different ways to analyse data, study's, etc Flashcards
meta analysis
-method of analysis data which produces an effect size
-examines data from a number of independent studies in the same subject to determine overall trends
effect size
quantitative measure of studies effect
case study
the detailed study of a single individual, institution or event using info from a range of sources e.g. family friends or person concerned
longitudinal (case studies)
follow group over extended amount of time
what observation is content analysis
indirect, observing the individual from the artifacts they produce e.g songs, books, paintings
meta analysis strengths
-reviewing from range of studies increases validity
-reduces contrast in studies by producing statistics
meta analysis weaknesses
-research designs in diff studies may vary meaning u cant truly compare them
-so arent always valid
case study strengths
-rich in depth data
-overlooked data likely to be identified
-used incases where experiments arent ethical e.g how respond to certain events
case study weaknesses
-difficult to generalise data
-as it is identified after the event we cannot be sure the apparent changed weren’t present originally
content analysis strengths
-based on what people actually do, real communications that are current and relevant
-high ecological validity
-when sources are obtained, findings can be replicated
content analysis weaknesses
- observer bias may reduce objectivity and validity of findings
-diff observers interpret the meaning of behavioural categories differently (e.g anger)
-lack internal validity
types of extraneous variables
demand characteristics, investigator effects, situational variables, participant variables
demand characteristics
if participant knows/guesses the experiment and changes their behaviour
investigator effects
any aspect of the researcher’s behaviour, appearance or gender that could affect participant responses
situational variables
features of a research situation that may influence participants behaviour e.g. order effects, heat, time of day
participant variables
differences between participants (e.g. IQ, age)
confounding variable (not extraneous)
variables that interfere with the effect of the IV and the DV
extraneous variable
variable that only effects the DV
content analysis
a method used to analyse qualitative data
benefits of content analysis
-high ecological validity
-high mundane realism
-analysis can be repeated so reliable
weakness of content analysis
-big culture bias and interpretation of verbal or written content affected by language and culture of observer
-observer bias- affects objectivity and validity
how to deal with validity in content analyses
researcher needs to ensure sample is representative
-use a double blind technique
how to deal with issues of reliability
-test retest (another researcher retests analysis)
-inter observer reliability (two or more observe same artefacts)
-training observers in use of coding system through practice
what average goes with nominal data
mode
Nominal data
categorical data- discrete and mutually exclusive
Ordinal data
ordered in some way but the intervals aren’t known/ not equal, lack of objectivity as its based on how you rate it
what average is used with ordinal data
the median
interval data
similar to ordinal data but we know the size of the difference e.g. Time in a race- objective and scientific
average used with interval data
the mean
similar to interval but has clear definition of 0- when 0 means none of that data e.g. temp
ratio data
limitation of nominal
overly simplistic- no measure of dispersion (spread of data)
limitation of ordinal data
intervals aren’t equal- an average cannot be used as a measure of central tendency
limitation of interval data
intervals are arbitrary- e.g. 100c is not 2x 50c
arbitary-
based on random choice or personal whim, rather than any reason or system.
strength of quantitative data
-easy to analyse statistically
-more objective
disadvantage of quantitative data
-lacks representativeness since its generated from closed questions answers which are narrow
-lack meaning and context
-not representation of true life, lack validity
strength of qualitative data
-rich detail
-can develop answers so high external validity
limitation of qualitative data
-subjective
-interpretations of data can rely on opinions- bias on conclusions drawn
strength of primary data
-authenticity/ specificity
-data generated will fit aims of experiment, reduce time wasted on checking the data is relevent
weakness of primary data
-designing and carrying out psychological study can take a lot of time and effort
-equipment needs purchasing so expensive
strength of secondary data
-less time consuming
-easier
