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2 edition of Altering the bias in differential school effectiveness using data envelopment analysis found in the catalog.

Altering the bias in differential school effectiveness using data envelopment analysis

Emmanuel Thanassoulis

Altering the bias in differential school effectiveness using data envelopment analysis

by Emmanuel Thanassoulis

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Published by University of Warwick. Warwick Business School Research Bureau in Coventry .
Written in English


Edition Notes

Statementby E. Thanassoulis.
SeriesWarwick Business School research papers -- No.139
ContributionsUniversity of Warwick. Warwick Business School Research Bureau.
ID Numbers
Open LibraryOL17164637M

Data Envelopment Analysis (DEA) evaluates the relative efficiency of decision-making units (DMUs) but does not allow for a ranking of the efficient units themselves. A modified version of DEA based upon comparison of efficient DMUs relative to a reference technology spanned by all other units is by: This paper attempts to clarify the impacts of information technology (IT) on accounting systems. The biggest impact IT has made on accounting is the ability of companies to develop and use computerized systems to track and record financial transactions. IT networks and computer systems have shortened the time needed by accountants to prepare Cited by:

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Altering the bias in differential school effectiveness using data envelopment analysis by Emmanuel Thanassoulis Download PDF EPUB FB2

This paper develops a data envelopment analysis based method for setting targets for diffentially effective schools to enable them to alter the bias of their differential effectiveness. The method also identifies role model schools which a differentially effective school can emulate in seeking to redress the imbalances in its by: Awareness of the extent and direction of a school's differential effectiveness is important.

Measures of overall performance of a differentially effective school can be misleading and targets seeking to improve the overall performance of such a school may not address its underlying inadequacies in raising the attainments of specific groups of pupils.

The mast appropriate data for assessing differential effectiveness would be at pupil or, at least, at ability range level. Such data is not generally available. This paper develops a data envelopment analysis (DEA) based method that can identify the existence, and indicate the direction of, differential effectiveness at a school using data covering the full range of pupil abilities.

This paper develops a data envelopment analysis based method for setting targets for diffentially effective schools to enable them to alter the bias of their differential effectiveness.

Studies of school effectiveness are briefly reviewed. pointing to the need to study effectiveness for sub-groups within each school as well as overall. The results of a multilevel analysis of a large dataset covering the years Differential Characteristics of Efficient Frontiers in Data Envelopment Analysis.

The implicit definition and nondifferentiability of efficient frontiers used in data envelopment analysis are two major obstacles to obtaining their differential characteristics, including various elasticity measures and marginal rates of substitution.

This paper describes the use of Excel to implement the basic Data Envelopment Analysis (DEA) algorithms. Excel's Solver can perform the optimisation required, whether nonlinear or linear programming formulations are used. DEA requires repeated : Nathan Proudlove.

Differential Game Model of Knowledge Flow in University-Industry Collaborative Innovation. Measuring the research performance of Chinese higher education institutions using data envelopment analysis. China Differential Game Model of Knowledge Flow in University-Industry Collaborative Innovation.

In: Strategy and Performance of Knowledge Author: Yu Yu, Yao Chen, Yao Chen, Qinfen Shi. Validity and Reliability in Education.

Schools all over the country are beginning to develop a culture of data, which is the integration of data into the day-to-day operations of a school in order to achieve classroom, school, and district-wide goals.

One of the biggest difficulties that comes with this integration is determining what data will Author: Ben Rubin. As part of the EPC Methods Guide, we intend that this paper will guide EPCs when selecting studies for inclusion in an SR.

Guidance is intended to reduce inconsistencies and risk of bias. Unfortunately, because there are no available studies to guide us how best to reduce this variation, what follows is based on fundamental principles of SRs and the experience of the EPC by: Data envelopment analysis (DEA) is a nonparametric data-based approach that is used to evaluate the performance of a set of homogeneous units; hence, inputs and outputs play a crucial role in this.

DEA results are biased. Generally speaking, the results are biased if they favour a particular unit and/or unfairly assess the efficiency of some inefficient units.

Although the actual DEA technique can be considered fair, bias can be introduced into the analysis through the omission of a critical factor or a lack of homogeneity in the field. A conceptual model taking the form of a Linear Equation was used and the Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) models of the Data Envelopment Analysis were employed to measure the relative efficiency of the components of ICT Infrastructure (Inputs) and the components of Education (Outputs).

"Estimating Effectiveness of the Control of Violence and Socioeconomic Development in Colombia: An Application of Dynamic Data Envelopment Analysis and Data Panel Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol.

(3), pagesFebruary. Data Envelopment Analysis (DEA) has been recognized as a valuable analytical research instrument and a practical decision support tool. DEA has been credited for not requiring a complete specification for the functional form of the production frontier nor the distribution of inefficient deviations from the frontier.

The effect of differential misclassification of the exposure or health outcome can bias the risk ratio, rate ratio, or odds ratio in either direction. The direction of bias is towards null if fewer cases are considered to be exposed or if fewer exposed subjects are considered to have. The majority of early evidence on school‐based physical activity interventions showed positive effects, but used self‐report measures, 16, 17 which have limited validity and differential bias across population subgroups.

18 Whilst more recent reviews are restricted to objective measures, they commonly combine data from a variety of tools Cited by: Data Envelopment Analysis reverses this role and employs mathematical programming to obtain ex post facto A separation into technical and scale efficiencies is accomplished by the methods developed in this paper without altering the latter conditions for use of DEA directly on observational data.

Cost Effectiveness and Resource Cited by: Data Envelopment Analysis reverses this role and employs mathematical A separation into technical and scale efficiencies is accomplished by the methods developed in this paper without altering the latter conditions for use of DEA directly on observational data.

Aggregation of inputs and outputs prior to Data Envelopment Analysis under Cited by: This work attempts to fill this gap by developing and empirically testing a tool, denoted ‘TCO-based DEA’, which can approximate the results of TCO in a more parsimonious way.

TCO-based DEA measures the efficiency of the supply relationship by using TCO cost drivers Cited by:. Dropout in randomised controlled trials is common and threatens the validity of results, as completers may differ from people who drop out.

Differing dropout rates between treatment arms is sometimes called differential dropout or attrition. Although differential dropout can bias results, it does not always do so.

Similarly, equal dropout may or may not lead to biased by: D. school registration Answer: D. school registration how to organize, how to reduce question order effects, and how to control researcher bias. social desirability bias. B. compare the two variables of the hypothesis using quantitative data analysis C.

systematically record the physical evidence D. all of the above Answer: D. all of.A new bi-level data envelopment analysis model for efficiency measurement and target setting Measurement, Vol.

Assessing the tourism performance of the Mediterranean coastal destinations: A combined efficiency and effectiveness approachCited by: