The field of view (FOV) in most head-mounted displays (HMDs) is no more than 60 degrees wide -- far narrower than our normal FOV of about 200 wide. This mismatch arises mostly from the difficulty and expense of building wide-FOV HMDs. Restricting a person's FOV, however, has been shown in real environments to affect people's behavior and degrade task performance. Previous work in virtual reality too has shown that restricting FOV to 50 or less in an HMD can degrade performance. I conducted experiments with a custom, wide-FOV HMD and found that performance is degraded even at the relatively high FOV of 112, and further at 48. The experiments used a prototype tiled wide-FOV HMD to measure performance in VR at up to 176 total horizontal FOV, and a custom large-area tracking system to establish new findings on performance while walking about a large virtua...

Sample-size determination is often an important step in planning a statistical study---and it is usually a difficult one. Among the important hurdles to be surpassed, one must obtain an estimate of one or more error variances, and specify an effect size of importance. There is the temptation to take some shortcuts. This paper offers some suggestions for successful and meaningful sample-size determination. Also discussed is the possibility that sample size may not be the main issue, that the real goal is to design a high-quality study. Finally, criticism is made of some ill-advised shortcuts relating to power and sample size. Key words: Power; Sample size; Observed power; Retrospective power; Study design; Cohen's effect measures; Equivalence testing; # I wish to thank Kate Cowles, John Castelloe, Steve Simon, two referees, an editor, and an associate editor for their helpful comments on earlier drafts of this paper. Much of this work was done with the support of the Obermann ...

Recently we have witnessed a welcomed increase in the amount of empirical evaluation of Software Engineering methods and concepts. It is hoped that this increase will lead to establishing Software Engineering as a well-defined subject with a sound scientifically proven underpinning rather than a topic based upon unsubstantiated theories and personal belief. For this to happen the empirical work must be of the highest standard. Unfortunately producing meaningful empirical evaluations is a highly hazardous activity, full of uncertainties and often unseen difficulties. Any researcher can overlook or neglect a seemingly innocuous factor, which in fact invalidates all of the work. More serious is that large sections of the communuity can overlook essential experimental design guidelines, which bring into question the validity of much of the work undertaken to date. In this paper, the authors address one such factor - Statistical Power Analysis. It is believed and will be demonstrated that a...

Methods for planning sample size (SS) for the standardized mean difference so that a narrow confidence interval (CI) can be obtained via the accuracy in parameter estimation (AIPE) approach are developed. One method plans SS so that the expected width of the CI is sufficiently narrow. A modification adjusts the SS so that the obtained CI is no wider than desired with some specified degree of certainty (e.g., 99 % certain the 95 % CI will be no wider than �). The rationale of the AIPE approach to SS planning is given, as is a discussion of the analytic approach to CI formation for the population standardized mean difference. Tables with values of necessary SS are provided. The freely available Methods for the Behavioral, Educational, and Social Sciences (K. Kelley, 2006a) R (R Development Core Team, 2006) software package easily implements the methods discussed.

package for R (R Development Core Team, 2007b), an open source statistical programming language and environment. MBESS implements methods that are not widely available elsewhere, yet are especially helpful for the idiosyncratic techniques used within the behavioral, educational, and social sciences. The major categories of functions are those that relate to confidence interval formation for noncentral t, F, and � 2 parameters, confidence intervals for standardized effect sizes (which require noncentral distributions), and sample size planning issues from the power analytic and accuracy in parameter estimation perspectives. In addition, MBESS contains collections of other functions that should be helpful to substantive researchers and methodologists. MBESS is a long-term project that will continue to be updated and expanded so that important methods can continue to be made available to researchers in the behavioral, educational, and social sciences. R is an open source statistical programming language and environment for (essentially) all operating systems that has gained a widespread following in quantitative disciplines (R Development Core Team, 2007b). This following is perhaps most prevalent in the statistical sciences, where many published works now provide R routines

This empirical research was undertaken as part of a multi-method programme of research to investigate unsupported claims made of object-oriented technology. A series of subject-based laboratory experiments, including an internal replication, tested the effect of inheritance depth on the maintainability of object-oriented software. Subjects were timed performing identical maintenance tasks on object-oriented software with a hierarchy of three levels of inheritance depth and equivalent object-based software with no inheritance. This was then replicated with more experienced subjects. In a second experiment of similar design, subjects were timed performing identical maintenance tasks on object-oriented software with a hierarchy of five levels of inheritance depth and the equivalent object-based software. The collected data showed that subjects maintaining object-oriented software with three levels of inheritance depth performed the maintenance tasks significantly quicker than those mainta...

Abstract—Although projective techniques continue to be widely used in clinical and forensic settings, their scientific status remains highly controversial. In this monograph, we review the current state of the literature concerning the psychometric properties (norms, reliability, validity, incremental validity, treatment utility) of three major projective instruments: Rorschach Inkblot Test, Thematic Apperception Test (TAT), and human figure drawings. We conclude that there is empirical support for the validity of a small number of indexes derived from the Rorschach and TAT. However, the substantial majority of Rorschach and TAT indexes are not empirically supported. The validity evidence for human figure drawings is even more limited. With a few exceptions, projective indexes have not consistently demonstrated incremental validity above and beyond other psychometric data. In addition, we summarize

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The author(s) shown below used Federal funds provided by the U.S. Department of Justice and prepared the following final report:

Microarray experiments can generate enormous amounts of data, but large datasets are usually inherently complex, and the relevant information they contain can be difficult to extract. For the practicing biologist, we provide an overview of what we believe to be the most important issues that need to be addressed when dealing with microarray data. In a microarray experiment we are simply trying to identify which genes are the most “interesting ” in terms of our experimental question, and these will usually be those that are either overexpressed or underexpressed (upregulated or downregulated) under the experimental conditions. Analysis of the data to find these genes involves first preprocessing of the raw data for quality control, including filtering of the data (e.g., detection of outlying values) followed by standardization of the data (i.e., making the data uniformly comparable throughout the dataset). This is followed by the formal quantitative analysis of the data, which will involve either statistical hypothesis testing or multivariate pattern recognition. Statistical hypothesis testing is the usual approach to “class comparison, ” where several experimental groups are being directly compared. The best approach to this problem is to use analysis of variance, although issues related to multiple hypothesis testing and probability estimation still need to be evaluated. Pattern recognition can involve “class prediction, ” for which a range of supervised multivariate techniques are available, or “class discovery, ” for which an even broader range of unsupervised multivariate techniques have been developed. Each technique has its own limitations, which need to be kept in mind when making a choice from among them. To put these ideas in context, we provide a detailed examination of two specific examples of the analysis of microarray data, both from parasitology, covering many of the most important points raised.