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This meta-scientific dissertation comprises three research articles that investigated the reproducibility of psychological research. Specifically, they focused on the reproducibility of eye-tracking research on the one hand, and studying preregistration (i.e., the practice of publishing a study protocol before data collection or analysis) as one method to increase reproducibility on the other hand.
In Article I, it was demonstrated that eye-tracking data quality is influenced by both the utilized eye-tracker and the specific task it is measuring. That is, distinct strengths and weaknesses were identified in three devices (Tobii Pro X3-120, GP3 HD, EyeLink 1000+) in an extensive test battery. Consequently, both the device and specific task should be considered when designing new studies. Meanwhile, Article II focused on the current perception of preregistration in the psychological research community and future directions for improving this practice. The survey showed that many researchers intended to preregister their research in the future and had overall positive attitudes toward preregistration. However, various obstacles were identified currently hindering preregistration, which should be addressed to increase its adoption. These findings were supplemented by Article III, which took a closer look at one preregistration-specific tool: the PRP-QUANT Template. In a simulation trial and a survey, the template demonstrated high usability and emerged as a valuable resource to support researchers in using preregistration. Future revisions of the template could help to further facilitate this open science practice.
In this dissertation, the findings of the three articles are summarized and discussed regarding their implications and potential future steps that could be implemented to improve the reproducibility of psychological research.
Differential equations yield solutions that necessarily contain a certain amount of regularity and are based on local interactions. There are various natural phenomena that are not well described by local models. An important class of models that describe long-range interactions are the so-called nonlocal models, which are the subject of this work.
The nonlocal operators considered here are integral operators with a finite range of interaction and the resulting models can be applied to anomalous diffusion, mechanics and multiscale problems.
While the range of applications is vast, the applicability of nonlocal models can face problems such as the high computational and algorithmic complexity of fundamental tasks. One of them is the assembly of finite element discretizations of truncated, nonlocal operators.
The first contribution of this thesis is therefore an openly accessible, documented Python code which allows to compute finite element approximations for nonlocal convection-diffusion problems with truncated interaction horizon.
Another difficulty in the solution of nonlocal problems is that the discrete systems may be ill-conditioned which complicates the application of iterative solvers. Thus, the second contribution of this work is the construction and study of a domain decomposition type solver that is inspired by substructuring methods for differential equations. The numerical results are based on the abstract framework of nonlocal subdivisions which is introduced here and which can serve as a guideline for general nonlocal domain decomposition methods.
Data fusions are becoming increasingly relevant in official statistics. The aim of a data fusion is to combine two or more data sources using statistical methods in order to be able to analyse different characteristics that were not jointly observed in one data source. Record linkage of official data sources using unique identifiers is often not possible due to methodological and legal restrictions. Appropriate data fusion methods are therefore of central importance in order to use the diverse data sources of official statistics more effectively and to be able to jointly analyse different characteristics. However, the literature lacks comprehensive evaluations of which fusion approaches provide promising results for which data constellations. Therefore, the central aim of this thesis is to evaluate a concrete plethora of possible fusion algorithms, which includes classical imputation approaches as well as statistical and machine learning methods, in selected data constellations.
To specify and identify these data contexts, data and imputation-related scenario types of a data fusion are introduced: Explicit scenarios, implicit scenarios and imputation scenarios. From these three scenario types, fusion scenarios that are particularly relevant for official statistics are selected as the basis for the simulations and evaluations. The explicit scenarios are the fulfilment or violation of the Conditional Independence Assumption (CIA) and varying sample sizes of the data to be matched. Both aspects are likely to have a direct, that is, explicit, effect on the performance of different fusion methods. The summed sample size of the data sources to be fused and the scale level of the variable to be imputed are considered as implicit scenarios. Both aspects suggest or exclude the applicability of certain fusion methods due to the nature of the data. The univariate or simultaneous, multivariate imputation solution and the imputation of artificially generated or previously observed values in the case of metric characteristics serve as imputation scenarios.
