京都大学 / 0048 / 新制・課程博士 / 博士(人間健康科学) / 甲第20058号 / 人健博第39号 / 新制||人健||3(附属図書館) / 京都大学大学院医学研究科人間健康科学系専攻 / (主査)教授 桂 敏樹, 教授 任 和子, 教授 川村 孝 / 学位規則第4条第1項該当 / Doctor of Human Health Sciences / Kyoto University / DFAM

This study tests the relationship of the 6 ideological variables and 7 contextual variables to shifts in ideological alignment through a latent class regression analysis for three periods of years (1972-1978, 1980-1992, 1993-2006). The latent class regression models determine the number of identifiable classes for each model. Using ideological realignment theory (Abramowitz and Saunders 1998) this study finds there has been a moderate polarization of opinions that has occurred, as well as, a moderate hardening of ideological beliefs with moral issues during the third time period becoming the driving force in ideological makeup. With regard to the culture wars hypothesis (Hunter 1991) there seems to be so much randomness in peoples overall ideological makeup that it hardly suggests a salient culture war is taking place. It also seems to matter very little what opinions individuals express on domain specific issues with regard to political ideology.

With a higher number of nocturnal tornado events, I surveyed residents of Alabama, Arkansas, Kentucky, Mississippi, and Tennessee (N = 487 for each sample) to determine the time, in minutes, it took to reach a decision on shelter-seeking. I utilized latent class analysis (LCA) to create class memberships, based on diurnal and nocturnal scenarios, to associate with time intervals. Four actors were identified for each scenario: Tech Users, Typical Actors, Non-Reactors, and Social Actors for the day sample, Tech Users, Typical Actors, Passive Actors, and Non-Reactors for the night sample. Time intervals were created and applied to each class. All class assignments except one, Traditional Actors in the night sample, used more time than allotted in an average tornado warning lead time (~15 minutes). Future studies may be necessary to determine a reduction in time needed for decision-making, such as establishing the most impactful warning sources.

The accurate modeling of travelers’ route choice decision making when faced with unreliable (risky) travel times is necessary for the assessment of policies aimed at improving travel time reliability. Two major objectives are studied in this thesis. The first objective is to evaluate the applicability of a process model to route choice under risk where the actual process of decision making is captured. Traditionally, we adopt “as-if” econometric models to predict people’s route choice decisions. The second objective is to investigate travelers’ capability to incorporate future real-time traffic information into their current route choice decision making. Two separate stated preference (SP) surveys were conducted for each objective. The first SP survey used an interactive map in a computer based test. The second SP survey used a full-scale high-fidelity driving simulator. Compared with econometric models, process models have been rarely investigated in travel decision making under risk. A process model aims to describe the actual decision making procedure and could potentially provide a better explanation to route choice behavior. A process model, Priority Heuristic (PH), developed by Brandstatter et al. (2006) is introduced to the travel choice context and its probabilistic version, Probabilistic Priority Heuristic (PPH), is developed and estimated in this study. With data collected from a stated preference (SP) survey which is based on an animated computer interface, one econometric model, Rank-Dependent Expected Utility (RDEU) model, and two other alternative models were compared with the PPH model in a cross validation test to investigate their data-fitting and predictive performance. Our results show that the PPH model outperforms the RDEU model in both data-fitting and predictive performance. This suggests that the process modeling paradigm could be a promising new area in travel behavior research. With the advance of information and telecommunication technology, real-time traffic information is increasingly more available to help travelers make informed route choice decisions when faced with unreliable travel times. A strategic route choice refers to a decision taking into account future diversion possibilities at downstream nodes based on real-time information not yet available at the time of decision-making. Based on the data collected from a driving simulator experiment and a matching PCbased experiment, a mixed Logit model with two latent classes, strategic and nonstrategic route choice, is specified and estimated. The estimates of the latent class probabilities show that a significant portion of route choice decisions are strategic and subjects can learn to make more strategic route choice as they have more experience with the decision scenarios. Non-parametric tests additionally show that network complexity adversely affects travelers’ strategic thinking ability in a driving simulator environment but not in a PC environment and a parallel driving task only affects strategic thinking ability in a difficult scenario but not a simple one. In addition, we find that people’s strategic thinking ability are influenced by their gender and driving experience (mileage) in the non-parametric analysis, but not in the modeling work. These findings suggest that a realistic route choice model with real-time traffic information should consider both strategic and non-strategic behavior, which vary with the characteristics of both the network and the driver.

京都大学 / 新制・論文博士 / 博士(農学) / 乙第13507号 / 論農博第2905号 / 新制||農||1095(附属図書館) / 学位論文||R4||N5407(農学部図書室) / (主査)准教授 沈 金虎, 教授 浅見 淳之, 教授 栗山 浩一 / 学位規則第4条第2項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

No description available.

No description available.

