Quantitative research

This article describes the basic tenets of quantitative research. The concepts of dependent and independent variables are addressed and the concept of measurement and its associated issues, such as error, reliability and validity, are explored. Experiments and surveys – the principal research designs in quantitative research – are described and key features explained. The importance of the double-blind randomised controlled trial is emphasised, alongside the importance of longitudinal surveys, as opposed to cross-sectional surveys. Essential features of data storage are covered, with an emphasis on safe, anonymous storage. Finally, the article explores the analysis of quantitative data, considering what may be analysed and the main uses of statistics in analysis

Importance-satisfaction analysis for marine-park hinterlands: A Western Australian case study

Tourist use of national and marine parks continues to increase worldwide. Effective management depends on being able to evaluate the quality of visitors' experiences, as well as protecting the natural environment. In tourism management, importance-performance analysis (IPA) has been used as part of quality management. It has recently been applied to national park management. This paper reconceptualises this analysis to one of importance satisfaction, enabling a focus on the quality of experience. Two methods, importanceperformance analysis and service quality gap, were modified and applied in the hinterland of Swan Estuary Marine Park in Western Australia. Both provided data useful for evaluating satisfaction, with the choice of method depending on the end user's resources and requirements as well as cognisance of each method's limitations. For most of the Marine Park attributes, satisfaction exceeded importance and hence no management attention is needed. Exceptions were the condition of the Swan River and associated footpaths, and the presence of litter and wildlife. For these, satisfaction was lower than importance, suggesting management attention is needed

Importance-performance analysis of retail website service quality

This study intends to empirically explore the customer’s perceived ranking of the importance of a range of on-line services, and their perceptions of the retailers’ performance in delivering these services. An online questionnaire survey has been conducted to gather the data from respondents. The data was analysed using Importance-Performance Analysis (IPA). The findings suggest areas of e-service quality where retailers could improve, based on the customers’ perceptions of the retailers’ performance against the importance of some e-service quality features and/or services on offer. Consequently, this study highlights that retailers should take active steps to understand their customers’ requirements, before developing an online customer services strategy. From a practical perspective, retailers could also apply the questionnaire developed for this study to canvas the opinions of customers, to help identify areas in which their performance needs to be improved

On Regularization Parameter Estimation under Covariate Shift

This paper identifies a problem with the usual procedure for L2-regularization parameter estimation in a domain adaptation setting. In such a setting, there are differences between the distributions generating the training data (source domain) and the test data (target domain). The usual cross-validation procedure requires validation data, which can not be obtained from the unlabeled target data. The problem is that if one decides to use source validation data, the regularization parameter is underestimated. One possible solution is to scale the source validation data through importance weighting, but we show that this correction is not sufficient. We conclude the paper with an empirical analysis of the effect of several importance weight estimators on the estimation of the regularization parameter.Comment: 6 pages, 2 figures, 2 tables. Accepted to ICPR 201

Benefits of InterSite Pre-Processing and Clustering Methods in E-Commerce Domain

This paper presents our preprocessing and clustering analysis on the clickstream dataset proposed for the ECMLPKDD 2005 Discovery Challenge. The main contributions of this article are double. First, after presenting the clickstream dataset, we show how we build a rich data warehouse based an advanced preprocesing. We take into account the intersite aspects in the given ecommerce domain, which offers an interesting data structuration. A preliminary statistical analysis based on time period clickstreams is given, emphasing the importance of intersite user visits in such a context. Secondly, we describe our crossed-clustering method which is applied on data generated from our data warehouse. Our preliminary results are interesting and promising illustrating the benefits of our WUM methods, even if more investigations are needed on the same dataset

Triangulation of Instrumentation and Data Source: a Stronger Method in Assessing English Language Needs

This paper proposes the importance of multiple instrumentation and data source (triangulation) in a needs analysis. Various data gathering methods developed in assessing learners' English language needs are reviewed. The justification of employing more than a single data gathering method and data source in a needs analysis is also presented by examining the strengths and weaknesses of each method and evaluating previous needs analyses carried out in some Asian countries. Highlights are then given to the methodology mostly implemented in assessing English needs in Indonesia and a recommendation is addressed to further studies on learners's needs in Indonesi

A random matrix analysis and improvement of semi-supervised learning for large dimensional data

This article provides an original understanding of the behavior of a class of graph-oriented semi-supervised learning algorithms in the limit of large and numerous data. It is demonstrated that the intuition at the root of these methods collapses in this limit and that, as a result, most of them become inconsistent. Corrective measures and a new data-driven parametrization scheme are proposed along with a theoretical analysis of the asymptotic performances of the resulting approach. A surprisingly close behavior between theoretical performances on Gaussian mixture models and on real datasets is also illustrated throughout the article, thereby suggesting the importance of the proposed analysis for dealing with practical data. As a result, significant performance gains are observed on practical data classification using the proposed parametrization