The SLS-Berlin: Validation of a German Computer-Based Screening Test to Measure Reading Proficiency in Early and Late Adulthood

Reading proficiency, i.e., successfully integrating early word-based information and utilizing this information in later processes of sentence and text comprehension, and its assessment is subject to extensive research. However, screening tests for German adults across the life span are basically non-existent. Therefore, the present article introduces a standardized computerized sentence-based screening measure for German adult readers to assess reading proficiency including norm data from 2,148 participants covering an age range from 16 to 88 years. The test was developed in accordance with the children’s version of the Salzburger LeseScreening (SLS, Wimmer and Mayringer, 2014). The SLS-Berlin has a high reliability and can easily be implemented in any research setting using German language. We present a detailed description of the test and report the distribution of SLS-Berlin scores for the norm sample as well as for two subsamples of younger (below 60 years) and older adults (60 and older). For all three samples, we conducted regression analyses to investigate the relationship between sentence characteristics and SLS-Berlin scores. In a second validation study, SLS-Berlin scores were compared with two (pseudo)word reading tests, a test measuring attention and processing speed and eye-movements recorded during expository text reading. Our results confirm the SLS-Berlin’s sensitivity to capture early word decoding and later text related comprehension processes. The test distinguished very well between skilled and less skilled readers and also within less skilled readers and is therefore a powerful and efficient screening test for German adults to assess interindividual levels of reading proficiency

Lessons Learned and Next Steps in Energy Efficiency Measurement and Attribution: Energy Savings, Net to Gross, Non-Energy Benefits, and Persistence of Energy Efficiency Behavior

This white paper examines four topics addressing evaluation, measurement, and attribution of direct and indirect effects to energy efficiency and behavioral programs: Estimates of program savings (gross); Net savings derivation through free ridership / net to gross analyses; Indirect non-energy benefits / impacts (e.g., comfort, convenience, emissions, jobs); and, Persistence of savings

Urban building energy modelling for retrofit analysis under uncertainty

Urban building energy modelling (UBEM) is a growing research field that seeks to expand conventional building energy modelling to the realm of neighbourhoods, cities or even entire building stocks. The aim is to establish frameworks for analysing combined urban e˙ects rather than those of individual buildings, which city governments, utilities and other energy policy stakeholders can use to assess the current environmental impact of our buildings, and, maybe more importantly, the future e˙ects that energy renovation programmes and energy supply infrastructure changes might have. However, the task of creating reliable models of new or existing urban areas is diÿcult, as it requires an enormous amount of detailed input data – data which is rarely available. A solution to this problem is the introduction of archetype modelling, which is used to break down the building stock into a manageable subset of semantic building archetypes, for which, it is possible to characterize their parameters. It is the focus of this thesis to explore and develop new methods for stochastic archetype characterization that can enable archetype-based UBEM to be used for accurate urban-scale time series analysis.The thesis is divided into three parts. The first part acts as an introduction to case study data of the residential building stock of detached single-family houses (SFHs) in Aarhus, Denmark, which is used throughout the thesis for demonstration purposes.The second part concerns the development of methods for archetype modelling. Bayesian methods for archetype parameter calibration are presented that incorporates the variability of the underlying cluster of buildings, and correlation between parameters, to enable informed predictions of unseen buildings from the archetype under uncertainty. The capabilities of archetype-based UBEM are further widened through the introduction of dynamic building energy modelling that allows for time series analysis.The third part of the thesis is devoted to demonstrating the usefulness of the proposed archetype formulation as a building block for urban-scale applications. An exhaustive test scheme is employed to validate the predictive performance of the framework before establishing a city-scale UBEM of approx. 23,000 SFHs in Aarhus. It is used to forecast citywide heating energy use from 2017 up until 2050 under uncertainty of energy renovations and climate change.Overall, the proposed archetype-based UBEM framework promises very useful for fast, flexible and reliable urban-scale time series analysis, including forecasting the effects of energy renovation or city densification, to establish an informed basis for energy policy decision-making

Developing Efficient Strategies For Global Sensitivity Analysis Of Complex Environmental Systems Models

Complex Environmental Systems Models (CESMs) have been developed and applied as vital tools to tackle the ecological, water, food, and energy crises that humanity faces, and have been used widely to support decision-making about management of the quality and quantity of Earth’s resources. CESMs are often controlled by many interacting and uncertain parameters, and typically integrate data from multiple sources at different spatio-temporal scales, which make them highly complex. Global Sensitivity Analysis (GSA) techniques have proven to be promising for deepening our understanding of the model complexity and interactions between various parameters and providing helpful recommendations for further model development and data acquisition. Aside from the complexity issue, the computationally expensive nature of the CESMs precludes effective application of the existing GSA techniques in quantifying the global influence of each parameter on variability of the CESMs’ outputs. This is because a comprehensive sensitivity analysis often requires performing a very large number of model runs. Therefore, there is a need to break down this barrier by the development of more efficient strategies for sensitivity analysis. The research undertaken in this dissertation is mainly focused on alleviating the computational burden associated with GSA of the computationally expensive CESMs through developing efficiency-increasing strategies for robust sensitivity analysis. This is accomplished by: (1) proposing an efficient sequential sampling strategy for robust sampling-based analysis of CESMs; (2) developing an automated parameter grouping strategy of high-dimensional CESMs, (3) introducing a new robustness measure for convergence assessment of the GSA methods; and (4) investigating time-saving strategies for handling simulation failures/crashes during the sensitivity analysis of computationally expensive CESMs. This dissertation provides a set of innovative numerical techniques that can be used in conjunction with any GSA algorithm and be integrated in model building and systems analysis procedures in any field where models are used. A range of analytical test functions and environmental models with varying complexity and dimensionality are utilized across this research to test the performance of the proposed methods. These methods, which are embedded in the VARS–TOOL software package, can also provide information useful for diagnostic testing, parameter identifiability analysis, model simplification, model calibration, and experimental design. They can be further applied to address a range of decision making-related problems such as characterizing the main causes of risk in the context of probabilistic risk assessment and exploring the CESMs’ sensitivity to a wide range of plausible future changes (e.g., hydrometeorological conditions) in the context of scenario analysis

