John Tyssedal

Reduced cognitive functions in a group of whiplash patients with demonstrated disturbances in the posture control system

Past studies examining whether or not cognitive changes actually have occurred as a result of a whiplash (WL) accident have produced varying results. The aim of this study was to identify possible cognitive dysfunctions in a group with persistent problems after whiplash due to injuries to the posture control system and related structures. The whiplash subjects (n = 23) were selected on the basis of their reduced gain in the Smooth Pursuit Neck Torsion test (SPNT). The WL group differed significantly from a closely matched control group on tests of learning and memory, and prolonged divided attention and concentration. After attempting to rule out other ways of interpreting these differences (such as pain, depression, medication, and premorbid health problems), these data were interpreted as lending support to the notion of a causal connection between the disturbed posture control system and some cognitive malfunctions.

SIXTEEN RUN DESIGNS OF HIGH PROJECTIVITY FOR FACTOR SCREENING

A two level orthogonal array design for n observations with k factors and of projectivity P provides an (n, k, P) factor screen for which every projection into P space produces a complete 2 P factorial, possibly with certain points replicated. Box and Tyssedal [1] rigorously investigated the screening properties of such designs derived from fractional factorials and Plackett Burman orthogonal arrays (OAs). For example, they showed that these always provided (12, 11, 3) and (20, 19, 3) screens, but for 16 runs only a (16, 8, 3) screen could be generated. In this paper it is shown that designs derived from a different class of OAs due to Hall [2] can produce sixteen run designs that can screen a larger number of factors and that in particular (16, 12, 3) screens and also a (16, 14, 3) screen can be obtained.

The Use of a 12-run Plackett–Burman Design in the Injection Moulding of a Technical Plastic Component

During injection moulding of technical plastic components it is often the case that 15–20 variables need to be set to operational conditions when the production of a new plastic components is started. In this case study, a 12-run Plackett–Burman design is used in the experimentation with eight factors and nine responses to obtain better operational conditions for the production of a certain product. A 12-run Plackett–Burman design belongs to the class of non-geometric orthogonal arrays. Most of these designs have very good projection properties compared with their run sizes, but due to partial aliasing among effects, standard methods of analysing fractional factorial two-level designs do not apply. In this paper, part of the planning of the experiment, the method of analysis used and the results achieved are presented. Substantial improvement in the operational conditions was obtained. Copyright

Graphical Aids for the Analysis of Two-Level Nonregular Designs

The complex alias pattern between main effects and two-factor interactions for two-level nonregular designs has been considered a problem when analyzing these designs. If only a few two-factor interactions are active, however, the pattern induced into the estimated main effects contrasts from the active interactions may be very structured. This is, in particular, true for the 12-run and the 20-run Plackett–Burman (PB) designs, probably the two most important designs for physical experimentation. This article presents a graphical method for the analysis of nonregular two-level designs. The method consists of two steps. The first step is called contrast plots interpretation and is directed toward revealing the cause for the pattern observed in contrast plots. The second step is called alias reduction and aims at simplifying the interpretation of the contrast plots by reducing the aliasing caused by effects that, with a high degree of certainty, may be considered active. The method is tested on the 12-run PB design both with simulated and real data and on the 20-run PB design for one particular case with real data. Supplementary materials (MINITAB codes for performing calculations and plots) are available online.

Introducing Statistical Design of Experiments to SPARQL Endpoint Evaluation

This paper argues that the common practice of benchmarking is inadequate as a scientific evaluation methodology. It further attempts to introduce the empirical tradition of the physical sciences by using techniques from Statistical Design of Experiments applied to the example of SPARQL endpoint performance evaluation. It does so by studying full as well as fractional factorial experiments designed to evaluate an assertion that some change introduced in a system has improved performance. This paper does not present a finished experimental design, rather its main focus is didactical, to shift the focus of the community away from benchmarking towards higher scientific rigor.

Factor screening in nonregular two-level designs based on projection-based variable selection

In this paper, we focus on the problem of factor screening in nonregular two-level designs through gradually reducing the number of possible sets of active factors. We are particularly concerned with situations when three or four factors are active. Our proposed method works through examining fits of projection models, where variable selection techniques are used to reduce the number of terms. To examine the reliability of the methods in combination with such techniques, a panel of models consisting of three or four active factors with data generated from the 12-run and the 20-run Plackett–Burman (PB) design is used. The dependence of the procedure on the amount of noise, the number of active factors and the number of experimental factors is also investigated. For designs with few runs such as the 12-run PB design, variable selection should be done with care and default procedures in computer software may not be reliable to which we suggest improvements. A real example is included to show how we propose factor screening can be done in practice.

Split-plot designs for multistage experimentation

ABSTRACT Most of today’s complex systems and processes involve several stages through which input or the raw material has to go before the final product is obtained. Also in many cases factors at different stages interact. Therefore, a holistic approach for experimentation that considers all stages at the same time will be more efficient. However, there have been only a few attempts in the literature to provide an adequate and easy-to-use approach for this problem. In this paper, we present a novel methodology for constructing two-level split-plot and multistage experiments. The methodology is based on the Kronecker product representation of orthogonal designs and can be used for any number of stages, for various numbers of subplots and for different number of subplots for each stage. The procedure is demonstrated on both regular and nonregular designs and provides the maximum number of factors that can be accommodated in each stage. Furthermore, split-plot designs for multistage experiments with good projective properties are also provided.

Projection Properties of Blocked Non-regular Two-level Designs

Minimum resolution IV (MinResIV) and Plackett and Burman (PB) designs are popular screening designs because of their run size efficiency and good projection properties. The purpose of this investigation is to find out which projection properties that can be maintained when these designs are blocked and also the efficiency by which the effects of interest can be estimated. MinResIV designs with 10–20 runs and PB designs with 12 and 20 are investigated. For the PB designs, design factor columns were used as blocking columns, while for the MinResIV designs, the main rule consisted of finding good arrangements for allocating mirror-image pair runs to the same blocks, a method inspired from Jacroux. As a criterion for a good blocking arrangements, we used maximum Ds - efficiency, which we found to be more generally applicable and provide better projection properties than previous suggested methods. It came out that for most of these designs, it was possible to find blocking schemes satisfying that for any three factors, either all main effects and their interactions or all main effects and their two-factor interactions could be estimated with a reasonable high efficiency when blocked. Copyright

Experiments for Multi-Stage Processes

Abstract Multi-stage processes are very common in both process and manufacturing industries. In this article we present a methodology for designing experiments for multi-stage processes. Typically in these situations the design is expected to involve many factors from different stages. To minimize the required number of experimental runs, we suggest using mirror image pairs of experiments at each stage following the first. As the design criterion, we consider their projectivity and mainly focus on projectivity P > 3 designs. We provide the methodology for generating these designs for processes with any number of stages and also show how to identify and estimate the effects. Both regular and non-regular designs are considered as base designs in generating the overall design.