1. Chapter 13 Analysing Quantitative Data
Zina O’Leary

2. 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 O’Leary (2009) The Essential Guide to Doing Your Research Project. London: Sage

3. “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 O’Leary (2009) The Essential Guide to Doing Your Research Project. London: Sage

4. 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 O’Leary (2009) The Essential Guide to Doing Your Research Project. London: Sage

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

6. 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 O’Leary (2009) The Essential Guide to Doing Your Research Project. London: Sage

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

8. 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 O’Leary (2009) The Essential Guide to Doing Your Research Project. London: Sage

9. 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 O’Leary (2009) The Essential Guide to Doing Your Research Project. London: Sage

10. 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 O’Leary (2009) The Essential Guide to Doing Your Research Project. London: Sage

11. 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 O’Leary (2009) The Essential Guide to Doing Your Research Project. London: Sage

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