levels of data ect....

• the levels of measurements:

  1. nominal
  2. ordinal
  3. ratio
  4. interval
• nominal data shows categories
• ordinal data is data that can be placed in ascending or descending order
• ratio data is measured on a numerical scale that has equal intervals and a true zero
• interval data is measured on a numerical scale that has equal intervals but can measure below zero
• graphical representations:

  • bar charts
  • scatter graph
  • histogram
  • line graph
  • pie chart
• bar chart shows the differences in the categories of data
• scatter graph shows relationships between co-variables
• histogram shows how grouped data is spread across the continuous scale
• line graph shows how a variable changes often over time
• pie chart shows how measures from distinct groups show s a proportion of the total scores
• distribution refers to how spread out ppts scores are over the measuring scale or how far they have dispersed
• normal distribution is a bell shaped curved graph where there is a symmetrical spread of scores before and after the mean
• positive skew shows most scores have low values however there is a tail end of very high scores which means the mean is right of the mode and median
• negative skew show most scores have high values with a tail of low scores so the mean is left of the mode and median
• in normal distribution the mode mean and median are generally in the same place and the dispersion is consistent and can be expressed as standard deviation
• standard deviation is a number which tells you how spread out the data is from the mean . the lower = the closer to the average and can only be used with normal disribution
• descriptive statistics are the measures of central tendancies and dispersion
• measures of central tendencies are the mean , mode and median
• advantages of mean:

  • use for further statistical analysis
  • appropriate to use with ordinal , interval and ratio data
• disadvantages of mean

  • affected by extreme scores
  • may produce a value that no ppt has achieved
• advantages of mode

  • unaffected by extreme scores
  • can be used with nominal data
• disadvantages of mode

  • ignores values by looking at the frequency so it may led to a biased representation
  • several modes- bimodal
• advantages of median

  • uses all values but is not as biased by extreme scores then the mean
  • used with ordinal data
• disadvantages of median

  • more open to bias then the mode
  • unhelpful for further statistical analysis
• range advantages

  • easier to calculate then standard deviation
  • takes into account extreme scores
• range disadvantages

  • affected by extreme scores
  • no info on how far the scores are spread around the mean
• standard deviation advantages

  • more precise information as it reflects each score
  • extreme scores have less of an impact
• standard deviation disadvantages

  • cant be used with nominal data
  • cant be used if the data is postively or negatively skewed
• calculating standard deviation:

  1. calculate the mean of the scores
  2. subtract the mean from each score
  3. square each answer
  4. add up the numbers
  5. divide the total by the number of scores-1
  6. square root answer
• probablity level = a numerical measure of the likelihood that something could happen
• significance level = the level of probability that the difference/relationship occurred by chance
• observed value = the number calculated using the statistical test
• critical value = the value which the observed value is compared with to see if it is significant
• inferential statistics

  1. select appropriate significance level
  2. identify inferential test - difference, data , design (no ric)
  3. calculate observed value
  4. compare with critical value
  5. accept or reject research and null hypothesis