5 research outputs found
Evaluation of scientific status of endocrine, diabetes, and metabolism research centers in Iran using exergy method
The aim of this study was to determine the scientific status of endocrine, diabetes and metabolism research centers in Iran using exergy method in terms of scientific publications indexed in the Web of Science, Scopus, and Google Scholar database in 2013. This study is a cross-sectional one. The study population included 6 Iranian endocrine, diabetes and metabolism research centers. Data were collected by referring to the Iranian Ministry of Health and Medical Education personally and also using the above-mentioned citation databases. Data were analyzed using Microsoft Excel and descriptive statistics. The results revealed that endocrine, diabetes and metabolism research centers of Shahid Beheshti and Tehran University of Medical Sciences were top centers in Iran in terms of the quality of scientific publications. Diabetes Research Center of Ahvaz University of Medical Sciences was found to have the weakest performance. This article is the results of the first study in Iran, regarding the evaluation of research centers by exergy analysis
Bibliometrics and scientometrics in India: An overview of studies during 1995-2014Part II: Contents of the articles in terms of disciplines and their bibliometric aspects
This part of the study highlights the contents of the published articles in terms of various disciplines or sub-disciplines and the bibliometric aspects discussed in these articles. The analysis of 902 papers published by Indian scholars during1995-2014 indicates that the main focus of bibliometrics/scientometrics is on assessment of science and technology in India in different sub-disciplines including contributions by Indian states and other individual countries followed by bibliometric analysis of individual journals. Papers dealing with bibliometric laws received a low priority as compared to other subdisciplines of bibliometrics/scientometrics. The analysis of data indicates that the share of theoretical studies using mathematical and statistical techniques which were missing in the earlier period (1970-1994) has increased during 1995-2014. The field of medicine as a discipline received the highest attention as compared to other disciplines
DISSERTATION
This particular research aims to investigate the p-index or performance indicator proposed by Prathap in 2010. The concept of analogy between such branches of physics as mechanical and electrical physics, kinetics, thermodynamics and scientometric field was assumed, and it seems to be an incredibly interesting consilience. Perceiving standard bibliometric measures as an energy which each paper carries, allow us to operate with these numbers in a new way. P index, which is calculated as p=X1/3 =(iC)1/3=(c/pC)1/3=(c2/p)1/3, where C is the total number of citations received and P is the total number of publications, were computed for 499 top productive authors affiliated with Malaysian institutuions, who were retrieved from Web of Science Database..
Statistical efficiency model based on the Ivanovic distance
U uvodnom poglavlju se opisuju predmet i cilj istraživanja, navode se polazne
hipoteze i metode istraživanja, daje sadržaj i opis disertacije uz navođenje ključnih aspekata
na koje će se disertacija usmeriti.
Drugo poglavlje je posvećeno konceptu efikasnosti i načinima za merenje
efikasnosti. Princip efikasnosti predstavlja jedan od bitnih postulata savremenog
poslovnog odlučivanja (Savić, 2011). Efikasnost se definiše kao sposobnost da se
minimiziraju ulaganja u ostvarivanju ciljeva organizacionih jedinica uz maksimizaciju
rezultata (Amado et al., 2011). Kod organizacija koje koriste jedan ulaz za kreiranje
jednog izlaza, efikasnost se definiše kao odnos izlaza prema ulazu. Problem se javlja
kod određivanja efikasnosti jedinica koje imaju više raznorodnih ulaza i koriste ih za
stvaranje više raznorodnih izlaza (Thanassoulis et al., 2012). Osnovni cilj istraživanja
doktorske disertacije je da kroz razvoj novog statističkog modela efikasnosti prevaziđe
problem raznorodnih varijabli. Stoga, potrebno je definisati pokazatelj efikasnosti koji
će sintetizovati sve indikatore u jednu vrednost. Problemi sa kojima se u tom procesu
susrećemo su izražavanje varijabli (ulaza i izlaza) u opsezima koji su međusobno
uporedivi i određivanje pondera koji se dodaju pojedinim ulazima i izlazima. Kao
jedna od metoda za merenje efikasnosti organizacionih jedinica u disertaciji se navodi
analiza obavijanja podataka (Data Envelopment Analysis - DEA). DEA metodu su
razvili Charnes, Cooper & Rhodes (1978), da bi merili efikasnost poslovanja
organizacionih jedinica i to pre svega onih koje ne stvaraju profit. Tvorci analize
obavijanja podataka su predložili neparametarski pristup za izračunavanje efikasnosti,
tako što su višestruke ulaze sveli na jedan "virtuelni" ulaz i višestruke izlaze sveli na
jedan "virtuelni" izlaz koristeći težinske koeficijente. Problem dodeljivanja težina su
rešili tako što su svakoj jedinici dopustili da odredi sopstvene težine sa ciljem da joj se
maksimizira efikasnost, uz ograničenje da te težine moraju biti nenegativne vrednosti i
da količnik virtuelnog izlaza i virtuelnog ulaza svake jedinice ne može biti veći od 1
(Martić, 1999). Pоrеd DEA mеtоdе zа mеrеnjе еfikаsnоsti, u disertaciji je pоsеbnа
pаžnjа usmеrеnа na аnаlizu stоhаstičkih grаnicа (SFA – Stochastic Frontier Analysis).