With regard to the concrete plethora of possible fusion algorithms, three classical imputation approaches are considered: Distance Hot Deck (DHD), the Regression Model (RM) and Predictive Mean Matching (PMM). With Decision Trees (DT) and Random Forest (RF), two prominent tree-based methods from the field of statistical learning are discussed in the context of data fusion. However, such prediction methods aim to predict individual values as accurately as possible, which can clash with the primary objective of data fusion, namely the reproduction of joint distributions. In addition, DT and RF only comprise univariate imputation solutions and, in the case of metric variables, artificially generated values are imputed instead of real observed values. Therefore, Predictive Value Matching (PVM) is introduced as a new, statistical learning-based nearest neighbour method, which could overcome the distributional disadvantages of DT and RF, offers a univariate and multivariate imputation solution and, in addition, imputes real and previously observed values for metric characteristics. All prediction methods can form the basis of the new PVM approach. In this thesis, PVM based on Decision Trees (PVM-DT) and Random Forest (PVM-RF) is considered.
The underlying fusion methods are investigated in comprehensive simulations and evaluations. The evaluation of the various data fusion techniques focusses on the selected fusion scenarios. The basis for this is formed by two concrete and current use cases of data fusion in official statistics, the fusion of EU-SILC and the Household Budget Survey on the one hand and of the Tax Statistics and the Microcensus on the other. Both use cases show significant differences with regard to different fusion scenarios and thus serve the purpose of covering a variety of data constellations. Simulation designs are developed from both use cases, whereby the explicit scenarios in particular are incorporated into the simulations.
The results show that PVM-RF in particular is a promising and universal fusion approach under compliance with the CIA. This is because PVM-RF provides satisfactory results for both categorical and metric variables to be imputed and also offers a univariate and multivariate imputation solution, regardless of the scale level. PMM also represents an adequate fusion method, but only in relation to metric characteristics. The results also imply that the application of statistical learning methods is both an opportunity and a risk. In the case of CIA violation, potential correlation-related exaggeration effects of DT and RF, and in some cases also of RM, can be useful. In contrast, the other methods induce poor results if the CIA is violated. However, if the CIA is fulfilled, there is a risk that the prediction methods RM, DT and RF will overestimate correlations. The size ratios of the studies to be fused in turn have a rather minor influence on the performance of fusion methods. This is an important indication that the larger dataset does not necessarily have to serve as a donor study, as was previously the case.
The results of the simulations and evaluations provide concrete implications as to which data fusion methods should be used and considered under the selected data and imputation constellations. Science in general and official statistics in particular benefit from these implications. This is because they provide important indications for future data fusion projects in order to assess which specific data fusion method could provide adequate results along the data constellations analysed in this thesis. Furthermore, with PVM this thesis offers a promising methodological innovation for future data fusions and for imputation problems in general.
Representation Learning techniques play a crucial role in a wide variety of Deep Learning applications. From Language Generation to Link Prediction on Graphs, learned numerical vector representations often build the foundation for numerous downstream tasks.
In Natural Language Processing, word embeddings are contextualized and depend on their current context. This useful property reflects how words can have different meanings based on their neighboring words.
In Knowledge Graph Embedding (KGE) approaches, static vector representations are still the dominant approach. While this is sufficient for applications where the underlying Knowledge Graph (KG) mainly stores static information, it becomes a disadvantage when dynamic entity behavior needs to be modelled.
To address this issue, KGE approaches would need to model dynamic entities by incorporating situational and sequential context into the vector representations of entities. Analogous to contextualised word embeddings, this would allow entity embeddings to change depending on their history and current situational factors.
Therefore, this thesis provides a description of how to transform static KGE approaches to contextualised dynamic approaches and how the specific characteristics of different dynamic scenarios are need to be taken into consideration.
As a starting point, we conduct empirical studies that attempt to integrate sequential and situational context into static KG embeddings and investigate the limitations of the different approaches. In a second step, the identified limitations serve as guidance for developing a framework that enables KG embeddings to become truly dynamic, taking into account both the current situation and the past interactions of an entity. The two main contributions in this step are the introduction of the temporally contextualized Knowledge Graph formalism and the corresponding RETRA framework which realizes the contextualisation of entity embeddings.
