David Takes on Goliath: An Analysis of Survey Evidence in a Trademark Dispute

2003 ◽  
Vol 45 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Philip Gendall ◽  
Janet Hoek
Keyword(s):  
New Zealand ◽  
Measurement Error ◽  
Survey Design ◽  
Sampling Error ◽  
Error Measurement ◽  
Question Wording ◽  
Forensic Research ◽  
Coverage Error ◽  
Consumer Confusion ◽  
Protection Legislation

Anheuser Busch, the brewers of Budweiser, sued a Czech brewer alleging that its beer brand, Budejovicky Budvar, created confusion in the marketplace and breached New Zealand consumer protection legislation that prohibits misleading and deceptive conduct. The case brought against Budejovicky Budvar included a survey of beer consumers and this was central to the action. This paper explores the robustness of the survey by evaluating its methodology in terms of the four errors that affect survey research: coverage error, measurement error, sampling error and non-response error. In each area, the survey adduced contained serious flaws that undermined its validity. Ironically, these flaws were largely avoidable. An alternative survey design that draws on past cases and empirical generalisations in question wording is proposed. While untested in court, this design takes greater cognisance of issues regarding forensic research that have been raised internationally. More attention to these issues should reduce the vulnerability of surveys to criticism and help ensure that the relevant public's voice is better represented in actions that allege consumer confusion.

The battle over employers’ demand for “more flexibility”

2016 ◽  
Vol 38 (6) ◽  
pp. 886-906 ◽  
Author(s):  
Erling Rasmussen ◽  
Barry Foster ◽  
Deirdre Farr
Keyword(s):  
New Zealand ◽  
Survey Design ◽  
Postal Questionnaire ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Policy Makers ◽  
Content Type ◽  
Employer Attitudes ◽  
Legislative Changes ◽  
Employment Legislation

Purpose The purpose of this paper is to place empirical research on New Zealand employers’ attitudes to collective bargaining and legislative change within the context of the long running debate of flexibility. Design/methodology/approach A cross-sectional survey design using a self-administered postal questionnaire, covering private sector employers with ten or more staff and including employers within all 17 standard industry classification. To explore particular issues, an additional in-depth interviews were conducted of 25 employers participating in the survey. Findings It is found that employers support overwhelmingly recent legislative changes though there are variations across industries and firm sizes. There is also considerable variation in terms of which legislative changes are applied in the workplace. Despite fewer constraints on employer-determined flexibility, there was a rather puzzling finding that most employers still think that employment legislation is even balanced or favouring employees. Originality/value Cross-sectional survey findings of New Zealand employer attitudes to legislative changes are few and provide valuable data for policy makers, unions, employers and employment relations researchers. The paper also contributes to a more comprehensive understanding of pressures to increase employer-determined flexibility in many western countries.

Adaptive Survey Design and Measurement Error

2017 ◽  
pp. 199-222
Author(s):  
Barry Schouten ◽  
Andy Peytchev ◽  
James Wagner
Keyword(s):  
Measurement Error ◽  
Survey Design

Critiquing the OECD’s Employment Protection Legislation Index for individual dismissals: The importance of procedural requirements

2019 ◽  
pp. 0143831X1985641 ◽  
Author(s):  
Mark Harcourt ◽  
Gregor Gall ◽  
Arjun Sree Raman ◽  
Helen Lam ◽  
Richard Croucher
Keyword(s):  
New Zealand ◽  
Empirical Evidence ◽  
Employment Protection ◽  
Employment Protection Legislation ◽  
Member States ◽  
Protection Legislation ◽  
Principal Method

Key EU agencies have successfully urged member states to scale back employment protection legislation as a solution to unemployment. The economic arguments for this reform are mixed, with recent empirical evidence largely unsupportive. Critics have also raised doubts about the accuracy of the OECD’s Employment Protection Legislation Index, which is the principal method EU agencies use to target so-called high-protection regimes. This article supplements existing criticisms of the OECD index by arguing that it fails to account for procedural requirements in assessing the difficulties and costs of carrying out individual dismissals. Evidence from New Zealand, ostensibly a low-protection country, demonstrates procedural requirements can pose the main impediments to carrying out individual dismissals. This suggests the need for revision of the OECD Employment Protection Legislation Index or the use of other indices instead.

