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In psychology and the social sciences, it is often of interest how complex structural relations among variables are moderated by profiles or combinations of persons’ attributes. Some state-of-the-art methods, such as latent class analysis, are well-suited for this purpose. However, they can lead to methodological problems (e.g., convergence issues) or interpretative difficulties (e.g., due to nondistinctive profiles). For these cases, two other approaches combining structural equation modeling with machine learning have been proposed, namely structural equation model (SEM) trees and SubgroupSEM. These approaches allow for exploration of how parameters of a SEM differ depending on combinations of a person's attributes. This can be useful for generating hypotheses for future research. In this paper, we provide an empirical illustration of SubgroupSEM using an example from research on the development of commitment in interdisciplinary study programs in German higher education and identify combinations of vocational interests related to exceptional development.

In this paper, we propose a classification scheme for study programs. The classification scheme makes it possible to classify study programs in respect of their interdisciplinary design. Interdisciplinary study programs do not fit particularly well into existing classification systems. The proposed classification scheme differentiates between interdisciplinary, multidisciplinary and monodisciplinary programs. It has been applied to the open responses of first-year students of starting cohort 5. The resulting classification is available in NEPS Scientific-Use-Files 13-0-0. We present the classification scheme, the coding process, and an overview of the results., NEPS Survey Papers

Jungwissenschaftler*innen Retreat der Fachgruppe Methoden und Evaluation der Deutschen Gesellschaft für Psychologie

Las tecnologías renovables y, más concretamente, las tecnologías de generación eléctrica basadas en fuentes de energía renovables constituyen un pilar básico en la transición energética necesaria para lograr los objetivos del Acuerdo de París, y su coste es un elemento relevante en la misma. El objetivo de este trabajo es identificar los rangos de costes nivelados probables de dichas tecnologías en el futuro, con especial atención a las tecnologías eólicas y solares. La revisión sistemática de la literatura ha permitido identificar 25 documentos con predicciones de los costes nivelados a futuro en distintos países del mundo. Nuestros resultados muestran un rango de costes en 2030 para la eólica terrestre de entre 16 y 129 €/MWh, entre 43 y 178 €/MWh para la eólica marina, entre 31 y 152 €/MWh para la solar fotovoltaica de techo y entre 14 y 117 €/MWh para suelo y de entre 44 y 105 €/MWh para la solar termoeléctrica. Los rangos de costes a 2050 serían menores, y a un nivel inferior: entre 15 y 40 €/MWh para la eólica terrestre, 25 y 80 €/MWh para la eólica marina, 9 y 83 €/MWh para la solar fotovoltaica y 45 y 101 €/ MWh para la solar termoeléctrica. Estos costes futuros suponen reducciones considerables con respecto a los costes actuales. En términos porcentuales, las mayores reducciones esperadas tendrán lugar en la eólica marina y la solar fotovoltaica, seguida de la solar termoeléctrica y la eólica terrestre.

This book provides an in-depth analysis of the concept of the Circular Economy (CE), as well as an assessment of the drivers and barriers for circular practices by firms, and its implications for managers in firms and public policy makers. It includes proposals for policy frameworks and instruments that will encourage the uptake of CE practices. The book is presented in three linked parts. The first part of the book provides a broad view of the topic, put into the wider context of sustainability. In the second part, the drivers of and barriers to the uptake of the CE are analysed, with a special focus on the micro-level not seen often in the previous studies on the CE. This book is of interest to researchers, policy makers and post-graduate students in areas such as environmental management and economics.

We investigate whether political ideology has an observable effect on decarbonization ambition, renewable power aims, and preferences for power system balancing technologies in four European countries. Based on the Energy Logics framework, we identify ideologically different transition strategies (state-centered, market-centered, grassroots-centered) contained in government policies and opposition party programs valid in 2019. We compare these policies and programs with citizen poll data. We find that ideology has a small effect: governments and political parties across the spectrum have similar, and relatively ambitious, decarbonization and renewables targets. This mirrors citizens' strong support for ambitious action regardless of their ideological self-description. However, whereas political positions on phasing out fossil fuel power are clear across the policy space, positions on phasing in new flexibility options to balance intermittent renewables are vague or non-existent. As parties and citizens agree on strong climate and renewable power aims, the policy ambition is likely to remain high, even if governments change.

Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2020; Aachen 1 Online-Ressource (iii, 148 Seiten) : Illustrationen, Diagramme (2020). = Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2020, In this thesis, we develop a statistical framework to investigate average and conditional effects of a treatment or intervention on a count outcome. For example, in the field of drug and substance use prevention, the effects of interventions on the outcome variable daily/weekly count of alcoholic drinks or cigarettes consumed can been examined. In clinical psychology, treatment effects on count outcomes such as instances of binge eating among patients with eating disorders or counts of classroom violations in children with ADHD can been examined. Further examples are treatment effects on counts of correctly answered items in a cognitive test, days of absenteeism, or counts of traffic violations. The framework is termed moment-based approach and is based on regression models with a logarithmic link function. The moment-based approach overcomes three major shortcomings of earlier approaches estimating treatment effects on count outcomes: First, effect definitions are based on the stochastic theory of causal effects (Steyer, Mayer, Fiege, 2014). This theory provides unambiguous definitions of atomic or individual causal effects as differences between conditional expectations under treatment and under control (i.e., a reference group). Further, causality conditions under which estimated average and conditional treatment effects may be interpreted as aggregates of atomic or individual causal effects are explicated. In contrast, in regression models with a logarithmic link function it is common to inspect treatment effects defined as ratios of conditional expectations under treatment and under control. We caution that these ratio effects have a different causal interpretation than difference effects. For example, the simple ratio of group averages in a randomized experiment does not represent an average of individual ratio effects -- unlike the simple difference of group averages, which is also an average of individual difference effects. Second, traditional approaches examining treatment effects on count outcomes, treat the size of treatment groups and values of observed covariates as fixed-by-design, that is, predetermined by the experimenter. If in fact group sizes and covariates are not predetermined -- as is the case in most psychological and social science studies --, this assumption can lead to underestimated standard errors and, thus, inflated Type 1 error rates and decreased power for average and conditional treatment effect estimates. The moment-based approach offers an alternative by treating both group sizes and covariates as random or stochastic variables, which can substantially improve statistical inferences. Third, the moment-based approach allows to account for measurement error in covariates. While earlier approaches were based on generalized linear models with a logarithmic link function (e.g., Poisson regression models), we use a structural equation modeling framework. In a negative binomial multigroup structural equation model, it is possible to simultaneously estimate measurement models for latent variables (i.e., decomposing true scores and measurement error in fallible indicators) and a negative binomial regression model with these latent variables as regressors. While ignoring measurement error in the generalized linear models might lead to biased estimation of both the regression coefficients and the corresponding effect estimates, these issues are circumvented in the moment-based approach. In this thesis, the moment-based approach is developed and extended in a step-by-step manner. First, we motivate the need for a new approach by presenting and discussing the three aforementioned shortcomings of earlier approaches (i.e., interpretation of exponentiated regression coefficients and marginal effects) in detail. Second, we introduce the key idea of the moment-based approach in a simple case considering only a single, but stochastic covariate using a generalized linear model. In a Monte Carlo simulation study, we compare the performance of a traditional (marginal effects) approach with the performance of the moment-based approach. Third, we extend the statistical framework to the negative binomial multigroup structural equation model and include multiple, possibly latent covariates as well as stochastic group sizes in our effect computations. Fourth, we relax the assumption of multivariate normally distributed covariates from previous steps and derive a generalized moment-based approach accounting for various kinds of normally and non-normally distributed covariates. As a result, we achieve a suitable approach for a broader range of applied data analyses. Finally, we discuss guidelines how and when the moment-based approach should be applied and have an outlook on possible further developments., Published by Aachen

The need for a sustainability transition is undisputed. Ecological innovation (eco-innovation) is a possible mechanism with which to reach a fully sustainable economy and society. Several types of eco-innovation exist, that is, end-of-pipe, efficiency or effectiveness related. While all contribute to the sustainability transition, they have different impacts, ranging from local optimization to systemic redesign, that is, for the circular economy. In that sense, past research has brought forward many definitions and conceptualizations. The aim of this chapter is to go a step further and contribute to a universal taxonomy by characterizing eco-innovations departing from the resource-based view of the firm. Therefore, four dimensions of eco-innovations are contrasted against six groups of firm resources. It is found that, depending on eco-innovation types and dimensions, the respective antecedents are different, that is, certain resources facilitate local optimization while systemic eco-innovations are the result of cooperation (joint efforts and resource bases) of several innovators. This chapter provides a precise conceptualization of the interaction between firm resources and eco-innovations.