Types of Data:
Qualitative –
Expressed in words eg. diary extract
Pros: easy to interpret/analyse, very detailed, high external validity
Cons: biased, harder to graph, not objective
Quantitative –
Data in the form of numbers eg. score
Pros: objective, less biased/subjective
Cons: less detailed, lower external validity
Primary –
Information that has been obtained first-hand by the researcher for the
purposes of a research project, gathered directly from participants
Pros: accurate/specific, recent, first hand
Cons: time consuming
Secondary –
Information that has already been collected by someone else (‘desk research’),
may be in journal articles, books, online, govt. records etc.
Pros: quick and easy, provides overview of lots of different research
Cons: less relevant/specific, outdated
Meta-analysis –
Secondary data from many studies with the same research questions and
methods of research is combined
Pros: can generalise data and let us view data with more confidence
Cons: publication bias (eg. file drawer effect) are big problems, leading to bias,
large effect size (overall statistical measure of relationship across variables in a
meta-analysis – overall finding)
Qualitative –
Expressed in words eg. diary extract
Pros: easy to interpret/analyse, very detailed, high external validity
Cons: biased, harder to graph, not objective
Quantitative –
Data in the form of numbers eg. score
Pros: objective, less biased/subjective
Cons: less detailed, lower external validity
Primary –
Information that has been obtained first-hand by the researcher for the
purposes of a research project, gathered directly from participants
Pros: accurate/specific, recent, first hand
Cons: time consuming
Secondary –
Information that has already been collected by someone else (‘desk research’),
may be in journal articles, books, online, govt. records etc.
Pros: quick and easy, provides overview of lots of different research
Cons: less relevant/specific, outdated
Meta-analysis –
Secondary data from many studies with the same research questions and
methods of research is combined
Pros: can generalise data and let us view data with more confidence
Cons: publication bias (eg. file drawer effect) are big problems, leading to bias,
large effect size (overall statistical measure of relationship across variables in a
meta-analysis – overall finding)
