Geomorphology is currently in a period of resurgence as we seek to explain the diversity, origins and dynamics of terrain on the Earth and other planets in an era of increased environmental awareness. Yet there is a great deal we still do not know about the physics and chemistry of the processes that weaken rock and transport mass across a planet's surface. Discovering and refi ning the relevant geomorphic transport functions requires a combination of careful fi eld measurements, lab experiments, and use of longer-term natural experiments to test current theory and develop new understandings. Landscape evolution models have an important role to play in sharpening our thinking, guiding us toward the right observables, and mapping out the logical consequences of transport laws, both alone and in combination with other salient processes. Improved quantitative characterization of terrain and process, and an ever-improving theory that describes the continual modifi cation of topography by the many and varied processes that shape it, together with improved observation and qualitative and quantitative modelling of geology, vegetation and erosion processes, will provide insights into the mechanisms that control catchment form and function. This paper reviews landscape theory -in the form of numerical models of drainage basin evolution and the current knowledge gaps and future computing challenges that exist.

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Sample size recommendations in confirmatory factor analysis (CFA) have recently shifted away from observations per variable or per parameter toward consideration of model quality. Extending research by Marsh, Hau, Balla, and Grayson (1998), simulations were conducted to determine the extent to which CFA model convergence and parameter estimation are affected by n as well as by construct reliability, which is a measure of measurement model quality derived from the number of indicators per factor (p/f) and factor loading magnitude. Results indicated that model convergence and accuracy of parameter estimation were affected by n and by construct reliability within levels of n. Sample size recommendations for applied researchers using CFA are presented herein as a function of relevant design characteristics.

structural equation modeling, effect sizes, structured means modeling, MIMIC models, power analysis, construct reliability,

We present types of constructs, individual- and cluster-level, and their confirmatory factor analytic validation models when data are from individuals nested within clusters. When a construct is theoretically individual level, spurious construct-irrelevant dependency in the data may appear to signal cluster-level dependency; in such cases, however, and consistent with theory, a single-level analysis with a correction for dependency may be appropriate. Regarding cluster-level constructs, we discuss two types—shared and configural—and present appropriate validation models. Illustrative validation analyses with individual, shared, and configural constructs are provided using empirical data as well as simple simulations demonstrating the spurious effects that can occur with nested data. The article concludes with future directions to be examined in construct validation in multilevel settings.

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Latent variable modeling is a popular and flexible statistical framework. Concomitant with fitting latent variable models is assessment of how well the theoretical model fits the observed data. Although firm cutoffs for these fit indexes are often cited, recent statistical proofs and simulations have shown that these fit indexes are highly susceptible to measurement quality. For instance, a root mean square error of approximation (RMSEA) value of 0.06 (conventionally thought to indicate good fit) can actually indicate poor fit with poor measurement quality (e.g., standardized factors loadings of around 0.40). Conversely, an RMSEA value of 0.20 (conventionally thought to indicate very poor fit) can indicate acceptable fit with very high measurement quality (standardized factor loadings around 0.90). Despite the wide-ranging effect on applications of latent variable models, the high level of technical detail involved with this phenomenon has curtailed the exposure of these important findings to empirical researchers who are employing these methods. This article briefly reviews these methodological studies in minimal technical detail and provides a demonstration to easily quantify the large influence measurement quality has on fit index values and how greatly the cutoffs would change if they were derived under an alternative level of measurement quality. Recommendations for best practice are also discussed.