Hierarchical Modeling
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Recent papers in Hierarchical Modeling
Advancing research on service quality requires clarifying the theoretical conceptualizations and validating an integrated service quality model. The purpose of this study is to facilitate and elucidate practical issues and decisions... more
Advancing research on service quality requires clarifying the theoretical conceptualizations and validating an integrated service quality model. The purpose of this study is to facilitate and elucidate practical issues and decisions related to the development of a hierarchical service quality model in mobile health (mHealth) services research. Conceptually, it extends theory by reframing service quality as a reflective, hierarchical construct and modeling its impact on satisfaction, intention to continue using and quality of life. Empirically, it confirms that PLS path modeling can be used to estimate the parameters of a higher order construct and its association with subsequent consequential latent variables in a nomological network. The findings of the study show that service quality is the third-order, reflective construct model with strong positive effects on satisfaction, continuance intentions and quality of life in the context of mHealth services. Finally, the study discusses the implications of hierarchical service quality modeling in electronic markets and highlights future research directions.
Most subjective probability aggregation procedures use a single probability judgement from each expert, even though it is common for experts studying real problems to update their probability estimates over time. This paper advances into... more
Most subjective probability aggregation procedures use a single probability
judgement from each expert, even though it is common for experts
studying real problems to update their probability estimates over time. This
paper advances into unexplored areas of probability aggregation by considering
a dynamic context in which experts can update their beliefs at random
intervals. The updates occur very infrequently, resulting in a highly sparse
dataset that cannot be modeled by standard time-series procedures. In response
to the lack of appropriate methodology, this paper presents a hierarchical
model that takes into account the expert’s level of self-reported expertise
and produces aggregate probabilities that are sharp and well-calibrated both
in- and out-of-sample. The model is demonstrated on a real-world dataset
that includes over 2,300 experts making multiple probability forecasts over a
period of two years on different subsets of 166 international political events.
judgement from each expert, even though it is common for experts
studying real problems to update their probability estimates over time. This
paper advances into unexplored areas of probability aggregation by considering
a dynamic context in which experts can update their beliefs at random
intervals. The updates occur very infrequently, resulting in a highly sparse
dataset that cannot be modeled by standard time-series procedures. In response
to the lack of appropriate methodology, this paper presents a hierarchical
model that takes into account the expert’s level of self-reported expertise
and produces aggregate probabilities that are sharp and well-calibrated both
in- and out-of-sample. The model is demonstrated on a real-world dataset
that includes over 2,300 experts making multiple probability forecasts over a
period of two years on different subsets of 166 international political events.
Increasingly, often ecologist collects data with nonlinear trends, heterogeneous variances, temporal correlation, and hierarchical structure. Nonlinear mixed‐effects models offer a flexible approach to such data, but the estimation and... more
Increasingly, often ecologist collects data with nonlinear trends, heterogeneous variances, temporal correlation, and hierarchical structure. Nonlinear mixed‐effects models offer a flexible approach to such data, but the estimation and interpretation of these models present challenges, partly associated with the lack of worked examples in the ecological literature.
We illustrate the nonlinear mixed‐effects modeling approach using temporal dynamics of vegetation moisture with field data from northwestern Patagonia. This is a Mediterranean‐type climate region where modeling temporal changes in live fuel moisture content are conceptually relevant (ecological theory) and have practical implications (fire management). We used this approach to answer whether moisture dynamics varies among functional groups and aridity conditions, and compared it with other simpler statistical models. The modeling process is set out “step‐by‐step”: We start translating the ideas about the system dynamics to a statistical model, which is made increasingly complex in order to include different sources of variability and correlation structures. We provide guidelines and R scripts (including a new self‐starting function) that make data analyses reproducible. We also explain how to extract the parameter estimates from the R output.
Our modeling approach suggests moisture dynamic to vary between grasses and shrubs, and between grasses facing different aridity conditions. Compared to more classical models, the nonlinear mixed‐effects model showed greater goodness of fit and met statistical assumptions. While the mixed‐effects approach accounts for spatial nesting, temporal dependence, and variance heterogeneity; the nonlinear function allowed to model the seasonal pattern.
Parameters of the nonlinear mixed‐effects model reflected relevant ecological processes. From an applied perspective, the model could forecast the time when fuel moisture becomes critical to fire occurrence. Due to the lack of worked examples for nonlinear mixed‐effects models in the literature, our modeling approach could be useful to diverse ecologists dealing with complex data.
We illustrate the nonlinear mixed‐effects modeling approach using temporal dynamics of vegetation moisture with field data from northwestern Patagonia. This is a Mediterranean‐type climate region where modeling temporal changes in live fuel moisture content are conceptually relevant (ecological theory) and have practical implications (fire management). We used this approach to answer whether moisture dynamics varies among functional groups and aridity conditions, and compared it with other simpler statistical models. The modeling process is set out “step‐by‐step”: We start translating the ideas about the system dynamics to a statistical model, which is made increasingly complex in order to include different sources of variability and correlation structures. We provide guidelines and R scripts (including a new self‐starting function) that make data analyses reproducible. We also explain how to extract the parameter estimates from the R output.
Our modeling approach suggests moisture dynamic to vary between grasses and shrubs, and between grasses facing different aridity conditions. Compared to more classical models, the nonlinear mixed‐effects model showed greater goodness of fit and met statistical assumptions. While the mixed‐effects approach accounts for spatial nesting, temporal dependence, and variance heterogeneity; the nonlinear function allowed to model the seasonal pattern.
Parameters of the nonlinear mixed‐effects model reflected relevant ecological processes. From an applied perspective, the model could forecast the time when fuel moisture becomes critical to fire occurrence. Due to the lack of worked examples for nonlinear mixed‐effects models in the literature, our modeling approach could be useful to diverse ecologists dealing with complex data.