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24 Cards in this Set
- Front
- Back
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Factorial Analysis of Variance (ANOVA)
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If the between groups variance is larger than the within groups variance, some of the between groups variance may be due to a treatment effect. The statistical analysis that allows you to compare the between groups variance to the within groups variance and thereby determine whether the treatment had an effect is called the analysis of variance (ANOVA).
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Repeated Measures (Within Groups) Design
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Each participant gets all levels or types of the treatment that the experimenter administers, and the participant is measured after receiving each level.
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Independent Groups Design
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Each participant in each group receives only one condition of the independent variable.
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Post hoc
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Looking at the ANOVA for patterns that were not considered before the test. Post hoc uses a correction for inflated type one error rates, and are flexible and can have many comparisons.
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Familywise Error Rate
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The probability of at least one Type 1 Error occurring in a family of comparisons. The more the comparisons, the higher the familywise error rate. .5 x comparisons.
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the mean squared estimate
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A ratio of the sum of squares and the degrees of freedom
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omnibus test
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A test which sees if the explained variance in data is significantly different than the unexplained variance.
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Mixed models factorial ANOVA
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a mixture of withn groups and independent groups testing
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Homogeneity of Variance
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The variance of the populations are equal. If the variances are not equal, this is hetrogeneity of variance.
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Disordinal interaction
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Lines crossing on the graph. Occurs when the differences between different levels of one factor reverse at some level of the other factor.
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Ordinal Interaction
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Lines do not cross but get closer together. Occurs when the differences between different levels of one factor are reduced (but not reversed) at some level of the other factor.
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Sphericity Assumption
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When the ranks of the participants stay the same. This is seen in repeated measures.
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Normality Assumption
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Where the distribution is in the normal bell curve range.
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Independence of Observations
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No observation is influenced by another.
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main effects
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The effect of a factor on a dependent variable.
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Fmax test
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Used with the Levene's test.
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priori
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planned tests before the Anova is done. small in number and inflexible, and normally do not contain a correction for inflated type one errors.
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simple effects
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compares group means and isolates the effects of one factor at different levels of another factor.
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Dunnett's test
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Compares the group means
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REGWQ test (Ryan's test)
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adjusts the critical value depending on the number of steps between means. an adjustment is made to alpaha, where alpha (number of steps) = 1 - (1 - a) ^ number of steps/number of means.
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Orthogonal
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Contrasts are orthogonal if they are not correlated, if they are independent.
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Funny looking t tiny j
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the effects of the independent variable
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funny looking e tiny i j
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the variability (error) associated with the individual observation
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Bonferroni
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The correction used to deflate the increased type one error.
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