Appendix: Statistical Methods
Stats are useful in the study of human behaviour
Help spot trends amid diversity
N=total number of observation or scores in a set
X= an observation or score
Sigma=Greek capital letter sigma, the sum of
Square root=the squre root of
Organizing Data
Constructing a Frequency Distribution
Frequency distribution
o How often each possible score actually occurred
Grouped frequency distribution
o Group adjacent scores into equal sized classes or intervals
o E.g. range 1-50
(1-5, 6-10, 11-15)
Graphing the Data
Bar graph/histogram
o Draw rectangles above each score, indicating the number of times it occurred
Frequency polygon/line graph
o Indicated by dot placed directly over score on horizontal axis, appropriate height
on vertical axis
Graphs exaggerate or mask differences, depending on units
Describing Data
Measuring Central Tendency
Central Tendency
o Characterize an entire set of data in terms of a single number
o Mean
Sum of scores/number of scores
o The Median
Sometimes extremely high score can dramatically raise the mean
Median more representative measure when extreme scores occur
o Mode
Score that occurs most often Measuring Variability
Tell whether scores are clustered closely around the mean or widely scattered
Range
Highest – Lowest scores
Standard Deviation
Gives on average how much scores differ from the mean
Small standard deviation tells us most scores clustered near the mean, therefore mean is
representative
Deviation Scores
o Subtract mean from each score, add up all the numbers
o Sum of scores will be zero
o √∑(X-M)^2/N
Transforming Scores
Working with scores that reveal where a person stands relative to others
Percentile Scores
Gives the percentage of people who scored at or below a given raw score
You never do better than 100 percent of a group when you are a member of the group
However only rank people do not tell how far ap
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