How to improve allometric equations to estimate forest biomass stocks? Some hints from a central African forest

2014 ◽  
Vol 44 (7) ◽  
pp. 685-691 ◽  
Author(s):  
Quentin Moundounga Mavouroulou ◽  
Alfred Ngomanda ◽  
Nestor Laurier Engone Obiang ◽  
Judicaël Lebamba ◽  
Hugues Gomat ◽  
...  
Keyword(s):  
Measurement Error ◽  
Bayesian Model Averaging ◽  
Sampling Error ◽  
Forest Biomass ◽  
Model Averaging ◽  
Allometric Equation ◽  
Allometric Equations ◽  
Data Set ◽  
Equation Error ◽  
Source Of Error

Predicting the biomass of a forest stand using forest inventory data and allometric equations involves a chain of propagation of errors going from the sampling error to the tree measurement error. Using a biomass data set of 101 trees in a tropical rain forest in Gabon, we compared two sources of error: the error due to the choice of allometric equation, assessed using Bayesian model averaging, and the biomass measurement error when tree biomass is calculated from tree volume rather than directly weighed. Differences between allometric equations resulted in a between-equation error of about 0.245 for log-transformed biomass compared with a residual within-equation error of 0.297. Because the residual error is leveled off when randomly accumulating trees whereas the between-equation error is incompressible, the latter turned out to be a major source of error at the scale of a 1 ha plot. Measuring volumes rather than masses resulted in an error of 0.241 for log-transformed biomass and an average overestimation of the biomass by 19%. These results confirmed the choice of the allometric equation as a major source of error but unexpectedly showed that measuring volumes could seriously bias biomass estimates.

Enzyme variation in orange roughy, Hoplostethus atlanticus (Teleostei: Trachichthyidae), from southern Australian and New Zealand waters

1992 ◽  
Vol 43 (6) ◽  
pp. 1561 ◽  
Author(s):  
NG Elliott ◽  
RD Ward
Keyword(s):  
Genetic Diversity ◽  
New Zealand ◽  
Sampling Error ◽  
Genetic Homogeneity ◽  
Gene Frequencies ◽  
Orange Roughy ◽  
Enzyme Variation ◽  
Enzyme Loci ◽  
Total Genetic Diversity ◽  
Hoplostethus Atlanticus

Orange roughy from six localities around the southern coasts of Australia showed no evidence of genetic subdivision when the products of 11 polymorphic enzyme loci were analysed electrophoretically. Samples ranged in size from 84 to 171 per locality. Gene frequencies were very similar in samples taken from New Zealand. The amount of total genetic diversity attributable to subdivision among samples is estimated at 0.55 to 0.22%, but bootstrapping procedures showed that much of this diversity could arise from sampling error. A minimum of around 200 migrants per locality per generation would be sufficient to maintain the observed genetic homogeneity, although actual numbers migrating are likely to be greater than this.

The sensitivity of measurement error in stand volume estimation

1990 ◽  
Vol 20 (6) ◽  
pp. 800-804 ◽  
Author(s):  
George Z. Gertner
Keyword(s):  
Measurement Error ◽  
Random Sample ◽  
Sampling Error ◽  
Regression Function ◽  
Volume Estimation ◽  
Simple Random Sample ◽  
Large Reduction ◽  
Stand Volume ◽  
Volume Function ◽  
Independent Variables

A method is given for approximating and evaluating the consequences of random and nonrandom errors in the independent variables of a nonlinear tree volume function that is used in the estimation of stand volume based on a simple random sample of plots. Sampling error, regression function error, and measurement error are accounted for with the method presented. An application is given where relatively moderate amounts of measurement error in the independent variables of a tree volume function can cause a relatively large reduction in the accuracy of estimated stand volume.

Effects of measurement error on Monte Carlo integration estimators of tree volume: critical height sampling and vertical Monte Carlo methods

2015 ◽  
Vol 45 (4) ◽  
pp. 463-470 ◽  
Author(s):  
Thomas B. Lynch
Keyword(s):  
Monte Carlo ◽  
Measurement Error ◽  
Sampling Error ◽  
Monte Carlo Integration ◽  
Critical Height ◽  
Volume Estimate ◽  
Cross Sectional ◽  
Individual Tree ◽  
Control Variate ◽  
Multiplicative Measurement Error

The effects of measurement error on Monte Carlo (MC) integration estimators of individual-tree volume that sample upper-stem heights at randomly selected cross-sectional areas (termed vertical methods) were studied. These methods included critical height sampling (on an individual-tree basis), vertical importance sampling (VIS), and vertical control variate sampling (VCS). These estimators were unbiased in the presence of two error models: additive measurement error with mean zero and multiplicative measurement error with mean one. Exact mathematical expressions were derived for the variances of VIS and VCS that include additive components for sampling error and measurement error, which together comprise total variance. Previous studies of sampling error for MC integration estimators of tree volume were combined with estimates of upper-stem measurement error obtained from the mensurational literature to compute typical estimates of total standard errors for VIS and VCS. Through examples, it is shown that measurement error can substantially increase the total root mean square error of the volume estimate, especially for small trees.

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