<p>In diagnostic accuracy studies, sensitivity and specificity are the most common measures to assess the performance of diagnostic or screening tests. The estimation of these measures can be done using empirical or model-based methods. The primary objective of this thesis is to use both the empirical and the model-based (logistic regression) approach to assess the effects of covariates on the performance of the visual inspection with acetic acid (VIA) and lugol iodine (VILI) tests using the data from women screened for cervical cancer in Kinshasa, the Democratic Republic of Congo. The secondary objectives are: first, to adjust for the false negative and false positive error rates by the two tests through latent class models (LCM), and second, to evaluate the effects of covariates on the agreement between the measurements of the two tests taken by nurse and physician through Kappa statistic.</p> <p>No particular pattern could be observed in the trend of empirically estimated sensitivity and specificity of the VIA and VILI tests measured by the nurse and by the physician across age and parity categories. From the logistic regression models, both age, parity, and their respective quadratic terms have significant effects on the probability of VIA and VILI tests to detect cervical cancer. However, there is no significant effect of marital status, smoking, and hybrid capture2 (HPV DNA) on the probability of VIA and VILI tests measured by nurse to detect cervical cancer while HPV DNA does in the probability of VIA and VILI tests measured by physician to detect cervical cancer. The trend of the estimated sensitivity of VIA and VILI tests measured by the nurse is not different across age groups but the specificity does vary. The trend of both the sensitivity and specificity of VIA and VILI tests are significantly different across parity groups. The reverse is the case for the sensitivity and specificity of VIA and VILI tests measured by physician across age and parity groups. The false negative and false positive error rates in the sensitivity and specificity of VIA and VILI tests measured by nurse are higher compared to that of physician. With Kappa statistic results, there is almost perfect agreement between the ratings by the nurse and physician for the dichotomized VIA and VILI test outcomes.</p> <p>In conclusion, there is a significant effects of age, parity and the quadratic term of age on the performance of VIA and VILI tests outcomes measured by nurse. On the VIA and VILI test outcomes measured by physician, age, parity, HPV DNA and quadratic term of age have shown significant effects on the performance of VIA and VILI tests outcomes measured by physician alone.</p> / Master of Science (MSc)

Research has shown that there are a number of risk factors that increase the odds of youth joining gangs, from individual- to family- to neighborhood-level risks. Studies have identified child abuse and other childhood traumatic experiences as influences on gang membership. Adverse childhood experiences (ACEs) provide a framework for how to measure and identify these traumatic events. This dissertation study uses longitudinal data from the Pittsburgh Youth Study (PYS) to inform the relationship between early life events and later gang membership. First, the count of total ACEs experienced by gang involved youth were compared to nongang youth. Then, latent class analysis was used to create groupings of ACEs to determine if particular classes of adverse events are associated with higher odds of gang membership during later adolescence. Using the longitudinal data structure of the PYS, additional latent classes were developed when breaking up the adversity into separate age ranges. ACE categories for the youngest cohort were able to be divided into early school entry (elementary school), early adolescence (middle school), and later adolescence (high school) due to their earlier age of first survey, and then these age-graded categories were added into the latent class model to determine if age specific adversity increased odds of gang membership. Lastly, covariates were added into the model to test if time-stable elements increased odds of belonging to one of the classes identified in the initial latent class analysis. The methods described above produced results, showing that gang involved youth have significantly more childhood adversity than nongang involved youth on average. When the latent class analysis was conducted, a three-class solution was found to be the most appropriate model, with classes with higher odds of adversity leading to greater odds of gang membership. There was no significant difference between two classes that had higher odds of adversity, though both included high rates of community violence experiences and parental separation. There were mixed findings on the impact of age specific adversity. Lastly, covariates were added into the model finding early school achievement plays a large role in predicting class membership, while familial financial strain does not. The findings from this dissertation have important implications for policy and practice around gang prevention and intervention in that they can help pinpoint constellations of risk factors. Evidence-based school intervention programs, such as The Fourth R-- an in-school intervention designed to reduce delinquency through positive relationship building with teachers, parents, and pro-social peers (Crooks et al., 2011)-- are important for reducing the odds of experiencing higher odds of adversity. Additionally, programs that work with youth who experience adversity can help reduce the hurt they perpetrate on others. / Criminal Justice

Heterogeneity in the profile of HIV-associated neuropsychological disorder (HAND) may obscure understanding of its etiology and prognosis. Despite longstanding acknowledgement of this heterogeneity, HAND diagnostic approaches such as the Frascati criteria characterize neuropsychological function based on the level of impairment, without regard to the pattern of strengths and weaknesses. Attention to these patterns may enhance etiologic and prognostic specificity. We used latent class analysis (LCA) to identify relatively homogeneous subtypes of neurocognitive function in adults with well-treated HIV infection. We compared the diagnostic agreement of latent classes and Frascati categories, as well as their associations with demographics, HIV markers and antiretroviral factors, comorbid medical and psychiatric conditions, and everyday functioning. LCA identified four classes, whose cognitive profiles are depicted in Figure 1: cognitively intact, mild-to-moderate motor/speed impairment, mild-to-moderate memory/visuoconstruction impairment, and moderate mixed impairment. Latent classes and Frascati categories demonstrated good agreement in the overall classification of impaired cognition but more disagreement regarding subtypes of impairment. Both latent classes and Frascati categories demonstrated unique associations with etiologic factors and significant associations with functional outcomes. However, only latent classes, not Frascati categories, were associated with HIV variables. Additionally, functional difficulties were significantly elevated in the motor impairment class but not the memory impairment class despite similar levels of cognitive impairment in the two groups. Findings support the utility of a diagnostic approach that accounts for both the level and pattern of neurocognitive impairment. Future research should examine the neuropathological mechanisms, longitudinal trajectories, and treatments of empirically identified HAND subtypes. / Psychology