Types of data

quantitative data

numerical data that can be statistically analysed and converted easily into a graphical  format. Experiments, structured observations, correlations and closed/rating‐scale questions from  questionnaires all produce quantitative data. 

strength of quantitative data

easy to analyse statistically - easy to conduct descriptive statistics or inferential tests that allow comparisons and trends to be made between groups

objective

limitation of quantitative data

lack of representativeness - found from closed questions, narrow responses, lacks validity

qualitative data

non‐numerical, language‐based data expressed in words which is collected through  semi‐structured or unstructured interviews and open questions in a questionnaire. It allows researchers to  develop an insight into the unique nature of human experiences, opinions and feelings. 

strength of qualitative data

rich detail obtained - increases external validity

limitation of qualitative data

subjective. Interpretations of the rich detailed research can only be relied on by the opinions and judgements of the researcher - bias

Hard to analyse - hard to find trends and make comparisons

primary data

data that has been collected for a specific reason and reported by the original researcher
it is data that the participant reports directly to the researcher (often an interview/questionnaire) or is witnessed first-hand (via an observation/experiment)

aka ‘field research’

strength of primary data

authentic because it is collected with the sole purpose of being for a specific investigation

limitation of primary data

time consuming and lots of effort

secondary data

information collected by other researchers for a purpose other than the investigation in which it is currently being used - already exists

aka ‘desk research’

strength of secondary research

less time consuming as the data already exists

limitation of secondary data

may not be accurate because the data has not been gathered for that specific investigation

meta analysis

investigators combine findings from multiple studies on a specific phenomenon to make an overall analysis of trends arising across researchst

strength of meta analysis

larger sample for conclusions to be drawn from - easier to generalise, increasing validity

limitation of meta analysis

bias since the researcher is selecting data from research that already exists

Publication bias