Monash University

About: Monash University is a education organization based out in Melbourne, Victoria, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 35920 authors who have published 100681 publications receiving 3027002 citations.

Dynamic self-organizing maps with controlled growth for knowledge discovery

Abstract: The growing self-organizing map (GSOM) algorithm is presented in detail and the effect of a spread factor, which can be used to measure and control the spread of the GSOM, is investigated. The spread factor is independent of the dimensionality of the data and as such can be used as a controlling measure for generating maps with different dimensionality, which can then be compared and analyzed with better accuracy. The spread factor is also presented as a method of achieving hierarchical clustering of a data set with the GSOM. Such hierarchical clustering allows the data analyst to identify significant and interesting clusters at a higher level of the hierarchy, and continue with finer clustering of the interesting clusters only. Therefore, only a small map is created in the beginning with a low spread factor, which can be generated for even a very large data set. Further analysis is conducted on selected sections of the data and of smaller volume. Therefore, this method facilitates the analysis of even very large data sets.

I. Using the Bland–Altman method to measure agreement with repeated measures

Abstract: Medical researchers often need to compare two methods of measurement, or a new method with an established one, to determine whether these two methods can be used interchangeably or the new method can replace the established one. – 6 In most of these situations, the ‘true’ value of the measured quantity is unknown. In a series of articles, Bland and Altman – 9 advocated the use of a graphical method to plot the difference scores of two measurements against the mean for each subject and argued that if the new method agrees sufficiently well with the old, the old may be replaced. Here the idea of agreement plays a crucial role in method comparison studies. There are numerous published clinical and laboratory studies evaluating agreement between two measurement methods using Bland–Altman analysis. The original Bland–Altman publication has been cited on more than 11 500 occasions—compelling evidence of its importance in medical research. The Bland–Altman method calculates the mean difference between two methods of measurement (the ‘bias’), and 95% limits of agreement as the mean difference (2 SD) [or more precisely (1.96 SD)]. It is expected that the 95% limits include 95% of differences between the two measurement methods. The plot is commonly called a Bland–Altman plot and the associated method is usually called the Bland–Altman method. The Bland–Altman method can even include estimation of confidence intervals for the bias and limits of agreement, but these are often omitted in research papers. The presentation of the 95% limits of agreement is for visual judgement of how well two methods of measurement agree. The smaller the range between these two limits the better the agreement is. The question of how small is small depends on the clinical context: would a difference between measurement methods as extreme as that described by the 95% limits of agreement meaningfully affect the interpretation of the results? Repeated measurements for each subject are often used in clinical research. Two recent articles in the British Journal of Anaesthesia use such a design. 6 When repeated measures data are available, it is desirable to use all the data to compare the two methods. However, the original Bland–Altman method was developed for two sets of measurements done on one occasion (i.e. independent data), and so this approach is not suitable for repeated measures data. However, as a naı̈ve analysis, it may be used to explore the data because of the simplicity of the method. Examples of the misuse of agreement estimation for repeated measures data can be found readily in the anaesthetic literature: Opdam and colleagues did repeated measurements of cardiac output in six subjects, but incorrectly analysed and plotted 251 paired data sets using the standard Bland–Altman technique. Niedhart and colleagues compared a processed EEG device’s electrode placement on each side of the head in 12 subjects, but analysed and plotted 22 860 paired data sets. Such examples of incorrect use are widespread in the anaesthetic and critical care literature. Bland and Altman have provided a modification for analysing repeated measures under stable or changing conditions, where repeated data were collected over a period of time. As an alternative, we propose using random effects models for this purpose.

Psychic Distance and Organizational Performance: An Empirical Examination of International Retailing Operations

Abstract: We develop a reconceptualization and operationalization of psychic distance that broadens the concept. We then empirically test this operationalization and investigate the relationship between psychic distance and organizational performance. Our results suggest that psychic distance, as a summary construct, explains a significant proportion of the variance in financial performance and strategic effectiveness. However, disaggregation of the construct substantially increases its explanatory power. The results also support a psychic distance paradox, where psychic distance has a positive relationship with organizational performance.