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Experimental Design, Statistical Analysis. CSCI 4800/6800 University of Georgia March 7, 2002 Eileen Kraemer. Research Design. Elements: Observations/Measures Treatments/Programs Groups Assignment to Group Time. Observations/Measure. Notation: ‘O’ Examples: Body weight - PowerPoint PPT Presentation
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Experimental Design, Statistical Analysis
CSCI 4800/6800University of GeorgiaMarch 7, 2002Eileen Kraemer
Research Design
Elements: Observations/Measures Treatments/Programs Groups Assignment to Group Time
Observations/Measure
Notation: ‘O’ Examples:
Body weight Time to complete Number of correct response
Multiple measures: O1, O2, …
Treatments or Programs
Notation: ‘X’ Use of medication Use of visualization Use of audio feedback Etc.
Sometimes see X+, X-
Groups
Each group is assigned a line in the design notation
Assignment to Group
R = randomN = non-equivalent groupsC = assignment by cutoffs
Time
Moves from left to right in diagram
Types of experiments
True experiment – random assignment to groupsQuasi experiment – no random assignment, but has a control group or multiple measuresNon-experiment – no random assignment, no control, no multiple measures
Design Notation ExampleR O1 X O1,2
R O1 O1,2
Pretest-posttest treatment
comparison group
randomized experiment
Design Notation Example
N O X O
N O O
Pretest-posttest
Non-Equivalent Groups
Quasi-experiment
Design Notation ExampleX O
Posttest Only
Non-experiment
Goals of design ..
Goal:to be able to show causalityFirst step: internal validity: If x, then y AND If not X, then not Y
Two-group Designs
Two-group, posttest only, randomized experiment
R X O
R O
Compare by testing for differences between means of groups, using t-test or one-way Analysis of Variance(ANOVA)
Note: 2 groups, post-only measure, two distributions each with mean and variance, statistical (non-chance) difference between groups
To analyze …
What do we mean by a difference?
Possible Outcomes:
Measuring Differences …
Three ways to estimate effect
Independent t-testOne-way Analysis of Variance (ANOVA)Regression Analysis (most general)
equivalent
Computing the t-value
Computing the variance
Regression Analysis
Solve overdetermined system of equations for β0 and β1, while minimizing sum of e-terms
Regression Analysis
ANOVA
Compares differences within group to differences between groupsFor 2 populations, 1 treatment, same as t-testStatistic used is F value, same as square of t-value from t-test
Other Experimental Designs
Signal enhancers Factorial designs
Noise reducers Covariance designs Blocking designs
Factorial Designs
Factorial Design
Factor – major independent variable Setting, time_on_task
Level – subdivision of a factor Setting= in_class, pull-out Time_on_task = 1 hour, 4 hours
Factorial Design
Design notation as shown2x2 factorial design (2 levels of one factor X 2 levels of second factor)
Outcomes of Factorial Design Experiments
Null caseMain effectInteraction Effect
The Null Case
The Null Case
Main Effect - Time
Main Effect - Setting
Main Effect - Both
Interaction effects
Interaction Effects
Statistical Methods for Factorial Design
Regression AnalysisANOVA
ANOVA
Analysis of variance – tests hypotheses about differences between two or more meansCould do pairwise comparison using t-tests, but can lead to true hypothesis being rejected (Type I error) (higher probability than with ANOVA)
Between-subjects design
Example: Effect of intensity of background
noise on reading comprehension Group 1: 30 minutes reading, no
background noise Group 2: 30 minutes reading,
moderate level of noise Group 3: 30 minutes reading, loud
background noise
Experimental Design
One factor (noise), three levels(a=3)Null hypothesis: 1 = 2 = 3
Noise None Moderate High
R O O O
Notation
If all sample sizes same, use n, and total N = a * nElse N = n1 + n2 + n3
Assumptions
Normal distributions
Homogeneity of variance Variance is equal in each of the
populations
Random, independent samplingStill works well when assumptions not quite true(“robust” to violations)
ANOVA
Compares two estimates of variance MSE – Mean Square Error, variances
within samples MSB – Mean Square Between, variance
of the sample means
If null hypothesis is true, then MSE approx = MSB, since
both are estimates of same quantity Is false, the MSB sufficiently > MSE
MSE
MSB
Use sample means to calculate sampling distribution of the mean,
= 1
MSB
Sampling distribution of the mean * nIn example, MSB = (n)(sampling dist) = (4) (1) = 4
Is it significant?
Depends on ratio of MSB to MSEF = MSB/MSEProbability value computed based on F value, F value has sampling distribution based on degrees of freedom numerator (a-1) and degrees of freedom denominator (N-a)Lookup up F-value in table, find p valueFor one degree of freedom, F == t^2
Factorial Between-Subjects ANOVA, Two factors
Three significance tests Main factor 1 Main factor 2 interaction
Example Experiment
Two factors (dosage, task)3 levels of dosage (0, 100, 200 mg)2 levels of task (simple, complex)2x3 factorial design, 8 subjects/group
Summary tableSOURCE df Sum of Squares Mean Square F pTask 1 47125.3333 47125.3333 384.174 0.000 Dosage 2 42.6667 21.3333 0.174 0.841 TD 2 1418.6667 709.3333 5.783 0.006 ERROR 42 5152.0000 122.6667 TOTAL 47 53738.6667
Sources of variation: Task Dosage Interaction Error
Results
Sum of squares (as before)Mean Squares = (sum of squares) / degrees of freedomF ratios = mean square effect / mean square errorP value : Given F value and degrees of freedom, look up p value
Results - example
Mean time to complete task was higher for complex task than for simpleEffect of dosage not significantInteraction exists between dosage and task: increase in dosage decreases performance on complex while increasing performance on simple
Results