This thesis bridges a gap in extant research by examining key factors that play a role in behavioral grocery shopping routines following minor and major disruptions. The present research involves two interrelated investigations incorporating mixed methodologies (Cresswell, 2003). Study 1 involves semi-structured in-depth interviews seeking to establish how goal-directed grocery shopping routines are developed over time. Utilizing a laddering approach of questioning (Gutman, 1997), respondents are probed on their routines (Brotherton, 2001) and goals, including end goals as described in the List of Values (Kahle & Kennedy, 1988). Three participants were interviewed on three occasions over an eight week period, until theoretical saturation was achieved. A significant contribution of Study 1 lies in the development of a conceptual framework for understanding factors associated with grocery shopping routines. This model reflects a working definition characterizing routines as goal-driven and value-guided heuristic strategies. It is proposed that routines are repetitive patterns of personal and private behavioral activities dependent upon situational and temporal contexts, and utilized for instrumental reasons. Risk-taking attitudes and personal values also shape goal-directed behaviors. Using structural equation modeling (SEM) procedures (Jöreskog & Sörbom, 1993), Study 2, an online experiment, aims to test and build upon the conceptual model emanating from Study 1. This study also investigates the impact of minor and major disruptions on routinized grocery shopping behavior. 612 participants were allocated across three experimental groups: situational contexts, anticipated temporal conditions, and repetitive value. Cohorts were assessed at baseline levels and received unique minor and major disruptions appropriate to their circumstance. Study 2 contributes through the large-scale SEM testing of a model of grocery shopping routinization. Overall, sound structural model fit demonstrates that the present model of grocery shopping routinization is explained by six distinct components including routinized behavior, goal-centeredness, situational contexts, anticipated temporal conditions, repetitive value, and risk-taking attitudes; and three dimensions of personal values: maturity, self-direction/achievement, and enjoyment. In terms of disruptions, findings indicate that routine strength is dependent on degree of situational, temporal, and instrumental interruptions. Disruptions can both facilitate and impede routines. Results demonstrate that regardless of goal stability, routines change when model components are disrupted. Findings suggest theoretical, research, and practical implications. This thesis expands decision making theory (Betsch, Fiedler, & Brinkmann, 1998) by demonstrating that, despite unwavering goals, new contexts arising from disruptions influence new behavioral deliberations. In relation to research implications, this thesis develops then subsequently tests a model of grocery shopping routinization. Despite routines becoming subconscious over time (Aarts & Dijksterhuis, 2000a), this study asserts that routines are intentional and involve goal-directed strategies for dealing with the environment. From an applied perspective, practitioners should be aware that routine-disrupted consumers remain goal-driven. Consumers are unlikely to forego focal goals (e.g., shop for weekly household meals) if these goals are non-negotiable. Present results suggest that consumers esteem maturity-related personal values, such as fostering and maintaining warm relationships with others and sense of belonging, when grocery sho pping.

Although understanding causation is an essential part of nearly every problem domain, it has resisted formal treatment in the languages of logic, probability, and even statistics. Autonomous artificially intelligent agents need to be able to reason about cause and effect. One approach is to provide the agent with formal, computational notions of causality that enable the agent to deduce cause and effect relationships from observations. During the 1990s, formal notions of causality were pursued within the AI community by many researchers, notably by Judea Pearl. Pearl developed the formal language of structural models for reasoning about causation. Among the problems he addressed in this formalism was a problem common to both AI and law, the attribution of causal responsibility or actual causation. Pearl and then Halpern and Pearl developed formal definitions of actual causation in the language of structural models. <p>Within the law, the traditional test for attributing causal responsibility is the counterfactual "but-for" test, which asks whether, but for the defendant's wrongful act, the injury complained of would have occurred. This definition conforms to common intuitions regarding causation in most cases, but gives non-intuitive results in more complex situations where two or more potential causes are present. To handle such situations, Richard Wright defined the NESS Test. Pearl claims that the structural language is an appropriate language to capture the intuitions that motivate the NESS test. While Pearl's structural language is adequate to formalize the NESS test, a recent result of Hopkins and Pearl shows that the Halpern and Pearl definition fails to do so, and this thesis develops an alternative structural definition to formalize the NESS test.

