Intervals for the assessment of measurement agreement: Similarities, differences, and consequences of incorrect interpretations

Several intervals have been proposed to quantify the agreement of two methods intended to measure the same quantity in the situation where only one measurement per method and subject is available. The limits of agreement are probably the most well-known among these intervals, which are all based on the differences between the two measurement methods. The different meanings of the intervals are not always properly recognized in applications. However, at least for small-to-moderate sample sizes, the differences will be substantial. This is illustrated both using the width of the intervals and on probabilistic scales related to the definitions of the intervals. In particular, for small-to-moderate sample sizes, it is shown that limits of agreement and prediction intervals should not be used to make statements about the distribution of the differences between the two measurement methods or about a plausible range for all future differences. Care should therefore be taken to ensure the correct choice of the interval for the intended interpretation.

Looking beneath the Surface: Differences in Decision-Making Structures across Processes

The previous chapter presented the overall decision-making structure in Swiss politics at the beginning of the 21st century. This provides us with a general picture and allows for a comparison over time with the decision-making structure in the 1970s. However, the analysis of the overall decision-making structure potentially neglects important differences between policy domains (Atkinson and Coleman 1989; Knoke et al. 1996; Kriesi et al. 2006a; Sabatier 1987). Policy issues vary across policy domains, as do the political actors involved. In addition, actors may hold different policy preferences from one policy domain to the next, and they may also collaborate with other partners depending on the policy domain at stake. Examining differences between policy domains is particularly appropriate in Switzerland. Because no fixed coalitions of government and opposition exist, actors create different coalitions in each policy domain (Linder and Schwarz 2008). Whereas important parts of the institutional setting are similar across policy domains, decision-making structures might still vary. As was the case with the cross-time analysis conducted in the two previous chapters, a stability of 'rules-in-form' might hide important variations in 'rules-in-use' also across different policy domains.

Differences in grain growth of calcite: a field-based modeling approach

Normal grain growth of calcite was investigated by combining grain size analysis of calcite across the contact aureole of the Adamello pluton, and grain growth modeling based on a thermal model of the surroundings of the pluton. In an unbiased model system, i.e., location dependent variations in temperature-time path, 2/3 and 1/3 of grain growth occurs during pro- and retrograde metamorphism at all locations, respectively. In contrast to this idealized situation, in the field example three groups can be distinguished, which are characterized by variations in their grain size versus temperature relationships: Group I occurs at low temperatures and the grain size remains constant because nano-scale second phase particles of organic origin inhibit grain growth in the calcite aggregates under these conditions. In the presence of an aqueous fluid, these second phases decay at a temperature of about 350 °C enabling the onset of grain growth in calcite. In the following growth period, fluid-enhanced group II and slower group III growth occurs. For group II a continuous and intense grain size increase with T is typical while the grain growth decreases with T for group III. None of the observed trends correlate with experimentally based grain growth kinetics, probably due to differences between nature and experiment which have not yet been investigated (e.g., porosity, second phases). Therefore, grain growth modeling was used to iteratively improve the correlation between measured and modeled grain sizes by optimizing activation energy (Q), pre-exponential factor (k0) and grain size exponent (n). For n=2, Q of 350 kJ/mol, k0 of 1.7×1021 μmns−1 and Q of 35 kJ/mol, k0 of 2.5×10-5 μmns−1 were obtained for group II and III, respectively. With respect to future work, field-data based grain growth modeling might be a promising tool for investigating the influences of secondary effects like porosity and second phases on grain growth in nature, and to unravel differences between nature and experiment.