ANOVA lectures 1 and 2 Flashcards
observed statistic is more extreme than critical statistic
reject Ho
p(obs|Ho) > α (i.e., p > .05), or observed statistic is not more extreme than critical statistic
the researcher would decide to retain Ho.
In ANOVA what is j?
Each level of the IV will be given a number, so j = 1 … J fixed levels
𝑛𝑗
number of participants in the j’th group
N
total number of participants in the experiment
How do you work out N?
Assume equal 𝑛𝑗 for PSYC3010, so N = 𝑛𝑗×𝐽
Between-subjects is…
= experiment where different participants have been assigned to different conditions
Fixed effects =
levels of the IV can be replicated across experiments
Null in ANOVA
𝐻0:𝜇1=𝜇2=𝜇3=⋯=𝜇𝐽
Ha in ANOVA
𝐻𝐴:not 𝐻0. Mu’s are not equal
alpha =
probability of making a Type 1 Error. Probability of falsely rejecting Ho when the true state of affairs is that Ho is True (nothing is happening) We should have retained Ho but instead we falsely rejected it.
For an independent samples t test can we draw directional conclusions?
Yes. If there is inequality between the two groups we know the direction.
One way?
An experiment with one independent variable (IV) with J levels
The General Linear Model (GLM)
Yij= μ + αj + εij. Score on the dependent variable (DV) for the ith person in the jth group (Yij)= Population grand mean (µ) + Effect parameter for group (aj) + Random error for ith person in the jth group (e)
what is mu in GLM?
everybody in the population has this
what is alpha j in GLM
effect parameters. boost we get for being in a specific group. systematic between group differences.
error in GLM
adjustment for being the person that they are. error, non systematic within group differences
Do all alpha js look the same?
No. each group has it’s own alpha j. so alpha 1, alpha 2, a3 etc. Ho is that all of the alpha js = 0. Ha is that some groups deviate from the grand mean (µ).
What about the alpha js is important for the GLM?
Sum of the alpha js have to equal zero or you have breached a requirement of the GLM, you’ve done something wrong.
What are the assumptions of error
1) Errors are independent (different people) 2) Have a mean of zero, i.e. E(εij) = 0 3) Are normally distributed 4) Has equal variance across conditions (Homogeneity of variance, or homoscedasticity) = spread of epsilons are the same across all groups.
In an experiment, all we have are the Yij observed scores, so we have to estimate these population parameters from sample data. How do we do this?
μ , αj , and εij are not usually known out ‘in the real world’. (use the sample mean Yij as an estimate of the population mean μ) Think using ‘deviations’. move the µ from left to right of the GLM equation. (Yij –Ybar) = (Ybarj-Ybar) + (Yij-Ybar).\ BUT sum of these deviations (alpha js) will always be 0, so we need to SQUARE them creating ANOVA.