The purpose of this study was the examination of age bias in social work professionals who have direct and influential contact with a growing segment of the population: older adults. Those who work most closely with older people may be at risk for age bias, although much of the research on age bias has been conducted with students rather than with those who work with older people. This study adds to the research on prejudice; the sources from which attitudes, values, prejudices, and stereotypical thinking arise were addressed. Key experiences with older individuals were found to predict age bias. Because social work ethical principles closely align with conditions known to reduce prejudice, it was hypothesized that higher ethics adherence would be associated with less age bias. Specific experiential factors were found to influence prejudice toward older people. Influences from family beliefs and from television and other media were associated with a non-biased attitude, as were influences from caregiving to older people. These sources of one's values and beliefs about older individuals were also found to predict the extent of one's knowledge of aging processes. Although ethics adherence was not a predictor of age bias, the discovery of the influence of family beliefs, media portrayals, and caregiving experiences revealed a need for awareness of ageist beliefs in a professional population that works extensively with older adults. / Ph. D.

People interact with different critical applications in day-to-day life. Some examples of critical applications include computer programs, anonymous vehicles, digital healthcare, smart homes, etc. There are inherent risks in these critical applications if they fail to perform properly. In my dissertation, we mainly focus on developing methodologies for performance improvement for software security and healthcare prognosis. Cryptographic vulnerability tools are used to detect misuses of Java cryptographic APIs and thus classify secure and insecure parts of code. These detection tools are critical applications as misuse of cryptographic libraries and APIs causes devastating security and privacy implications. We develop two benchmarks that help developers to identify secure and insecure code usage as well as improve their tools. We also perform a comparative analysis of four static analysis tools. The developed benchmarks enable the first scientific comparison of the accuracy and scalability of cryptographic API misuse detection. Many published detection tools (CryptoGuard, CrySL, Oracle Parfait) have used our benchmarks to improve their performance in terms of the detection capability of insecure cases. We also examine the need for performance improvement for healthcare applications. Numerous prediction applications are developed to predict patients' health conditions. These are critical applications where misdiagnosis can cause serious harm to patients, even death. Due to the imbalanced nature of many clinical datasets, our work provides empirical evidence showing various prediction deficiencies in a typical machine learning model. We observe that missed death cases are 3.14 times higher than missed survival cases for mortality prediction. Also, existing sampling methods and other techniques are not well-equipped to achieve good performance. We design a double prioritized (DP) technique to mitigate representational bias or disparities across race and age groups. we show DP consistently boosts the minority class recall for underrepresented groups, by up to 38.0%. Our DP method also shows better performance than the existing methods in terms of reducing relative disparity by up to 88% in terms of minority class recall. Incorrect classification in these critical applications can have significant ramifications. Therefore, it is imperative to improve the performance of critical applications to alleviate risk and harm to people. / Doctor of Philosophy / We interact with many software using our devices in our everyday life. Examples of software usage include calling transport using Lyft or Uber, doing online shopping using eBay, using social media via Twitter, check payment status from credit card accounts or bank accounts. Many of these software use cryptography to secure our personal and financial information. However, the inappropriate or improper use of cryptography can let the malicious party gain sensitive information. To capture the inappropriate usage of cryptographic functions, there are several detection tools are developed. However, to compare the coverage of the tools, and the depth of detection of these tools, suitable benchmarks are needed. To bridge this gap, we aim to build two cryptographic benchmarks that are currently used by many tool developers to improve their performance and compare their tools with the existing tools. In another aspect, people see physicians and are admitted to hospitals if needed. Physicians also use different software that assists them in caring the patients. Among this software, many of them are built using machine learning algorithms to predict patients' conditions. The historical medical information or clinical dataset is taken as input to the prediction models. Clinical datasets contain information about patients of different races and ages. The number of samples in some groups of patients may be larger than in other groups. For example, many clinical datasets contain more white patients (i.e., majority group) than Black patients (i.e., minority group). Prediction models built on these imbalanced clinical data may provide inaccurate predictions for minority patients. Our work aims to improve the prediction accuracy for minority patients in important medical applications, such as estimating the likelihood of a patient dying in an emergency room visit or surviving cancer. We design a new technique that builds customized prediction models for different demographic groups. Our results reveal that subpopulation-specific models show better performance for minority groups. Our work contributes to improving the medical care of minority patients in the age of digital health. Overall, our aim is to improve the performance of critical applications to help people by decreasing risk. Our developed methods can be applicable to other critical application domains.

