Conjoint Analysis

Image quality preferences among radiographers and radiologists. A conjoint analysis

by Borgny Ween

Published in Radiography. Volume 11, Issue 3, Aug 2005, Pages 191–197.
http://dx.doi.org/10.1016/j.radi.2005.03.002


On 25hottest list at ScienceDirect Oct-Dec 2008

Key words:
Experimental study, Digital imaging, Digital radiography, Measuring image quality, Post-processing, Radiography (diagnostic)

Purpose

The aim of this study was to investigate the image quality preferences among radiographers and... more Purpose

The aim of this study was to investigate the image quality preferences among radiographers and radiologists. The radiographers' preferences are mainly related to technical parameters, whereas radiologists assess image quality based on diagnostic value.

Methods

A conjoint analysis was undertaken to survey image quality preferences; the study included 37 respondents: 19 radiographers and 18 radiologists. Digital urograms were post-processed into 8 images with different properties of image quality for 3 different patients. The respondents were asked to rank the images according to their personally perceived subjective image quality.

Results

Nearly half of the radiographers and radiologists were consistent in their ranking of the image characterised as ‘very best image quality’. The analysis showed, moreover, that chosen filtration level and image intensity were responsible for 72% and 28% of the preferences, respectively. The corresponding figures for each of the two professions were 76% and 24% for the radiographers, and 68% and 32% for the radiologists. In addition, there were larger variations in image preferences among the radiologists, as compared to the radiographers.

Conclusions

Radiographers revealed a more consistent preference than the radiologists with respect to image quality. There is a potential for image quality improvement by developing sets of image property criteria.

Drug Attribute Importance and Impact of Patient Out-of-Pocket Expense on Prescription Drug Selection: Survey of Midwestern Physicians

by Gregory Wellman

Choice-based conjoint analysis was used to construct and analyze multiple panels of drug products from which a sample... more Choice-based conjoint analysis was used to construct and analyze multiple panels of drug products from which a sample of Midwestern physicians were asked to choose. The panels were varied on the drug attributes of brand name, efficacy, frequency of administration, side effects, and patient out-of-pocket expense based on prescription benefit program reimbursement. An acute medication (antibiotic in the treatment of community-acquired pneumonia) and a chronic medication (antidepressant) were simulated for physicians. For both types of agents, patient out-of-pocket expense was the most important attribute, followed by slightly different arrangements of frequency of administration, effectiveness, and side effects, which were statistically significant.

Pilot study of a hierarchical Bayes method for utility estimation in a choice-based conjoint analysis of prescription benefit plans including medication therapy management services

by Gregory Wellman

BACKGROUND:Consumers face an array of multiattribute prescription benefit insurance programs that include different... more BACKGROUND:Consumers face an array of multiattribute prescription benefit insurance programs that include different access points (retail, supermarket, Internet, etc) and levels of pharmacist interaction (including medication therapy management services [MTMSs]). Because of this, there is a need for more sophisticated information to drive prescription benefit plan design.
OBJECTIVES:A pilot study to determine if choice-based conjoint (CBC) analysis with hierarchical Bayes (HB) estimation for individual level part-worths could provide a stable model for attribute preferences for prescription benefit insurance; to pilot test the addition of MTMSs to a prescription benefit management model; and to pilot and compare logit-based utility estimates to HB estimations in a conjoint market simulator.
METHODS:A mail-based survey was conducted using a random sample of 1500 residents of the United States. A CBC analysis instrument was developed to provide a single-stated choice from a selection of different prescription benefit plans. Choice tasks were varied based on the attributes: co-payment, pharmacy access, formulary, level of pharmacist interaction including MTMSs and monthly premium. Analysis included logit-based and HB estimation for utilities, and preference share market simulation testing.
RESULTS:The utility estimations from HB analysis were consistent with those seen in the logit-based analysis. A goodness of fit of 83% (root likelihood) was achieved in the HB utility estimations with only 4 choice tasks per respondents and the inclusion of MTM-like services. There was convergence on preference shares from the market simulation between the 2 estimation methods.
CONCLUSIONS:The use of CBC analysis with HB estimation provided utilities similar to those estimated using aggregated logit-based methods, with the added benefit of respondent specific part-worth scores for each attribute level. A larger sample, changes in the instrument design, more panels (tasks) per respondent, and selection of conjoint methods may allow for more predictive information from market simulators.

Drug Attributes and Patient Out-of-Pocket Cost Impact on Preference:: Conjoint Analysis of Physicians, Pharmacists, and Consumers

by Gregory Wellman

The objective of this study was to develop an understanding of the impact of drug attributes on preference, for... more The objective of this study was to develop an understanding of the impact of drug attributes on preference, for decision makers. Conjoint analysis of physicians, pharmacists, and consumers was conducted in an Ohio PPO. Subjects rated drug scenarios that varied on select attributes. Functional status was the noneconomic attribute with the greatest impact on preference. Frequency of administration and side effects were the factors that had the least impact. Physicians, pharmacists, and consumers did not tend to differ in the relative importance placed on noneconomic attributes. These three groups did differ in the manner in which they incorporated cost into the drug selection process.

