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Once information is summarised, easier to identify patterns and interpret meanings. Sometimes this can be a simple descriptive process if patterns or meaning are obvious.

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Your research methods may include the collection of information (data) which can be interpreted or analysed to frame answers to your research questions or increase knowledge of your research topic. You can collect this information in a variety of ways (interviews, surveys, experiments, observations, critical appraisal of texts, literature or works of art or other artefacts). Different collection methods will require different types of management.

Numerical or quantitative information is obtained from research methods such as surveys of populations or from repeated experimental procedures. When recording the data it is important to include detailed information (eg dates and place of collection, methods of measurement, units of measurement) to minimise confusion. Numerical data are usually recorded on printed datasheets, then stored in spreadsheet format.

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Graphs must have axes labelled and all units of measurement clearly shown.

Qualitative (non-numerical) information may be recorded during interviews with human participants, often on video or audiotape, possibly with supporting notes, and may be transcribed into written form later. Other qualitative information describing and interpreting texts or artefacts may also be recorded in written form and stored on index cards or as Word files. This material may be coded for themes using software programs (eg ) that search for keywords or strings, or it may be done manually. The transcripts may also be treated as texts for analysis.

The University has purchased QSR NVivo software licences for research degree students in the Divisions of Business; Education, Arts and Social Sciences; and Health Sciences.

Whether you are collecting new data or accessing existing data, you need to consider:
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This page presents an algorithm solving black box global optimization problems using mainly methods from data analysis and an implementation of this algorithm. As documentation, you may download a copy of the thesis.

Dissertation 2015 | Thesis | Data Analysis

The main goal of my thesis was to cross the barrier between the fields of optimization and data analysis by applying methods from data analysis to optimization problems. In particular, I applied data analysis techniques to obtain information about black box functions, i.e. functions for which we do not know an algebraic expression.

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I presented both an algorithm and a reference implementation to solve optimization problems where: using methods from data analysis: The algorithm is a so-called incomplete global optimizer, i.e. it attempts to find a global solution for the optimization problem, but is unable to guarantee globality. In fact, it cannot even guarantee always finding a local optimum, due to the lack of gradients and any sort of global information. Despite this lack of guarantees, the algorithm performs well in practice.

Thesis Data Analysis Tools | Graduate Thesis

The implementation is licensed under the GNU General Public License, version 3 or later, with special exceptions allowing to link with the third-party optimizers used.

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