One challenge in tourism market segmentation research is finding a statistical clustering method that can use data from the commonly used qualitative (categorical scale) survey instrument. Current proven methods require the use of quantitative (ratio or interval scale) data. However, quantitative survey instruments are seldom used. Many quantitative clustering methods severely restrict the number of attributes measured despite the fact that segmentation analysis works best when it measures all the multistate attributes that visitors identify as influencing their tourist experience. This study demonstrated that multistate categorical survey data could be successfully used. Using data from a bed-and-breakfast survey (229 guests), a two-stage analysis method was employed. First, multiple correspondence analysis was used to spatially map each of the attributes, and then cluster analysis was used to identify market segments. It is believed this method can be more practical in the field of applied tourism research.

The purpose of this study was to identify the clinical and plantar loading variables related to hallux valgus. Fifty-one healthy control subjects and 40 subjects with a diagnosis of moderate hallux valgus deformity of similar age and body weight were recruited for this study. Clinical measurements of pain, first metatarsophalangeal joint range of motion, and single-leg resting calcaneal stance position were obtained. Biomechanical measurements were obtained using a capacitive pressure platform. Plantar loading variables were calculated for seven regions of the plantar surface. A univariate analysis followed by a stepwise logistic regression was used to analyze the data. The results indicated that high values for pain, single-leg resting calcaneal stance position, hallux region peak pressure and force-time integral, and central forefoot region force-time integral increased the likelihood of hallux valgus.

We evaluated the use of a simple rake sampling technique for predicting the biomass of submersed aquatic vegetation. Vegetation sampled from impounded areas of the Mississippi River using a rake sampling technique, was compared with vegetation harvested from 0.33-m 2 quadrats. The resulting data were used to model the relationship between rake indices and vegetation biomass (total and for individual species). We constructed linear regression models using log-transformed biomass data for sites sampled in 1999 and 2000. Data collected in 2001 were used to validate the resulting models. The coefficient of determination (R 2 ) for predicting total biomass was 0.82 and ranged from 0.59 (Potamogeton pectinatus) to 0.89 (Ceratophyllum demersum) for individual species. Application of the model to estimate total submersed aquatic vegetation is illustrated using data collected independent of this study. The accuracy and precision of the models tested indicate that the rake method data may be used to predict total vegetation biomass and biomass of selected species; however, the method should be tested in other regions, in other plant communities, and on other species.

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Analyses of 94 Fund for the Improvement of Post-Secondary Education (FIPSE)-sponsored drug-prevention programs and their outcomes used the Core Survey to identify 34 institutions where college students' binge drinking increased (M = 5.44%) and 60 institutions where it decreased (M = -4.59%) during 2 years of program operation. The authors used an inductively derived taxonomy of prevention program elements, student variables, student substance use, use-related variables, and institutional variables to compare the 2 groups of institutions. Only prevention program elements discriminated between groups. Factor analysis of discriminating elements identified 8 prevention factors that improved base-rate prediction of institutional decrease in binge drinking by 28.1%. Factor synthesis yielded a 3-construct binge-drinking prevention model based on student participation and involvement strategies, educational and informational processes, and campus regulatory and physical change efforts. This model improved base-rate prediction of decreased binge drinking by 33.2%.

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population is in the same direction as the rejection region tail. We show the differences in power for shifts to both the left and right and highlight how the power is affected when the parent population is skewed. Moreover, we examine the effects of skewness, kurtosis, shifting, and the direction of the test on the power of the sign test as well.Much has been written on the fact that the distribution of the t-statistic inherits a skewness that is opposite of the parent population. Boos and Hughes-Oliver (2000) clearly illustrate this fact and show that t-intervals constructed for rightskewed parent populations, for example, will have upper and lower bounds that are to the left of what they would be under a normally distributed parent, and vice-versa. In the context of hypothesis testing this translates into true tail probabilities that are higher than nominal in the upper tail, resulting in fewer rejections, and lower than nominal tail probabilities in the lower tail, resulting in more rejections than there would be under a normal parent for one-tailed tests.While it is true that the sign test requires no assumptions regarding the distribution of the parent population, skewness, shifting, and particularly kurtosis affect the power of the test. The power of the sign test is greatly diminished as kurtosis decreases. Simulations by Ott and Longknecker (2001) show that the t-test is uniformly more powerful than the sign test for normal parent populations and that the sign test is more powerful in cases of heavily tailed or highly skewed parent populations. Our study agrees for skewed parent populations when the shift is in the same direction as the skewness, but not when the shift is in the opposite direction. Simulations by Randles and Wolfe (1979) showed that the power of the sign test is lower for low kurtosis distributions than it is for high kurtosis distributions. We also demonstrate the vulnerability of the sign test to low kurtosis parent populations and provide insight as to why this is so.

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