two-dimensional, radiographic templating was compared to demographic-based regression predictions on a prospective 181 consecutive patients undergoing index TKA in their ability to accurately predict intraoperative implanted sizes. Surgeons were blinded of any predictions. Patient gender, height, weight, age, and ethnicity/race were predictive of implanted TKA component size. The regression model more accurately predicted implanted component size compared to radiographically templated sizes for both the femoral ( P = 0.04) and tibial ( P < 0.01) components. The regression model exactly predicted femoral and tibial component sizes in 43.7 and 43.7% of cases, was within one size 90.1 and 95.6% of the time, and was within two sizes in every case. Radiographic templating exactly predicted 35.4 and 36.5% of cases, was within one size 86.2 and 85.1% of the time, and varied up to four sizes for both the femoral and tibial components. The regression model averaged within 0.66 and 0.61 sizes, versus 0.81 and 0.81 sizes for radiographic templating for femoral and tibial components. A demographic-based regression model was created based on patient-specific demographic data to predict femoral and tibial TKA component sizes. In a prospective patient series, the regression model more accurately and precisely predicted implanted component sizes compared to radiographic templating.Prospective cohort, level II.