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.

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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