Experimental Methods T1.txt

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Author:
Arukio
ID:
175215
Filename:
Experimental Methods T1.txt
Updated:
2012-10-03 06:52:40
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Experimental Design
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Experimental methods and design
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  1. Four Basic Canons of Science
    • Determinism: Events have meaningful, systematic causes.
    • Empiricism: The method of making observations. (Making observations is the best method.)
    • Parsimony: If we have two competing theories, we should choose the simpler or more frugal of the two.
    • Testability: You must be able to realistically test the theory (involves Validation, Falsification & Qualification).
  2. Quasi-Experiment
    Naturally occurring grouping variable, but analyzed like an experiment.
  3. Independent variables vs. grouping variables
    • Variable that is manipulated by experimenter
    • In a Quasi-experiment, called the grouping variable (GV)
  4. Scales of Measurement: NOIR
    •  Nominal: – Numbers are names only, no real order.
    • – Use Frequencies and Chi-Square

    • Ordinal:– rank ordered, but don’t know how far apart scores are.
    • Sprite is favorite, coke is 2nd

    •  Interval:– Tells how far apart values are, but no true 0 point. Equal interval between units.
    • Sprite>Coke>>>>Pepsi>>>>>>>Tea

    •  Ratio: – like interval but with a true 0 point
    • Temperature
  5. Descriptive Statistics
    • Measures of Central tendency: Mean, median, mode
    • Measures of Variability: Range, variance, standard deviation
    • Range is the difference between the largest and smallest value
    • Variance is the average squared deviation of each score from the mean
    • Standard deviation is the square root of the variance.
  6. Formulas
  7. Type I and Type II errors
    Type I error: a true null hypothesis is rejected (false positive)

    Type II error: Failing to regect a false null (false negative)
  8. Factors, levels, and between/within- subjects
    designs
    • Factors: # of IVs
    • Levels: Treatment conditions PER IV
    • Between/Within Group: Between- People are in different groups. Within- People test out all groups and compare to self.
  9. Generalizability
    • Statistical generalizability: Allows you to generalize to the population from which you randomly selected.
    • Practical generalizability: You can generalize to similar individuals (i.e., college students)
    • Situational Generalizability: Can findings from the lab be applied to real life? How is the research setting different from other settings? How were the variables (IV’s & DV’s) operationalized?
  10. Grouping variable
    • Typically categorical
    • Male, female
    • 1, 2, 3, 4
    • etc
  11. The Values
    • T= Grand Total
    • t= # of grand totals (always 1)
    • A= Total for each condition
    • a= # of levels for the IV
    • AS= Individual scores
    • s= # of participants per condition
    • as= Total # of participants

    • [T]=T2/as
    • [A]=A12+A22/s
    • [AS]=AS2

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