Price indices play a vital role in economic measurement as they reflect price levels and measure price fluctuations. Price level measures are used with macroeconomic indicators to express them in real terms. These measures are also used to index wages, rents, and pensions. Furthermore, they are used as a reference for monetary policy conducted by central banks. Therefore, the provision of accurate price indices is one of the most important goals of National Statistical Institutes (NSIs), and numerous studies have been devoted to this goal. This cumulative dissertation also contributes to this goal. It contains four chapters, each of which represents a separate research. The first two studies are devoted to the treatment of seasonal products by using different price index methods. The first research is co-authored with Ken van Loon. The third research is dedicated to finding the most accurate method to make price predictions for missing products. The fourth research is focused on the treatment of products by using different price index methods when products’ quality characteristics are available.

The application of machine learning and deep learning methods to hydrological modelling has advanced significantly in recent years, offering alternatives to traditional conceptual and physically based approaches. Within the numerous algorithms, long short-term memory (LSTM) networks have proven themselves particularly useful for the task of streamflow modelling. This thesis provides a collection of publications that investigate the capabilities, limitations and interpretability of LSTM for the purpose of streamflow modelling and climate change impact assessment within the lowland Ems catchment in Northwest Germany. Within a comparative performance evaluation, LSTM and its predecessor, the recurrent neural network, demonstrate superior accuracy compared to the conceptual HBV model across various statistical performance metrics. However, a decline in performance was observed during low-flow conditions in certain sub-catchments. The evaluation of the flow duration curve revealed that the ML models more effectively capture the water balance, while HBV better represents streamflow dynamics. To enhance the interpretability of LSTM, six explainable artificial intelligence techniques were applied. These methods consistently identified seasonal patterns in the temporal relevance of hydroclimatic input data. In combination with an observed correlation between the internal LSTM states and catchment-scale soil moisture dynamics, the findings suggest that LSTM models are capable of implicitly learning the relevant hydrological processes. Following, the capabilities of LSTM to model climate change impact scenarios, particularly when they extend beyond historically observed climate conditions, are addressed. An ensemble of climate change projections is provided as hydroclimatic input to evaluate the performance of LSTMs and conceptual models. While all models reveal heterogeneous alterations in streamflow under future climate conditions, significant differences emerge based on the model type. Results provide evidence that LSTMs, in combination with the temperature-based Haude formula for estimating potential evaporation, work inadequately under altered climatic regimes, raising concerns about their applicability in long-term projections. The study also indicates the potential need to incorporate physical constraints into LSTM architectures to ensure model robustness and hydrological plausibility beyond the historical training range. Collectively, this thesis contributes important insights into the applicability and interpretability of LSTM models in streamflow modelling. Despite the presence of a physically realistic representation of soil moisture dynamics of the Ems catchment, no robust change signals for streamflow under climate change can be derived. Those results underscore the potential of LSTM model approaches for accurate streamflow simulation, however, they require us to always critically question LSTM results, particularly when they are applied outside the training range.

This dissertation examines how individuals unlock their personal power by investigating individual differences in self-regulation, in particular, how situational conditions interact with the personality dispositions of action versus state orientation. Action-oriented individuals are well able to regulate their affective states and to bridge the intention–behavior gap, showing initiative, implementing demanding intentions, and resisting temptations. State-oriented individuals, by contrast, often struggle to regulate affect and experience difficulties enacting intentions, especially under demanding conditions, tending to hesitate and ruminate. While extensive research has highlighted the advantages of action orientation across various domains such as education and health, this thesis challenges the prevailing one-sided perspective that presents action orientation as inherently superior and frames state orientation negatively. Drawing on Personality Systems Interactions theory, the dissertation adopts a dynamic view that understands these dispositions as context-sensitive rather than fixed. The central assumption is that action and state orientation each require different kinds of situational conditions to fully unlock their potential. Across six empirical studies (overall N = 1,067) using a multimethod approach that combines experimental and survey-based research in diverse populations and contextual settings, this dissertation examines (1) action and state orientation as distinct dispositions, (2) their dynamic interaction with situational factors, and (3) ways to support each in mobilizing personal power. Overall, the findings show that each disposition offers unique advantages - they simply require different situational conditions for their potential to unfold.

Norming of psychological tests is crucial for the accurate interpretation of test scores. Conventional norming, which relies on subgroups, may introduce bias and requires large samples (uneconomic) to achieve high precision (i.e., low standard errors) of the estimated norm scores. Continuous norming has been proposed as a solution to reduce bias and resolve the dilemma between economy and precision. Continuous norming estimates norm scores based on the entire normative samples – rather than subgroups – using (non-linear) regression. The aim of this dissertation is to examine continuous norming methods to improve both test development and psychological diagnostics. To this end, this dissertation comprises four research studies. This first study includes both a systematic review of continuous norming and an empirical study. The systematic review introduces different continuous norming methods and highlights their respective advantages and limitations. The empirical study compares the precision of conventional and continuous norms and investigates the presentation of continuous norms in classical norm tables. The second study provides a systematic overview of German-language tests, showing that continuous norming methods are rarely applied and that descriptions of the norming process in test manuals are often scarce. Such a scarce reporting hinders a critical evaluation of the estimated norm scores. To address this issue, the third study introduces guidelines for reporting on norm-referenced scores. The guidelines cover relevant aspects along the entire norming process and provide detailed information on each aspect. One aspect, where the guidance is limited, is determining the required sample size. The fourth study addresses this gap by providing empirical based guidance to determine the required sample size for continuous norming. For a comprehensive evaluation of continuous norming, I combined the findings of these studies with (1) an illustration of the effects of bias and precision on individual diagnostics and the financial costs of normative studies, (2) an updated systematic literature search, and (3) an examination of applied norming practices in recently published tests. This integration allowed the extraction of seven best practices for continuous norming. The first best practice is to favor continuous over conventional norming, as it effectively resolves the economy-precision dilemma: continuous norming produces less biased norms than conventional norming and requires smaller sample sizes to achieve high precision. Despite these advantages, continuous norming is still rarely applied in German-language tests. The guidelines and sample-size recommendations provided in this dissertation may facilitate a wider adoption of continuous norming methods, thereby improving both test development and psychological diagnostics.

This thesis seeks to improve the understanding of evolution and habitat as key factors forming tadpole morphology (i.e. of larvae of the order Anura), uncovers existing gaps in current research and recommends strategies and directions for future research. The present study improves the knowledge about the influences of evolution and habitat on the bauplan of tadpoles in a global scale ensuring maximum standardization and comparability of the data. In relation to the total number of tadpoles assumed to exist, only a small proportion has been described and only a few of them have been identified genetically. The lack of a global standard for their description makes it difficult to compare data. Using the tadpole of a harlequin frog (Atelopus) from Guiana region, it is shown that only an integrative approach with morphological and genetic data can solve taxonomic problems. In the study area of Madagascar, it becomes evident that in this region the common genetic history only has little influence on morphology, in contrast to the aquatic way of life. Tadpoles from flowing waters develop larger eyes, more robust tail muscles and smaller fins to cope better with current conditions and move more efficiently. In an additional study, the examination is extended to an almost global level. To achieve the intended standardization, over 1000 individuals (tadpoles) from 144 species have been examined. It can be shown that the common evolutionary history on a global scale influences morphology as strongly as the habitat. In addition, the influence of specialized nutrition and the climate is investigated.