Robustness of a Class of Partial Diallel Cross Designs to the Unavailability of a Complete Block of Observations

Complete and partial diallel cross designs are examined as to their construction and robustness against the loss of a block of observations. A simple generalized inverse is found for the information matrix of the line effects, which allows evaluation of expressions for the variances of the line-effect differences with and without the missing block. A-efficiencies, based on average variances of the elementary contrasts of the line-effects, suggest that these designs are fairly robust. The loss of efficiency is generally less than 10%, but it is shown that specific comparisons might suffer a loss of efficiency of as much as 40%.     

The Alliance Experience Transfer Effect: The Case of Industry Convergence in the Telecommunications Equipment Industry

A central premise of the industry change literature is that firms change their strategic actions when an industry changes. Industry convergence (IC), the blending of boundaries between industries creating competition among firms that previously did not compete, is increasingly impacting many industries and is a salient case of industry change. Acquisitions are an important action shaping the course of IC because they trigger imitation and bandwagon effects further accelerating IC. This paper focuses on why and when learning from alliances reduces uncertainty resulting in acquisitions during IC: an ‘alliance experience transfer effect’. We demonstrate the utility of this mechanism for the substitution‐based form of IC that occurred between the telecommunications equipment and computer networking industries. Our key insight is that when the extent of IC is low there are significant transfer effects but, as the extent of IC increases, firms have access to an expanding volume and diversity of information sources that reduce uncertainty, thus weakening the transfer effect mechanism. We contribute to the alliance–acquisition relationship and learning literatures by demonstrating that the alliance experience transfer effect mechanism explains changes in firm strategic action (alliancing and acquiring) as the extent of IC changes. We also introduce a semi‐convergence perspective by directly measuring the extent of IC.     

Differential predictors of academic achievement: individual and familial factor interactions with community poverty

We present an analysis of the contexts within which adolescents graduate from high school and enroll in college. Data from the National Longitudinal Study of Adolescent Health show that adolescents’ school engagement, maternal academic monitoring, and community poverty significantly interact to explain differences in high school graduation and college enrollment rates (n?=?7100). To examine this association, we performed weighted logistic regression analyses, controlling for gender, race, block level unemployment, and block level median income. Findings suggest that protective factors can help youth overcome the challenges associated with community poverty and achieve academically in low and medium levels of poverty, but that youth in high-poverty contexts may need more resources to reach higher levels of academic attainment.     

Assessing robustness of crossover designsto subjects dropping out

In some crossover experiments, particularly in medical applications, subjects may fail to complete their sequences of treatments for reasons unconnected with the treatments received. A method is described of assessing the robustness of a planned crossover design, with more than two periods, to subjects leaving the study prematurely. The method involves computing measures of efficiency for every possible design that can result, and is therefore very computationally intensive. Summaries of these measures are used to choose between competing designs. The computational problem is reduced to a manageable size by a software implementation of Polya theory. The method is applied to comparing designs for crossover studies involving four treatments and four periods. Designs are identified that are more robust to subjects dropping out in the final period than those currently favoured in medical and clinical trials.     

Orthogonally Blocked Mixture Experiments in Ellipsoidal Restricted Regions

In practical situations involving mixtures formed from several ingredients, interest is sometimes centered on the response in an ellipsoidal neighborhood around a standard formulation. We show that standard, orthogonally blocked, response surface designs, defined on a q ? 1 dimensional unit sphere, may be transformed into similarly orthogonally blocked q-ingredient mixture designs defined within an ellipsoid centered at the standard formulation. The method is illustrated using several examples of mixture experiments with three, four, and five ingredients, arranged in two, three, or four orthogonal blocks, obtained by projecting standard central composite designs and Box–Behnken designs into the ellipsoidal mixture region. Rotations of the resulting designs within the ellipsoidal regions are also considered.     

Trimmed and Outer Means and Their Variances

Some properties of trimmed and outer means for the normal situation are considered; in particular, the mean of the outer half of the sample has the same variance as the mean of the inner half. An inequality involving variances of unbiased estimators of location and their complements is derived and some of its consequences are examined.     

Mixture designs for five components in orthogonal blocks

The methods developed by John and Draper et al. of partitioning the blends (runs) of four mixture components into two or more orthogonal blocks when fitting quadratic models are extended to mixtures of five components. The characteristics of Latin squares of side five are used to derive rules for reliably and quickly obtaining designs with specific properties. The designs also produce orthogonal blocks when higher order models are fitted.     

Projection designs for mixture experiments in orthogonal blocks

Mixture experiments are often carried out in the presence of process variables, such as days of the week or different machines in a manufacturing process, or different ovens in bread and cake making. In such experiments it is particularly useful to be able to arrange the design in orthogonal blocks, so that the model in tue mixture vanauies may ue iitteu inucpenuentiy or tne UIOCK enects mtrouuceu to take account of the changes in the process variables. It is possible in some situations that some of the ingredients in the mixture, such as additives or flavourings, are present in soian quantities, pernaps as iuw a.s 5% ur even !%, resulting in the design space being restricted to only part of the mixture simplex. Hau and Box (1990) discussed the construction of experimental designs for situations where constraints are placed on the design variables. They considered projecting standard response surface designs, including factorial designs and central composite designs, into the restricted design space, and showed that the desirable property of block orthogonality is preserved by the projections considered. Here we present a number of examples of projection designs and illustrate their use when some of the ingredients are restricted to small values, such that the design space is restricted to a sub-region within the usual simplex in the mixture variables.     

D-optimal mixture component-amount designs for quadratic and cubic models

When the total amount of a mixture of ingredients needs to be taken into account (in addition to the composition of its ingredients), an experimental design requires several levels of the amount. Designs for such situations are discussed, and D-optimal choices are made for fitting quadratic and cubic models, for various numbers of experimental units.