The multidimensional analysis of preference data: an explorative strategy for the conjoint analysis

by Giuseppe Giordano

Ph.D Thesis - Tesi di dottorato, 1997

L'analisi multidimensionale dei dati di preferenza: una strategia esplorativa per la conjoint analysis L'analisi multidimensionale dei dati di preferenza: una strategia esplorativa per la conjoint analysis

Solving multiattribute design problems with analytic hierarchy process and conjoint analysis: An empirical comparison

by Roland Helm

Preference measurement with conjoint analysis and AHP: An empirical comparison

by Roland Helm

Den Blick am Markt: Conjoint-Analyse für eine marktorientierte Innovationsplanung

by Roland Helm

Forecasting for Marketing

by J Armstrong

Co-authored with Roderick J. Brodie. Published in Graham J. Hooley and Michael K. Hussey (Eds.),

Research on forecasting is extensive and includes many studies that have tested alternative methods in order to... more Research on forecasting is extensive and includes many studies that have tested alternative methods in order to determine which ones are most effective. We review this evidence in order to provide guidelines for forecasting for marketing. The coverage includes intentions, Delphi, role playing, conjoint analysis, judgmental bootstrapping, analogies, extrapolation, rule-based forecasting, expert systems, and econometric methods. We discuss research about which methods are most appropriate to forecast market size, actions of decision makers, market share, sales, and financial outcomes. In general, there is a need for statistical methods that incorporate the manager's domain knowledge. This includes rule-based forecasting, expert systems, and econometric methods. We describe how to choose a forecasting method and provide guidelines for the effective use of forecasts including such procedures as scenarios.

A multidimensional approach to conjoint analysis

by Giuseppe Giordano

Factorial Conjoint Analysis Based Methodologies

by Giuseppe Giordano

“Findings from Evidence-based Forecasting: Methods for Reducing Forecast Error

by J Armstrong

Forthcoming (after revisions) in the International Journal of Forecasting.

Empirical comparisons of reasonable approaches provide evidence on the best forecasting procedures to use under given... more Empirical comparisons of reasonable approaches provide evidence on the best forecasting procedures to use under given conditions. Base on this evidence, I summarize the progress made over the past quarter century with respect to methods for reducing forecasting error. Seven well-established methods have been shown to improve accuracy: combining forecasts and Delphi help for all types of data; causal modeling, judgmental bootstrapping and structured judgment help with cross-sectional data; and causal models and trend-damping help with time-series data. Promising methods for cross-sectional data include damped causality, simulated interaction, structured analogies, and judgmental decomposition; for time-series data, they include segmentation, rule-based forecasting, damped seasonality, decomposition by causal forces, damped trend with analogous data, and damped seasonality. The testing of multiple hypotheses has also revealed methods where gains are limited: these include data mining, neural nets, and Box-Jenkins methods. Multiple hypotheses testing should be conducted on widely used but relatively untested methods such as prediction markets, conjoint analysis, diffusion models, and game theory.

Conjoint Analysis and MDS Approach to Brand Improvement of an Aerosol Product

by Charles Vincent

Vincent Charles *
CENTRUM Católica, Lima, Peru
Mukesh Kumar
CENTRUM Católica, Lima, Peru
Tulika Anand
National Institute of Entrepreneurship, Noida, India
Journal of CENTRUM Cathedra ● Volume 4, Issue 1, 2011 ● 27-43

Consumers decide which aerosol product to purchase depending upon its different features or attributes. The importance... more Consumers decide which aerosol product to purchase depending upon its different features or attributes. The importance that consumers give to each attribute, however, differs from one consumer to another. The ability to identify the importance of different attributes of aerosols from the consumers’ perspective is essential for improving an existing brand or launching a completely new brand of aerosol. The purpose of this study is to identify feasible offerings of aerosols from a company’s point of view and the positioning of a comparatively new brand of room air freshener among all other existing brands in the capital city of Bihar (Patna) in India. The study makes use of conjoint analysis and the multidimensional scaling technique to identify (a) the attributes of the room air freshener and their corresponding levels from the consumers’ perspective, (b) the importance of each attribute of the room air freshener and its contribution in influencing the consumers’
purchase decision, (c) the best and the worst combinations of attributes and their levels from the consumers’ point of view, and (d) the potential opportunities for the new brand of room air freshener in the perceptual map of the consumers’ mind. Keywords: conjoint analysis, MDS, aerosol product

Modeling preference data

by Alberto Maydeu-Olivares

Maydeu-Olivares, A. & Böckenholt, U. (2009). Modeling preference data.  In R. Millsap &  A. Maydeu-Olivares (Eds.) (2009). Handbook of Quantitative Methods in Psychology. (pp. 264-282). London: Sage.