exam 3 Flashcards
two-way ANOVA
we have two factors, we are interested in the effects of both factors on the same dependent variable, can have ANOVA with 3 or 4 factors
conditions matrix
if we have 2 factors and each factor has 2 levels, we have 4 possible combos of factors, we call this a 2X2 factorial design
three mean comparison
in a 2 factor design, we can test 3 hypotheses all at one: 1) does IV1 have an effect on DV? 2) does IV2 have an effect on DV? 3) does IV1 and IV2 interact to affect the DV?
main effects
first F tests the main effect of factor A as if factor B wasn’t there, second F tests the main effect of factor B as if factor A wasn’t there
interaction
the third F tests for an interaction, based on whether there is any more variance between groups that we haven’t already accounted for
writing about interactions: what to report in two-way ANOVA
same that you report in one-way ANOVA; means, F statistic, df, p-value for overall F, effect size (eta squared)
two-way tables of means
report the means for each cell ( combo of two factors) and rows/columns means
- put levels of factor A in rows and levels of factor B in columns
- use extra columns to left and extra column on top to include factor names
- put SD for each condition in parentheses after the mean
two-way ANOVA table
expand ANOVA table to include SS, df, MS, F and p for each of three tests
- divide into “between and “within” first then break down between variables
- add “*” to help reader quickly see which p values are significant
Writing about two-way ANOVA table
- report the results of each F test separately, but be clear about which effect you’re talking about
- when you have a significant interaction, you need to explain the form of the interaction
- then refer reader to tables and figures for further details
connecting your hypotheses: two-way ANOVA table
as you report your results, link them to hypotheses so reader can keep track of whether you were right or not
conventions for plotting interactions
- an interaction involves at least 3 variables
- DV/outcome always goes on vertical axis
- IV/predictor goes on horizontal axis
- in ANOVA, IV predictor is categorical
- other IV/predictor variable is represented by different lines
how to interpret interaction plots
- do lines cross? if yes; disordinal interaction, if no; ordinal interaction
- are lines separated? if yes, consistent with main effect of IV 2
- do the lines slope? if lines slope up or down thats consistent with main effect of IV1
correlational research
- comes in many forms (direct observation, surveys, analysis of existing data),
- involves more stats than a simple correlation
correlation vs t-test and ANOVA
t-tests and ANOVA are used to compare means of dif groups
when should you use correlation?
useful
- any time predictor variable is continuous
- any time you have at least 1 continuous variable and no strong expectations about which one predicts the other
- correlations are symmetrical, it doesn’t matter which variable comes first
when to use one-sample t-test?
when we know or can assume the pop mean but we don’t know the pop SD
independent samples t-test
comparing two samples with different people in each sample
-also called a between participants design
basic conceptual formula for any t-test
t= difference between means / estimated SE
standard error
error in one sample= SM1= square root of S1^2/n1
pooled standard error
if two samples are not the same size, we need to account for that difference
- pooled variance= Sp^2= SS+S2/df1+df2
- we use pooled variance instead of sample variance to calculate the SE
the pearson correlation
- indicated by the lower-case r (for sample, p for pop)
- r= degree to which X and Y vary together/ degree to which X and Y vary separately
- more shared variability= strong relationship= high correlation
calculating correlation
-we are interested in whether the deviations for one variable are similar to the deviations for the other
simple linear regression
-one predictor= one outcome
things we need to accomplish in a regression analysis
- find a straight line that best fits the data (Y=bX+a), what are best values for b and a?
- evaluate how well that line fits the data