data handling Flashcards
what is quantitative data?
-numerical
what is qualitative data?
-non-numerical
-data expresses meanings, feelings and descriptions
what do qualitative studies produce?
-subjective, detailed, less reliable data, of a descriptive nature
what do quantitative studies produce?
-objective, less detailed, more reliable data
how does qualitative data become quantitative?
-content analysis
what do each of the types of studies produce?
-experiments - mainly quantitative
-observations - quantitative
-questionnaires - both
-interviews - both
-correlational data - quantitative
-case studies - qualitative
what’s the difference between primary and secondary data?
-primary = collected by the researcher specifically for the research aim (more reliable and valid)
-secondary = data originally collected for another research aim (gives clearer insight)
what is a meta-analysis
-a statistical technique for combining multiple findings
-allows for identification of trends and relationships
-useful for when smaller studies have found contradictory or weak results
what is content analysis?
-a method of quantifying qualitative data through coding units, commonly used with media research
-coding units can involve words, themes, characters, time and space etc
straights of content analysis
-easy to perform, non-invasive and inexpensive
-compliments other methods (especially useful in longitudinal research, detecting trends over time)
-reliability (others use same units)
weaknesses of content analysis
-descriptive (doesn’t reveal underlying reasons)
-flawed results (limited available results)
-lack of causality (not performed under controlled conditions)
what is thematic analysis?
-qualitative methods for identifying, analysing and reporting themes in data
-patterns are identified through data coding
-organises describes and interprets
-identified themes become the categories for analysis
-6 stages
what are the 6 stages of thematic analysis?
1-familiarisation with the data
2-coding (that identify important features)
3-search for themes
4-review themes (check themes against the data, refine themes)
5-define and name themes
6-writing up (combining all the info)
what are descriptive statistics?
provide a summary of a set of data drawn from a sample, applies to a whole target population
-involves measures of central tendency and measures of dispertion
what are measures of central tendency?
-summaries large amounts of data into averages
-mean (mid-point)
-median (central score)
-mode (most common)
pros and cons of mean
+most accurate measure of central tendency
+uses all the data
-less useful for skewed scores
-mean score may not be an actual score in the data
pros and cons of median
+not affected by freak scores
+easier to calculate than mean
+can be used with ordinal data (Ranks) unlike mean
-not as sensitive, doesn’t use all scores
-can be unrepresentative in small data
pros and cons of mode
+less prone to distortion by extreme data
+sometimes makes more sense than the other 2
-can be more than one mode
-doesnt use all the data
what are measures of dispersion
-provide measures of the variability of scores
-range
-standard deviation
what are the pros and cons of range
+fairly easy and quick
+takes full account of extreme values
-can be distorted by freak values
-doesnt show whether data is clustered or even around the mean
what is standard deviation?
-measures the spread of scores from the mean
1-calculate mean
2-subtract the mean from each score
3-square all the scores
4-add the squared scores together
5-divide the sum of the squares by the sum of the scores minus 1 (variance)
6-calculate the square root of the variance (the standard deviation)
pros and cons of standard deviation
+more sensitive dispersion measure
+allows for interpretation of individual scores
-more complicated to calculate
-less meaningful if data isn’t normally distributed
