qualitative data
data expressed descriptively with language
quantitative data
data expressed in numerical form
strengths of qualitative data
richer detail
unexpected insights
greater external validity
weaknesses of qualitative data
difficult to analyse and spot patterns (cannot be put on a graph)
interpretation is subjective » can introduce bias
low reliability
strengths of quantitative data
simple to analyse and draw comparisons
numbers are objective » less open to bias
weaknesses of quantitative data
less meaningful than qualitative data
does not represent real life » low external validity
primary data
original, unpublished data collected by a researcher for a specific aim
examples of primary data
experiments, observations, surveys, interviews
strengths of primary data
data obtained from participants is specific to that investigation
more reliable, as researchers have control over the data collection process
weakness of primary data
requires time and effort
secondary data
preexisting data collected and published for a different research aim
examples of secondary data
books, databases, literature reviews, web research
strengths of secondary data
inexpensive
easily accessible
weaknesses of secondary data
may be outdated, incomplete or poor quality
may not match researcher’s needs
meta-analysis
statistical technique for combining the results of many smaller studies into a single larger study
when is meta-analysis useful?
when smaller studies have contradictory / unclear findings
how is data collected for meta-analysis?
researchers develop a selection criteria for what studies to include, then valid data is identified, compiled and analysed
strengths of meta-analysis
creates highly generalisable results
can identify trends where smaller studies have been contradictory
weakness of meta-analysis
suffers from publication bias » researchers are 10x more likely to publish positive results