types of data Flashcards
define qualitative data
- data that is expressed in words & non-numerical
- may take form of written description of thoughts, feelings & opinions of participants eg. transcript from interviews, extract from a diary
examples of qualitative methods of data collection
those concerned with the interpretation of language eg. from an interview or unstructured observation
define quantitative data
- data that can be counted, usually given as numbers
- open to being analysed statistically & easily converted to graphs, charts etc.
examples of quantitative methods of data collection
usually gather numerical data in form of individual scores from participants eg. number of words person able to recall in memory experiment
describe the overlap between qualitative & quantitative data
researchers who collect quantitative data during an experiment may interview participants to gain a more qualitative insight into their experience of the investigation
define primary data
information obtained first-hand by the researcher for the purposes of the investigation
–> in psychology, it’s often gathered directly from the participants
examples of how primary data is collected
- conducting an experiment
- questionnaire
- interview
- observation
define secondary data
- data collected by someone other than the person conducting the research (already exists prior)
- data has often been subject to statistical testing & thus, significance is unknown
where would secondary data be found (examples)
- journal articles
- books
- websites
- statistical information from government eg. census
- population records
- employee absence records within an organisation
describe meta-analysis
- form of research method that uses secondary data
- refers to process where number of studies are identified which have investigated same aim/hypothesis
- results of studies can be pooled together & joint conclusion produced
- (experimental research - IV measured in same way) possible to perform statistical analysis & calculate effect size which provides overall statistical measure of difference/relationship between variables across number of studies
evaluation of qualitative data
+)
P: offers researcher more richness of detail than quantitative data
E: it is much broader in scope & gives participant/respondent opportunity to more fully report their thoughts, feelings & opinions
T: gives it greater external validity as provides researcher with more meaningful insight into participants worldview
-)
P: difficult to analyse
E: doesn’t lend itself to being summarised statistically so patterns & comparisons within/between data are hard to identify
T: conclusions often rely on subjective interpretations of researcher & these may be subject to bias - especially if the researcher has preconceptions of the findings
evaluation of quantitative data
(basically opposite to qualitative)
+)
P: simple to analyse
E/T: comparisons between groups easily drawn
+)
P: data in numerical form is more objective & less open to bias
-)
P: much narrower in meaning/detail
E/T: ‘fail’ to represent ‘real life’ = low mundane realism/external validity
evaluation of primary data
+)
P: fits the job
E/T: authentic data obtained from participants for purpose of particular investigation - eg. questionnaires & interviews can be designed to target specific information that the researcher requires
-)
P: producing primary data takes time & effort of researcher
E/T: eg. conducting an experiment takes considerable planning, preparation & resources whereas secondary data may be accessed quickly (eg. matter of minutes)
evaluation of secondary data
+)
P: inexpensive & easily accessed with minimal effort
E/T: when examining secondary data, the researcher may find the already existing necessary information & so, no need to conduct primary data collection
-)
P: substantial variation in quality & accuracy of data
E: the data may appear valuable but further investigation may show it to be outdated/incomplete & the content of the data may not directly match the researchers needs/objectives
T: which challenges the validity of any conclusions
evaluation of meta-analysis
+)
P: create a larger, more varied sample & results can be generalised across much larger populations
E/T: increases validity
-)
P: prone to publication bias (file drawer problem)
E: researcher may not select all relevant studies & choose to leave out studies with negative/non-significant results
T: conclusions will be biased as only represent some of relevant data