Although understanding causation is an essential part of nearly every problem domain, it has resisted formal treatment in the languages of logic, probability, and even statistics. Autonomous artificially intelligent agents need to be able to reason about cause and effect. One approach is to provide the agent with formal, computational notions of causality that enable the agent to deduce cause and effect relationships from observations. During the 1990s, formal notions of causality were pursued within the AI community by many researchers, notably by Judea Pearl. Pearl developed the formal language of structural models for reasoning about causation. Among the problems he addressed in this formalism was a problem common to both AI and law, the attribution of causal responsibility or actual causation. Pearl and then Halpern and Pearl developed formal definitions of actual causation in the language of structural models. <p>Within the law, the traditional test for attributing causal responsibility is the counterfactual "but-for" test, which asks whether, but for the defendant's wrongful act, the injury complained of would have occurred. This definition conforms to common intuitions regarding causation in most cases, but gives non-intuitive results in more complex situations where two or more potential causes are present. To handle such situations, Richard Wright defined the NESS Test. Pearl claims that the structural language is an appropriate language to capture the intuitions that motivate the NESS test. While Pearl's structural language is adequate to formalize the NESS test, a recent result of Hopkins and Pearl shows that the Halpern and Pearl definition fails to do so, and this thesis develops an alternative structural definition to formalize the NESS test.

Seagrass bed is a highly productivity ecosystem, it also provides habitats for animals and plays an important role in stabilizing the substrate. The sea anemone Phymanthus strandesi is very abundant in the seagrass bed of Thalassia hemprichii on Hsiao-Liuchiu Island. Structural Equation Modeling (SEM) was used to investigate the relationship between P. strandesi and some environmental factors, which affect the distribution of this species at Tuozaiping tidal flat (N 22¢X20"55' E 120¢X21"49'), Hsiao-Liuchiu Island. Light and temperature were also manipulated in the laboratory to test their effect on the hiding response of P. strandesi. The results of SEM show that the abundance of T. hemprichii showed very weak positive relation with P.strandesi. On the other hand, soil depth on the seagrass bed might be the main factor that affects the distribution of P. strandesi. In high a temperature situation (i.e. over 38¢XC), all the sea anemones in the experimental container hided into the sand. However, only some sea anemones hid when exposed to strong light (i.e. 5030 lum/ft²) after one and half hours.

Abstract The communication and information technology, particularly the Internet, has dramatically changed the way we have done business before. Therefore. Some different work arrangements have emerged in the technology-based business, such as telecommuting, mobile office, hoteling, satellite office, etc. The number of telecommuters in the US today is up to 25 million. In Taiwan, China Productivity Center (CPC), Taiwan Xerox, Taiwan IBM, and Taiwan HP have implemented the mobile office system for several years. Arthur Andersen and Sun also built their flexible office last year. As the literatures indicated that telecommuting did increase organizational flexibility, job efficiency, employee satisfaction, productivity, customer satisfaction, while reduce commuting time and transportation costs, office spaces. However, the teleworkers felt more isolated as a result of working in a remote environment. Their interpersonal relationship and communication with supervisor or co-worker all got worse. Besides, managers also worried about telecommuting will reduce their authority and control power. The main purpose of this study is to understand how Internet affect the individual worker¡¦s work style, especially what are the key factors being considered in telecommuting. Finally, the proposed telecommuting model would be empirically examined in the selected information technology-based organizations.

The purpose of the study was to examine multilevel regression models in the context of multilevel structural equation modeling (SEM) in terms of accuracy of parameter estimates, standard errors, and fit indices in normal and nonnormal data under various sample sizes and differing estimators (maximum likelihood, generalized least squares, and weighted least squares). The finding revealed that the regression coefficients were estimated with little to no bias among the study design conditions investigated. However, the number of clusters (group level) appeared to have the greatest impact on bias among the parameter estimate standard errors at both level-1 and level-2. In small sample sizes (i.e., 300 and 500) the standard errors were negatively biased. When the number of clusters was 30 and cluster size was held at 10, the level-1 standard errors were biased downward by approximately 20% for the maximum likelihood and generalized least squares estimators, while the weighted least squares estimator produced level-1 standard errors that were negatively biased by 25%. Regarding the level-2 standard errors, the level-2 standard errors were biased downward by approximately 24% in nonnormal data, especially when the correlation among variables was fixed at .5 and kurtosis was held constant at 7. In this same setting (30 clusters with cluster size fixed at 10), when kurtosis was fixed at 4 and the correlation among variables was held at .7, both the maximum likelihood and generalized least squares estimators resulted in standard errors that were biased downward by approximately 11%. Regarding fit statistics, negative bias was noted among each of the fit indices investigated when the number of clusters ranged from 30 to 50 and cluster size was fixed at 10. The least amount of bias was associated with the maximum likelihood estimator in each of the data normality conditions examined. As sample size increased, bias decreased to near zero when the sample size was equal to or greater than 1,500 with similar results reported across estimation methods. Recommendations for the substantive researcher are presented and areas of future research are presented.

