964 resultados para Multivariate analyses


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The present study explored the connections among adolescents' sense of self, sexuality, and perceptions of risk. Such an exploration may help educators to further understand why adolescents engage in risk-taking behaviours such as unprotected sex. The study involved secondary analysis on the data collected from the Youth Lifestyle Choices - Community University Research Alliance 2000 (YLC - CURA) Youth Resilience Questionnaire (YRQ). Participants were 300 male and female students in Grades 9, 1 1 and OAC. Data analyses involved both descriptive and inferential statistics (correlational and multivariate analysis). Chi-square analyses were performed on the open-ended self-description question. Separate analyses were conducted on gender and age (grade levels). Correlational analyses revealed that adolescents with a more positive sense of self were more likely to perceive sexual involvement as a relatively high-risk behaviour. Specifically, results found that male adolescents were less likely than females to perceive sex to be risky. Results are discussed in relation to previous research in the area of selfcognitions and risk-taking sexual behaviour. Results are also discussed in terms of educational implications in that the current results may provide the beginnings of a framework for more holistic sexual education programs.

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The overall objective of this study was to investigate factors associated with long-term survival in axillary node negative (ANN) breast cancer patients. Clinical and biological factors included stage, histopathologic grade, p53 mutation, Her-2/neu amplification, estrogen receptor status (ER), progesterone receptor status (PR) and vascular invasion. Census derived socioeconomic (SES) indicators included median individual and household income, proportions of university educated individuals, housing type, "incidence" of low income and an indicator of living in an affluent neighbourhood. The effects of these measures on breast cancer-specific survival and competing cause survival were investigated. A cohort study examining survival among axillary node negative (ANN) breast cancer patients in the greater Toronto area commenced in 1 989. Patients were followed up until death, lost-to-follow up or study termination in 2004. Data were collected from several sources measuring patient demographics, clinical factors, treatment, recurrence of disease and survival. Census level SES data were collected using census geo-coding of patient addresses' at the time of diagnosis. Additional survival data were acquired from the Ontario Cancer Registry to enhance and extend the observation period of the study. Survival patterns were examined using KaplanMeier and life table procedures. Associations were examined using log-rank and Wilcoxon tests of univariate significance. Multivariate survival analyses were perfonned using Cox proportional hazards models. Analyses were stratified into less than and greater than 5 year survival periods to observe whether known markers of short-tenn survival were also associated with reductions in long-tenn survival among breast cancer patients. The 15 year survival probabilities in this cohort were: for breast cancerspecific survival 0.88, competing causes survival 0.89 and for overall survival 0.78. Estrogen receptor (ER) and progesterone receptor (PR) status (Hazard Ratio (HR) ERIPR- versus ER+/PR+, 8.15,95% CI, 4.74, 14.00), p53 mutation (HR, 3.88, 95% CI, 2.00, 7.53) and Her-2 amplification (HR, 2.66, 95% CI, 1.36, 5.19) were associated with significant reductions in short-tenn breast cancer-specific survival «5 years following diagnosis), however, not with long-term survival in univariate analyses. Stage, histopathologic grade and ERiPR status were the clinicallbiologieal factors that were associated with short-term breast cancer specific survival in multivariate results. Living in an affluent neighbourhood (top quintile of median household income compared to the rest of the population) was associated with the largest significant increase in long-tenn breast cancer-specific survival after adjustment for stage, histopathologic grade and treatment (HR, 0.36, 95% CI, 0.12, 0.89).

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Studies on persistence and degradation of the synthetic pyrethroid insecticides, permethrin and fenvalerate, were carried out under natural environmental conditions of the Niagara Peninsula. Permethrin and fenvalerate were treated on apple foliage atrat~s of 0.21 kg(AI)!ha and 0.14 kg(AI)/ha, respectively. The initial cis- and trans-permethrin spray deposits were found to be 13.5 ppm and 19.2 ppm, respectively and 38.0 ppm was observed for the fenvalerate treated sample. Twenty-three days and 84 days after spray application, permethrin residues were 4.0 ppm and 2.7 ppm for the cis-isomer, whereas they were 7.9 ppm and 4.7 ppm for the trans-isomer, respectively. Residues of fenvalerate 23 days and 84 days after spray application were 13.4 ppm and 8.0 ppm, respectively. The values of observed half-life of cis-permethrin, trans-permethrin and fenvalerate were found to be 42 days, 46 days and 51 days, respectively. Studies were extended to quantitatively determine some of the major degradation compounds of permethrin and fenvalerate, which were expected to be produced as results of ester cleavage of the parent compounds. A permethrin treated sample, 84 days after initial spray application, showed 0.25 and 0.8 ppm of cis- and trans-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropanecarboxylic acid (C12CA (18), respectively. These two acids were not found as free acids, but found as conjugated compounds. The other expected degradation compounds, 3-phenoxybenzyl alcohol (PBalc (~)),3-phenoxybenz.aldehyde (PBald (38)) and 2- (4-chlorophenyl) isovaleric acid (CPIA (31)) were not detected by the methods employed in this study. The results indicate that these degradation compounds were not present, or, if they were present, their concentrations were too low to detect by the methods used.

