PSYC 3010 QUESTIONS AND COMPLETE SOLUTIONS

1. Factorial Design - Has at least two factors (IVs), each with at least two levels - Two IVs can be examined simultaneously 1. Advantages of Factorial Design - More economical in terms of participants - Allows us to examine the interaction of independent variables (assess generalisability) 1. Interactions in Factorial Designs - One IV interacts with another when the effects of one are different depending on the level of the other - And when it changes (moderates or qualifies) the impact of a second IV on the DV 2. Variance - "Dispersion or spread of scores around a point of central tendency, e.g. mean" - Error Variance: cannot be explained; should go up with more observations - Treatment Variance: systematic differences due to our IV 2. Three Questions of Two-Way ANOVA 1. Variance due to factor A? (df a-1) 2. Variance due to factor B? (df b-1) 3. Variance due to AxB interaction? (df(a-1)(b-1)) 2. Structural Model of 2-way ANOVA Xijk = mew. + aj + Bk + aBjk + eijk - X (Specific DV) e.g. height, age, gender - mew. - the grand mean (e.g. 1.5m) for IV - aj - the effect of the j-th treatment of factor A (e.g. effect of being male or female) - Bk - the effect of the k-th treatment of factor B (e.g. effect of age) - aBjk - effect of differences in factor A treatments at different levels of factor B treatments (interaction between age and gender) - eijk = error for i person in the j-th and k-th treatments (anything left over after main effects are removed which is not error is due to the interaction) 2. Variance and Significance The more variability attributable to the effects, the more significant they are 2. Assumptions of ANOVA - Population: normally distributed (normality) and have the same variance (homogeneity of variance) - Samples: Independent; obtained by random sampling; at least two observations and equal n - Data (DV Scores): measured on continuous scale for mathematical operations (mean, SD, variance)