Finally, we demonstrate how situational contextualisation can be realized even in static environments, where all object entities are passive at all times.
For this, we introduce a novel task that requires the combination of multiple context modalities and their integration with a KG based view on entity behavior.
Social entrepreneurship is a successful activity to solve social problems and economic challenges. Social entrepreneurship uses for-profit industry techniques and tools to build financially sound businesses that provide nonprofit services. Social entrepreneurial activities also lead to the achievement of sustainable development goals. However, due to the complex, hybrid nature of the business, social entrepreneurial activities are typically supported by macrolevel determinants. To expand our knowledge of how beneficial macro-level determinants can be, this work examines empirical evidence about the impact of macro-level determinants on social entrepreneurship. Another aim of this dissertation is to examine the impact at the micro level, as the growth ambitions of social and commercial entrepreneurs differ. At the beginning, the introductory section is explained in Chapter 1, which contains the motivation for the research, the research question, and the structure of the work.
There is an ongoing debate about the origin and definition of social entrepreneurship. Therefore, the numerous phenomena of social entrepreneurship are examined theoretically in the previous literature. To determine the common consensus on the topic, Chapter 2 presents
the theoretical foundations and definition of social entrepreneurship. The literature shows that a variety of determinants at the micro and macro levels are essential for the emergence of social entrepreneurship as a distinctive business model (Hartog & Hoogendoorn, 2011; Stephan et al., 2015; Hoogendoorn, 2016). It is impossible to create a society based on a social mission without the support of micro and macro-level-level determinants. This work examines the determinants and consequences of social entrepreneurship from different methodological perspectives. The theoretical foundations of the micro- and macro-level determinants influencing social entrepreneurial activities were discussed in Chapter 3. The purpose of reproducibility in research is to confirm previously published results (Hubbard et al., 1998; Aguinis & Solarino, 2019). However, due to the lack of data, lack of transparency of methodology, reluctance to publish, and lack of interest from researchers, there is a lack of promoting replication of the existing research study (Baker, 2016; Hedges & Schauer, 2019a). Promoting replication studies has been regularly emphasized in the business and management literature (Kerr et al., 2016; Camerer et al., 2016). However, studies that provide replicability of the reported results are considered rare in previous research (Burman et al., 2010; Ryan & Tipu, 2022). Based on the research of Köhler and Cortina (2019), an empirical study on this topic is carried out in Chapter 4 of this work.
Given this focus, researchers have published a large body of research on the impact of microand macro-level determinants on social inclusion, although it is still unclear whether these studies accurately reflect reality. It is important to provide conceptual underpinnings to the field through a reassessment of published results (Bettis et al., 2016). The results of their research make it abundantly clear that the macro determinants support social entrepreneurship.
In keeping with the more narrative approach, which is a crucial concern and requires attention, Chapter 5 considered the reproducibility of previous results, particularly on the topic of social entrepreneurship. We replicated the results of Stephan et al. (2015) to establish the trend of reproducibility and validate the specific conclusions they drew. The literal and constructive replication in the dissertation inspired us to explore technical replication research on social entrepreneurship. Chapter 6 evaluates the fundamental characteristics that have proven to be key factors in the growth of social ventures. The current debate reviews and references literature that has specifically focused on the development of social entrepreneurship. An empirical analysis of factors directly related to the ambitious growth of social entrepreneurship is also carried out.
Numerous social entrepreneurial groups have been studied concerning this association. Chapter 6 compares the growth ambitions of social and traditional (commercial) entrepreneurship as consequences at the micro level. This study examined many characteristics of social and commercial entrepreneurs' growth ambitions. Scholars have claimed to some extent that the growth of social entrepreneurship differs from commercial entrepreneurial activities due to objectivity differences (Lumpkin et al., 2013; Garrido-Skurkowicz et al., 2022). Qualitative research has been used in studies to support the evidence on related topics, including Gupta et al (2020) emphasized that research needs to focus on specific concepts of social entrepreneurship for the field to advance. Therefore, this study provides a quantitative, analysis-based assessment of facts and data. For this purpose, a data set from the Global Entrepreneurship Monitor (GEM) 2015 was used, which examined 12,695 entrepreneurs from 38 countries. Furthermore, this work conducted a regression analysis to evaluate the influence of various social and commercial characteristics of entrepreneurship on economic growth in developing countries. Chapter 7 briefly explains future directions and practical/theoretical implications.
