‘Pick Any’ Measures Contaminate Brand Image Studies

Dolničar, Sara; Rossiter, John R.; Grün, Bettina

doi:10.2501/ijmr-54-6-821-834

Cited by 17 publications

(15 citation statements)

References 13 publications

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“…Survey respondents ticked “yes” if they felt that an attribute applied to a specific source of water or “no” otherwise. This format is known as forced choice binary format or the binary with inferred threshold measure and has been shown to lead to the most reliable results in terms of test-retest reliability in brand image measurement ( Dolnicar and Grün, 2013; Dolnicar et al., 2012; Dolnicar and Leisch, 2012; Rossiter, Dolnicar and Grün, in press ).…”

Section: Methodsmentioning

confidence: 99%

Branding water

2014

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Branding is a key strategy widely used in commercial marketing to make products more attractive to consumers. With the exception of bottled water, branding has largely not been adopted in the water context although public acceptance is critical to the implementation of water augmentation projects. Based on responses from 6247 study participants collected between 2009 and 2012, this study shows that (1) different kinds of water – specifically recycled water, desalinated water, tap water and rainwater from personal rainwater tanks – are each perceived very differently by the public, (2) external events out of the control of water managers, such as serious droughts or floods, had a minimal effect on people's perceptions of water, (3) perceptions of water were stable over time, and (4) certain water attributes are anticipated to be more effective to use in public communication campaigns aiming at increasing public acceptance for drinking purposes. The results from this study can be used by a diverse range of water stakeholders to increase public acceptance and adoption of water from alternative sources.

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Section: Methodsmentioning

confidence: 99%

Branding water

2014

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“…On the other hand, when the consumer gives an answer of "no," the logical implication is that one or both of these subattributes is lacking. Also worthy of note is that this measure's answer options are "forced" binary in that the rater has to answer either yes or no, whereas in an earlier study Rossiter and Bellman (2012) employed the same item with a yes-only, or "pick any," answer option whose "unforced" nature, as demonstrated by Dolnicar, Rossiter, and Grün (2012), makes answering the measure unstable on repeated measurements such as used in brand tracking studies.…”

Section: Figure 7 About Herementioning

confidence: 99%

How to use C-OAR-SE to design optimal standard measures

Rossiter

2016

EJM

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The paper begins by dismissing convergent validation, the core procedure in Nunnally's (1978) and Churchill's (1979) psychometric method of measure design which allows alternative measures of the same construct. The method of dismissal is the mathematical demonstration that an alternative measure, no matter how highly its scores converge with those from the original measure, will inevitably produce different findings. The only solution to this knowledgethreatening problem is to agree on an optimal measure of each of our major constructs and to use only that measure in all future research, as is standard practice in the physical sciences. The paper concludes by proposing an extension of Rossiter's C-OAR-SE method to design optimal standard measures of judgment constructs, the most prevalent type of construct in marketing. Findings: Findings: The findings are, first, the mathematical dismissal of the accepted practice of convergent validation of alternative measures of the same construct, which paves the way for, second, the proposal of five new C-OAR-SE-based construct models for designing optimal standard measures of judgment constructs, three of which require a multiple-item measure and two of which a single-item measure. Practical implications: Practical implications: The common practice of accepting alternative measures of the same construct causes major problems for the social sciences: when different measures are used, it becomes impossible, except by remote chance, to replicate findings; meta-analyses become meaningless because the findings are averaged over different measures; and empirical generalizations cannot be trusted when

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“…However, Dolnicar, Rossiter, and Grün (2012) raised three concerns about the validity of the Pick-Any measure (see also Dolnicar and Rossiter, 2008). The main concern is that 'free choosing' in Pick Any allows respondents to omit, and therefore underreport, beliefs about the brands; the proposed Level-Free Forced-Choice Binary measure is not affected by this problem because it encourages respondents to carefully consider every one of the attributes that is presented on the questionnaire.…”

Section: Introductionmentioning

confidence: 99%

Why the Level-Free Forced-Choice Binary Measure of Brand Benefit Beliefs Works So Well

Rossiter

Dolničar

Grün

2015

International Journal of Market Research

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The level-free version of the Forced-Choice Binary measure of brand benefit beliefs was introduced in a recent article in IJMR (Dolnicar et al. 2012) and was shown to yield more stable – hence more reliable and trustworthy – results than the shorter ‘Pick-Any’ measure and the longer ‘7-Point Scale’ measure. The aims of the present article are (1) to explain how and why the Level-Free Forced-Choice Binary measure works so well, and (2) to point out its advantages over other belief measure formats - advantages that, importantly, include prevention of all forms of response bias.

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‘Pick Any’ Measures Contaminate Brand Image Studies

Cited by 17 publications

References 13 publications

Branding water

Branding water

How to use C-OAR-SE to design optimal standard measures

Why the Level-Free Forced-Choice Binary Measure of Brand Benefit Beliefs Works So Well

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