Category: Learning modules
Receiver Operating Curve (ROC) is an extension of such classifications. Performance of binary classifier system in the case of ROC analysis can be tested.
classification in supervised learning, Supervised learningData visualization is a technique for data representation in the form of tables, charts and diagrams. This article explains representation of demographic information of the participants (e.g. teachers) based on nodes in Nvivo.
nvivo data representation, nvivo moduleNvivo coding query eases the understanding of nodes and their interconnections. Given the vast array of nodes generated, researchers find it difficult to connect two nodes.
nvivo analysis, nvivo moduleNvivo memo can be prepared and linked to sources, nodes and case nodes. Like nodes, a memo also can be typed or directly imported to Nvivo.
nvivo data processing, nvivo moduleQueries can be generated in Nvivo by 3 ways; words, content and matrix coding. Not only words but also by number of nodes or classification in different nodes and attributes. For that purpose, Nvivo matrix coding query is useful.
nvivo analysis, nvivo moduleApart from sources, nodes, classifications, queries and maps, the Nvivo main folder also contains the option of ‘Reports’ (see figure below). ‘Reports’ contains a summary of the project.
nvivo module, nvivo result summarisationThe present article shows extensions of ARCH, i.e. GARCH model in STATA. Like ARCH model, ARCH extensions like Generalised ARCH (GARCH) model too need squared residuals as determinants of the equation’s variance.
STATA for data analysisThis article shows the application of one of the other multidimensional scaling methods Individual Differences Scaling (INDSCAL) using Hamlet II. INDSCAL compares the co-occurrence of matrices obtained from comparable search lists.
analysis and scaling, hamlet