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Chapter 13 Analysing Quantitative Data Zina OLeary

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Dealing with Data Data interpretation is a major hurdle in any research study. Effective data analysis involves: keeping your eye on the main game managing your data engaging in the actual process of analysis and effectively presenting your data. Zina OLeary (2009) The Essential Guide to Doing Your Research Project. London: Sage

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Doing Stats Being able to do statistics no longer means being able to work with formulae. It is much more important for researchers to be familiar with the language and logic of statistics, and be competent in the use of statistical software. Zina OLeary (2009) The Essential Guide to Doing Your Research Project. London: Sage

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Data Management Data management involves: familiarizing yourself with appropriate software systematically logging in and screening your data entering the data into a programme and cleaning your data. Zina OLeary (2009) The Essential Guide to Doing Your Research Project. London: Sage

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Data Types Different data types demand discrete treatment, so its important to be able to distinguish variables by: cause and effect (dependent or independent) and measurement scales (nominal, ordinal, interval, and ratio). Zina OLeary (2009) The Essential Guide to Doing Your Research Project. London: Sage

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Descriptive Statistics Descriptive statistics are used to summarize the basic feature of a data: measures of central tendency (means, mode, and median) dispersion (range, quartiles, variance, and standard deviation) and distribution (skewness and kurtosis). Zina OLeary (2009) The Essential Guide to Doing Your Research Project. London: Sage

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Shape of the Data Areas under the Normal Curve So Zina OLeary (2009) The Essential Guide to Doing Your Research Project. London: Sage

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Inferential Statistics Inferential statistics allow researchers to assess their ability to draw conclusions that extend beyond the immediate data, i.e.: if a sample represents the population if there are differences between two or more groups if there are changes over time or if there is a relationship between two or more variables. Zina OLeary (2009) The Essential Guide to Doing Your Research Project. London: Sage

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Statistical Significance Statistical significance is captured through a p-value - the probability that your findings are more than coincidence. The lower the p-value, the more confident researchers can be that findings are genuine. Zina OLeary (2009) The Essential Guide to Doing Your Research Project. London: Sage

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Types of Analysis Statistical analysis can be: Univariate - exploring one variable at a time Bivariate - exploring the relationship between two variables Multivariate - exploring the relationship between three or more variables. Multivariate analysis allows for the most sophisticated data interrogation. Zina OLeary (2009) The Essential Guide to Doing Your Research Project. London: Sage

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Selecting the Right Statistical Test Selecting the right statistical test relies on knowing: the nature of your variables their scale of measurement their distribution shape and the types of question you want to ask. Zina OLeary (2009) The Essential Guide to Doing Your Research Project. London: Sage

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Data Presentation Presenting quantitative data often involves the production of graphs and tables. These need to be: 1.Selectively generated so that they make relevant arguments 2.Informative yet simple, so that they aid the readers understanding. Zina OLeary (2009) The Essential Guide to Doing Your Research Project. London: Sage

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