Analysing Data

• Analysing Qualitative Data:

  > content analysis

  > thematic analysis
• Content Analysis:

  > changes large amounts of qualitative data into quantitative, by identifying meaningful codes that can be counted so the data can be presented in a graph

  > coding is the initial process of a content analysis, where qualitative data is placed into meaningful categories
• How to conduct a Content Analysis:

  > read the transcript/watch the video

  > identify/create coding - provide an example

  > re-read the transcript or re-watch the video and tally each time a code appears

  > present the quantitative data in a graph or table
• Thematic Analysis:

  > identifies emergent themes, enabling us to present the data in a qualitative format

  > used with interview recordings, diary entries, newspapers etc
• How to conduct a Thematic Analysis:

  > read the transcript (or create a transcript if data is in another form, such as recording)

  > read and re-read the transcript (familiarisation)

  > identify coding by looking for words that appear repeatedly

  > combine these codes into three or four themes

  > present the data in a qualitative format
• Content and Thematic Analysis AO3:

  :) High reliability

  :( Researcher bias
• Assessing Reliability of Content Analysis:

  > test re-test

  > inter-rater reliability
• Improving Reliability of Content Analysis:

  > operationalising
• Assessing Validity of Content Analysis:

  > face validity

  > concurrent validity
• Improving Validity of Content Analysis:

  > operationalising

  > training researchers
• Analysing Quantitative Data - Descriptive Statistics:

  > measures of central tendency

  > measures of dispersion
• Measures of Central Tendency:

  > mean

  > median

  > mode
• Measures of central tendency - the general term for any measure of the average value in a set of data
• Measures of Central Tendency - Mode:

  > most common or popular number in a set of scores

  > there can be more than one mode

  > used with nominal data
• Measures of Central Tendency - Mode AO3:

  :) easy to calculate

  :) less prone to distortion by extreme values

  :( does not take into account all scores
• Measures of Central Tendency - Median:

  > central or middle score in a list of ranked/ordered scores

  > if there are two central scores, add together and divide by 2

  > used with ordinal data
• Measures of Central Tendency - Median AO3:

  :) easy to calculate

  :) less prone to distortion by extreme values

  :( does not take into account all scores
• Measures of Central Tendency - Mean:

  > all scores added up and divided by the total number of scores

  > mathematical average

  > used with interval data
• Measures of Central Tendency - Mean AO3:

  :) Most accurate - uses all data

  :( Affected by extreme scores
• Measures of Dispersion:

  > range

  > standard deviation
• Measures of dispersion - based on the spread of scores (how far the score varies from the mean or range)
• Measures of Dispersion - Range:

  > the spread of data from the smallest to the largest

  > calculated by subtracting the lowest value from the highest and adding 1

  > used for ordinal data
• Measures of Dispersion - Range AO3:

  :) Easy and quick

  :( Can be distorted by extreme scores
• Measures of Dispersion - Standard Deviation:

  > measure of spread around the mean - the higher the standard deviation, the more spread

  > more spread = less consistency and more individual differences

  > used for interval data
• Measures of Dispersion - Standard Deviation AO3:

  :) Most precise

  :) Less prone to distortion by extreme values

  :( More complicated and time-consuming
• Distributions:

  > shows the spread of a data set (how many standard deviations the scores are from the mean)

  > shows all the possible values in the data set and how frequently each occurs

  > displayed on a frequency graph
• Distributions:

  > normal distribution

  > skewed distribution - positive or negative
• Normal Distribution:

  > the curve is symmetrical

  > the curve extends outward but never touches 0

  > the mean, median and mode all occupy around the same mid-point on the curve
• Skewed Distribution - Positive Skew:

  > most of the data is concentrated on the left of the graph

  > the long tail is on the right side of the peak of data
• Skewed Distribution - Negative Skew:

  > most of the data is concentrated on the right of the graph

  > the long tail is on the left side of the peak of data
• Discrete data - information that can be categorised into groups

  > the data can only appear in one category
• Continuous data - data that can be measured using scientific tools

  > examples - height, weight, time
• Graphical Representations:

  > bar charts

  > histograms

  > scattergraphs
• Graphical Representations - Bar Charts:

  > used to display discrete data

  > categories will appear as words

  > used to compare conditions

  > the bars never touch
• Graphical Representations - Histograms:

  > used to display continuous data

  > represents frequencies

  > the bars always touch
• Graphical Representations - Scattergraphs:

  > used to display a relationship between two co-variables

  > represent correlations

  > each plot represents one participant, but two scores