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Supported data models (conceptual, logical, physical) Supported notations Forward engineering Reverse engineering Model/database comparison and synchronization Teamwork/repository Database Workbench: Conceptual, logical, physical IE (Crow’s foot) Yes Yes Update database and/or update model No Enterprise Architect
A phenomenographic data analysis sorts qualitatively distinct perceptions which emerge from the data collected into specific "categories of description." [1] [2] [3] [8] The set of these categories is sometimes referred to as an "outcome space." These categories (and the underlying structure) become the phenomenographic essence of the ...
Quantitative research using statistical methods starts with the collection of data, based on the hypothesis or theory. Usually a big sample of data is collected – this would require verification, validation and recording before the analysis can take place. Software packages such as SPSS and R are typically used for this purpose. Causal ...
These data sources include interview transcripts, videos of social interactions, notes, verbal reports [8] and artifacts such as books or works of art. The case study method exemplifies qualitative researchers' preference for depth, detail, and context. [11] [12] Data triangulation is also a strategy used in qualitative research. [13]
Learning styles refer to a range of theories that aim to account for differences in individuals' learning. [1] Although there is ample evidence that individuals express personal preferences on how they prefer to receive information, [2]: 108 few studies have found validity in using learning styles in education.
A concept model (a model of a concept) is quite different because in order to be a good model it need not have this real world correspondence. [3] In artificial intelligence, conceptual models and conceptual graphs are used for building expert systems and knowledge-based systems ; here the analysts are concerned to represent expert opinion on ...