Types of Data

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original, unpublished data collected by a researcher for a specific aim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• may be outdated, incomplete or poor quality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