Please enable JavaScript.
Coggle requires JavaScript to display documents.
Research methods: Data handling and analysis - Coggle Diagram
Research methods: Data handling and analysis
Thematic analysis
is an identification of themes through examining data - examine the data and identify key themes then report these themes in results and extracts from the data will support the existence of the themes
Types of data
Secondary data
= Information that has already been collected by someone else or for a different purpose (meta analysis)
P - It is inexpensive and easily accessed requiring minimal effort
L - There may be some variation and a lack of accuracy in the data - may be outdated or incomplete
Primary data
= Information that has been obtained first-hand by the researcher for the purpose of the investigation - gathered directly from ppts
S - It is authentic data obtained from the ppts themselves for the purpose of a particular experiment so it targets the information the researcher requires
L - Requires time and effort - requires planning, preparation and resources
Meta-analysis
= Combining the findings from a number of studies on a particular topic to create an overall conclusion
Measurements of central tendency
Mean
= The average calculated by adding up all the values in a set of data and dividing the number by the number of values
P - Takes into account all values to calculate the average
L - Very small and large values can affect the mean
Median
= The central value in a set of data when values are lowest to highest
P - Median is not affected by very large or small values
L - If there is an even number of numbers it is found by averaging the two middle numbers so may not actually exist as a score in the data
Mode
= Most frequently occurring value in a set of data
P - The mode can be used if the data set is nominal
L - Can be more than one mode and all values can be modal so not always representative of the data
Measures of dispersion:
Range
= The difference between the highest and lowest values (subtract high from low)
P - Easy to calculate
L - Affected by extreme values
Standard deviation
= To what extent the values in a data set deviate from the mean
P - Shows whether or not data is clustered around the mean and not affected by outliers
L - Difficult to calculate and does not show full range of data
Distribution
Positive skew
= The long tail is on the positive (right) side of the peak and most of the distribution is concentrated on the left side of the (mode at highest peak, mean pulled to right, median in-between)
Negative skew
= Long tail on negative (left) side of the peak and most of the distribution is concentrated on the right (mode is highest, median is in the middle and the mean is pulled to the left)
Normal distribution
= A symmetrical spread of frequency data that forms a bell-shaped pattern - the mean, median and mode are all located at the highest peak
The tails of the curve never touch the horizontal x-axis as more extreme scores are always possible
Quantitative data
= Data that can be counted, usually given as numbers
Can be presented through graphs, tables, scattergrams, bar charts and histograms
S - Simple to analyse so comparisons between groups can be easily drawn
S - Data is numerical so tends to be more objective and less open to bias
L - The data is narrower in meaning and detail and may not represent real life
Qualitative data
= Data expressed in words and non-numerical
S - Offers more detail and is broader in scope giving the ppt the chance to fully report their feelings, thoughts and opinions of a given subject
S - Greater external validity as it provides the researcher with a more meaningful insight into the ppts worldview
L - Difficult to analyse as patterns and comparisons within and between data may be hard to identify
L - Conclusions often rely on subjective interpretations and may be subject to bias esp if the researcher has preconceptions about what they are expecting to find out
Content analysis
= A coding system where categories are used to analyse data before the experiment
To covert qualitative data into quantitative data for easier interpretation
1) Data is collected
2) Researcher reads through ir examines data making themselves familiar with it
3) The researcher identifies the coding units
4) The data is analysed by applying the coding units
5) A tally is made of the number of times that a coding unit appears
Limitations:
Subjective/researcher bias - Coding and identifying themes involve interpretation so different researchers may reach different conclusions unless coding is clear
Loss of context - By reducing data into codes the full meaning or intention behind words or actions may be lost
Low reliability if not standardised - If coding rules are not well defined other researchers may not produce the same results making replication difficult
Strengths:
High ecological validity - studies real-life material so findings are more reflective of natural behaviour
Can be qualitative and quantitative- Allows researchers to explore meanings and also count how often something appears
Unobtrusive method - uses existing data so ppts are not directly involved - no risk of demand characteristics or ethical issues
Useful for tracking trends over time - Researchers can analyse how topics have changed by comparing media from different decades - reliable as coding units are not open to interpretation
Scattergram
= A type of graph that represents the strength and direction of the relationship between co-variables in a correlational analysis
Histogram
= Shows frequency but the are of the bars represents the frequency and bars touch each other
Bar chart
= The frequency of each variable is represented by the height of the bars - used when data is divided into categories (discrete data)
Correlation co-efficient
= A number between -1 and +1 that represents the direction and strength of a relationship between co-variables
The closer the co-efficient is to +1 or -1 the stronger/weaker the relationship is
Levels of measurement:
Nominal - categorised data (City of birth)
Ordinal - categorised and ranked (Language ability)
Interval - categorised, ranked and evenly spaced (Test scores)
Percentage
= Divide the smaller number by the larger number and then multiply the result by 100