This study investigates one-sided thermal damage of carbon fiber reinforced polymers (CFRP) by means of depth resolved infrared spectroscopy, tomography and mechanical testing. All CFRP samples are thermally irradiated at one side with a heat flux of 50 kW/m2 over various time intervals. ATR-FTIR spectroscopy along a ground incline plane through the sample allows a chemical characterization of the thermal degradation of the polymer matrix into depth. Developing delaminations are observed with a depth-resolved gray-value-analysis of microfocused computed X-ray tomographic (µCT) data. Mechanical behavior is determined by tensile, compressive, and interlaminar shear strength (ILSS) testing of specimens taken from different depths of the irradiated samples. The depth profiles show how pronounced damage phenomena like matrix degradation and the development of delaminations are after one-sided thermal loading and how they influence strength in different ways. Compressive strength and ILSS is found to be more sensitive towards thermal damage than tensile strength, as they are most influenced by formed delaminations at higher thermal loads.
A common challenge in designing empirical studies is determining an appropriate sample size. When more complex models are used, estimates of power can only be obtained using Monte Carlo simulations. In this tutorial, we introduce the R package mlpwr to perform simulation-based power analysis based on surrogate modeling. Surrogate modeling is a powerful tool to guide the search for study design parameters that imply a desired power or meet a cost threshold (e.g., in terms of monetary cost). mlpwr can be used to search for the optimal allocation when there are multiple design parameters, e.g., when balancing the number of participants and the number of groups in multilevel modeling. At the same time, the approach can take into account the cost of each design parameter, and aims to find a cost-efficient design. We introduce the basic functionality of the package, which can be applied to a wide range of statistical models and study designs. Additionally, we provide two examples based on empirical studies for illustration: one for sample size planning when using an item response theory model, and one for assigning the number of participants and the number of countries for a study using multilevel modeling.
Research suggests that belief in conspiracy theories (CT) stems from basic psychological mechanisms and is linked to other belief systems (e.g. religious beliefs). While previous research has extensively examined individual and contextual variables associated with CT beliefs, it has not yet investigated the role of culture. In the current research, we tested, based on a situated cultural cognition perspective, the extent to which culture predicts CT beliefs. Using Hofstede’s model of cultural values, three nation-level analyses of data from 25, 19 and 18 countries using different measures of CT beliefs (Study 1, N = 5,323; Study 2a, N = 12,255; Study 2b, N = 30,994) revealed positive associations between Masculinity, Collectivism and CT beliefs. A cross-sectional study among US citizens (Study 3, N = 350), using individual-level measures of Hofstede’s values, replicated these findings. A meta-analysis of correlations across studies corroborated the presence of positive links between CT beliefs, Collectivism, r = .31, 95%CI = [.15; .47] and Masculinity, , r = .39, 95%CI = [.18; .59]. Our results suggest that in addition to individual-differences and contextual variables, cultural factors also play an important role in shaping CT beliefs.
The planning of adequately powered research designs increasingly goes beyond determining a suitable sample size. More challenging scenarios demand simultaneous tuning of multiple design parameter dimensions and can only be tackled using Monte Carlo simulation in case that no analytical approach is available. Since there are usually several possible solutions, we are often interested in a specific solution that is optimal, for example, in terms of the financial cost of an experiment. We introduce a new surrogate modeling framework based around machine learning predictions to address these types of problems. Possible applications include finding design parameters that imply a desired power at minimum cost or, alternatively, maximum power at a cost threshold. As surrogate models, which form the basis for the prediction during the framework iterations, we implement Gaussian process regression and support vector regression. Our implementation is publicly available in an R package. We investigate the approach in an extensive simulation study using six different hypothesis test scenarios with single and multidimensional design parameters from the fields of multilevel modeling, item response theory, and others. The results demonstrate the high efficiency of this novel framework for simulation-based design optimization.
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