| Lesson objective | To explore what the basics are for research methods |
| Lesson outcomes | • Assess how to use the structure PET • Analyse different sampling techniques • Explain the basics |
The basics
Throughout this module, you need to be using key words over and over again to show you know your stuff (AO1).
To start with, we will look at Primary and Secondary data.
Primary data is data which you, yourself, have personally acquired. For example: Questionnaires, interviews etc.
Secondary data is data which you are using, BUT someone else has acquired. E.g. BBC website, Youtube etc.
Both of these have positive and negative reasons when using them.
Primary data:
- Reliable and you control specifically what you want to measure.
- You understand exactly what your data shows.
- Up to date, providing you didn’t do it ages ago.
Secondary data:
1) Usually, secondary data is able to complete large scale studies which you yourself are not able to.
2) Secondary data has usually been analysed a hundred times by professionals and academics all over the world.
3) Secondary data is much faster to use, as really you don’t even have to get off your backside.
The next two key terms you need to use are qualitative and quantitative data (big words I know).
Qualitative data is data where normally a person can give as much information as they want. This makes analysing it harder, as you the data could be part of numerous categories, however, by not limiting the participant you could find you have better data. An example of qualitative data are open ended questions (we will look at this later).
Quantitative data is where normally a person gives data in a numerical form which can be easily placed into a chart. This makes analysing it easier, but as stated, could make your results limited. An example of quantitative data are questionnaires.
Factors affecting influence, choice of research
What you need to know: You need to know the following:
- Key terms.
- Positivism and Interpretivism perspectives on methodology.
- How to construct a PET paragraph.
Key terms: When discussing research methods, you need to be aware of two different perspectives.
Methodological perspective: This is what we will be discussing on this page (Interpretivism and Positivism).
Generalisability: How true can the data reflect the target population. Does it use men and women of all ages from all parts of the world? You need to explain how this affects your data.
Validity: Does this experiment measure its hypothesis? Does it attempt to prove what it aims to. This does not mean the hypothesis has to be right, but if I am measuring rainfall in summer and go to a nearby field and count sheep…. it isn’t really helpful is it.
Reliability: If you were to repeat the experiment, how easily could this be done? A good experiment has high reliability and is not a one trick pony.
The great debate: When looking at research methods there are two point of views that you must consider (Positivist and Interpretivist).
Sampling
Sampling is key when studying a large population. The true art is to increase your generalisability. Depending on your sampling size, depends on how this is achieved.
Sampling techniques falls into two categories:
- Representative sampling.
- Non representative sampling.
Representative Sampling
Random sampling.
Random sampling is simple. It is random. If you put 100 names into a hat and draw ten. Each person in that hat has an equal chance of being chosen.
Systematic random sampling.
There are two stages to this research method. 1) Place the names into some sort of order. This can be anything from oldest to youngest to gender or alphabetical order. 2) Choose the nth number from the list. E.g. I choose every 10th person or every 3rd person.
Stratified random sampling.
This is where, again, participants are placed into groups. So, if a researcher was investigating age and gender in a high school, the researcher would choose 10 males and 10 females from each year 7-11 to generate his sample size.
Quota sampling.
Quota sampling means to take a very tailored sample that’s in proportion to some characteristic or trait of a population. For example, you could divide a population by their age, how much they earn, their ethnicity etc. The population is divided into groups and samples are taken from these groups making sure that the correct proportions are representative of the population e.g. if your population sample is 25% African, you make sure your sample size is. It is considered to be a non-probability sampling technique.
Non-Representative Sampling
Snowball sampling.
Snowball sampling is using a target and then using their friends and their friends etc to generate your population sample.
Opportunity sampling.
This is simple, if you stand in a street, whoever you find is the target.
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