Chapter 13 Analysing Quantitative Data Zina OLeary.

Chapter 13Analysing Quantitative Data

Zina O’Leary

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

“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

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.


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).


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).


Shape of the Data

Areas under the Normal Curve

So


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.


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.


Types of AnalysisStatistical 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.


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.


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 reader’s understanding.


Chapter 13 Analysing Quantitative Data Zina OLeary.

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