This thesis contains four parts that are all connected by their contributions to the Efficient Market Hypothesis and decision-making literature. Chapter two investigates how national stock market indices reacted to the news of national lockdown restrictions in the period from January to May 2020. The results show that lockdown restrictions led to different reactions in a sample of OECD and BRICS countries: there was a general negative effect resulting from the increase in lockdown restrictions, but the study finds strong evidence for underreaction during the lockdown announcement, followed by some overreaction that is corrected subsequently. This under-/overreaction pattern, however, is observed mostly during the first half of our time series, pointing to learning effects. Relaxation of the lockdown restrictions, on the other hand, had a positive effect on markets only during the second half of our sample, while for the first half of the sample, the effect was negative. The third chapter investigates the gender differences in stock selection preferences on the Taiwan Stock Exchange. By utilizing trading data from the Taiwan Stock Exchange over a span of six years, it becomes possible to analyze trading behavior while minimizing the self-selection bias that is typically present in brokerage data. To study gender differences, this study uses firm-level data. The percentage of male traders in a company is the dependent variable, while the company’s industry and fundamental/technical aspects serve as independent variables. The results show that the percentage of women trading a company rises with a company’s age, market capitalization, a company’s systematic risk, and return. Men trade more frequently and show a preference for dividend-paying stocks and for industries with which they are more familiar. The fourth chapter investigated the relationship between regret and malicious and benign envy. The relationship is analyzed in two different studies. In experiment 1, subjects had to fill out psychological scales that measured regret, the two types of envy, core self-evaluation and the big 5 personality traits. In experiment 2, felt regret is measured in a hypothetical scenario, and the subject’s felt regret was regressed on the other variables mentioned above. The two experiments revealed that there is a positive direct relationship between regret and benign envy. The relationship between regret and malicious envy, on the other hand, is mostly an artifact of core self-evaluation and personality influencing both malicious envy and regret. The relationship can be explained by the common action tendency of self-improvement for regret and benign envy. Chapter five discusses the differences in green finance regulation and implementation between the EU and China. China introduced the Green Silk Road, while the EU adopted the Green Deal and started working with its own green taxonomy. The first difference comes from the definition of green finance, particularly with regard to coal-fired power plants. Especially the responsibility of nation-states’ emissions abroad. China is promoting fossil fuel projects abroad through its Belt and Road Initiative, but the EU’s Green Deal does not permit such actions. Furthermore, there are policies in both the EU and China that create contradictory incentives for economic actors. On the one hand, the EU and China are improving the framework conditions for green financing while, on the other hand, still allowing the promotion of conventional fuels. The role of central banks is also different between the EU and China. China’s central bank is actively working towards aligning the financial sector with green finance. A possible new role of the EU central bank or the priority financing of green sectors through political decision-making is still being debated.
Left ventricular assist devices (LVADs) have become a valuable treatment for patients with advanced heart failure. Women appear to be disadvantaged in the usage of LVADs and concerning clinical outcomes such as death and adverse events after LVAD implant. Contrary to typical clinical characteristics (e.g., disease severity), device-related factors such as the intended device strategy, bridge to a heart transplantation or destination therapy, are often not considered in research on gender differences. In addition, the relevance of pre-implant psychosocial risk factors, such as substance abuse and limited social support, for LVAD outcomes is currently unclear. Thus, the aim of this dissertation is to explore the role of pre-implant psychosocial risk factors for gender differences in clinical outcomes, accounting for clinical and device-related risk factors.
