Indiana University-Purdue University Indianapolis (IUPUI) / In many longitudinal studies, the time scales upon which we assess the primary outcomes are anchored by pre-specified events. However, these anchoring events are often not observable and they are randomly distributed with unknown distribution. Without direct observations of the anchoring events, the time scale used for analysis are not available, and analysts will not be able to use the traditional longitudinal models to describe the temporal changes as desired. Existing methods often make either ad hoc or strong assumptions on the anchoring events, which are unveri able and prone to biased estimation and invalid inference. Although not able to directly observe, researchers can often ascertain an interval that includes the unobserved anchoring events, i.e., the anchoring events are interval censored. In this research, we proposed a two-stage method to fit commonly used longitudinal models with interval censored anchoring events. In the first stage, we obtain an estimate of the anchoring events distribution by nonparametric method using the interval censored data; in the second stage, we obtain the parameter estimates as stochastic functionals of the estimated distribution. The construction of the stochastic functional depends on model settings. In this research, we considered two types of models. The first model was a distribution-free model, in which no parametric assumption was made on the distribution of the error term. The second model was likelihood based, which extended the classic mixed-effects models to the situation that the origin of the time scale for analysis was interval censored. For the purpose of large-sample statistical inference in both models, we studied the asymptotic properties of the proposed functional estimator using empirical process theory. Theoretically, our method provided a general approach to study semiparametric maximum pseudo-likelihood estimators in similar data situations. Finite sample performance of the proposed method were examined through simulation study. Algorithmically eff- cient algorithms for computing the parameter estimates were provided. We applied the proposed method to a real data analysis and obtained new findings that were incapable using traditional mixed-effects models. / 2 years

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Syftet med denna empiriska studie var att granska hur sjuksköterskan dokumente-rar smärta på en postoperativ avdelning och hur åtgärder i samband med smärt-lindring följs upp. Studien genomfördes på Universitetssjukhuset i Malmö genom granskning av journaler för 14 patienter som givit sitt samtycke att delta i studien. Analysen resulterade i tre kategorier: god/tillfredsställande, delvis tillfredsställan-de och bristfällig/obefintlig dokumentation. Resultatet av studien visade brister gällande dokumentation av smärta. Avsaknad av användning av visuell analog skala, smärtans karaktär och uppföljning av smärtlindring kunde noteras i ett fler-tal journaler. Bristerna var särskilt uttalade i fråga om dokumentation av uppföljning. Vidare forskning på området är angelägen för att kunna ta itu med problemet och i framtiden bör sjuksköterskan få fördjupa sina kunskaper i utbildningen eller på sin arbetsplats. / The aim of this empirical study was to examine how nurses document pain in a postoperative unit and the follow up of the alleviation of pain. The study was car-ried out in Malmö University Hospital, Sweden, through examining journals from 14 patients whom agreed to participate. The analyse identified three categories: good/satisfying, partly satisfying and deficient/non-existing documentation. Fur-thermore the study showed qualitative shortages concerning documentation of pain e.g. Lack of using a pain scale, pain character and follow up of alleviation of pain could be seen in many journals. Further research in this area is needed to be able to handle the problem and in service training as well as increased focus on the documentation problems in basic nurse training is recommended.

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The estimation of standard errors is essential to statistical inference. Statistical variability is inherent within data, but is usually of secondary interest; still, some options exist to deal with this variability. One approach is to carefully model the covariance structure. Another approach is robust estimation. In this approach, the covariance structure is estimated from the data. White (1980) introduced a biased, but consistent, robust estimator. Long et al. (2000) added an adjustment factor to White's estimator to remove the bias of the original estimator. Through the use of simulations, this project compares restricted maximum likelihood (REML) with four robust estimation techniques: the Standard Robust Estimator (White 1980), the Long estimator (Long 2000), the Long estimator with a quantile adjustment (Kauermann 2001), and the empirical option of the MIXED procedure in SAS. The results of the simulation show small sample and asymptotic properties of the five estimators. The REML procedure is modelled under the true covariance structure, and is the most consistent of the five estimators. The REML procedure shows a slight small-sample bias as the number of repeated measures increases. The REML procedure may not be the best estimator in a situation in which the covariance structure is in question. The Standard Robust Estimator is consistent, but it has an extreme downward bias for small sample sizes. The Standard Robust Estimator changes little when complexity is added to the covariance structure. The Long estimator is unstable estimator. As complexity is introduced into the covariance structure, the coverage probability with the Long estimator increases. The Long estimator with the quantile adjustment works as designed by mimicking the Long estimator at an inflated quantile level. The empirical option of the MIXED procedure in SAS works well for homogeneous covariance structures. The empirical option of the MIXED procedure in SAS reduces the downward bias of the Standard Robust Estimator when the covariance structure is homogeneous.

Current design theory sets out many rules and guidelines for designers, but good design is still difficult to replicate. Often the design principles found in the manuals are misapplied, resulting in designs that (1) do not fulfill their purpose and (2) disrupt the clarity of information. This thesis will review and provide experimental data supporting a model of visual form/visual purpose connections based on the semiotic of C.S. Peirce. This model was first used by Amare and Manning (2005, 2006, 2007, 2008, 2009) to evaluate and explain both effective and ineffective visual information design. This thesis will extend their approach, reporting on the results of four experiments to test the aesthetic appeal and information retention from various visual designs. The four experiments presented in this thesis show that viewer's ability to recall information does not coincide with designs that they find the most visually stimulating or visually pleasing. High indicative contrasts allow for higher retention rate, but those contrasts do not necessarily conform to viewer's aesthetic preferences. Low indicative contrast options have a lower retention rate, but are preferred aesthetically by viewers. Peircean analysis accounts for this disconnect between usability and preference and can help designers find the balance that is needed between these competing purposes in visual information design.

Empirical research is inexact and error-prone leading researchers to agree that replication of experiments is a necessary step to validating empirical results. Unfortunately, replicating experiments requires substantial investments in manpower and time. These resource requirements can be reduced by incorporating component reuse when building tools for empirical experimentation. Bokeo is an initiative within the Sequoia Lab of the BYU Computer Science Department to develop a platform to assist in the empirical study of software engineering. The i3Persistence Framework is a component of Bokeo which enables researchers to easily build and rapidly deploy tools for empirical experiments by providing an easy-to-use database management service. We introduce the i3Persistence Framework of Bokeo to assist in the development of software to replicate experiments and conduct studies on data from open-source repositories.