This study developed a new typology to better understand patterns of change in rural counties. A cluster analysis was performed to group rural Texas counties by the population percent change and per capita personal income percent change that occurred between the years 2000 and 2007. A stable five-cluster solution was selected as the most appropriate. The clusters were described as Declining Population/Stable Economy, Growing Population/Growing Economy, Declining Population/Growing Economy, Growing Population/Stable Economy, and Declining Population/Declining Economy based on the means of the cluster variates. The clusters were then profiled to determine how they differed on a series of identified factors that have been found in the literature to affect population and economic growth in rural areas. Clusters were found to differ on net migration, foreign born migration, race/ethnicity of residents, percentage of commuters, economic dependence status, and number of two and four-year education institutions. Generated maps of the clusters revealed that bordering a neighboring state or country may play a role in a county’s population and economic growth; thus, it is recommended that additional attention needs to be given to understanding and facilitating cross border collaborations. Recommendations were also made for community development efforts to focus on improving educational access in rural counties and developing services to draw in foreign born immigrants. / text

<p>The present thesis concerns middle-aged women’s subjective well-being (SWB). The interest is focused on the importance of childhood factors, social circumstances, and personality for middle-aged women’s general SWB. Data were taken from the longitudinal research program Individual Development and Adaptation (IDA, Magnusson & Bergman, 2000) and concerned a sample of about 300 women. The main analyses were made on data collected at age 43, but data collected at age 13 and age 49 were also used to elucidate the purposes of this thesis. The results can be summarized as follows: 1) In a Swedish sample of middle-aged women, social circumstances had only a moderate effect on general SWB variables. The strongest relationship was found between marital status and global life satisfaction. When personality factors were controlled for, they wiped out nearly all relationships between the social circumstances variables and SWB, except for those between global life satisfaction and marital status or unemployment; 2) The level of general SWB was found to be considerably higher for Swedish employed women as compared to their counterparts in Lithuania and different socio-demographic variables predicted SWB in those two countries. For the Swedish sample, family-oriented variables were the strongest predictors of SWB, while for the Lithuanian sample income and educational level were more important; 3) Results from applying longitudinal structural equation modeling suggested that optimism in adolescence influenced optimism in middle age, which in its turn had both a direct influence on global life satisfaction and an indirect influence via the negative affect dimension. In relation to a number of different adjustment factors measured in adolescence it was found that optimism was the only factor that was constantly related to SWB 30 years later; 4) Typical patterns of general SWB were identified. Cluster analyses at age 43 and age 49 separately resulted in similar well-functioning six cluster solutions at both ages, indicating structural stability across six years. In addition to the typical high/low/average SWB clusters that could be to some degree expected from variable-oriented results, a cluster with intense affect and one with very low GLS emerged. All clusters except the latter one showed individual stability across six years.</p>

This thesis comprises two stages that empirically investigate and evaluate the perceptions and importance of service elements expectations of professional baseball spectator in Taiwan. Study I is designed to collect the perception of spectators service elements expectations focus groups meetings, which also help the research to develop an appropriate research instrument for the evaluation of the importance of service elements expectations to Taiwan professional baseball spectators. Study II collected 1020 questionnaire survey samples and used cluster analysis approach to segment TPB spectators into six meaningful groups by service elements expectations. The thesis concludes that, firstly, the successful use of expectation to segment spectators proves the potential of expectation as a typology with which to categorise customers. Secondly, TPB spectators with different levels of team identification failed to have great differences in their service expectations, even though two service factors ( subsidiary services and social and educational services ) were evaluated as less important by respondents, they were still evaluated that six service expectation factors are all important to them. Finally, this study provided a different angle for sports organisers to consider, and an outline for assisting managers design service packages that are highly responsive to the target market.

Methods for determining clusters of data under- specified constraints have recently gained popularity. Although general constraints may be used, we focus on clustering methods with the constraint of a minimal cluster size. In this dissertation, we propose two constrained k-means algorithms: Linear Programming Algorithm (LPA) and Genetic Constrained K-means Algorithm (GCKA). Linear Programming Algorithm modifies the k-means algorithm into a linear programming problem with constraints requiring that each cluster have m or more subjects. In order to achieve an acceptable clustering solution, we run the algorithm with a large number of random sets of initial seeds, and choose the solution with minimal Root Mean Squared Error (RMSE) as our final solution for a given data set. We evaluate LPA with both generic data and simulated data and the results indicate that LPA can obtain a reasonable clustering solution. Genetic Constrained K-Means Algorithm (GCKA) hybridizes the Genetic Algorithm with a constrained k-means algorithm. We define Selection Operator, Mutation Operator and Constrained K-means operator. Using finite Markov chain theory, we prove that the GCKA converges in probability to the global optimum. We test the algorithm with several datasets. The analysis shows that we can achieve a good clustering solution by carefully choosing parameters such as population size, mutation probability and generation. We also propose a Bi-Nelder algorithm to search for an appropriate cluster number with minimal RMSE.

