Multilevel Models

2018 ◽  
Author(s):  
Peter Miksza ◽  
Kenneth Elpus
Keyword(s):  
Data Structures ◽  
Statistical Approach ◽  
Multilevel Models ◽  
Testing Hypotheses ◽  
Nested Data ◽  
Data Analyses ◽  
Multiple Measurements ◽  
Research Questions ◽  
Multiple Levels ◽  
Common Application

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 some sort. Multilevel models can also be applied to longitudinal data analyses such that the “levels” do not refer to objects nested within groups but instead refer to multiple measurements (e.g., measures made at different occasions/time points) nested within individuals. The chapter illustrates some of the major considerations and basic steps for performing multilevel analyses so that the reader can begin to imagine how to apply this technique to the reader’s own research questions.

scholarly journals The Preregistration Revolution

2017 ◽  
Author(s):  
Brian A. Nosek ◽  
Charles R. Ebersole ◽  
Alexander Carl DeHaven ◽  
David Thomas Mellor
Keyword(s):  
Hypothesis Testing ◽  
Hindsight Bias ◽  
Hypothesis Generation ◽  
Human Reasoning ◽  
Effective Solution ◽  
Testing Hypotheses ◽  
Research Outcomes ◽  
Research Findings ◽  
Research Questions ◽  
The Ideal

Progress in science relies on generating hypotheses with existing observations and testing hypotheses with new observations. This distinction between postdiction and prediction is appreciated conceptually, but is not respected in practice. Mistaking generation of postdictions with testing of predictions reduces the credibility of research findings. However, ordinary biases in human reasoning such as hindsight bias make it hard to avoid this mistake. An effective solution is to define the research questions and analysis plan prior to observing the research outcomes--a process called preregistration. A variety of practical strategies are available to make the best possible use of preregistration in circumstances that fall short of the ideal application, such as when the data are pre-existing. Services are now available for preregistration across all disciplines facilitating a rapid increase in the practice. Widespread adoption of preregistration will increase distinctiveness between hypothesis generation and hypothesis testing and will improve the credibility of research findings.

Understanding the family care dyad: A comparison of two multilevel models

2020 ◽  
Vol 19 (2) ◽  
pp. 178-184 ◽  
Author(s):  
Karen S Lyons ◽  
Christopher S Lee
Keyword(s):  
Heart Failure ◽  
Cardiovascular Disease ◽  
Multilevel Models ◽  
Family Care ◽  
Illness Experience ◽  
Matched Pairs ◽  
Model Data ◽  
Cardiovascular Research ◽  
The Family ◽  
Research Questions

Although there has been increasing attention on a dyadic perspective of illness, contemporary dyadic research methods are still rarely utilized in cardiovascular disease. The focus of this paper is to describe the advantages of two types of multilevel dyadic models (the matched pairs model and the lesser known incongruence model). Data exemplars in a sample of heart failure family dyads are used to illustrate the distinct advantages of these two related multilevel dyadic models with particular emphasis on alignment with research questions. The more commonly known matched pairs model examines separate outcomes for each member of the dyad, controlling for the interdependent nature of the data. By re-parameterizing this model into a univariate dyadic outcomes model, researchers can address distinct, and sometimes more appropriate, research questions (e.g. incongruent appraisals of the illness experience). This paper promotes greater application of these methods in cardiovascular research to further understanding of the dyadic experience and more appropriately target interventions.

Multilevel Models

2009 ◽  
Author(s):  
Bradford S. Jones
Keyword(s):  
Random Effects ◽  
Data Structures ◽  
Multilevel Model ◽  
Multilevel Models ◽  
Great Promise ◽  
Hierarchical Data ◽  
Hierarchical Data Structures ◽  
Fixed And Random Effects

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. It generally tries to convey the scope of multilevel models but in a very compact way. Multilevel models provide great promise for exploiting information in hierarchical data structures. There are a range of alternatives for such data and it bears repeating that sometimes, simpler-to-apply correctives are best.

Opportunities and Challenges of Engaged Indigenous Scholarship

2018 ◽  
Vol 14 (3) ◽  
pp. 449-462 ◽  
Author(s):  
Andrew H. Van de Ven ◽  
Alan D. Meyer ◽  
Runtian Jing
Keyword(s):  
Case Studies ◽  
Quantitative Data ◽  
Abductive Reasoning ◽  
Indigenous Research ◽  
Field Surveys ◽  
Testing Hypotheses ◽  
Qualitative And Quantitative ◽  
Research Reporting ◽  
Research Questions

Management and Organization Review (MOR) is announcing a renewed initiative that seeks to encourage and publish research reporting engaged indigenous scholarship in China. MOR invites empirical as well as conceptual studies of indigenous phenomena related to management and organizations. MOR welcomes exploratory studies of new, emerging, and/or poorly understood indigenous research questions that employ abductive reasoning and creative hunches, as opposed to testing hypotheses deduced from non-indigenous Western theories. Data on indigenous phenomena can come from any source, including qualitative and quantitative data from case studies, field surveys, experiments, and ethnographies.

scholarly journals Nested data structures in array frameworks

2020 ◽  
Vol 1525 ◽  
pp. 012053
Author(s):  
Jim Pivarski ◽  
David Lange ◽  
Peter Elmer
Keyword(s):  
Data Structures ◽  
Nested Data
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