Organising and interpreting data Flashcards
what are the different types of data
primary secondary, qualitative, quanititative, objective and subjective
why do researchers use more than 1 type of data
the research findings is more robust (assesses the hypothesis more accurately)
primary data
data collected first hand by a researcher e.g experimentation, observation, study
secondary data
data sourced from others prior researcg, not directly from the current researcher e.g literature review
quantitative
data that is expressed numerically, such as test scores or measurements of weight
examples of collection of quantitative data
closed ended surveys, rating scales, multiple choice questions
qualititative data
data that is expressed non-numerically, it may be converted into quantitative data using systematic methods and analyses
collection of qualitative data
open ended questionaires and interviews
objective data
factual data that is observed and measured independently of person opinion, collected using tools that ensure that same results are obtained by different researchers
examples of objective data
amount of brain waves per second, milliseconds taken to respond to stimuli
subjective data
data that is informed by personal opinion, perception or interpretation, comes from participants own qualitative descriptions and self reports, not same for different researchers
benefits of subjective data
provides rich, qualitative, descriptions of person experience
self reports
in the form of surveys, questionarries, interviews, is subjective but can be both qualitative and quanititative
why do we process quantitative data
to make meaningful comparisons, observations, patterns, summerise organise and describe the raw unprocessed data
outliers
values that differ significantly from other values in a data set, as they make the mean a less accurate summary of the average data value
why cant outliers be removed
conformation bias
when are outliers more likely to occur
in data sets with large sample sizees
how to calculate percetage
given number/total number x 100
how to calculate percentage change
new percentage-old percentage / old number x 100
what are measures of central tendency
descriptive statictics that summarise a data set by describing the centre of the districution of the data set with a single value
three measures of central tendency
mean, median, mode
mean
average of the data set, calculated by adding up the total of all data values then dividing this total by the number of data values in the set, better when data values are distributed around a ‘centre’
when is mean less helpful
when data values are widely distributed, in which case that data set is likely to be influenced by extreme values and outliers
median
the middle value in a data set ordered from lowesr to highest, best used to identify a typical response when the data is not evenly distributed around the centre of outliers
