A multilevel research lens for dealing with complexity in IB research

2021 ◽  
Author(s):  
Bo Nielsen
Keyword(s):  
Multilevel Research

scholarly journals A new approach to institutional domain analysis: Multilevel research fronts structure

2007 ◽  
Vol 74 (3) ◽  
pp. 331-344 ◽  
Author(s):  
Sandra Miguel ◽  
Félix Moya-Anegón ◽  
Víctor Herrero-Solana
Keyword(s):  
Domain Analysis ◽  
New Approach ◽  
Research Fronts ◽  
Multilevel Research

scholarly journals From Droplets to Process: Multilevel Research Approach to Reduce Emissions from LNG Processes

2015 ◽  
Vol 64 ◽  
pp. 3-12 ◽  
Author(s):  
Sigurd Weidemann Løvseth ◽  
Ingrid Snustad ◽  
Amy Leigh Brunsvold ◽  
Geir Skaugen ◽  
Per Eilif Wahl ◽  
...  
Keyword(s):  
Research Approach ◽  
Multilevel Research ◽  
Reduce Emissions

A Multilevel Approach to Investigating Cross-National Differences in Negotiation Processes

2004 ◽  
Vol 9 (3) ◽  
pp. 471-484 ◽  
Author(s):  
◽  
Evert Van de Vliert
Keyword(s):  
Research Approach ◽  
Psychological States ◽  
Individual Level ◽  
Country Level ◽  
National Differences ◽  
The Social ◽  
Multilevel Research ◽  
Negotiation Processes ◽  
The Impact ◽  
Cross National

AbstractThe impact of the psychological states of the negotiators, the social conditions of negotiations, and the behavior of negotiators on the outcomes of negotiations differs from country to country. Various suboptimal, individual-level, and country-level solutions have been suggested to predict and explain such cross-national variations. Drawing inspiration from a series of cross-cultural studies on job satisfaction and motives for volunteer work that successfully employed multilevel modeling, we propose a multilevel research approach to more accurately examine the generalizability of negotiation models across countries.

Anti-consumption for Environmental Sustainability: Conceptualization, Review, and Multilevel Research Directions

2016 ◽  
Vol 148 (2) ◽  
pp. 411-435 ◽  
Author(s):  
Nieves García-de-Frutos ◽  
José Manuel Ortega-Egea ◽  
Javier Martínez-del-Río
Keyword(s):  
Environmental Sustainability ◽  
Research Directions ◽  
Multilevel Research

Team-Level Constructs

2019 ◽  
Vol 6 (1) ◽  
pp. 325-348
Author(s):  
David Chan
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Future Research ◽  
Methodological Issues ◽  
Research Directions ◽  
Substantive Content ◽  
Multilevel Research ◽  
Future Research Directions ◽  
Changes Over Time ◽  
Over Time

Studies of team-level constructs can produce new insights when researchers explicitly take into account several critical conceptual and methodological issues. This article explicates the conceptual bases for multilevel research on team constructs and discusses specific issues relating to conceptual frameworks, measurement, and data analysis. To advance programmatic research involving team-level constructs, several future research directions concerning issues of substantive content (i.e., changes in the nature of work and teams, member-team fit, linking team-level constructs to higher-level constructs) and strategic approaches (i.e., the construct's theoretical roles, dimensionality and specificity, malleability and changes over time, relationships with Big Data) are proposed.

scholarly journals Multiple Imputation of Multilevel Missing Data

SAGE Open ◽  
2016 ◽  
Vol 6 (4) ◽  
pp. 215824401666822 ◽  
Author(s):  
Simon Grund ◽  
Oliver Lüdtke ◽  
Alexander Robitzsch
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Multilevel Models ◽  
R Package ◽  
Data Sets ◽  
Multilevel Data ◽  
Statistical Knowledge ◽  
Multilevel Research ◽  
User Friendly ◽  
High Degree

The treatment of missing data can be difficult in multilevel research because state-of-the-art procedures such as multiple imputation (MI) may require advanced statistical knowledge or a high degree of familiarity with certain statistical software. In the missing data literature, pan has been recommended for MI of multilevel data. In this article, we provide an introduction to MI of multilevel missing data using the R package pan, and we discuss its possibilities and limitations in accommodating typical questions in multilevel research. To make pan more accessible to applied researchers, we make use of the mitml package, which provides a user-friendly interface to the pan package and several tools for managing and analyzing multiply imputed data sets. We illustrate the use of pan and mitml with two empirical examples that represent common applications of multilevel models, and we discuss how these procedures may be used in conjunction with other software.

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