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1.6.6 Data Analysis Methods Selected

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  • You are in Phase 1 Category

    Phase 1:

    Starting to Design Your Doctoral Research

Quick View of Quantitative Analysis

During quantitative analysis, data are crunched using basic mathematical tests to identify characteristics and/or compare one group to another, searching for possible correlation. The purpose of statistical analysis is to clarify the variance between data to uncover deeper meaning. As a simple example, if a dot is placed on an infinite field, it is impossible to accurately describe its placement. However, if it is placed among a group of dots on paper, this description becomes relatively easy as we can measure the relationship to other dots and their relationship to the field. When employed, statistical analysis techniques create a discussion of characteristics inherent within and between sets of data.

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PhD Common Sense Statistics

Written by Dr Bob Zenhausern as the first in a series of articles outlining the logic and theory of statistics with corresponding excel worksheets to help the PhD student sort out what needs to be done for their statistical analysis.

Welcome to the Introductory module of Common Sense Statistics.  The purpose of this article is to let you explore the important procedures in statistics with minimal theoretical background and with the insights gained by teaching statistics for 35 years.  All calculation will depend on the use of a spreadsheet and a demonstration workbook is attached below.

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Mixed Methods Analysis

The analysis of qualitative and quantitative evidence should first be undertaken individually; allowing the researcher to reflect on what was learned from each.  Then you might spend some time considering the similarities and differences between what you learned in each - some of this will be tied to the similarities and differences between mixed methods and qualitative or quantitative considered singly. This article leads you through those considerations and, through this discussion, you should develop a deeper understanding of your own data and where your discussion of that analysis should go next.   

One major similarity between mixed methodologies and qualitative and quantitative taken separately is that the researcher needs to maintain his or her focus on the original purpose behind their methodological choices.

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