Associations Between Subjective Tinnitus and Cognitive Performance: Systematic Review and Meta-Analyses

Tinnitus is the perception of sound in the absence of a corresponding external sound source, and bothersome tinnitus has been linked to poorer cognitive performance. This review comprehensively quantifies the association between tinnitus and different domains of cognitive performance. The review protocol was preregistered and published in a peer-reviewed journal. The review and analyses were reported according to Preferred Reporting Items for Systematic Review and Meta-analysis guidelines. Peer-reviewed literature was searched using electronic databases to find studies featuring participants with tinnitus who had undertaken measures of cognitive performance. Studies were assessed for quality and categorized according to an established cognitive framework. Random-effects meta-analyses were performed on various cognitive domains with potential moderator variables assessed where possible. Thirty-eight records were included in the analysis from a total of 1,863 participants. Analyses showed that tinnitus is associated with poorer executive function, processing speed, general short-term memory, and general learning and retrieval. Narrow cognitive domains of Inhibition and Shifting (within executive function) and learning and retrieval (within general learning and retrieval) were also associated with tinnitus

Perceived Satisfaction of Counseling Doctoral Students With Their Dissertation Chairperson: Examining Selection Criteria and Chairperson Behaviors

The relationship between doctoral students and their chairperson has been linked to students\u27 successful completion of their dissertation and program of study (Gardner, 2009; Lovitts, 2001). It is often the case that failure to complete the dissertation is what prevents doctoral students from completing their degree. When students do not successfully complete their degrees, attrition rates rise and programs and students feel the burden, both financially and as an investment of time. (Bair & Haworth, 2004). Studies indicate that many students fall short of completing the dissertation, or take much longer than expected, due to a lack of supervision or mentorship (Garcia, Malott, & Brethower, 1988). Specifically, the single most frequent finding in a meta-synthesis study addressing doctoral attrition across 118 research studies was that successful degree completion is related to the amount and quality of contact between a doctoral student and her or his advisor (Bair & Haworth, 2004). The current study followed a non-experimental survey research design. The survey was developed by the researcher based on previous literature on dissertation advising, as well as from themes generalized from a qualitative pilot study that examined criteria used by recent counseling Ph.D. graduates to select their dissertation chairperson. The survey assessed counseling doctoral students\u27 and recent graduates\u27 perceived overall satisfaction with their dissertation chairperson. Additionally, the study examined criteria used by students when selecting their chairperson as well as perceived chairperson behaviors as predictors of overall satisfaction. Demographic variables of the doctoral students were also examined. A sample of counselor education doctoral students (N = 133), both past and present, participated in the current study. Results indicate that the selection criteria component, Collaborative Style, and the chairperson behavior components, Personal Connection and Work Style, were most influential in predicting counseling doctoral students\u27 overall satisfaction with their dissertation chairperson. Additionally, students who self-selected their dissertation chairs were shown to be more satisfied overall than their counterparts who were assigned their chairperson. Significant differences were not found in the demographic variables. Recommendations for further research and implications of the findings are discussed

The reliability of evidence review methodology in environmental science and conservation

Given the proliferation of primary research articles, the importance of reliable environmental evidence reviews for informing policy and management decisions is increasing. Although conducting reviews is an efficient method of synthesising the fragmented primary evidence base, reviews that are of poor methodological reliability have the potential to misinform by not accurately reflecting the available evidence base. To assess the current value of evidence reviews for decision-making we appraised a systematic sample of articles published in early 2015 (N = 92) using the Collaboration for Environmental Evidence Synthesis Assessment Tool (CEESAT). CEESAT assesses the methodology of policy-relevant evidence reviews according to elements important for objectivity, transparency and comprehensiveness. Overall, reviews performed poorly with a median score of 2.5/39 and a modal score of zero (range 0–30, mean 5.8), and low scores were ubiquitous across subject areas. In general, reviews that applied meta-analytical techniques achieved higher scores than narrative syntheses (median 18.3 and 2.0 respectively), as a result of the latter consistently failing to adequately report methodology or how conclusions were drawn. However, some narrative syntheses achieved high scores, illustrating that the reliability of reviews should be assessed on a case-by-case basis. Given the potential importance of reviews for informing management and policy, as well as research, it is vital that overall methodological reliability is improved. Although the increasing number of systematic reviews and meta-analyses highlight that some progress is being made, our findings suggest little or no improvement in the last decade. To motivate progress, we recommend that an annual assessment of the methodological reliability of evidence reviews be conducted. To better serve the environmental policy and management communities we identify a requirement for independent critical appraisal of review methodology thus enabling decision-makers to select reviews that are most likely to accurately reflect the evidence base