Empathy and memory are two central aspects that make us human. In the following work, I combined these two areas with genetics and asked how they would interrelate against the background of age. At study, 28 younger and 32 older adults went through an item recognition/source memory paradigm with neutral and emotional (i.e., angry) faces. Dispositional empathy was measured using the Interpersonal Reactivity Index (IRI) and the Empathy Quotient (EQ). Further, 13 single-nucleotide polymorphisms (SNPs) from mainly oxytocin receptors (OXTR) were extracted. Results revealed that older adults had a lower score on the Fantasy dimension of the IRI. Younger and older adults did not differ in hit rate, but older adults showed a higher false alarm rate for neutral source memory. For emotional item recognition, older adults showed a higher liberal response bias whereas, for neutral source memory, younger adults showed a higher conservative response bias. For both memory and empathy, main effects and age interactions were found for OXTR rs237887, rs237897, rs2254298, rs4564970, and rs4686302. These findings illustrated the close interconnectivity of memory, empathy, and genetics over the human life span.

The present monograph deals with the problem of sex differences in test results from various angles. Initially, the aim was to investigate whether the use of test results in selection could be considered fair in spite of sex differences in test score averages. As work progressed, the aim was specified towards clarifying in what manner test item content is related to sex differences in results and whether the observed differences are consistent over different groupé of men and women. After a brief review of some research results on sex differences in cognitive abilities, the Swedish Scholastic Aptitude Test (SSAT) is described. The SSAT is the measuring instrument in the following empirical studies. In chapter four there is a survey of a number of models which aim at correcting for unfair group differences in test scores when the tests are to be used in selection. Two models are examined empirically. In chapter five such models are examined that aim to identify individual test items giving deviant results. The conclusion of these two studies is that statistical models can not solve the problem of group differences in test scores, since what constitutes fairness is mainly a value problem. This cannot be dealt with in a strictly technical manner. Chapter six is devoted to analyses of test item content and sex differences in all subtests on vocabulary and general knowledge which have been used in the SSAT between 1977 and 1983. The conclusion from these analyses is that test item content seems to determine whether men or women obtain higher test scores. Some subcategories of items seem to favour men and others favour women. The extent to which the testees are able to predict which items favour one sex or the other is studied in chapter seven. The testees could only make appropriate judgements to a very limited extent. In chapter eight the significance of age and education for sex differences in test scores is studied. Furthermore, sex differences on individual items are studied for men and women having the same score on the subtest level. Sex differences in scores on individual test items could not be eliminated by equalizing age, education or subtest achievement respectively. Finally, the results from all the studies are summarized and discussed in view of their significance for the validity of the tests. / digitalisering@umu

As men and women return from serving on the frontlines of Operations Enduring Freedom (OEF; Afghanistan) and Iraqi Freedom (OIF; Iraq), many struggle with emotional or behavioral difficulties stemming from the stresses of battle. However, research has shown that these service members may be unwilling or unable to recognize or report such difficulties due to such factors as amnesia, avoidance, or cognitive impairment. Hence, the burden to recognize distress and encourage treatment increasingly falls on peers, friends, and especially intimate partners. Given that this responsibility is often placed on significant others, it is imperative to determine which symptoms are amenable to detection by informants and which are not. The current study examined the ability of female spouses of Vietnam veterans to report on various indicators of posttraumatic stress disorder (PTSD) using the Mississippi Scale for Combat-Related PTSD. Item response theory (IRT) analyses were conducted with a dataset composed of both self- and informant reports using the same items regarding the same individual in order to examine the item-level properties. Results from these analyses indicated that the ability of both spouses and veterans to detect PTSD symptoms varies across item content and that items themselves do not relate equally to, or become diagnostic at the same level of, PTSD. Overall, veterans showed greater sensitivity to their own symptoms and were able to provide more information than their spouses for nearly every item rated by independent experts to be overt or covert. However, some items provided greater information when endorsed by the spouse versus the veteran even though, consistent with the majority of other items, these items were endorsed by the spouse only once the PTSD symptoms had reached greater severity. Implications of these findings as well as future directions for research regarding observer reports of PTSD symptomatology were explored.