Оvо је аltеrnаtivni pristup оdrеđivаnjа grаnicе еfikаsnоsti kоrišćеnjеm
еkоnоmеtriјskih modela.
U trećem poglavlju pažnja se posvećuje multivarijacionoj statističkoj analizi.
Sam termin multivarijacione analize se koristi da predstavi multivarijacioni aspekt
analize podataka, u smislu da su mnogobrojne opservacije izmerene na velikom broju
promenljivih. Multivarijaciona statistička analiza obezbeđuje mogućnost analize
kompleksnih nizova podataka, tamo gde ima mnogo nezavisnih i zavisnih
promenljivih koje su međusobno korelisane na različitim nivoima povezivanja. U
okviru trećeg poglavlja, disertacija u značajnoj meri ističe dve ključne statističke
tehnike: faktorsku i analizu glavnih komponenata, kao i klaster analizu. Faktorska
analiza i analiza glavnih komponenata su statističke tehnike koje se koriste za
identifikaciju relativno malog broja faktora koji se mogu koristiti za predstavljanje
odnosa između grupa mnogobrojnih, međusobno povezanih, promenljivih.In section Introduction we define area of research and our main goals, we point out
crucial hypothesis and method of research, we provide contents and description of
dissertation with emphasising crucial aspects of our work.
Second section is committed to the concept of efficiency and methods for
evaluating and measuring efficiency. Principle of efficiency is one of the most important
parts of contemporary business decision making (Savić, 2011). Efficiency should be
defined as a capability to minimize the input means in order to achieve maximum
results (Amado et al., 2011). With organizations which use one input to define one
output, efficiency is measured as an output divided by input. However, real problem
emerges when we have to measure efficiency of organizational systems which use
many different types on inputs to create different outputs (Thanassoulis et al., 2012).
Main idea of this research was to create novel statistical model of efficiency which can
overcome problems with different types of input/output variables. Thus, it is necessary to
integrate all indicators into one value. Issue at stake is how to overcome fact that variables
are measured in different units, and how to provide appropriate weighting factors. As one
of the frequently used methods, Data Envelopment Analysis – DEA will be explained.
DEA method was developed by Charnes, Cooper & Rhodes (1978), in order to measure
efficiency of organizational units, in particular a non-profit one. Creator of this
method suggested a nonparametric approach for evaluating of efficiency. The
approach was based on the idea that multiple inputs and outputs are integrated into
one virtual input and output. Weighting issue was resolved by allowing each Decision
Making Unit (DMU) to determine its own weights, all of this with idea for each of
them to maximize its efficiency. Only constrain was that weights had to be a positive
number, and that quotient of virtual outputs and virtual inputs cannot be larger than
1 (Martić, 1999). Besides DEA method for measuring efficiency, in dissertation
Stochastic Frontier Analysis (SFA) was also elaborated. This is alternative approach
towards measuring efficiency with the extended usage of econometrics models.
In third chapter, attention is being shifted to the concept of multivariate data
analysis. The term has to explain multivariate aspect of data analysis, in a way that
many observations are collected on large number of variables. Multivariate data
analysis provides us with the opportunity to analyze complex data sets, with many
mutually dependent and independent variables occur. In third chapter dissertation is
mostly focused on two crucial statistical methods: principal component analysis
(PCA) & factor analysis, and cluster analysis. Factor analysis and PCA are most
commonly used to identify relatively small number of factors which represent
complex interrelations between all variables. These methods are very usefully in
identifying hidden dimensions of observed phenomenon. Main difference between
these two methods is the way of data examination. Factor analysis is mostly concern
about covariance, while PCA is examining variances. Both of them have similar goals
and procedures, thus factor analysis can be observed as a special case of PCA (Bulajić,
2002). Also, significant attention is committed to the cluster analysis as a multivariate
data analysis method. It is frequently used for grouping objects, so the similar objects
IX
create group which differs from the other groups