# Data week 6

version from 2017-11-06 13:04

## Section

what is an ANOVAANalysis Of VAriance
when do we use ANOVAin situations where we want to compare more than 2 conditions. maintains alpha/significance level at 5%.
why couldn't you use several t-testsby running 3 tests, 5% probability of an error is multiplied by 3 therefore the chance is 15%.
What do both ANOVA and t-tests comparemeans between groups. but ttest for 2 groups. more than 2 = anova = more efficient.
What are the assumptions for ANOVA-The Dependent Variable (DV) comprises data measured at interval/ratio level. -Normal Distribution -Homogeneity of Variance -In the case of independent groups designs, independent random samples must have been taken from each population.
Homogeneity of variance -Variance is a measure how data are spread around the mean (variance  SD2). -Two or more variances between or within groups are compared: similar in size = homogeneous. -Use Levene's test of Homogeneity of Variance (between groups design) and Mauchly’s Test of Sphericity (within groups design). want number to be greater than 0.05.
How to work out normal distribution Use Kolmogorov-Smirnov (large sample) or Shapiro-Wilk (small sample) tests to confirm normal distribution
How do you present alpha P values always 0.05 or p<0.005, not .000, always have number before decimal.
ANOVA looks for what differences between the means of the groups. when the means are v different, we say that there is a greater degree of variation between the conditions- called between groups variance.
What does between groups variance arise fromexperimental error, treatment/condition effects, individual difference to the condition.
between groups variance can be thought of as variation between the columns of data. Variation within a group=withing column.
within groups variance can be called error variance.
what can cause within groups varianceexperimental error, individual differences.
Partitioning the variance ANOVA takes account of the variance within the conditions and compares this to the variance between conditions. If the variance between conditions is much larger than the variance within conditions we can say that our IV is having a larger effect on scores than the individual differences are. comparison of variance due to nuisance factors compared to variance due to our experimental manipulation is called partitioning the variance.
Why do we calculate and F-ratio We want to see if our manipulation of the IV is responsible for the differences between scores (rather than being due to error variance). To do this we (well, SPSS will) calculate the ratio of the variance due to our manipulation of the IV and the error variance. This ratio is called the F-ratio (‘F’ stands for a statistician named Fisher).
how to work out F ratio variance due to manipulation of IV / error variance.
what does and f-ratio less than 1 indicate.IV is not significant. greater F-ration the better >1.
how do we find out if the f -ratio is significant if the F-ration is larger than 1, we need to decide if the value is large enough to be statistically significant. SPSS can calculate whether the effect of the IV is sufficiently larger than the nuisance variables. will report a p-level for a given F-ratio. p= probability of getting f by chance.
ANOVA terminology; Factors These are the independent variables. In our study, the Factor is Condition.
ANOVA terminology; Levels of factors In our study we have three levels of factors. Our IV (condition) has been manipulated into (1) constant low level of music, (2) no music and (3) intermittent music.
ANOVA terminology; withing or between participants Whether each factor is a within or between participant factor. In our study we investigated the differences between different conditions
ANOVA terminology; between subject factors These are factors that vary between participants. So in our study, each participant will only experience one level of a factor. Either no noise, intermittent noise or constant noise. This is a Between-Subjects Design (aka Independent Measures Design).
ANOVA terminology; within-subject factors These are factors that vary within a participant. In a different study, we may wish to administer all of the noise conditions to each participant to see how they perform. This is a Within-Subjects Design (aka as Repeated Measures Design).
what is does 'main effect' mean used to describe the ID effect of a factor.
what does the term 'interactions' assess the combine effect of the factors.