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There is an emerging awareness that children with poor motor abilities are at particular risk for overweight. This cross-sectional study examined the influence of physical activity behaviour on the relationship between motor proficiency and body composition. Participants were 1287 (646 males, 641 females) Grade 6 students in the Physical Health Activity Study project. Height, weight, waist girth, and motor proficiency (Bruininks-Oseretsky Test of Motor Performance BOTMP-SF) were assessed. Physical activity behaviours were also evaluated with a multifaceted approach and reported for school-based, non-school based physical activity, free-time play, and sedentary activities (Participation Questionnaire), and leisure time exercise (Godin-Shephard Leisure Time Exercise Questionnaire GS). Overweight was defined by BMI scores: boys :::20.6-21.2 and <25.1-26.0; girls: ::: 20.7-21.7and <25.4-26.7 and obesity was defined as: boys:::: 25.1-26.0; girls: :::25.4-26.7. Children were classified as case group (CG,::; 10% on BOTMP-SF), borderline case group (BC, > 10% to ::; 20% on BOTMP-SF) or non-case group. Analyses of variance (ANOVAs) uncovered a significant difference in overweight and obesity between the case group and non-case group. Normal-weight children reported higher participation in organized school-sports (intra-mural and inter-school teams). The CG reported significantly lower participation in school sports teams and lower GS results, with a trend towards lower participation in all active pursuits. They also reported a significantly higher duration of television watching and book reading. There were no significant differences between motor proficiency groups by gender, age, nonschool sports, or free-time activity. Multivariate ordinal logistic regression analysis showed that the case group was 10.9 times more likely to be overweight/obese than their peers. No single aspect of physical activity was able to explain the difference in odds ratios for the motor proficiency groups. However, for the entire cohort, children who participated in more organized school sports were less likely to be overweight/obese. These findings confirm that children with low motor proficiency are at significant risk of developing overweight. It is evident that these children have generally attenuated activity levels and heightened levels of sedentary pursuits. School-based activities appear particularly limited, and are the one area where children have near autonomy in their decision to pursue active opportunities. The promotion of school-based programs, specifically intramural sports may be an important aspect in increasing children's overall activity levels. It is also essential to consider the needs of those children with low motor proficiency when designing activity promotion programs. Future research should further explore motor proficiency and overweight/obesity.

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I t is generally accepted among scholars that individual learning and team learning contribute to the concept we refer to as organizational learning. However, a small number of quantitative and qualitative studies that have investigated their relationship reported contradicting results. This thesis investigated the relationship between individual learning, team learning, and organizational learning. A survey instrument was used to collect information on individual learning, team learning, and organizational learning. The study sample comprised of supervisors from the clinical laboratories in teaching hospitals and community hospitals in Ontario. The analyses utilized a linear regression to investigate the relationship between individual and team learning. The relationship between individual and organizational learning, and team and organizational learning were simultaneously investigated with canonical correlation and set correlation. T-test and multivariate analysis of variance were used to compare the differences in learning scores of respondents employed by laboratories in teaching and those employed by community hospitals. The study validated its tests results with 1,000 bootstrap replications. Results from this study suggest that there are moderate correlations between individual learning and team learning. The correlation individual learning and organizational learning and team learning and organizational learning appeared to be weak. The scores of the three learning levels show statistically significant differences between respondents from laboratories in teaching hospitals and respondents from community hospitals.

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In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g., the parametric bootstrap) which may be applied when the bounds are not conclusive.

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In the context of multivariate regression (MLR) and seemingly unrelated regressions (SURE) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. in this paper, we propose finite-and large-sample likelihood-based test procedures for possibly non-linear hypotheses on the coefficients of MLR and SURE systems.

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In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995. Our empirical results reveal the following. Whereas univariate exact tests indicate significant serial correlation, asymmetries and GARCH in some equations, such effects are much less prevalent once error cross-equation covariances are accounted for. In addition, significant departures from the i.i.d. hypothesis are less evident once we allow for non-Gaussian errors.

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We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross-equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared to simulation-based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal, Student t; normal mixtures and stable error models. In the Gaussian case, finite-sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi-stage Monte Carlo test methods. For non-Gaussian distribution families involving nuisance parameters, confidence sets are derived for the the nuisance parameters and the error distribution. The procedures considered are evaluated in a small simulation experi-ment. Finally, the tests are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995.