Data Analysis Flashcards
What is quantitive data?
data presented with numbers which allows for quick comparison between individuals
What is qualitative data?
data presented with words
- provides depth/detail of situation
What are strengths of using quantitative data?
- means/ranges can be calculated
- easy to enter numbers into tables/display data in graphs or charts
- precise details used
- easy to check for reliability
- easy to test for hypotheses
- easy to analyse
What is a limitation of using qualitative data?
- can be difficult/time consuming to analyse as involves looking for trends and/or categorisation
- subjective
- hard to test hypotheses
What are strengths of using qualitative data?
- allows for detailed descriptions; rich/informative data
- useful for attitudes, opinions, beliefs
What are limitations of using quantitative data?
- reduces complex behaviour to a number
- important information may be lost
What is primary data?
data collected/observed directly from first-hand experience by researcher for the purpose of their particular investigation
What are the strengths of using primary data?
- control researcher has; data collected designed to fit aims and/or hypotheses of the study
- not been altered in any way by any other researchers, reduces likeliness of investigator bias or subjectivity
What are limitations of using primary data?
- lengthy and time consuming, possibly expensive
What is secondary data?
data collected by someone other than the researcher (usually for a purpose that differs from that of the researcher)
What are the strengths of using secondary data?
- no need to design study, go through ethical committees, collect participants etc; more convenient and less expensive to obtain
- possible may have already been subjected to inferential statistical testing, known whether or not it is significant
What are the limitations of using secondary data?
- for some studies, the data will not fit the specific aims and/or hypothesis of the current researcher, may not match their needs
- may be substantial variation in the quality and accuracy of secondary data, information may appear valuable initially, but turns out to be incomplete
What is meta-analysis?
method where, rather than conducting research, primary data from other studies is re-analysed and consequently, uses secondary data - data from a large number of studies is combined
What are the strengths of using meta-analysis?
- technique is useful when a number of small studies have found contradictory or weak results as by combining the data from these studies it may be possible to identify common trends that are not noticeable in a single study
- reviewing the results from a number of studies, rather than just one, can increase the validity of the conclusions drawn as they are based on a larger sample of participants
What are the limitations of using meta-analysis?
- individual studies may have different designs so may not be truly comparable, may lead to a misleading conclusion
- it’s difficult to come up with the right criteria for accepting/rejecting studies to be part of the meta-analysis
- problem of publication bias (file-drawer problem) studies that give positive results may be over-represented in meta-analysis and any conclusions based on these studies will not take into account the studies that failed to get published
What is nominal data?
or categorical; the lowest level - measuring the frequency of occurrence in each category
What is ordinal data?
measurements place in rank order or in terms of relative position (in relation to others in the group)
What is interval data?
when the data measured on a scale are made up of equal units
What is ratio data?
same as interval; when data measured on a scale is made up of equal units BUT, ratio has a fixed 0 (no negative values) e.g. weight, height, temperature
What is the mode?
when data is arranged in numerical order and the value which occurs most frequently is identified
When/why is using the mode useful?
- for nominal data
- not affected by outliers
- can make more sense than average (e.g. for age just saying 2 rather than 2.4)
When/why is using the mode not useful
- there can be more than one mode in a set of data (data is bimodal) making it more difficult to use the mode as a summary value in the data
- does not take into account all the other values, loses a lot of information