In the first article, gender differences in pre-implant characteristics of patients registered in The European Registry for Patients with Mechanical Circulatory Support (EUROMACS) were investigated. It was found that women and men differed in multiple pre-implant characteristics depending on device strategy. In the second article, gender differences in major clinical outcomes (i.e., death, heart transplant, device explant due to cardiac recovery, device replacement due to complications) were evaluated for patients in the device strategy destination therapy in the Interagency Registry for Mechanically Assisted Circulation (INTERMACS). Additionally, the association of gender and psychosocial risk factors with the major outcomes were analyzed. Women had similar probabilities to die on LVAD support, and even higher probabilities to experience explant of the device due to cardiac recovery compared with men in the destination therapy subgroup. Pre-implant psychosocial risk factors were not associated with major outcomes. The third article focused on gender differences in 10 adverse events (e.g., device malfunction, bleeding) after LVAD implant in INTERMACS. The association of a psychosocial risk indicator with gender and adverse events after LVAD implantation was evaluated. Women were less likely to have psychosocial risk pre-implant but more likely to experience seven out of 10 adverse events compared with men. Pre-implant psychosocial risk was associated with adverse events, even suggesting a dose response-relationship. These associations appeared to be more pronounced in women.
In conclusion, women appear to have similar survival to men when accounting for device strategy. They have higher probabilities of recovery, but higher probabilities of device replacement and adverse events compared with men. Regarding these adverse events, women may be more susceptible to psychosocial risk factors than men. The results of this dissertation illustrate the importance of gender-sensitive research and suggest considering device strategy when studying gender differences in LVAD recipients. Further research is warranted to elucidate the role of specific psychosocial risk factors that lead to higher probabilities of adverse events, to intervene early and improve patient care in both, women and men
Today, almost every modern computing device is equipped with multicore processors capable of efficient concurrent and parallel execution of threads. This processor feature can be leveraged by concurrent programming, which is a challenge for software developers for two reasons: first, it introduces a paradigm shift that requires a new way of thinking. Second, it can lead to issues that are unique to concurrent programs due to the non-deterministic, interleaved execution of threads. Consequently, the debugging of concurrency and related performance issues is a rather difficult and often tedious task. Developers still lack on thread-aware programming tools that facilitate the understanding of concurrent programs. Ideally, these tools should be part of their daily working environment, which typically includes an Integrated Development Environment (IDE). In particular, the way source code is visually presented in traditional source-code editors does not convey much information on whether the source code is executed concurrently or in parallel in the first place.
With this dissertation, we pursue the main goal of facilitating and supporting the understanding and debugging of concurrent programs. To this end, we formulate and utilize a visualization paradigm that particularly includes the display of interactive glyph-based visualizations embedded in the source-code editor close to their corresponding artifacts (in-situ).
To facilitate the implementation of visualizations that comply with our paradigm as plugins for IDEs, we designed, implemented and evaluated a programming framework called CodeSparks. After presenting the design goals and the architecture of the framework, we demonstrate its versatility with a total of fourteen plugins realized by different developers using the CodeSparks framework (CodeSparks plugins). With focus group interviews, we empirically investigated how developers of the CodeSparks plugins experienced working with the framework. Based on the plugins, deliberate design decisions and the interview results, we discuss to what extent we achieved our design goals. We found that the framework is largely target programming-language independent and that it supports the development of plugins for a wide range of source-code-related tasks while hiding most of the details of the underlying plugin development API.
In addition, we applied our visualization paradigm to thread-related runtime data from concurrent programs to foster the awareness of source code being executed concurrently or in parallel. As a result, we developed and designed two in-situ thread visualizations, namely ThreadRadar and ThreadFork, with the latter building on the former. Both thread visualizations are based on a debugging approach, which combines statistical profiling, thread-aware runtime metrics, clustering of threads on the basis of these metrics, and finally interactive glyph-based in-situ visualizations. To address scalability issues of the ThreadRadar in terms of space required and the number of displayable thread clusters, we designed a revised thread visualization. This revision also involved the question of how many thread clusters k should be computed in the first place. To this end, we conducted experiments with the clustering of threads for artifacts from a corpus of concurrent Java programs that include real-world Java applications and concurrency bugs. We found that the maximum k on the one hand and the optimal k according to four cluster validation indices on the other hand rarely exceed three. However, occasionally thread clusterings with k > 3 are available and also optimal. Consequently, we revised both the clustering strategy and the visualization as parts of our debugging approach, which resulted in the ThreadFork visualization. Both in-situ thread visualizations, including their additional features that support the exploration of the thread data, are implemented in a tool called CodeSparks-JPT, i.e., as a CodeSparks plugin for IntelliJ IDEA.
