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Chapter 12

PSYB01 Chapter 12

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Anna Nagy

PSYB01 - Chapter 12 Understanding Research Results: Description and Correlation Two reasons for statistics: 1) Statistics are used to describe data 2) Used to make inferences on data Scales of Measurement: A Review  Variable must have operational definition + Have 2/more levels of variable .  Levels of variables can be described using four scales: 1) Nominal 2) Ordinal 3) Interval 4) Ratio  Scale used determines types of statistics that appropriate when results of study are analyzed .  Level of nominal scale variable: Have no numerical, quantitative properties .  Levels are simply different categories / groups .  Eye colour, hand dominance etc.  Variable with ordinal scale level: Minimal quantitative distinctions .  Rank order level of variable from lowest to highest .  Internal and ration scale variable: More detailed quantitative properties  Internal scale variable: Intervals between level are equal in size . - Generally have 5/more quantitative levels . - No absolute zero point, no absence of mood .  Ratio scale variable: Equal interval, have absolute zero points . - e.g. time, weight, length . Three ways in describing results: 1) Comparing group percentage:  e.g. 60% of female like to travel, 40% of male do not . 2) Correlate scores of individual on 2 variables:  Do not need distinct groups of subjects, measured on 2 variables with numerical value. 3) Comparing group means:  Compare the mean responses of participants . .  Frequency Distribution: Indicate # of individual that receive possible score on variable .  e.g. how many students receive score on variable .  Pie chart: Useful when represent nominal scale .  Bar graph: Useful when represent nominal scale .  Frequency polygons: Useful when represent interval/ratio scales . - Use line to represent frequencies .  Histogram: Uses bars to display frequency distribution for quantitative variable .  Able to indicate any outliers .  Descriptive statistics: Allow to make precise statements about datas .  Two statistics are needed to describe the data .  Used to describe Central tendency: How participants scored overall .  Used to describe Variability: How widely the distribution of score is spread .  Central Tendency: Mean, median, mode .  Mean: Appropriate when score are measured on interval or ratio scale .  Median: Appropriate when score are measured on ordinal scale, take accounts of rank order .  Useful with interval and ratio variables .  Mode: Most frequent score, Appropriate when score are measured on nominal scale .  Median/Mode can be better indicator than mean if few unusual score bias the mean .  Variability: Using Range/ Standard deviation, average deviation of scores from mean .  Appropriate when score are measured on interval and ratio scale .  Effect size: Refers to strength of association between variables .  Value range from 0.00 to 1.00 (Small=0.10-0.20 , Medium=0.30, Larger= above 0.40) .  For experiment more than 2 variables/conditions .  r2 : Percentage value represents % of variance in one variable, that account for second variable .  Criterion variable: In predicting future behavior .  Predictor variable: On basis of person’s score on other variable .  Used in Regression equation = Y= a+bX .  Multiple correlation: Used to combine #
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