Research Methods (cont) Flashcards
content analysis, assessing and improving reliability and validity, probability and significance, features of science, descriptive statistics and display of quantitative data
content analysis
way of analysing and transforming qualitative data into quantitative data
secondary source content e.g. adverts, films, diaries, transcribed verbal communication
ways of categorising data
(content analysis)
top-down
- pre-defined categories before research
bottom-up
- allow categories to emerge from content
- read/watch first, then again
- provides more detail, won’t miss important themes
quantitative analysis
(content analysis)
create coding system and tally each time a behavioural category occurs
should be pre-defined and clearly operationalised
- less subjective, limits misinterpretation
statistical analysis can then be carried out
makes more scientific, reliable, valid
quantitative analysis process
(content analysis)
data collected
examines data to familiarise (bottom-up)
identify coding units
data analysed by applying coding units
tally of each tome coding unit appears
qualitative (thematic) analysis
(content analysis)
familiarise with data
generate initial codes
search for initial emerging themes (lots of different codes to sort in to themes)
review themes, collapse into each other, cross over
define and name
write up
content analysis strengths
reliable
easily replicated, standardisation of coding units and pre-existing secondary data
coded more than once (intra-rater reliability) or by different researchers (inter-rater reliability)
can check for consistency
but, subjectivity may affect findings, define codes differently, decreasing consistency
content analysis strengths
ethical
already in public domain (no privacy issues)
does not involve direct use of participants
less ethical guidelines that may restrict research
but, still some ethical considerations
researcher needs to ensure they have consent of stakeholders to analyse confidential material e.g. medical records
can be difficult - make some topics difficult to investigate
content analysis weaknesses
prone to subjective analysis
involves interpreting qualitative data from secondary sources alongside a coding system
affected by gender or cultural background of researcher
prone to researcher bias
mean
interval / ratio data
adding all scores and dividing by number of scores
if fairly even distribution around centre
mean strengths
accurate and sensitive measure
takes all numbers into consideration
highly representative
is numerical centre point of actual values
used to calculate standard deviation
mean weaknesses
can be skewed by anomalies
rogue scores can significantly increase or decrease mean score
not representative
not always an actual score (e.g. 2.5 children)
not accurate reflection of data set
median
ordinal data (or higher)
middle score when data in ordered list, middle scores averaged
when extreme high or low scores
median weaknesses
may not be an actual score
not representative
not appropriate in small data sets or when there are large differences
median strengths
unaffected by extreme scores
only concerned with middle
more accurate and representative
quick and easy to calculate
mode
nominal data
most common score
can be bi-modal or multi-modal if multiple common scores
least meaningful, especially when multiple modes
mode strengths
unaffected by extreme scores
more representative
always an actual score
accurate representation
mode weaknesses
sometimes doesn’t have a mode or has many
limited usefulness
doesn’t use all data
accuracy questioned
measures of central tendency
how close scores are to average
mean
median
mode
measures of dispersion
how spread out scores are
provide fuller picture
range
standard deviation
analyse how far away scores are from average responses e.g. spread or variability
normally large dispersion is due to individual differences or poor experimental control
range
ordinal data
difference between highest and lowest score
subtract lowest from highest score
range strengths
easy and simple to calculate
takes into account extreme values
range weaknesses
ignores most of data
doesn’t reflect true distribution
easily distorted by extreme values
standard deviation strengths
precise as all values accounted for
accurate representative of distribution
detailed conclusions made
allows for interpretation of individual scores in terms of how it falls from the mean (130 IQ = 2 SD away from the mean)
standard deviation
interval / ratio data
indicates average distances of scores around the mean
takes every score into account
larger SD, more spread out relative to the mean
measures collectively how much individual scores deviate from the mean, presenting this as a single number –> how much data is dispersed