With various empirical studies, including anecdotal usage scenarios, a usability test, web surveys, hands-on sessions, questionnaires and interviews, we investigated quality aspects of the in-situ thread visualizations and their corresponding tools. First, by a demonstration study, we illustrated the usefulness of the ThreadRadar visualization in investigating and fixing concurrency bugs and a performance bug. This was confirmed by a subsequent usability test and interview, which also provided formative feedback. Second, we investigated the interpretability and readability of the ThreadFork glyphs as well as the effectiveness of the ThreadFork visualization through anonymous web surveys. While we have found that the ThreadFork glyphs are correctly interpreted and readable, it remains unproven that the ThreadFork visualization effectively facilitates understanding the dynamic behavior of threads that concurrently executed portions of source code. Moreover, the overall usability of CodeSparks-JPT is perceived as "OK, but not acceptable" as the tool has issues with its learnability and memorability. However, all other usability aspects of CodeSparks-JPT that were examined are perceived as "above average" or "good".
Our work supports software-engineering researchers and practitioners in flexibly and swiftly developing novel glyph-based visualizations that are embedded in the source-code editor. Moreover, we provide in-situ thread visualizations that foster the awareness of source code being executed concurrently or in parallel. These in-situ thread visualizations can, for instance, be adapted, extended and used to analyze other use cases or to replicate the results. Through empirical studies, we have gradually shaped the design of the in-situ thread visualizations through data-driven decisions, and evaluated several quality aspects of the in-situ thread visualizations and the corresponding tools for their utility in understanding and debugging concurrent programs.
Knowledge acquisition comprises various processes. Each of those has its dedicated research domain. Two examples are the relations between knowledge types and the influences of person-related variables. Furthermore, the transfer of knowledge is another crucial domain in educational research. I investigated these three processes through secondary analyses in this dissertation. Secondary analyses comply with the broadness of each field and yield the possibility of more general interpretations. The dissertation includes three meta-analyses: The first meta-analysis reports findings on the predictive relations between conceptual and procedural knowledge in mathematics in a cross-lagged panel model. The second meta-analysis focuses on the mediating effects of motivational constructs on the relationship between prior knowledge and knowledge after learning. The third meta-analysis deals with the effect of instructional methods in transfer interventions on knowledge transfer in school students. These three studies provide insights into the determinants and processes of knowledge acquisition and transfer. Knowledge types are interrelated; motivation mediates the relation between prior and later knowledge, and interventions influence knowledge transfer. The results are discussed by examining six key insights that build upon the three studies. Additionally, practical implications, as well as methodological and content-related ideas for further research, are provided.
This dissertation focusses on research into the personality construct of action vs. state orientation. Derived from the Personality-Systems-Interaction Theory (PSI Theory), state orientation is defined as a low ability to self-regulate emotions and associated with many adverse consequences – especially under stress. Because of the high prevalence of state orientation, it is a very important topic to investigate factors that help state-oriented people to buffer these adverse consequences. Action orientation, in contrast, is defined as a high ability to self-regulate own emotions in a very specific way: through accessing the self. The present dissertation demonstrates this theme in five studies, using a total of N = 1251 participants with a wide age range, encompassing different populations (students, non-student population (people from the coaching and therapy sector), applying different operationalisations to investigate self-access as a mediator or an outcome variable. Furthermore, it is tested whether the popular technique of mindfulness - that is advertised as a potent remedy for bringing people closer to the self -really works for everybody. The findings show that the presumed remedy is rather harmful for state-oriented individuals. Finally, an attempt to ameliorate these alienating effects, the present dissertation attempts to find theory-driven, and easy-to-apply solution how mindfulness exercises can be adapted.