Surprising Robustness of the Self-Explicated Approach to Customer Preference Structure Measurement

1997 ◽  
Vol 34 (2) ◽  
pp. 286-291 ◽  
Author(s):  
V. Srinivasan ◽  
Chan Su Park
Keyword(s):  
Utility Function ◽  
Conjoint Analysis ◽  
Predictive Validity ◽  
The Self ◽  
Preference Structure ◽  
Combined Approach ◽  
Customer Preference ◽  
Full Profile ◽  
Structure Measurement

The authors introduce customized conjoint analysis, which combines self-explicated preference structure measurement with full-profile conjoint analysis. The more important attributes for each respondent are identified first using the self-explicated approach. Full-profile conjoint analysis customized to the respondent's most important attributes then is administered. The conjoint utility function on the limited set of attributes then is combined with the self-explicated utility function on the full set of attributes. Surprisingly, the authors find that the self-explicated approach by itself yields a slightly (but not statistically significantly) higher predictive validity than does the combined approach.

Improving Predictive Power of Conjoint Analysis by Constrained Parameter Estimation

1983 ◽  
Vol 20 (4) ◽  
pp. 433-438 ◽  
Author(s):  
V. Srinivasan ◽  
Arun K. Jain ◽  
Naresh K. Malhotra
Keyword(s):  
Parameter Estimation ◽  
Conjoint Analysis ◽  
Predictive Validity ◽  
Predictive Power ◽  
A Priori ◽  
A Priori Knowledge ◽  
Constrained Estimation ◽  
First Choice ◽  
Full Profile ◽  
Different Levels

The prediction of first choice preferences by the full-profile method of conjoint analysis can be improved significantly by imposing constraints on parameters based on a priori knowledge of the ordering of part worths for different levels of an attribute. Constrained estimation however, has little effect on the predictive validity of the tradeoff method because the preference judgments within rows (or columns) of tradeoff tables have largely the same role as the constraints.

Completely Unacceptable Levels in Conjoint Analysis: A Cautionary Note

1988 ◽  
Vol 25 (3) ◽  
pp. 293-300 ◽  
Author(s):  
Paul E. Green ◽  
Abba M. Krieger ◽  
Pradeep Bansal
Keyword(s):  
Conjoint Analysis ◽  
Predictive Validity ◽  
Cautionary Note ◽  
Theoretical Terms ◽  
Full Profile ◽  
Attribute Levels ◽  
The Impact

An experiment is reported on the extent to which respondents adhere to the implications of choosing the “completely unacceptable” level in hybrid conjoint (and related) applications. The findings indicate that the form of the instructions matters, but that respondents often ignore the implications of previous responses when responding to full-profile options containing unacceptable attribute levels. The authors discuss the impact of this inconsistency on internal predictive validity in both empirical and theoretical terms.

Predictive Validity of the Self-Appraisal Questionnaire (SAQ)

2000 ◽  
Vol 15 (11) ◽  
pp. 1183-1191 ◽  
Author(s):  
WAGDY LOZA ◽  
AMEL LOZA-FANOUS
Keyword(s):  
Predictive Validity ◽  
The Self

Bayesian Prediction in Hybrid Conjoint Analysis

2002 ◽  
Vol 39 (2) ◽  
pp. 253-261 ◽  
Author(s):  
Frenkel Ter Hofstede ◽  
Youngchan Kim ◽  
Michel Wedel
Keyword(s):  
Prior Information ◽  
Current Model ◽  
The Self ◽  
Attribute Importance ◽  
Individual Level ◽  
Special Cases ◽  
Full Profile ◽  
Credible Intervals ◽  
Competing Models ◽  
The Individual

The authors propose a general model that includes the effects of discrete and continuous heterogeneity as well as self-stated and derived attribute importance in hybrid conjoint studies. Rather than use the self-stated importances as prior information, as has been done in several previous approaches, the authors consider them data and therefore include them in the formulation of the likelihood, which helps investigate the relationship of self-stated and derived importances at the individual level. The authors formulate several special cases of the model and estimate them using the Gibbs sampler. The authors reanalyze Srinivasan and Park's (1997) data and show that the current model predicts real choices better than competing models do. The posterior credible intervals of the predictions of models with the different heterogeneity specifications overlap, so there is no clear superior specification of heterogeneity. However, when different sources of data are used—that is, full profile evaluations, self-stated importances, or both—clear differences arise in the accuracy of predictions. Moreover, the authors find that including the self-stated importances in the likelihood leads to much better predictions than does considering them prior information.

