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Experimental Design, Statistical AnalysisPowerPoint Presentation

Experimental Design, Statistical Analysis

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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
- Time to complete
- Number of correct response

- Examples:
- 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 = random
- N = non-equivalent groups
- C = assignment by cutoffs

Time

- Moves from left to right in diagram

Types of experiments

- True experiment – random assignment to groups
- Quasi experiment – no random assignment, but has a control group or multiple measures
- Non-experiment – no random assignment, no control, no multiple measures

Goals of design ..

- Goal:to be able to show causality
- First step: internal validity:
- If x, then y
AND

- If not X, then not Y

- If x, then y

Two-group Designs

- Two-group, posttest only, randomized experiment

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?

Three ways to estimate effect

- Independent t-test
- One-way Analysis of Variance (ANOVA)
- Regression Analysis (most general)
- equivalent

Regression Analysis

Solve overdetermined system of equations for β0 and β1, while minimizing sum of e-terms

ANOVA

- Compares differences within group to differences between groups
- For 2 populations, 1 treatment, same as t-test
- Statistic 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 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 shown
- 2x2 factorial design (2 levels of one factor X 2 levels of second factor)

Outcomes of Factorial Design Experiments

- Null case
- Main effect
- Interaction Effect

Statistical Methods for Factorial Design

- Regression Analysis
- ANOVA

ANOVA

- Analysis of variance – tests hypotheses about differences between two or more means
- Could 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

Notation

- If all sample sizes same, use n, and total N = a * n
- Else N = n1 + n2 +n3

Assumptions

- Normal distributions
- Homogeneity of variance
- Variance is equal in each of the populations

- Random, independent sampling
- Still 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

MSB

- Use sample means to calculate sampling distribution of the mean,
= 1

MSB

- Sampling distribution of the mean * n
- In example, MSB = (n)(sampling dist) = (4) (1) = 4

Is it significant?

- Depends on ratio of MSB to MSE
- F = MSB/MSE
- Probability 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 value
- For 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 table

SOURCE df Sum of Squares Mean Square F p

Task 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 freedom
- F ratios = mean square effect / mean square error
- P 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 simple
- Effect of dosage not significant
- Interaction exists between dosage and task: increase in dosage decreases performance on complex while increasing performance on simple

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