Experimental Designs II - Assignment Example

For example, in a 2x2 factorial ANOVA with levels A1 and A2 of Factor Aand levels B1 and B2. An ANOVA test would test the significant differences between the marginal means, which are called simple Main Effects of each factor. This is because they illustrate the overall difference between the levels of each factor, independently of the levels of the other. The ANOVA also tests for the significance differences between the Cell Means; in other words, the four means relevant to the AxBinteraction effect (Jackson, 2012).

According to Jackson, the test also determines if the effects of the independent variable (IV) are independent of each other, or whether the effects of one IV depend on the level of the factor. Key effects are differences in means over levels of one factor that is collapsed over levels of the other factor (s) (Jackson, 2012). No.6 The difference between a complete factorial design and an incomplete factorial design is laid out in how experimental conditions are dealt with. A Complete Factorial Design (CFD) consists of all factors and levels of each factor, it is also capable of estimating all factors, and their interactions (Jackson, 2012).

An incomplete factorial design is arrived at when experimental conditions are removed from a complete factorial design. . No.8 The difference between a two way ANOVA and a three-way ANOVA is that a two-way ANOVA test is used when there are more than one IV requiring multiple observations for each IV. The two-way test determines the main effect contributions of each IV and indicates if there is a significant interaction effect amongst the IVs. The three way ANOVA is used to determine the effect of three nominal predictor variables that are based on a continuous outcome variable.

The three-way test evaluates the effect of the IV on the expected outcome together with their relationship in the outcome (Jackson, 2012). Random factors are considered to have no statistical impact on a given data set, unlike systematic factors that are considered to hold statistical significance. No. 10 Source df SS MS F A 1 60 60 1.420 B 2 40 20 24.170 AxB 2 90 81 0.125 Error 30 200 100 1.884 Total 35 390 261 27.599 a). Factorial notation –1x2 = 2 b). There are 2 conditions in this particular study. c). Number of subjects in the study – 3 d).

Main effect for B, no significant interaction Source df SS MS F A 2 40 20 0.85 B 3 60 18 9 AxB 6 150 130 0.867 Error 72 150 75 Total 83 400 243 15.717 a). Factorial notation – 2 x 3. b). There are 6 conditions. c). Subjects in study - 2 d). No main effects. There is a significant interaction. Source df SS MS F A 1 60 60 132 B 2 40 20 98 AxB 2 90 45 135 Error 30 200 6.67 Total 35 390 131.67 245 a). Factorial notation – 1 x 2 b). There are 3 conditions. c). Subjects in study - 2 d). Main effect for B.

No significant interaction. Source df SS MS F A 1 10 10 0.10 B 1 60 60 30 Error 36 80 40 Total 39 150 110 30.10 a). Factorial notation – 1 x 1 b). 1 condition c). Subjects in study - 1 d). Main effect for A and B. Significant