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AbstractWith perfect information from flawlessly designed and executed evaluations of social programs in short supply, evaluators are urged to look to gathering many kinds of evidence and analyzing it by multiple methods to reduce the incidence of erroneous conclusions.

SummaryThe sums of squares associated with the independent variables in a multiple regression equation depend on the order in which these variables are introduced. Two methods have been proposed in the literature to avoid this inconvenience: "forward selection" or "backward elimination".With forward selection the independent variables are introduced in successive stages. The order is not predetermined but at each stage that variable is taken as the next one which produces the highest reduction in the residual sum of squares of the dependent variable.With backward elimination on the other hand, we start with the complete regression equation and eliminate the independent variables from it in the order in which they produce the smallest increases in the residual sum of squares.This paper describes a simple and convenient computational lay‐out which can be used for both procedures. In forward selection we start with the matrix of product sums, and in bacward elimination we work from the inverse matrix.In addition these techniques are applied to a variety of practical examples in order to see what results they lead to and what pitfalls may be encountered.

Prioritising items for management attention has been advocated in operations management for a long time, normally using ABC analysis (inventory control). This focuses attention on the "A" category items to maximise managerial effectiveness. Empirical evidence shows that this is a reasonable rule for allocating scarce resource‐management time but presents difficulties when the manager has to take more than one important dimension of a situation into account. A joint criteria matrix is put forward within the ABC framework and an industrial application given. The joint criteria matrix has practical utility provided ranking on some scale of measurement is realistic. The appropriate number of categories must be defined by the user. Combining criteria will probably require different analytical approaches, e.g. goal programming or heuristic approaches. Utilisation of the matrix by managers can provide an explicit method for taking a range of criteria into account in the development of inventory policies.

AbstractConjoint analysis is widely used for estimating the effects of a large number of treatments on multidimensional decision-making. However, it is this substantive advantage that leads to a statistically undesirable property, multiple hypothesis testing. Existing applications of conjoint analysis except for a few do not correct for the number of hypotheses to be tested, and empirical guidance on the choice of multiple testing correction methods has not been provided. This paper first shows that even when none of the treatments has any effect, the standard analysis pipeline produces at least one statistically significant estimate of average marginal component effects in more than 90% of experimental trials. Then, we conduct a simulation study to compare three well-known methods for multiple testing correction, the Bonferroni correction, the Benjamini–Hochberg procedure, and the adaptive shrinkage (Ash). All three methods are more accurate in recovering the truth than the conventional analysis without correction. Moreover, the Ash method outperforms in avoiding false negatives, while reducing false positives similarly to the other methods. Finally, we show how conclusions drawn from empirical analysis may differ with and without correction by reanalyzing applications on public attitudes toward immigration and partner countries of trade agreements.

The nature of family research is such that several family members may be asked to comment on identical items. The purpose of this article is to discuss several approaches for analysis of multiple respondent data applicable to family research. Analysis techniques will focus on one dependent variable. Of special consideration in analysis of multiple respondent data is the violation of statistical assumptions, specifically, the assumption of sphericity. One method of handling this assumption violation will be addressed. An example from a recent family-focused study will be provided to demonstrate differences in significance obtained through use of one-way analysis of variance (ANOVA), repeated measures ANOVA, and Hotellings T2.