Evaluation of the Caregiver Preparedness Scale in African American and White Caregivers of Persons With Dementia During Post-Hospitalization Transition

2021 ◽  
pp. JNM-D-20-00087
Author(s):  
Ashley Kuzmik ◽  
Marie Boltz ◽  
Barbara Resnick ◽  
Rhonda BeLue
Keyword(s):  
African American ◽  
Family Caregivers ◽  
Measurement Invariance ◽  
Factor Structure ◽  
Predictive Validity ◽  
White Family ◽  
White Caregivers ◽  
Structure Measurement

Background and PurposeThe Preparedness for Caregiving Scale (PCS) is a widely used instrument to measure caregiver preparedness. The purpose was to evaluate the PCS in African American and White caregivers of patients with dementia upon discharge from the hospital.MethodsFactor structure, measurement invariance, and predictive validity of the PCS were assessed in a sample of 292 family caregivers/patient dyads.ResultsOne-factor structure of the PCS and measurement invariance by race was fully supported. Predicative validity revealed significant association between the PCS and anxiety (β = −.41, t = −7.61(287), p < .001), depression (β = −.44, t = −8.39(287), p < .001), and strain (β = −.48, t = −9.29(287), p < .001).ConclusionThe PCS is a valid and meaningful tool to measure preparedness in African American and White family caregivers of persons with dementia during post-hospitalization transition.

An Approach to Modeling Customer Preference Uncertainty by Applying Bootstrap to Choice-Based Conjoint Analysis Data

2010 ◽  
Author(s):  
Swithin S. Razu ◽  
Shun Takai
Keyword(s):  
Product Development ◽  
Conjoint Analysis ◽  
New Product Development ◽  
Market Share ◽  
New Product ◽  
Analysis Data ◽  
Product Development Process ◽  
Product Concept ◽  
Customer Preference ◽  
Market Shares

Analysis of customer preferences is among the most important tasks in a new product development. How customers come to appreciate and decide to purchase a new product affects the products market share and therefore its success or failure. Unfortunately, when designers select a product concept early in the product development process, customer preference response to the new product is unknown. Conjoint analysis is a statistical marketing tool that has been used to estimate market shares of new product concepts by analyzing data on the product ratings, rankings or concept choices of customers. This paper proposes an alternative to traditional conjoint analysis methods that provide point estimates of market shares. It proposes two approaches to model market share uncertainty; bootstrap and binomial inference applied to choice-based conjoint analysis data. The proposed approaches are demonstrated and compared using an illustrative example.

Private Self-Consciousness as a Moderator Variable?

1987 ◽  
Vol 60 (1) ◽  
pp. 303-312
Author(s):  
John W. Osborne ◽  
T. O. Maguire ◽  
N. Angus
Keyword(s):  
Construct Validity ◽  
Predictive Validity ◽  
The Self ◽  
Self Report ◽  
Use Of Self ◽  
Moderator Variable ◽  
Method Variance ◽  
Methodological Difficulties

Previous studies suggested that private self-consciousness may function as a moderator of the predictive validity of self-report measures of personality. This paper critically examined the construct validity of the Self-consciousness Scale used to measure private self-consciousness. The conceptual and methodological difficulties involved in measuring private self-consciousness are discussed with particular reference to the ubiquity of self-consciousness theory and the problem of method variance associated with the exclusive use of self-report in validating the Self-consciousness Scale. A phenomenologically derived profile of test experience is offered as a way of checking the validity of self-reported measures.

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