Abstract 1 – A Geographic Information System for Evaluating Residential Pesticide Exposure and Prostate Cancer Incidence Agricultural pesticide exposure is hypothesized to be a risk factor for prostate cancer, and such exposures are of particular concern for men living in farming communities where large-scale pesticide applications occur. Prostate cancer incidence data were obtained from the State Health Registry of Iowa for the years 1996 through 2006, and county and census tract level age-adjusted incidence rates were calculated. Historical crop-specific land use records and pesticide sales data for the state of Iowa during 1990 were integrated into a geographic information system (GIS), where estimates of predicted exposure to the four most commonly used pesticides in Iowa (atrazine, metolachlor, cyanazine, alachlor) were produced. Ecological correlation between pesticide exposure and prostate cancer incidence was evaluated using Spearman’s (rank) correlation coefficient and linear regression analysis. Statistically significant associations between prostate cancer incidence and percent of acres of corn and soybean crops were found at both the county (r=0.22, p=.031 and r=0.33, p=.001, respectively) and census tract (r=0.10, p=.007 and r=0.13, p<.001, respectively) level. The associations between percent of land exposed to the specific pesticides and prostate cancer were not statistically significant. Our findings suggest that residential proximity to corn and soybean fields, and by association the pesticides used on those crops, is correlated with increased prostate cancer risk, but that the increase in risk is not correlated with exposure to the four most commonly used pesticides in Iowa in 1990. Findings from this study underscore the need for continued investigation of the association between agricultural exposures and prostate cancer incidence. Abstract 2 – Spatial Analysis of Prostate Cancer Incidence and Residential Pesticide Exposure in Iowa A statistically significant positive association between prostate cancer incidence and residential proximity to corn and soybean fields in Iowa exists. Research suggests that exposure to pesticides used on these crops increases prostate cancer risk. The objective of this study was to investigate clustering of prostate cancer risk in the presence of potential exposure to pesticides in Iowa. Prostate cancer incidence data (1996-2006) were obtained from the State Health Registry of Iowa. Using SaTScan software, clusters of high and low prostate cancer risk were identified. Ecological correlation between exposure to the four most commonly used pesticides (atrazine, metolachlor, cyanazine, alachlor) in Iowa during 1990 and residence in a cluster of relatively high or low prostate cancer incidence was evaluated using Pearson’s chi-square test statistic and logistic regression analysis. Clusters of increased prostate cancer risk were associated with a greater percentage of land used for all crops of interest (i.e., corn and soybean farming (p <0.001), corn farming (p <0.001), soybean farming (p <0.001)) and low exposure to alachlor (p =0.032) than did clusters with decreased risk of prostate cancer. After adjustment for percent of land used for each crop type, no association between pesticide exposure and prostate cancer risk was observed. Residence in or near agricultural communities increases prostate cancer risk. Our findings suggest that residential proximity to exposures specific to corn and soybean farming increases prostate cancer risk. Evaluation of exposure to less commonly used pesticides and those used in lower quantities is needed.   Abstract 3 – Multilevel Analysis of Residential Pesticide Exposure and Prostate Cancer Incidence An association between residential exposure to factors specific to corn and soybean farms in Iowa exists. The objectives of this study were to statistically assess spatial autocorrelation in prostate cancer incidence in Iowa and to evaluate the effect of residential exposure to the most commonly used pesticides for corn and soybean farms in Iowa in 1990 on prostate cancer incidence. Prostate cancer incidence data were obtained from the State Health Registry of Iowa for the years 1996 through 2006. Spatial patterning of age-adjusted incidence rates was assessed via Moran’s I global index of spatial autocorrelation. A hierarchical regression modeling approach with an assumed Poisson distribution was used to characterize the relationship between census tract level prostate cancer incidence and exposure to pesticides. Statistically significant spatial patterning of prostate cancer incidence, corn and soybean fields and pesticide use (p<.001 for all variables) was observed. After adjustment for individual and area level characteristics, prostate cancer risk increased by approximately 25% for each percentage point increase in percent of land used for corn and soybean crops. Prostate cancer risk was approximately 25% higher for Black men exposed to corn and soybean fields compared to white men exposed to corn and soybean fields. Results from this study support the need for further evaluation of residential exposure to environmental hazards specific to corn and soybean farming.