This dissertation consists of two essays. First essay investigates the implications of researcher data requirement on the risk-adjusted returns of firms. Using the monthly CRSP data from 1925 to 2013, we present evidence that firms which survive longer have higher average returns and lower standard deviation of annualized returns than the firms which do not. I further demonstrate that there is a positive relation between firms’ survival and average performance. In order to account for the positive correlation between survival and average performance, I model the relation of survival and pricing errors using a Farlie-Gumbel-Morgenstern joint distribution function and fit resulting the moment conditions to the data. Our results show that even a low correlation between firm survival time and pricing errors can lead to a much higher correlation between the survival time and average pricing errors. Failure to adjust for this data selection biases can result in over/under estimates of abnormal returns by 5.73 % in studies that require at least five years of returns data. Second essay examines diversification benefits of commodity futures portfolios in the light of the rapid increase in investor participation in commodity futures market since 2000. Many actively managed portfolios outperform traditional buy and hold portfolios for the sample period from January, 1986 to October, 2013. The evidence documented through traditional intersection test and stochastic discount factor based spanning test indicates that financializaiton has reduced segmentation of commodity market with equity and bond market and has increased the riskiness of investing in commodity futures markets. However, diversifying property of commodity portfolios have not disappeared despite the increased correlation between commodity portfolios returns and equity index returns.

Purpose: The tendency to prioritize negative or threatening social information, a cognitive process known as cognitive bias, has been linked to the development of social anxiety. Given the increased risk for social anxiety among adolescents who stutter (aWS), this project extended the research on cognitive bias to aWS to inform our understanding of the psychosocial factors associated with stuttering in adolescence – the period of development when social anxiety typically emerges. The purpose of this two-part study was to examine group and individual differences in two forms of cognitive bias among aWS and typically fluent controls (TFC) – attentional and interpretation biases. Methods: A sample of 102 adolescents (49 aWS and 53 TFC; 13- to 19-years-old) completed a self-report measure of social anxiety, a computerized attentional bias task, and a computerized interpretation bias task. To assess attentional bias, neutral-negative face pairs were presented in a modified dot-probe paradigm in which response times to engaging and disengaging from neutral, fearful, and angry expressions were measured. To assess interpretation bias, ambiguous verbal and nonverbal social scenarios were presented in a vignette-based recognition task, after which participants endorsed possible negative and positive interpretations of those scenarios. Results: The aWS and TFC reported comparable degrees of social anxiety, although female aWS reported higher levels than male aWS. For the attentional bias task, aWS were faster to engage with fearful faces than to maintain attention on neutral faces, and they were also faster to disengage from fearful and angry faces than to maintain attention on those negative faces. TFC did not demonstrate an attentional preference for any particular face type. For the interpretation bias task, while aWS and TFC rated negative and positive interpretations of verbal and nonverbal scenarios similarly, social anxiety moderated the effect of interpretation characteristics on endorsement of those interpretations; participants with greater social anxiety endorsed negative interpretations of verbal scenarios to a greater degree than those with lower social anxiety, and participants with lower social anxiety endorsed positive interpretations of verbal and nonverbal scenarios to a greater degree than those with higher social anxiety. Conclusions: This study contributes to the existing literature in several meaningful ways. First, this sample of aWS and TFC demonstrated comparable rates of social anxiety, which counters many other reports of group differences in social anxiety in this population. Second, it supports previous preliminary accounts of attentional bias among individuals who stutter. The present findings are novel in that aWS’ rapid engagement with and rapid disengagement from negative faces were observed in the absence of group differences in social anxiety. Third, the results challenge the speculation that stuttering is associated with negative interpretation bias – a relationship that has been proposed in the literature but never empirically investigated. Taken together, these findings provide the groundwork for continued investigation into the role of social information processing on psychosocial outcomes for aWS.

[Truncated abstract] It is well established that anxiety vulnerability is characterised by two biased patterns of selective information processing (Mathews & MacLeod, 1986; Mogg & Bradley, 1998). First anxiety is associated with an attentional bias, reflecting the selective allocation of attention to threatening stimuli in the environment (Mathews & MacLeod, 1985; MacLeod, Mathews & Tata, 1986; MacLeod & Cohen, 1993). Second anxiety is associated with an interpretive bias, reflecting a disproportionate tendency to resolve ambiguity in a threatening manner (Mogg et al., 1994). These characteristics are shown by normal individual high in trait anxiety (Mathews, Richards & Eysenck, 1989; Mogg, Bradley & Hallowell, 1994; Mathews & MacLeod, 1994), and by examining clinically anxious patients who repeatedly report elevated trait anxiety levels (MacLeod, Mathews & Tata, 1986; Mogg & Bradley, 1998). '...' Two alternative hypotheses regarding this relationship are proposed. One hypothesis is that attentional and interpretive biases are concurrent expressions of a single underlying biased selectivity mechanism that characterises anxiety vulnerability (the Common Mechanism account). In contrast, a quite different hypothesis is that attentional and interpretive biases are independent cognitive anomalies that represent separate pathways to anxiety vulnerability (the Independent Mechanisms account). The present research program was designed to empirically test the predictions that differentiate the Common Mechanism and Independent Mechanisms accounts. The general methodological approach that was adopted was to employ bias manipulation tasks from the literature that have been developed and validated to directly modify one class of processing bias (i.e. attentional bias or interpretive bias). The effect of these direct bias manipulation tasks on a measure of the same class of processing bias or the other class of processing bias was then examined. The Common Mechanism and Independent Mechanisms accounts of the relationship between attentional and interpretive bias generate differing predictions concerning the impact of directly manipulating one class of processing bias upon a measure of the other class of processing bias. The central difference between the alternate accounts is their predictions regarding cross-bias transfer, that is the transfer of training effects from direct manipulation of one class of processing bias to a measure of the other class of processing bias. Whereas the Common Mechanism account predicts that such cross-bias transfer will occur, the Independent Mechanisms account does not predict such transfer. A series of seven studies is reported in this thesis. There was some difficulty achieving successful bias modification using bias manipulation approaches established in the literature; however when such manipulation was achieved no cross-bias transfer was observed. Therefore the obtained pattern of results was consistent with the Independent Mechanisms (IM) account, and inconsistent with the Common Mechanism (CM) account. A more detailed version of the IM account is developed to more fully accommodate the specific results obtained in this thesis.

