Results 231 to 240 of about 383,443 (287)
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Psychometrika, 2006
Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling—model disturbances, random coefficients, and future response ...
Frees, Edward W., Kim, Jee-Seon
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Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling—model disturbances, random coefficients, and future response ...
Frees, Edward W., Kim, Jee-Seon
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IEICE Transactions on Communications, 2005
The widespread use of the Internet raises issues regarding intellectual property. After content is downloaded, no further protection is provided on that content. DRM (Digital Rights Management) technologies were developed to ensure secure management of digital processes and information.
Hwang, S +2 more
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The widespread use of the Internet raises issues regarding intellectual property. After content is downloaded, no further protection is provided on that content. DRM (Digital Rights Management) technologies were developed to ensure secure management of digital processes and information.
Hwang, S +2 more
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The Multilevel Regression Model
2014The multilevel regression model Social and behavioral research often concerns data that have a hierarchical structure, with individuals nested within groups. In multilevel analysis, such data structures are viewed as a multistage sample from a hierarchical population. For example, in educational research we may have a sample of schools, and within each
Hox, Joop, Wijngaards, Leoniek
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Multilevel Mixture Factor Models
Multivariate Behavioral Research, 2012Factor analysis is a statistical method for describing the associations among sets of observed variables in terms of a small number of underlying continuous latent variables. Various authors have proposed multilevel extensions of the factor model for the analysis of data sets with a hierarchical structure.
Varriale R., Vermunt J. K.
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2009
Abstract This article addresses multilevel models in which units are nested within one another. The focus is primarily two-level models. It also describes cross-unit heterogeneity. Moreover, it assesses the fixed and random effects from the multilevel model.
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Abstract This article addresses multilevel models in which units are nested within one another. The focus is primarily two-level models. It also describes cross-unit heterogeneity. Moreover, it assesses the fixed and random effects from the multilevel model.
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Multilevel and Longitudinal Modeling
2014This chapter reviews high points of survey methodology literature. It outlines more specifically why survey research may be valuable to social psychologists. The chapter explains the utility of various study designs. It also reviews several standard designs, including cross-sectional, repeated cross-sectional, panel and mixed designs, and discusses ...
Schoemann, A.M. +2 more
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Multilevel Modeling in the Context of Growth Modeling
Annals of Nutrition and Metabolism, 2014Multilevel modeling is a flexible approach for the analysis of nested data structures, such as those encountered in longitudinal studies with repeated measures of an outcome of interest taken across time and nested within subjects. The baseline score on the outcome and rate of change vary across subjects, and subject level predictor variables may be ...
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2018
This chapter introduces a statistical approach for analyzing nested data structures that both accounts for the dependence of observations due to hierarchical arrangements and allows for testing hypotheses at multiple levels. The most common application of multilevel models is for analyses of objects (e.g., people) nested within groups or clusters of ...
Peter Miksza, Kenneth Elpus
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This chapter introduces a statistical approach for analyzing nested data structures that both accounts for the dependence of observations due to hierarchical arrangements and allows for testing hypotheses at multiple levels. The most common application of multilevel models is for analyses of objects (e.g., people) nested within groups or clusters of ...
Peter Miksza, Kenneth Elpus
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A Model of Recoverability in Multilevel Systems
IEEE Transactions on Software Engineering, 1978Backward error recovery (that is, resetting an erroneous state of a system to a previous error-free state) is an important general technique for recovery from faults in a system, especially those faults which were not foreseen. However, the provision of backward error recovery can be complex, particularly if the implementation of the system is ...
Anderson T, Lee PA, Shrivastava SK
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