AN ALTERNATING LEAST‐SQUARES PROCEDURE FOR ESTIMATING MISSING PREFERENCE DATA IN PRODUCT‐CONCEPT TESTING

S. DeSarbo; Paul E. Green; J. Douglas Carroll

doi:10.1111/j.1540-5915.1986.tb00219.x

AN ALTERNATING LEAST‐SQUARES PROCEDURE FOR ESTIMATING MISSING PREFERENCE DATA IN PRODUCT‐CONCEPT TESTING

S. DeSarbo, Paul E. Green, J. Douglas Carroll

Marketing

Research output: Contribution to journal › Article › peer-review

22 Scopus citations

Abstract

Product‐concept testing is a popular activity in marketing research. Often the number of new product/service concepts under study far exceeds the time available for any single respondent. Respondents therefore may receive only a subset of the concepts comprising the total design. Researchers are interested in making plausible imputations for the missing evaluations of any given respondent. This paper proposes a model and an iterative estimation procedure to impute missing entries for each evaluator. The model and the procedure incorporate (1) the internal structure of the response matrix and (2) an ancillary matrix of (nonmissing) respondent background data; they also (3) allow for individual differences in respondents' uses of the numerical rating scale. The model is applied to both real and synthetic data. Suggestions also are given on how the data imputations may be used in market segmentation and product‐line decisions.

Original language	English (US)
Pages (from-to)	163-185
Number of pages	23
Journal	Decision Sciences
Volume	17
Issue number	2
DOIs	https://doi.org/10.1111/j.1540-5915.1986.tb00219.x
State	Published - Apr 1986

All Science Journal Classification (ASJC) codes

General Business, Management and Accounting
Strategy and Management
Information Systems and Management
Management of Technology and Innovation

Access to Document

10.1111/j.1540-5915.1986.tb00219.x

Cite this

@article{74cb3b82d9814208968981761e9b4e33,

title = "AN ALTERNATING LEAST‐SQUARES PROCEDURE FOR ESTIMATING MISSING PREFERENCE DATA IN PRODUCT‐CONCEPT TESTING",

abstract = "Product‐concept testing is a popular activity in marketing research. Often the number of new product/service concepts under study far exceeds the time available for any single respondent. Respondents therefore may receive only a subset of the concepts comprising the total design. Researchers are interested in making plausible imputations for the missing evaluations of any given respondent. This paper proposes a model and an iterative estimation procedure to impute missing entries for each evaluator. The model and the procedure incorporate (1) the internal structure of the response matrix and (2) an ancillary matrix of (nonmissing) respondent background data; they also (3) allow for individual differences in respondents' uses of the numerical rating scale. The model is applied to both real and synthetic data. Suggestions also are given on how the data imputations may be used in market segmentation and product‐line decisions.",

author = "S. DeSarbo and Green, {Paul E.} and Carroll, {J. Douglas}",

year = "1986",

month = apr,

doi = "10.1111/j.1540-5915.1986.tb00219.x",

language = "English (US)",

volume = "17",

pages = "163--185",

journal = "Decision Sciences",

issn = "0011-7315",

publisher = "Wiley-Blackwell Publishing Ltd",

number = "2",

}

TY - JOUR

T1 - AN ALTERNATING LEAST‐SQUARES PROCEDURE FOR ESTIMATING MISSING PREFERENCE DATA IN PRODUCT‐CONCEPT TESTING

AU - DeSarbo, S.

AU - Green, Paul E.

AU - Carroll, J. Douglas

PY - 1986/4

Y1 - 1986/4

N2 - Product‐concept testing is a popular activity in marketing research. Often the number of new product/service concepts under study far exceeds the time available for any single respondent. Respondents therefore may receive only a subset of the concepts comprising the total design. Researchers are interested in making plausible imputations for the missing evaluations of any given respondent. This paper proposes a model and an iterative estimation procedure to impute missing entries for each evaluator. The model and the procedure incorporate (1) the internal structure of the response matrix and (2) an ancillary matrix of (nonmissing) respondent background data; they also (3) allow for individual differences in respondents' uses of the numerical rating scale. The model is applied to both real and synthetic data. Suggestions also are given on how the data imputations may be used in market segmentation and product‐line decisions.

AB - Product‐concept testing is a popular activity in marketing research. Often the number of new product/service concepts under study far exceeds the time available for any single respondent. Respondents therefore may receive only a subset of the concepts comprising the total design. Researchers are interested in making plausible imputations for the missing evaluations of any given respondent. This paper proposes a model and an iterative estimation procedure to impute missing entries for each evaluator. The model and the procedure incorporate (1) the internal structure of the response matrix and (2) an ancillary matrix of (nonmissing) respondent background data; they also (3) allow for individual differences in respondents' uses of the numerical rating scale. The model is applied to both real and synthetic data. Suggestions also are given on how the data imputations may be used in market segmentation and product‐line decisions.

UR - http://www.scopus.com/inward/record.url?scp=84985854755&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84985854755&partnerID=8YFLogxK

U2 - 10.1111/j.1540-5915.1986.tb00219.x

DO - 10.1111/j.1540-5915.1986.tb00219.x

M3 - Article

AN - SCOPUS:84985854755

SN - 0011-7315

VL - 17

SP - 163

EP - 185

JO - Decision Sciences

JF - Decision Sciences

IS - 2

ER -

AN ALTERNATING LEAST‐SQUARES PROCEDURE FOR ESTIMATING MISSING PREFERENCE DATA IN PRODUCT‐CONCEPT TESTING

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this