Visual graph representations are increasingly used to represent, display, and explore scenarios and the structure of organizations. The graph representations of scenarios are readily understood, and commercial software is available to create and manage these representations. The purpose of the research presented in this paper is to explore whether these graph representations support quantitative assessments of the underlying scenarios. The underlying structure of the scenarios is the information that is being targeted in the experiment and the extent to which the scenarios are similar in content. An experiment was designed that incorporated both the contents of the scenarios and analysts’ graph representations of the scenarios. The scenarios’ content was represented graphically by analysts, and both the structure and the semantics of the graph representation were attempted to be used to understand the content. The structure information was not found to be discriminating for the content of the scenarios in this experiment; but, the semantic information was discriminating.

/pdf/an-experiment-on-graph-analysis-methodologies-for-scenarios-4u2yvztlt2.pdf

An Experiment on Graph Analysis Methodologies for Scenarios

Analysis goals associated with collections of scenarios include: • Summarizing a collection of scenarios – the summarization could take the form of identification of typical patterns of relations within the collection • Retrieve similar scenarios/cases – the retrieval could be based on parts of scenarios; the similar ‘complete’ scenarios could be used to suggest additional lines of investigation • Find “relation paths” between key actors A signature-vector strategy is presented that addresses the above analysis goals. The broad strategy has been applied in various settings.

A Data Signature Approach for Analyzing, Manipulating and Understanding Collections of Graphical Summaries of Scenarios

For many reasons, researchers place their data in multiple electronic datasets and later find that they wish to combine those datasets for a particular analysis. This article reports on an analysis of the extant literature on combining complex datasets and provides both a tutorial and related syntax that combines non-matching datasets (i.e., from different sources) in such a way that all data is contained in the final combined dataset with identification of data source. This syntax represents an improvement over the existing SPSS (v23-v27) combining dataset routines in terms of 1) expanding the types of datasets that can be combined, 2) completeness of the resulting dataset, and 3) facilitating management of multiple and/or divergent data sets. An additional benefit of using this process is the incorporation of a method to test the accuracy of the merged data and thus verify the data quality The SPSS syntax along with instructions and examples are reviewed in the article.
Keywords: SPSS, syntax, datasets, merging, longitudinal data

Merging Complex or Divergent Datasets using SPSS: A Method and Tutorial

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A Data Signature Approach for Analyzing, Manipulating and Understanding Collections of Graphical Summaries of Scenarios

Merging Complex or Divergent Datasets using SPSS: A Method and Tutorial