In a preregistered study, we investigated whether two different procedures increased people’s recognition and motivation to self-regulate personal bias and also recognition and motivation to combat systemic bias. Non-Black undergraduates (N = 467) were randomly assigned to either a IAT procedure (i.e., took a racial IAT, received fixed feedback indicating racial bias, and received an explanation for why people may hold implicit biases), a discrimination experiences procedure (i.e., read about Black people’s discrimination experiences across various institutional contexts), or a control procedure (i.e., rated their preferences for common consumer products). Then, participants completed measures assessing recognition of and motivation to combat personal and systemic bias. Among average IMS participants, results indicated that the IAT procedure significantly increased recognition of personal racial bias, compared to the control procedure. The discrimination experiences procedure significantly increased motivation to combat systemic bias, support for policies aimed at addressing inequality, and motivation to self-regulate personal bias, compared to both the control and IAT procedures. We also found that the IAT heightened negative self-directed affect especially among higher IMS participants, which in turn was associated with increased acknowledgement of and motivation to combat not only personal but also systemic bias. Finally, among all participants, the discrimination experiences procedure heightened negative other-directed affect, which in turn was associated with increased recognition of and motivation to combat systemic bias. Although additional research is needed, these initial results may suggest that personal bias interventions influence personal bias outcomes but do not similarly influence systemic bias outcomes. In contrast, systemic bias interventions may be more likely to influence awareness of and motivation to combat both personal and systemic bias. These results pave the way for future investigation into the nature of crossover effects between personal and systemic bias procedures.

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Bakgrund: Efter många år av högkonjunktur är den svenska ekonomin prognostiserad att föras in i en lågkonjunktur under 2023. Hög inflation hanteras med stigande räntor vilket påverkar investerare på flera sätt, men det finns också en rad börspsykologiska faktorer som kan ha en inverkan på investerare och deras beslut. En åldersgrupp som aldrig investerat under en lågkonjunktur är generation Z. Det är därför intressant att undersöka hur börspsykologiska faktorer påverkar generation Z:s investeringsbeslut under ingången av en lågkonjunktur. Syfte: Syftet med arbetet är därför att undersöka och åskådliggöra vilka börspsykologiska faktorer som har en påverkan på generation Z:s beslutsfattande i ingången av en lågkonjunktur.  Metod: Studien har antagit en kvantitativ insamlingsmetod med en deduktiv ansats, detta genom en genomgripande litteraturstudie följt av en enkätundersökning. Analysen har antagit ett deskriptivt förhållningssätt men har också bestått av enkel linjär regression. Slutsats: Resultatet av undersökningen visar att det finns tendenser av samtliga börspsykologiska biaser i generation Z. Av regressionsanalysen att döma går det dock endast att utläsa signifikanta samband mellan biaserna overconfidence, herding behaviour och anchoring bias mot generation Z:s investeringsbeslut under ingången av en lågkonjunktur.

Demographic factors can influence criminal justice system outcomes. We examine legal system processing in 12 U.S. states from 1976-1991. Variables included: 1) race, age, and gender; 2) violent, sexual, and drug- and alcohol-related charges; 3) level of charge; 4) charges at arrest, trial, and final disposition; 5) time-lengths between each stage; 6) dismissal, plea bargaining, and conviction; and 7) final sentencing length. Significant differences in arrest, prosecution, plea bargaining, charge severity, and final sanctioning were observed dependent on race, gender, age, and the intersectionality of these characteristics. Implications for research policy to reduce the impact of disparities are discussed.

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