Investigating psychology 1 – Generating Research Questions
Research Questions – must be answerable with a study
Types of research questions –
Descriptive – what characterises the phenomenon?
Comparative – how is it related to other things?
Explanatory – what explains these relationships?
Normative – how can this phenomenon be changed?
Types of variables –
Categorical/ nominal – variables take on values that merely represent different
categories or groups. There is no order to them and they cant be ranked – e.g.
birthplaces
Ordinal - Variables take on values that are in an order or can be ranked, but
the distance between the values is not known. e.g. A-level grades. A is better
than B, B is better than C, but you can’t say that they are “better” by the same
amount.
Scale/interval/ continuous - Variables take on values that are on a uniform
scale- the distance between each point is the same. e.g. Age: Someone who is
20 is twice as old as someone who is 10.
Dichotomising continuous variables –
Continuous variables are usually more powerful than categorical variables –
(interval data is better than nominal data)
Continuous variables can be turned into categorical data – interval can go back
to nominal data or even ordinal data
The type of variable used can influence research design – some variables are set
and others can be decided on.
Two main types of quantitative design –
Investigating differences = experimental design
Investigating relationships = correlational design
Experimental design –
Research Questions – must be answerable with a study
Types of research questions –
Descriptive – what characterises the phenomenon?
Comparative – how is it related to other things?
Explanatory – what explains these relationships?
Normative – how can this phenomenon be changed?
Types of variables –
Categorical/ nominal – variables take on values that merely represent different
categories or groups. There is no order to them and they cant be ranked – e.g.
birthplaces
Ordinal - Variables take on values that are in an order or can be ranked, but
the distance between the values is not known. e.g. A-level grades. A is better
than B, B is better than C, but you can’t say that they are “better” by the same
amount.
Scale/interval/ continuous - Variables take on values that are on a uniform
scale- the distance between each point is the same. e.g. Age: Someone who is
20 is twice as old as someone who is 10.
Dichotomising continuous variables –
Continuous variables are usually more powerful than categorical variables –
(interval data is better than nominal data)
Continuous variables can be turned into categorical data – interval can go back
to nominal data or even ordinal data
The type of variable used can influence research design – some variables are set
and others can be decided on.
Two main types of quantitative design –
Investigating differences = experimental design
Investigating relationships = correlational design
Experimental design –
