Quantitative research methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques. Typically large sample sizes

1. Introduction to
Quantitative
Research
Dr Ryan Thomas Williams

2. Statistics
‘Some individuals use statistics as a drunk man uses lamp-posts- for
support rather than illumination.’
A.E. Houseman
‘There are lies, damned lies, and statistics.’
Benjamin Disraeli

3. Reflection on numbers
‘I can prove anything by statistics except the truth.’
George Canning

4. What is quantitative research?
• Quantitative research methods emphasize objective measurements
and the statistical, mathematical, or numerical analysis of data
collected through polls, questionnaires, and surveys, or by
manipulating pre-existing statistical data using computational
techniques.
• Typically large sample sizes

5. Research strategies
• Descriptive (A description…)
• Correlational (the relationship…)
• Quasi-Experimental (the effect…)
• Experimental Research (the effect…)

6. Quantitative sampling
• Probability sampling seeks representative-ness and thus makes
generalisations in the research
• Several types of probability samples such as: simple random
sampling, systematic sampling, random stratified sampling, and
cluster sampling

8. Examples of data collection
• Experiments
• Controlled observations
• Structured interviews
• Surveys
• Polls

9. Key terms
• theoretical - concerned with developing, exploring or testing the
theories or ideas
• empirical - based on observations and measurements of reality -- on
what we perceive of the world around us
• probabilistic - post-positivist view of science - certainty as non-
attainable hence based on probabilities. Part of the reason we have
seen statistics become so dominant in social research is that it allows
us to estimate probabilities for the situations we study.
• causal - cause-effect relationships

10. Variables
• Variables are data with different values based on their source, usually
represented in a dataset as a column
• Any value that is not a constant can be a variable
• Example: height, salary, sales, quantity

11. What is a variable?
• A variable is not only something that you measure, but also
something that you can manipulate and control
• Descriptive research questions must contain dependent variables.
• For comparative and/or relationship-based research questions, you
will deal with both dependent and independent variables.
• An independent variable is a variable that is being manipulated in an
experiment in order to observe the effect this has on a dependent
variable (sometimes called an outcome variable).

12. Example
If we were interested in investigating the relationship between gender
and attitudes towards maternity pay through policy, the
independent variable would be gender and the attitudes towards
maternity pay through policy

13. Types of variables
• Categorical (i.e., nominal, dichotomous and ordinal variables)
• Continuous/numeric (i.e., interval and ratio variables)
• Important for statistical tests. We cannot use categorical variables for
Pearson’s, for example.

14. Categorical variables
• Nominal data does not have a natural scale. For example, what is
your marital status? Single, Married, Widowed, Divorced.
• Ordinal data have a natural scale. For example, please state your
socio-ecomomic status? <£30k, £30-50k, >£50k

15. Continuous variables
• Interval data is measured along a numeric scale and the distance
between each data point is equal. For example, temperate (Celcius),
or IQ scores. The answer could be any score between the theoretical
minimum and maximum
• Ratio data is similar to interval data but 0 has a true value. For
example, height, weight, and speed. You cannot be -1.5m.

16. Hypothesis
• A hypothesis makes a prediction of the expected outcome in a given
situation
• Usually: how the manipulation of the independent variable will
influence the behaviour of a dependent variable
• The hypothesis is tested in an experiment
• Experimental design ensures that what you are doing is genuinely
(and solely) responsible for the results

17. Accept or reject hypothesis
• If the experiment works, the hypothesis is shown to be probably
correct
• Can’t prove 100% truth
• If it fails, it could be because
• The hypothesis is wrong
• The experimental design is faulty

18. Null hypothesis
• Experiments are generally set up to demonstrate or support (rarely
“prove” , note) a hypothesis
• The null hypothesis H0 is that any observed changes in behaviour are
due to chance
• The alternate hypothesis H1 is the hypothesis you are trying to
demonstrate
• Usually, the best you can do is refute H0 thus showing that H1 is
probably correct (with a measurable degree of likelihood: statistical
significance)