Warehouses are a key aspect of modern supply chains and play a vital role in the success or failure of businesses today. Figures from the USA indicate that the capital- and operating costs of warehouses represent about 22 % of a company’s logistics costs while figures from Europe indicate 25 % (Baker & Canessa, 2009). Because warehousing is such an important function within a company, the authors chose to focus on this in their thesis. The purpose of this thesis has been to examine the significance of a Warehouse Management System (WMS) and item identification with attention to logistic efficiency. With this as a starting point, the thesis also intends to examine the needs and possibilities with a WMS and some sort of item identification at a specific company and also present suggestions of how to move forward with this type of work.

Information Systems (IS) research frequently uses survey data to measure the interplay between technological systems and human beings. Researchers have developed sophisticated procedures to build and validate multi-item scales that measure real world phenomena (latent constructs). Most studies use the so-called classical test theory (CTT), which suffers from several shortcomings. We first compare CTT to Item Response Theory (IRT) and subsequently apply a Rasch model approach to measure hedonic aspects of websites. The results not only show which attributes are best suited for scaling hedonic information systems, but also introduce IRT as a viable substitute that overcomes severall shortcomings of CTT. (author´s abstract) / Series: Research Report Series / Department of Statistics and Mathematics

<p>This dissertation is devoted to modeling complex data from the</p><p>Bayesian perspective via constructing priors with latent structures.</p><p>There are three major contexts in which this is done -- strategies for</p><p>the analysis of dynamic longitudinal data, estimating</p><p>shape-constrained functions, and identifying subgroups. The</p><p>methodology is illustrated in three different</p><p>interdisciplinary contexts: (1) adaptive measurement testing in</p><p>education; (2) emulation of computer models for vehicle crashworthiness; and (3) subgroup analyses based on biomarkers.</p><p>Chapter 1 presents an overview of the utilized latent structured</p><p>priors and an overview of the remainder of the thesis. Chapter 2 is</p><p>motivated by the problem of analyzing dichotomous longitudinal data</p><p>observed at variable and irregular time points for adaptive</p><p>measurement testing in education. One of its main contributions lies</p><p>in developing a new class of Dynamic Item Response (DIR) models via</p><p>specifying a novel dynamic structure on the prior of the latent</p><p>trait. The Bayesian inference for DIR models is undertaken, which</p><p>permits borrowing strength from different individuals, allows the</p><p>retrospective analysis of an individual's changing ability, and</p><p>allows for online prediction of one's ability changes. Proof of</p><p>posterior propriety is presented, ensuring that the objective</p><p>Bayesian analysis is rigorous.</p><p>Chapter 3 deals with nonparametric function estimation under</p><p>shape constraints, such as monotonicity, convexity or concavity. A</p><p>motivating illustration is to generate an emulator to approximate a computer</p><p>model for vehicle crashworthiness. Although Gaussian processes are</p><p>very flexible and widely used in function estimation, they are not</p><p>naturally amenable to incorporation of such constraints. Gaussian</p><p>processes with the squared exponential correlation function have the</p><p>interesting property that their derivative processes are also</p><p>Gaussian processes and are jointly Gaussian processes with the</p><p>original Gaussian process. This allows one to impose shape constraints</p><p>through the derivative process. Two alternative ways of incorporating derivative</p><p>information into Gaussian processes priors are proposed, with one</p><p>focusing on scenarios (important in emulation of computer</p><p>models) in which the function may have flat regions.</p><p>Chapter 4 introduces a Bayesian method to control for multiplicity</p><p>in subgroup analyses through tree-based models that limit the</p><p>subgroups under consideration to those that are a priori plausible.</p><p>Once the prior modeling of the tree is accomplished, each tree will</p><p>yield a statistical model; Bayesian model selection analyses then</p><p>complete the statistical computation for any quantity of interest,</p><p>resulting in multiplicity-controlled inferences. This research is</p><p>motivated by a problem of biomarker and subgroup identification to</p><p>develop tailored therapeutics. Chapter 5 presents conclusions and</p><p>some directions for future research.</p> / Dissertation

