Statistics

• What are the types of data?

  quantitative - numerical(scientific)e.g. mean

  qualitative - rich and detailed descriptions(less scientific)e.g words/pictures
• What are the strengths and weaknesses of quantitative data 


  Strength

  P: easy to make statistical comparisons objectively without the risk of biased interpretation

  C: this increases validity as we can accurately measure what what intended

  weakness

  P: tells us what has been found but not WHY behaviour happens

  C: this reduces validity as we‘re not accurately measuring what was intended
• What are the strengths and weaknesses of qualitative data

  Strength

  P: rich in detail about how people behave and shows the true nature of human behaviour

  C: increases validity as we can accurately measure what was intended

  weakness

  P: introduces opinion which must be interpreted as it is subjective

  C: reduces validity as we‘re not accurately measuring what was intended
• What are the types of data collection


  Primary - collected by the researcher to use in the current study (first hand)

  secondary - data was collected for another purpose

  (second hand)
• Strengths and weaknesses of data collection

  Primary

  strength- can be designed to fit the aims of the study

  weakness- lengthy/expensive to recruit participants, plan and carry out the studies, it takes a long time

  secondary

  strength- simpler, quicker and cheaper to use someone else’s data (statistical testing may already tell us wether data is significant)

  weakness- data may not fit the needs of the study
• Levels of data

  Nominal - separate categories e.g. Grouping people according to their heights: tall, medium, short

  ordinal - data is ordered in some way e.g. highest to lowest

  interval - measured using units of equal intervals e.g. m, cm , mm
• strengths and weaknesses of nominal data

  Strength

  easy to generate from closed questions and easy to gather large amounts of data quickly

  weakness

  can only use the mode (most frequently occurring category)
• Strengths and weaknesses of ordinal data

  Strength

  more information than nominal

  weakness

  gaps between values aren’t equal so difficult to calculate a mean
• Strengths and weaknesses of interval data

  Strength

  more information as data is directly comparable and highly reliable

  weakness

  participants can demonstrate a variable that the scale doesn’t measure
• What are the measures of central tendency 


  Mean, median, mode
• strengths and weaknesses of mean (central tendency)

  strength - most representative which reflects values of all data (sensitive measure)

  weakness - can’t be used with nominal data, unrepresentative if there’s extreme values
• Strengths and weaknesses of median

  Strength - not affected by extreme scores

  weakness - not as sensitive as mean. Doesn’t take all of the data into consideration
• Strengths and weaknesses of mode

  Strength - useful for nominal data, not affected by extreme scores

  weakness - not useful when there are several modes or no modes
• What are the measures of dispersion (spread)


  range

  variance

  standard deviation
• strengths and weaknesses of the range

  Strength - very easy to calculate

  weakness - affected by extreme values, doesn’t use all values
• strengths and weaknesses of variance

  Strength - precise measure of spread as all data is used

  weakness - may hide some characteristics ( extreme values)
• Strengths and weaknesses of standard deviation

  Strength - precise measures (all data used). Produces smaller, more useable numbers than variance

  weakness - may hide characteristics, harder to calculate than range
• Levels of data

  Histogram - interval/continuous

  line graph - interval/continuous

  pie chart - nominal
• NEVER plot raw data on a graph
• Standard deviation formula

  
• Standard normal distribution 


  E.g. IQ test

  
• Positively skewed distribution 


  E.g if an exam is too hard

  
• Negatively skewed distribution 


  E.g. If an exam is too easy

  
• How to calculate angles in a pie chart

  1)add values to get a total

  2)divide each value by total and x100 to get a %

  3)multiply % by 360 to get an angle
• to work out order of magnitude round to one significant figure

  e.g. 327 and 40,626,674

  400 and 40,000,000

  5 more zero‘s so 100,000x bigger