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...

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citis patients in some year in some country, it is much cheaper to draw a two-stage sample with a first stage of, say, # Contribution to Multilevel Modelling of Health Statistics, A. Leyland and H. Goldstein (eds.), Wiley, 2001. 1 10 hospitals, than to draw a simple random sample of 100 patients -- who might be dispersed over 99 hospitals. On the other hand, the usual rationale for multilevel analysis resides in the research question at hand: the phenomena under study themselves have a multilevel structure, as is evident, e.g., when studying contextual e#ects in a study of outcome measures for individuals nested in organisations (hospitals, schools, etc.), or in a longitudinal study where individual development as well as individual di#erences are relevant. When a multistage sample is drawn, it usually is likely that population elements within psu's will be more alike than elements of di#erent psu's. Some kind of

with 1 or 2 uppermost, S 2 if it lands with 3 or 4 uppermost, or S 3 if it lands with 5 or 6 uppermost. Note that in general the set of possible samples need not consist of all possible samples of a given size (and indeed it can be useful to consider cases where the sample size is random), and not all possible samples need have the same probability. Once one has a method for finding a sample, we need to have a method of computing an estimate of any quantity of interest. For example, one could take the mean of the specimens in the sample. Alternatives to Random Sampling There are various alternative ways in which a sample can be taken. For example, take the most easily obtainable specimens. The pitfalls in such a procedure, which can be referred to as accessibility or haphazard sampling, are obvious in that such a sample is unlikely to be any any real sense `representative'

ernative ways in which a sample can be taken. For example, take the most easily obtainable specimens. The pitfalls in such a procedure, which can be referred to as accessibility or haphazard sampling, are obvious in that such a sample is unlikely to be any any real sense `representative'. A second method would be to number the specimens in some more or less systematic manner and then take every nth specimen for some suitable value of n, which can be referred to as a systematic sample. However, there are warnings about its use to be garnered from section 5.2 of Gray and Gee (1972). The 1966 sample census attempted to use a systematic sample for caravan sites and for hospitals, schools, etc. The sort of thing that went wrong was that on one caravan site where the random start was 8, the enumerator took the 8th, 16th, 24th, : : : caravans, instead of the 8th, 18th, 28th, : : : (i.e. took the random start as the sampling interval). In the hospital and school records, interviewers were to

The authors would like to thank Thom Baguley for helpful suggestions and comments to previous versions of this article. Correspondence to this article should be addressed to Ken Kelley, Department of