Doctor of Philosophy / Department of Human Nutrition / Edgar Chambers IV / Preference mapping is a method that provides product development directions for developers to see a whole picture of products, liking and relevant descriptors in a target market. Many statistical methods and commercial statistical software programs offering preference mapping analyses are available to researchers. Because of numerous available options, there are two questions addressed in this research that most scientists must answer before choosing a method of analysis: 1) are the different methods providing the same interpretation, co-ordinate values and object orientation; and 2) which method and program should be used with the data provided? This research used data from paint, milk and fragrance studies, representing complexity from lesser to higher. The techniques used are principal component analysis, multidimensional preference map (MDPREF), modified preference map (PREFMAP), canonical variate analysis, generalized procrustes analysis and partial least square regression utilizing statistical software programs of SAS, Unscrambler, Senstools and XLSTAT. Moreover, the homogeneousness of consumer data were investigated through hierarchical cluster analysis (McQuitty’s similarity analysis, median, single linkage, complete linkage, average linkage, and Ward’s method), partitional algorithm (k-means method), nonparametric method versus four manual clustering groups (strict, strict-liking-only, loose, loose-liking-only segments). The manual clusters were extracted according to the most frequently rated highest for best liked and least liked products on hedonic ratings. Furthermore, impacts of plotting preference maps for individual clusters were explored with and without the use of an overall mean liking vector. Results illustrated various statistical software programs were not similar in their oriented and co-ordinate values, even when using the same preference method. Also, if data were not highly homogenous, interpretation could be different. Most computer cluster analyses did not segment consumers relevant to their preferences and did not yield as homogenous clusters as manual clustering. The interpretation of preference maps created by the highest homogeneous clusters had little improvement when applied to complicated data. Researchers should look at key findings from univariate data in descriptive sensory studies to obtain accurate interpretations and suggestions from the maps, especially for external preference mapping. When researchers make recommendations based on an external map alone for complicated data, preference maps may be overused.

This study explored how the structural aspects of a social network (that is, number of social ties, frequency of contact, as well as social participation), along with the functional aspect (social support), relate to mental health. Using data from the baseline questionnaire for the tracking cohort of participants in the Canadian Longitudinal Study on Aging, community-dwelling older adults aged 65-85 years old were studied. Cluster analysis was used to group individuals into different clusters, based on their structural social network characteristics. Six clusters were found, ranging from most socially integrated, to moderately integrated, to socially isolated. Univariate analyses indicated that as level of social integration decreased, individuals fared increasingly worse in terms of their mental health outcomes. Furthermore, a series of mediation analyses showed that social support mediated the relationship between social integration level, and mental health, an effect that was strongest for the most socially isolated individuals. / May 2016

Different psychosocial factors influence the experience and adaptation to pain. Previous cluster analytic studies using the Minnesota Multiphasic Personality Inventory-2nd edition described psychologically different subgroups of pain patients that had been shown valuable in determining outcome. However, these studies had limited applicability to medico-legal pain populations because they did not use newly developed scales or describe important medico-legal factors that have large effects on symptom endorsement. Using three methods of clustering, the current investigation explored the subgroups that resulted when using all the MMPI-2 and the newly developed MMPI-2-RF (Restructured Form) scales on a large and well-described population of medico-legal spine pain patients. Result demonstrated that the best solution for the current sample was the two-cluster solution when a traditional method was used. However, the best solution was the three-cluster solution when all MMPI-2 scales and a method that used all MMPI-2-RF scales were used. Thus, the three-cluster solution was considered the most adequate solution to differentiate patients in medico-legal settings. Moreover, results demonstrated that subgroup membership was not conditioned to spine related organic factors. Instead, malingering, education, ethnic background and legal status differentiated pain subgroups. Lastly, results demonstrated a dose-response relationship between perceived outcome and subgroup profile elevation. The current results are relevant for understanding the circumstances that can influence spine pain recovery and for informing decisions regarding possible interventions.

This master thesis deals with beer market segmentation in the Czech Republic. The main aim of this thesis is to find out whether there is only one big segment or more different segments can be found there. The thesis is divided into two parts, the methodological --theoretical and the practical one. Segmentation, targeting and positioning are described in the first part. The second part is devoted to secondary resources analysis and content analysis focused on beer advertisements. The main part of practical section is to depicts primary quantitative research which is the basis for market segmentation done via SPSS statistic programme. At the end of this master thesis all segments are described in detail and marketing recommendation for each of them are also added there.

The main goal of the Master's Thesis is to discover significant differences in consumers' behaviour. Based on these differences it determinates and describes the segmenents of consumers. An important goal is to design marketing strategies for these segments as well. The theoretical part of the thesis includes the explanation of the segmentation process. The analytical part covers the characteristics of the Czech facial care market and the analysis of the Market&Media&Lifestyle data. The practical part of the thesis focuses on the process of segmentation using questionares and the IBM SPSS Statistics programme. The output of the thesis reveals three segments of the market and suggests relevant marketing strategies for them.