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A Systematic Framework for Multilevel Theorizing in Information Systems Research
Information systems (IS) research usually investigates phenomena at one level of analysis at a time. However, complex IS phenomena may be difficult to address from such a single-level perspective. A multilevel perspective offers an alternative means to examine phenomena by simultaneously accounting for multiple levels of analysis. Although useful guidelines for theory development are widely available, they give little specific attention to developing theory that is conceptualized and analyzed at multiple levels. Multilevel theorizing or developing theory from a multilevel perspective is more complex and involves unique challenges. To promote multilevel theorizing in the IS discipline, we focus on addressing challenges involved in multilevel theorizing and propose a holistic framework for systematically developing theory from a multilevel perspective. Drawing from the organization science and IS literature, the proposed framework harmonizes and synthesizes previous guidelines, providing a practical basis for conceptualizing and studying multilevel phenomena.
The online appendix is available at
https://doi.org/10.1287/isre.2017.0690
.
Journal Article
Overview of the Multilevel Research Perspective: Implications for Theory Building and Empirical Research
A multilevel perspective in information systems (IS) research helps researchers to understand phenomena simultaneously at multiple levels of analysis. In understanding and employing the multilevel perspective, researchers may face challenges in relation to the value contribution, the terminology, and the critical differences between multilevel and single-level research. To address the challenges, we synthesize contemporary thinking on the multilevel perspective. In particular, we clarify the various value contributions of the multilevel perspective, offer a consistent terminology for conducting multilevel research, and holistically overview the guidelines in relation to specifying, operationalizing, and testing theoretical models. This tutorial helps researchers to holistically understand the multilevel perspective to allow them to more deeply appreciate the nuanced assumptions underlying the perspective. Thus, this paper contributes by helping researchers to more effectively and more flexibly engage in multilevel research.
Journal Article
Review of Multilevel Voltage Source Inverter Topologies and Analysis of Harmonics Distortions in FC-MLI
We review the most common topology of multi-level inverters. As is known, the conventional inverters are utilized to create an alternating current (AC) source from a direct current (DC) source. The two-level inverter provides various output voltages [(Vdc/2) and (−Vdc/2)] of the load. It is a successive method, but it makes the harmonic distortion of the output side, Electromagnetic interference (EMI), and high dv/dt. We solve this problem by constructing the sinusoidal voltage waveform. This is achieved by a “multilevel inverter” (MLI). The multilevel inverter creates the output voltage with multiple DC voltages as inputs. Many voltage levels are combined to produce a smoother waveform. During the last decade, the multilevel inverter has become very popular in medium and high-power applications with some advantages, such as the reduced power dissipation of switching elements, low harmonics, and low EMIs. We introduce the information about several multilevel inverters such as the diode-clamped multilevel inverter (DC-MLI), cascaded H-bridge multilevel inverter (CHB-MLI), and flying-capacitor multilevel inverter (FC-MLI) with Power systems CAD (PSCAD) simulation. It is shown that THD is 28.88% in three level FC-MLI while THD is 18.56% in five level topology. Therefore, we can decrease the total harmonic distortion adopting the higher-level topology.
Journal Article
On Multilevel Picard Numerical Approximations for High-Dimensional Nonlinear Parabolic Partial Differential Equations and High-Dimensional Nonlinear Backward Stochastic Differential Equations
Parabolic partial differential equations (PDEs) and backward stochastic differential equations (BSDEs) are key ingredients in a number of models in physics and financial engineering. In particular, parabolic PDEs and BSDEs are fundamental tools in pricing and hedging models for financial derivatives. The PDEs and BSDEs appearing in such applications are often high-dimensional and nonlinear. Since explicit solutions of such PDEs and BSDEs are typically not available, it is a very active topic of research to solve such PDEs and BSDEs approximately. In the recent article (E et al., Multilevel Picard iterations for solving smooth semilinear parabolic heat equations,
arXiv:1607.03295
) we proposed a family of approximation methods based on Picard approximations and multilevel Monte Carlo methods and showed under suitable regularity assumptions on the exact solution of a semilinear heat equation that the computational complexity is bounded by
O
(
d
ε
-
(
4
+
δ
)
)
for any
δ
∈
(
0
,
∞
)
where
d
is the dimensionality of the problem and
ε
∈
(
0
,
∞
)
is the prescribed accuracy. In this paper, we test the applicability of this algorithm on a variety of 100-dimensional nonlinear PDEs that arise in physics and finance by means of numerical simulations presenting approximation accuracy against runtime. The simulation results for many of these 100-dimensional example PDEs are very satisfactory in terms of both accuracy and speed. Moreover, we also provide a review of other approximation methods for nonlinear PDEs and BSDEs from the scientific literature.
Journal Article
r2mlm: An R package calculating R-squared measures for multilevel models
Multilevel models are used ubiquitously in the social and behavioral sciences and effect sizes are critical for contextualizing results. A general framework of R-squared effect size measures for multilevel models has only recently been developed. Rights and Sterba (
2019
) distinguished each source of explained variance for each possible kind of outcome variance. Though researchers have long desired a comprehensive and coherent approach to computing R-squared measures for multilevel models, the use of this framework has a steep learning curve. The purpose of this tutorial is to introduce and demonstrate using a new R package –
r2mlm
– that automates the intensive computations involved in implementing the framework and provides accompanying graphics to visualize all multilevel R-squared measures together. We use accessible illustrations with open data and code to demonstrate how to use and interpret the R package output.
Journal Article