Thesis (M.A.)--University of Notre Dame, 2009. / Thesis directed by Scott E. Maxwell for the Department of Psychology. "December 2009." Includes bibliographical references (leaves 70-72).

Structural equation model (SEM) is a general approach to analyze multivariate data. It is a relatively comprehensive model and combines useful characteristics from many statistical approaches, thus enjoys a variety of advantages when dealing complex relationships. This report gives a brief introduction to SEM, focusing especially the comparison of SEM and OLS regression. A simple tutorial of how to apply SEM is also included with the introduction and comparison. SEM can be roughly seen as OLS regression added with features such as simultaneous estimation, latent factors and autocorrelation. Therefore, SEM enjoys a variety of advantages over OLS regression. However, it is not always the case that SEM will be the optimal choice. The biggest concern is the complexity of SEM, for simpler model will be preferable for researchers when the fitness is similar. Two simulation cases, one requires special features of SEM and one satisfies assumptions of OLS regression, are applied to illustrate the choice between SEM and OLS regression. A study using data from US life insurers in the year 1994 serves as a further illustration. The conclusion is when special features of SEM is required, SEM fits better and will be the better choice, while when OLS regression assumptions are satisfied, SEM and OLS regression will fit equally well, considering the complexity of SEM, OLS regression will be the better choice. / text

This dissertation focuses on results of multi-group SEM models estimated using data from the National Longitudinal Study of Adolescent Health (Add Health) in order to determine appropriate measurement and structural models for the relationship between socioeconomic status (SES) and health among six young adult U.S. social groups. Examining the links between SES and health during young adulthood is important because while there is a strong, documented link between lower SES and poorer health (Adler & Snibbe, 2003), young adults can exercise a considerable amount of agency with regard to their own SES and health. Young adults make critical decisions about pursuing post-secondary education, entering the workforce, and practicing healthy behaviors--activities which differ in their immediate and long-term economic and health payoff (Mirowsky & Ross, 2003; Elder, 1985; 1994). Yet, the nature of SES and its links with health for members of various gender and racial/ethnic groups is not entirely clear. Literature suggests that occupation, education, and income are neither defined nor linked among women in the same ways that they are for men (APA, 2007). Self-assessment of health is also thought to differ by gender and ethnicity (Krause & Jay, 1994). Moreover, limited research has addressed the unique mediating pathways by which aspects of SES affect health for specific social groups (Matthews, Gallo, & Taylor, 2010). In this work, I estimate measurement models for several aspects of SES among African American, Latina, and White men and women, then link aspects of SES with each other and with health using structural equation modeling. I also examine the unique mediating pathways by which aspects of SES are linked with health for these groups. / text

The Integrative Neuropsychological Theory of Executive-Related Abilities and Component Transactions (INTERACT; Garcia-Barrera, 2011) is a novel perspective on executive function(s), and the functional interactions among those neural systems thought to underlie them. INTERACT was examined in this validation study using structural equation modeling. A novel battery of computerized tasks was implemented in a sample of 218 healthy, adult, university students. Each of the derived indicator variables represented a specific aspect of performance, and corresponded with one of the five distinct executive components of INTERACT. After eliminating tasks that demonstrated poor psychometric properties, overall model fit was excellent, χ2 = 36.38, df = 44, p = .786; CFI = 1.00; RMSEA = .000. Further, INTERACT was superior to six alternative measurement models, which were theoretically-based. Although the structural model of INTERACT was too complex to be tested here, a novel analysis of the data was introduced to test the interactions among INTERACT’s components. This analysis demonstrated the significant utility of INTERACT’s fundamental theoretical predictions. Given the outcome of this initial validation study, the predictive power of INTERACT should continue to be exploited in future studies of executive function(s), and should be extended to explore executive systems in unique populations. / Graduate