Measuring and modeling the (limited) reliability of free choice attitude questions

Rungie, Cam; Laurent, Gilles; Riley, Francesca Dall’Olmo; Morrison, Donald G.; Roy, Tirthankar

doi:10.1016/j.ijresmar.2004.11.005

Cited by 32 publications

(43 citation statements)

References 15 publications

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“…The full binary format is not identical to the binary format (free choice) used in the data sets of Rungie et al (2005). Our binary format forced respondents to answer each brand-attribute combination by either agreeing or disagreeing; whereas the free-choice format gives respondents more flexibility in Deleted: one Deleted: one naming only selected brand-attribute combinations.…”

Section: Deleted: Instablementioning

confidence: 99%

“…Prior studies reveal that brand images are not very reliable, as they are typically measured in industry, which might be due to the answer format typically used (Rungie et al, 2005). The practical implication is that brand image data -as currently collected in consumer surveys -is not a valid source of market information.…”

Section: Introductionmentioning

confidence: 99%

“…It does not account for consumer heterogeneity, although targeting specific sub-groups of the marketplace, and thus harvesting knowledge about consumer heterogeneity, is fundamental to brand-based industry marketing. Almost a decade after Castleberry et al (1994), Rungie et al (2005) reinvestigated brand image stability, and empirically demonstrated, over several data sets, the instability of brand images when measured in a binary way. This result throws the stability of binary answer formats into question, although the authors state explicitly that "a similar lack of reliability may exist for attitude questions in a Likert format" (Rungie et al, 2005, p. 317).…”

Section: Introductionmentioning

confidence: 99%

“…Rungie et al (2005) propose an improved model that describes the aggregate relationship of RR and RL in which the coefficient c is referred to as "reliability":…”

Section: Introductionmentioning

confidence: 99%

“…From a theoretical perspective, not all brand-attribute associations lack stability (lack of stability was concluded by Castleberry et al, 1994;Dall'Olmo Riley et al, 1997;Rungie et al, 2005 …”

Section: Introductionmentioning

confidence: 99%

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Question Stability in Brand Image Measurement: Comparing Alternative Answer Formats and Accounting for Heterogeneity in Descriptive Models

Dolničar¹,

Grün²

2007

Australasian Marketing Journal

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This article originally published as Dolnicar, S and Grun, B, Question stability in brand image measurement: Comparing alternative answer formats and accounting for heterogeneity in descriptive models, Australasian Marketing Journal, 15(2), 2007, 26-41. Question stability in brand image measurement -Comparing alternative answer formats and accounting for heterogeneity in descriptive models. AbstractHigh quality image data on how consumers perceive brands is essential to make good brand management decisions. Prior studies reveal that brand images are not very reliable, as they are typically measured in industry, which might be due to the answer format typically used (Rungie et al., 2005). The practical implication is that brand image data -as currently collected in consumer surveys -is not a valid source of market information. We challenge this implication. Using three measures of stability we test whether the binary answer format produces image data less reliable than alternative formats. We investigate whether the aggregate descriptive model of brand image stability proposed by Rungie et al. can be improved by accounting for heterogeneity. Results indicate that, compared to alternative formats, binary answer formats lead to equal stability levels, and most brand-attribute associations are stable. Unstable associations typically fail to describe adequately the brands under study. Practical implications include that binary brand-attribute associations can be used safely to measure brand images. Also, practitioners can get guidance about required brand management measures by discriminating between stable and unstable brand-attribute associations. A model that helps managers classify brand-attribute associations into stable or unstable is proposed in the article. McDonalds is expensive. This is not plausible given that consumer heterogeneity is widely acknowledged and target markets frequently form the basis of most brand managers marketing activities.In this paper we investigate the extent to which answer formats used in brand image studies affect the stability of brand-attribute associations and we propose a model which accounts for consumer heterogeneity.The results are of major importance for managers who rely on empirical brand image data.The study demonstrates (1) that brand images are more stable than previously reported and that brand image data therefore represents a valid basis for the development of marketing activities, (2) that all answer formats lead to equally stability levels, (3) that heterogeneity exists in brand-attribute associations, thus making it possible for managers to design customized strategies for different attributes, and (4) that the reliability of brand image data depends strongly on how the brand image survey is designed. Most importantly, brand images should be measured among consumers for whom the product category is meaningful. 4 Question stability in brand image measurementComparing alternative answer formats and accounting for heterogeneity in descriptive models AbstractHig...

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Section: Deleted: Instablementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Rungie et al (2005) propose an improved model that describes the aggregate relationship of RR and RL in which the coefficient c is referred to as "reliability":…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Question Stability in Brand Image Measurement: Comparing Alternative Answer Formats and Accounting for Heterogeneity in Descriptive Models

Dolničar¹,

Grün²

2007

Australasian Marketing Journal

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A stochastic model of consideration set sizes

Trinh

2014

J of Consumer Behaviour

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This paper proposes a new model of consideration set sizes, which relies on a stochastic modelling approach to better understand data patterns. The paper combines the Poisson distribution with the lognormal distribution proposed by Hauser and Wernerfelt (1990) to create the Poisson lognormal (PLN) model of consideration set sizes. An advantage of PLN is that it allows variance within a given individual consumer. This is a crucial factor for brand managers who wish to choose a particular market to promote their brand, as a higher within‐individual variance suggests that there is more chance for a consumer to change their consideration set size, whereas a lower within‐individual variance indicates that a consumer tends to stick to his/her consideration set size. The paper then uses 10 datasets including service, durable and fast‐moving consumer goods across four countries to validate the new model and compare it with the lognormal model. The results show that PLN gives a good fit to these data. It outperforms the lognormal model. The average mean absolute percentage error of the PLN model is 12 per cent, whereas that of the lognormal model is 26 per cent. For managerial implications, the paper proposes a better tool to help brand managers analyse the nature and intensity of competition that is facing their brands. Also, relying on its stochastic element, the proposed model can help brand managers predict future brand consideration by their consumers, as well as evaluate any change in brand consideration, caused by marketing activity such as sales promotion and advertising. Copyright © 2014 John Wiley & Sons, Ltd.

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Rossiter

2010

Measurement for the Social Sciences

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Measuring and modeling the (limited) reliability of free choice attitude questions

Cited by 32 publications

References 15 publications

Question Stability in Brand Image Measurement: Comparing Alternative Answer Formats and Accounting for Heterogeneity in Descriptive Models

Question Stability in Brand Image Measurement: Comparing Alternative Answer Formats and Accounting for Heterogeneity in Descriptive Models

A stochastic model of consideration set sizes

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