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Experimental Design

Experimental Design. Internal Validation. Experimental Design. I. Definition of Experimental Design. II. Simple Experimental Design. III. Complex Experimental Design. IV. Quasi-Experimental Design. V. Threats to Validity. Experimental Design. I. Definition of Experimental Design.

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Experimental Design

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  1. Experimental Design Internal Validation

  2. Experimental Design I. Definition of Experimental Design II. Simple Experimental Design III. Complex Experimental Design IV. Quasi-Experimental Design V. Threats to Validity

  3. Experimental Design I. Definition of Experimental Design Control over the sequence and proportion of the independent variable involving: 1) at least two conditions (i.e. an independent variable); 2) random assignment of subjects to conditions; and 3) the measurement of some outcome (i.e. dependent variable)

  4. 1. Post-test Control Group Designs (t-test) E R X -> O1 C R -> O2 Experimental Design II. Simple Experimental Design Example 2. Pre-Post-test Control Group Designs (t-test) E R O1 X -> O3 C R O2 -> O4

  5. Experimental Design II. Simple Experimental Design 3. Soloman Four Group Design (t-test) E1 R X1 -> O1 C1 R -> O2 E2 R O1 X1 -> O3 C2 R O2 -> O4 4. Analysis of Variance (ANOVA) E1 R X1 -> O1 E2 R X2 -> O2 E3 R X3 -> O3 Example

  6. Experimental Design • Complex Experimental Design (Factorial Designs) uses ‘Two Way Analysis of Variance’ 1. Completely Randomized Designs (CRD) (This example is a 2x3 CRD) C-E1C-E2 C-E3 R-E1 O11 O12 O13 R-E2 O21 O22 O23 Main Effects Interaction Effects

  7. Experimental Design III. Complex Experimental Design (cont.) • Incomplete Designs (IRD) Split Plot Design (This example is a 2x3 SPD) • C-E1C-E2C-E3 • R-E1 - O12 O13 • R-E2 O21 O22 - • Repeated Measures Designs (RMD) • Latin Square Design( This example is a 4x4 LSD) • O1 O2 O3 O4 O2 O3 O4 O1 O3 O4 O1 O2 O4 O1 O2 O3

  8. Experimental Design IV. Quasi-Experimental Design 1. One Shot Case Study E O1 X ->O2 2. Non-Equivalent Control Group Design E O1 X -> O3 C O2 -> O4 3. Interrupted Time-Series Design E O1 O2 O3 X O4 O5 O6

  9. Experimental Design V. Threats to Validity 1. History = confounding of IV over time 2. Maturation = age / experience contaminate 3. Testing = subjects come to understanding IV 4. Regression to the Mean = extreme scores regress 5. Selection of Participants = non-random assignment 6. Mortality = subject attrition 7. Diffusion of Treatments = lack of control group Back to the Beginning End Presentation

  10. Step 1 State the hypotheses: Ho: = ; H1: < Two Sample t-test Problem: Suppose you wanted to know if students who work (the experimental condition) take fewer units than students who do not (the control condition). If a sample of 25 working students yielded a mean of 12 units with an unbiased standard deviation of 3 units and 25 who do not work took an average 15 units with an unbiased standard deviation of 4 units, could you conclude that the population of students not working take significantly more units? Step 2: Specify the distribution: (t-distribution) Step 3: Set alpha (say .05; one tail test, N>30, therefore t= 1.65) Step 4: Calculate the outcome: Step 5: Draw the conclusion: Reject Ho: 3.0 > 1.65 Working students take significantly fewer units. Back

  11. Step 1: State hypotheses: Ho: 1 = 2 = 3; H1: Ho is false Multiple Sample Test (ANOVA) Problem: Suppose your instructor divides your class into three sub-groups, each receiving a different teaching strategy (experimental condition). If the following test scores were generated, could you assume that teaching strategy affects test results? Step 2: Specify the distribution: (F-distribution) Step 3: Set alpha (say .05; therefore F = 3.89) Step 4: Calculate the outcome: Grand Mean = 150 Step 5: Draw the conclusion: Retain Ho: 1.54 < 3.89 Type of instruction does not influence test scores. Back

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