# Experimental Methods T1.txt

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 Author: Arukio ID: 175215 Filename: Experimental Methods T1.txt Updated: 2012-10-03 06:52:40 Tags: Experimental Design Folders: Description: Experimental methods and design Show Answers:

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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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