Measure (data warehouse)

About: Measure (data warehouse) is a research topic. Over the lifetime, 2517 publications have been published within this topic receiving 42751 citations.

A Cluster Separation Measure

Abstract: A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster. The measure can be used to infer the appropriateness of data partitions and can therefore be used to compare relative appropriateness of various divisions of the data. The measure does not depend on either the number of clusters analyzed nor the method of partitioning of the data and can be used to guide a cluster seeking algorithm.

Answering the Call for a Standard Reliability Measure for Coding Data

Abstract: In content analysis and similar methods, data are typically generated by trained human observers who record or transcribe textual, pictorial, or audible matter in terms suitable for analysis. Conclusions from such data can be trusted only after demonstrating their reliability. Unfortunately, the content analysis literature is full of proposals for so-called reliability coefficients, leaving investigators easily confused, not knowing which to choose. After describing the criteria for a good measure of reliability, we propose Krippendorff's alpha as the standard reliability measure. It is general in that it can be used regardless of the number of observers, levels of measurement, sample sizes, and presence or absence of missing data. To facilitate the adoption of this recommendation, we describe a freely available macro written for SPSS and SAS to calculate Krippendorff's alpha and illustrate its use with a simple example.

A Slacks Based Measure of Efficiency in Data Envelopment Analysis

Abstract: In this paper, we will propose a Slacks-Based measure (SBM) of efficiency in DEA. This scalar measure deals directly with the input surplus and the output shortage of the decision making unit (DMU) concerned. It is unit invariant and monotone decreasing with respect to input surplus and output shortage. Furthermore, this measure is decided only by consulting with the reference set of the DMU and is not affected by statistics over the whole data set. The new measure has a close connection with other measures proposed so far, e.g., CCR and BCC. The dual side of this model can be interpreted as profit maximization, in contrast to the ratio maximization of the CCR model. Numerical experiments show its validity as an efficiency measurement tool and its compatibility with other measures of efficiency.

A Slacks-based Measure of Super-Efficiency in Data Envelopment Analysis

Abstract: In most models of Data Envelopment Analysis (DEA), the best performers have the full efficient status denoted by unity (or 100%), and, from experience, we know that usually plural Decision Making Units (DMUs) have this “efficient status”. To discriminate between these efficient DMUs is an interesting subject. This paper addresses this “super-efficiency” issue by using the slacks-based measure (SBM) of efficiency, which the author proposed in his previous paper [European Journal of Operational Research 130 (2001) 498]. The method differs from the traditional one based on the radial measure, e.g. Andersen and Petersen model, in that the former deals directly with slacks in inputs/outputs, while the latter does not take account of the existence of slacks. We will demonstrate the rationality of our approach by comparing it with the radial measure of super-efficiency. The proposed method will be particularly useful when the number of DMUs are small compared with the number of criteria employed for evaluation.

Development and testing of a short form of the patient activation measure.

Abstract: Objective The Patient Activation Measure (PAM) is a 22-item measure that assesses patient knowledge, skill, and confidence for self-management. The measure was developed using Rasch analyses and is an interval level, unidimensional, Guttman-like measure. The current analysis is aimed at reducing the number of items in the measure while maintaining adequate precision. Study methods We relied on an iterative use of Rasch analysis to identify items that could be eliminated without loss of significant precision and reliability. With each item deletion, the item scale locations were recalibrated and the person reliability evaluated to check if and how much of a decline in precision of measurement resulted from the deletion of the item. Data sources The data used in the analysis were the same data used in the development of the original 22-item measure. These data were collected in 2003 via a telephone survey of 1,515 randomly selected adults. Principal Findings. The analysis yielded a 13-item measure that has psychometric properties similar to the original 22-item version. The scores for the 13-item measure range in value from 38.6 to 53.0 (on a theoretical 0-100 point scale). The range of values is essentially unchanged from the original 22-item version. Subgroup analysis suggests that there is a slight loss of precision with some subgroups. Conclusions The results of the analysis indicate that the shortened 13-item version is both reliable and valid.