PhD Research Methods: Quantitative Research
Quantitative Research is one style of research used in the social sciences - anthropology, sociology and the like, the other style of research is qualitative research. Quantitative research uses empirically observed data, and then mathematical models or theories to interpret said data. Generally, it is in the form of statistics, percentages, or other such styles.
So what can this sort of research be used for? If you’re doing a PhD , you’re likely to come across it. One example would be looking at the average age of mothers for their first child. In some cases, mixed-method interpretation may result – that would be using qualitative research in tandem with quantitative. This is a fairly common usage of data gained via quantitative research.
But what types of quantitative research methods are there?
The different types of quantitative research methods
As with any style of research, there are a variety of ways to go about gathering the data. Here we’ll look at some of the different types of quantitative research methods, and some of the kinds of data they’re intended to gather.
Surveys are one of the easiest ways to gather data and they can be targeted at any particular data that the researcher intends to gather. These can be useful regardless of how specific the data you wish to analyse in – you could have a survey targeted at students to work out the average age and which classes are taken, or something aimed specifically at 22-year-old mathematics students to find out how many hours a week they worked. The major advantage of this research method is that it is easy to set up, and depending on the way the data is gathered, it can be easy to analyse the data found. A disadvantage, however, is that it can be biased by the questions asked and the answers suggested.
#2 Laboratory Experiments
Laboratory experiments can be another good way to obtain data – the variables are easily controlled, and the results should be easily repeatable. The only problem is that for many aspects of social science, it is hard to use laboratory experiments. So, for a psychologist looking at responses to certain material it may be possible to use this method. However, if an anthropology student is looking at certain culturally specific behaviour, attempting to study this in a laboratory experiment is almost certain to fail. It’s a useful method of quantitative research, but not one suited for every researcher.
#3 Mathematical Modelling
Mathematical modelling is used to describe systems in mathematical language. It can be used to describe things such as population growth, and look at its relation to different locations. It can even be used to model more abstract things such as making a model of rational behaviour for a consumer. Mathematical modelling can help draw out the different kinds of links between variables. However, it is worth remembering that these models don’t necessarily show causality, merely the possibility of such. That, and mathematical models can’t always take into account all the possible variables that come with the study of humanity such as is found in the social sciences. They are useful, however, for giving a general formula or pattern that can be used as a reference point for further studies.
Econometrics is relating economic data to mathematics. This can be used to relate things such as GSP to unemployment, or education to expected wage. This can be particularly useful when looking at the economic variables in areas of social science. However, as with mathematical modelling, these models are general overviews, rather than being applicable on a necessarily individual scale
Types of Data used in Quantitative Research
These methods record various types of data, but just what data do they record?
Numerical data is exactly what it sounds like – numbers. Things such as a measurement of age, or size.
Non-numerical data can be things such as a position on a scale, or other such information that is not necessarily expressed through numbers.
Continuous data is data that could take any measurement within a range – for example, time.
Discrete data is data that can only take known values – such as the number of children in a house being chosen from the options “1, 2, 3 or 4”, rather than being left open.