data handling Flashcards
strength of quantitative data
- Tends to be collected through objective measures.
- Tends to be highly reliable.
- Can be analysed through inferential statistics.
- scientific.
- Easy to analyse.
weaknesses of quantitative data
- Method of measurement may limit participant’s response which means that it may lack validity.
- Can lack data.
- Can lack construct validity – may not capture the complexity of what is happening e.g. human emotion.
strengths of qualitative data
- Data can be very valid – often allows for participants to freely express themselves.
- Less likely for key information and observations to be ‘lost’ when simplifying down data.
weaknesses of qualitative data
- Data can be very subjective, as data may be processed through interpretation of the participant’s responses.
- Data is individual so can be hard to generalise.
- Different to analyse.
primary and secondary data
- Primary data - any data which is collected directly from the participants by the researcher
- Secondary data - data which has already been gathered by someone else other than the researcher
raw data tables
- used as it is a clear but quick way to record the scores for each person within each condition or when when recording how many word are remembered on both white and red paper
3 measures of central tendency needed
- mean
- median
- mode
mean
def = The average of a piece of data.
calculated = Adding up all of the scores and dividing this total by how many scores there were.
advantage = More sensitive than the median, as it makes use of all of the values within the data.
disadvantage = Can be misrepresentative if there is an extreme variable.
median
def = The middle of a piece of data.
calculated = Placing all of the scores within the data into a set (often smallest to largest) and finding which score is in the middle of the list.
advantage = it is not affected by extreme values, so can give a representative value.
disadvantage = Less sensitive than the mean, as it doesn’t take into account all of the values.
mode
def = the most frequent score within data.
calculated = Categorise results that have similar properties (e.g. same score on a test) and find the category with the most scores.
advantage = Data in categories can be useful.
disadvantage = It is not a useful way of describing data when there are several modes.
measures of dispersion
- range
- variance
- standard deviation
range
- Find the largest and smallest value. Subtract the smallest value from the largest then add 1.
- advantage = Quick and easy way of getting an idea of how dispersed the results gathered are e.g. small range = similar results.
- disadvantage = Is sensitive due to it being easily affected by an extreme value within the results.
variance
- Calculate the mean score per condition in the experiment, For each participant you then subtract the mean score from their score. This is ‘d’ (the difference), Then, you square each ‘d’ score. (d2), Finally, you calculate the mean of these d2 scores
- advantage = Representative as every value within the data is used. Not sensitive to extreme values.
- disadvantage = Final answer is a squared number and therefore not in the same units are the original data used.
standard deviation
- Workout the variance using the technique above, Calculate what the square root of the variance is.
- advantage = Representative as every value within the data is used. Not sensitive to extreme values.
- disadvantage = Time consuming and more difficult to calculate than the range score
levels of data
- nominal
- ordinal
- interval