Data mining is an important process of extracting desirable knowledge from existing databases for specific purposes. Nearly all transactions in real-world databases involve items bought, quantities of the items, and the time periods in which they appear. In the past, temporal quantitative mining was proposed to find temporal quantitative rules from a temporal quantitative database. However, the quantitative values of items are not suitable to human reasoning. To deal with this, the fuzzy set theory was applied to the temporal quantitative mining because of its simplicity and similarity to human reasoning. In this thesis, we thus handle the problem of mining fuzzy temporal association rules from a publication database, and propose three algorithms to achieve it. The three algorithms handle different lifespan definitions, respectively. In the first algorithm, the lifespan of an item is evaluated from the time of the first transaction with the item to the end time of the whole database. In the second algorithm, an additional publication table, which includes the publication date of each item in stores, is given, and thus the lifespan of an item is measured by its entire publication period. Finally in the third algorithm, the lifespan of an item is calculated from the end time of the whole database to its earliest time in the database for the item to be a fuzzy temporal frequent item within the duration. In addition, an effective itemset table structure is designed to store and get information about itemsets and can thus speed up the execution efficiency of the mining process. At last, experimental results on two simulation datasets compare the mined fuzzy temporal quantitative itemsets and rules with and without consideration of lifespans of items under different parameter settings.

In order to mitigate the burden of government expenditure and improve the management of university, national universities in Taiwan have implemented the system of University Endowment Fund. The establishment of University Endowment Fund System has enacted for 16 years since 1996. The maintenance of National University mostly depends on government support in the past. And the college maintenance fund turned into half government support and half self-raised fund after the University Endowment Fund System has been enact. The investment gain is the most undeveloped item in five self-raised items. Most of National universities take bank deposit as its only investment. As a result, this paper aims to study about university investment. Investigate the latest law and regulation of national university and find the characteristic of college investment. Take National Sun Yat-sen University for example and establish the investment policy for national university. Select appropriate invest stock for National Sun Yat-sen University, and the select mechanism depends on the investment policy. Take the cash dividend to improve the financial income. The University Endowment Fund can improve its asset allocation gradually in the future.

Data mining techniques have been widely applied to extract desirable knowledge from existing databases for specific purposes. In real-world applications, a database usually involves the time periods when transactions occurred and exhibition periods of items, in addition to the items bought in the transactions. To handle this kind of data, temporal data mining techniques are thus proposed to find temporal association rules from a database with time. Most of the existing studies only consider different item lifespans to find general temporal association rules, and this may neglect some useful information. For example, while an item within the whole exhibition period may not be a frequent one, it may be frequent within part of this time. To deal with this, the concept of a hierarchy of time is thus applied to temporal data mining along with suitable time granules, as defined by users. In this thesis, we thus handle the problem of mining temporal association rules with a hierarchy of time granules from a temporal database, and also propose three novel mining algorithms for different item lifespan definitions. In the first definition, the lifespan of an item in a time granule is calculated from the first appearance time to the end time in the time granule. In the second definition, the lifespan of an item in a time granule is evaluated from the publication time of the item to the end time in the time granule. Finally, in the third definition, the lifespan of an item in a time granule is measured by its entire exhibition period. The experimental results on a simulation dataset show the performance of the three proposed algorithms under different item lifespan definitions, and compare the mined temporal association rules with and without consideration of the hierarchy of time granules under different parameter settings.

The application of diagnostic thresholds plays an important role in the classification of mental disorders. Despite their importance, many diagnostic thresholds are set arbitrarily, without much empirical support. This paper seeks to introduce and analyze a new empirically based way of setting diagnostic thresholds for a category of mental disorders that has historically had arbitrary thresholds, the personality disorders (PDs). I analyzed data from over 2,000 participants that were part of the Methods to Improve Diagnostic Assessment and Services (MIDAS) database. Results revealed that functional outcome scores, as measured by Global Assessment of Functioning (GAF) scores, could be used to identify diagnostic thresholds and that the optimal thresholds varied somewhat by personality disorder (PD) along the spectrum of latent severity. Using the Item response theory (IRT)-based approach, the optimal threshold along the spectrum of latent severity for the different PDs ranged from θ = 1.50 to 2.25. Effect sizes using the IRT-based approach ranged from .34 to 1.55. These findings suggest that linking diagnostic thresholds to functional outcomes and thereby making them less arbitrary is an achievable goal. This study has introduced a new and uncomplicated way to empirically set diagnostic thresholds while also taking into consideration that items within diagnostic sets may function differently. Although purely an initial demonstration meant only to serve as an example, by using this approach, there exists the potential that diagnostic thresholds for all disorders could one day be set on an empirical basis.