Scratching down numbers (stem-and-leaf) -- Schematic summaries (pictures and numbers) -- Easy re-expression -- Effective comparison (including well-chosen expression) -- Plots of relationship -- Straightening out plots (using three points) -- Smoothing sequences -- Parallel and wandering schematic plots -- Delineations of batches of points -- Using two-way analyses -- Making two-way analyses -- Advanced fits -- Three-way fits -- Looking in two or more ways at batches of points -- Counted fractions -- Better smoothing -- Counts in bin after bin -- Product-ratio plots -- Shapes of distribution -- Mathematical distributions -- Postscript.
A practical introduction to using Mplus for the analysis of multivariate data, this volume provides step-by-step guidance, complete with real data examples, numerous screen shots, and output excerpts. The author shows how to prepare a data set for import in Mplus using SPSS. He explains how to specify different types of models in Mplus syntax and address typical caveats--for example, assessing measurement invariance in longitudinal SEMs. Coverage includes path and factor analytic models as well as mediational, longitudinal, multilevel, and latent class models. Specific programming tips an
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In: International journal of social ecology and sustainable development: IJSESD ; an official publication of the Information Resources Management Association, Band 8, Heft 4, S. 32-47
The amount of data generated worldwide has reached unprecedented levels, and its rate of growth continues to increase exponentially. Brought about by rapid advances in information technologies, as well as changes in lifestyles and business strategies, this explosion in the quantity of data has given impetus to a fast-growing demand for data storage, which, in turn, has paved the way for large-scale data centers. This article addresses the potential economic impact of the construction of a "data center region" in a developing country, using as a case study a development project in Konya, Turkey. The core focus of the analysis is on whether such a data center region could create positive spillovers that trigger further development in a developing region.
Purpose This study aims to assess the efficiency of Brazil, Russia, India, China, South Africa (BRICS) countries in achieving sustainable development by analyzing their ability to convert resources and technological innovations into sustainable outcomes.
Design/methodology/approach Using data envelopment analysis (DEA), the study evaluates the economic, environmental and social efficiency of BRICS countries over the period 2010–2018. It ranks these countries based on their sustainable development performance and compares them to the period 2000–2007.
Findings The study reveals varied efficiency levels among BRICS countries. Russia and South Africa lead in certain sustainable development aspects. South Africa excels in environmental sustainability, whereas Brazil is efficient in resource utilization for sustainable growth. China and India, despite economic growth, face challenges such as pollution and lower quality of life.
Research limitations/implications The study's findings are constrained by the DEA methodology and the selection of variables. It highlights the need for more nuanced research incorporating recent global events such as the COVID-19 pandemic and geopolitical shifts.
Practical implications Insights from this study can inform targeted and effective sustainability strategies in BRICS nations, focusing on areas such as industrial quality improvement, employment conditions and environmental policies.
Social implications The study underscores the importance of balancing economic growth with social and environmental considerations, highlighting the need for policies addressing inequality, poverty and environmental degradation.
Originality/value This research provides a unique comparative analysis of BRICS countries' sustainable development efficiency, challenging conventional perceptions and offering a new perspective on their progress.
In this paper, we examine the social and economic development level of the Russian Federation regions in 2021 using data from the Federal State Statistics Service. We employ multidimensional data analysis techniques to reduce the non-orthogonal variables via factor analysis to a small, orthogonal factor space, determine the optimal number of clusters via tree classification, and perform cluster analysis in the factor space. We calculate the position of the regions in the factor space, determine the location, composition, and statistical characteristics of clusters, and compute the volumes and densities of clusters. We identify the densest clusters, including those with close proximity and regions with significant variations from the average level of social and economic development. The study utilizes machine and graphical methods of computational mathematics and has both theoretical and practical implications.
