Assessing Computer Network Efficiency Using Data Envelopment Analysis and Multicriteria Decision Analysis Techniques

2014 ◽  
Vol 22 (5-6) ◽  
pp. 260-278 ◽  
Author(s):  
Nikolaos Ploskas ◽  
Emmanouil Stiakakis ◽  
Panayotis Fouliras
Keyword(s):  
Data Envelopment Analysis ◽  
Decision Analysis ◽  
Computer Network ◽  
Multicriteria Decision Analysis ◽  
Data Envelopment ◽  
Network Efficiency ◽  
Multicriteria Decision ◽  
Analysis Techniques ◽  
Using Data

scholarly journals A comparison of multicriteria decision analysis techniques for determining beekeeping suitability

Apidologie ◽  
2020 ◽  
Vol 51 (4) ◽  
pp. 481-498 ◽  
Author(s):  
Fatih Sarı ◽  
Durmuş Ali Ceylan ◽  
Mustafa Mete Özcan ◽  
Mehmet Musa Özcan
Keyword(s):  
Decision Analysis ◽  
Multicriteria Decision Analysis ◽  
Multicriteria Decision ◽  
Analysis Techniques

EVALUATION OF ECO-EFFICIENCY BY MULTICRITERIA DECISION ANALYSIS. CASE STUDY OF ECO-INNOVATED AND ECO-DESIGNED PRODUCTS FROM RECYCLABLE WASTE

2018 ◽  
Vol 17 (8) ◽  
pp. 1791-1804 ◽  
Author(s):  
Maria Gavrilescu ◽  
Elena-Diana Comanita ◽  
Petronela Cozma ◽  
Isabela Maria Simion ◽  
Mihaela Rosca
Keyword(s):  
Decision Analysis ◽  
Multicriteria Decision Analysis ◽  
Multicriteria Decision

Data Envelopment Analysis (DEA) in the Psychological Sciences

2019 ◽  
Author(s):  
Jeffrey A. Shero ◽  
Sara Ann Hart
Keyword(s):  
Data Envelopment Analysis ◽  
Aggressive Behavior ◽  
Personality Traits ◽  
Educational Administration ◽  
Latent Variable ◽  
Organizational Management ◽  
Data Envelopment ◽  
Targeted Interventions ◽  
Component Skills ◽  
Using Data

Using methods like linear regression or latent variable models, researchers are often interested in maximizing explained variance and identifying the importance of specific variables within their models. These models are useful for understanding general ideas and trends, but often give limited insight into the individuals within said models. Data envelopment analysis (DEA), is a method with roots in organizational management that make such insights possible. Unlike models mentioned above, DEA does not explain variance. Instead, it explains how efficiently an individual utilizes their inputs to produce outputs, and identifies which input is not being utilized optimally. This paper provides readers with a brief history and past usages of DEA from organizational management, public health, and educational administration fields, while also describing the underlying math and processes behind said model. This paper then extends the usage of this method into the psychology field using two separate studies. First, using data from the Project KIDS dataset, DEA is demonstrated using a simple view of reading framework identifying individual efficiency levels in using reading-based skills to achieve reading comprehension, determining which skills are being underutilized, and classifying and comparing new subsets of readers. Three new subsets of readers were identified using this method, with direct implications leading to more targeted interventions. Second, DEA was used to measure individuals’ efficiency in regulating aggressive behavior given specific personality traits or related skills. This study found that despite comparable levels of component skills and personality traits, significant differences were found in efficiency to regulate aggressive behavior on the basis of gender and feelings of provocation.

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