Specialised Statistical Procedures

Ray W. Cooksey
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Ray W. Cooksey: University of New England, UNE Business School

Chapter Chapter 9 in Illustrating Statistical Procedures: Finding Meaning in Quantitative Data, 2020, pp 557-693 from Springer

Abstract: Abstract In this chapter, we explore more specialised statistical procedures as well as a fundamental concept, Bayesian statistical inference. The procedures we discuss and illustrate include: Rasch models and item response theory (alternative approaches for assessing measurement quality); survival/failure analysis (useful for predicting how long a particular outcome event will take to be observed); quality control charts (useful for tracing, measuring and analysing the quality of products, services and processes); conjoint measurement and choice modelling (sophisticated experimental/statistical methodologies for assessing consumer decision making); multi-level models (for estimating multiple regression models at different levels of analysis); classification and regression trees (to facilitate detection of prediction and interaction patterns in data); social network analysis (a quantitative approach to understanding and displaying the nodes, interactions and relational connections in a social network); specialised forms of regression analysis (e.g. robust, multinomial, fuzzy, nonlinear, ridge, generalised least squares, 2-stage, tobit, probit, ordinal, mediated and moderated regression models); data mining (a range of methods for detecting and learning patterns in data, particularly in large data sets); text mining (useful for detecting and learning concepts and patterns in qualitative data); and simulation and computational modelling (useful for building/testing statistical, mathematical or virtual models of the world).

Keywords: Bayesian statistical inference; Rasch modelling; Item response theory; Survival/failure analysis; Quality control charts; Conjoint measurement; Choice modelling; Multi-level models; Specialised regression models; Classification & regression trees; Data mining; Text mining; Simulation; Computational modelling (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-15-2537-7_9

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DOI: 10.1007/978-981-15-2537-7_9

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