Stats

Question 1

between experimental designs

Answer 1

different participants in each condition

so difference between groups

Question 2

within subjects

Answer 2

the same participants in each condition

difference between treatments

Question 3

similarities of how experiments are ran both between and within

Answer 3

nonexperimental conditinos held constant
dependent variabel measured identically
different formula used in statistical tests for these designs

Question 4

what are factorial designs

Answer 4

designs with

• one dv
• two or more independent variables (unlike t-tests and one way ANOVA)

Question 5

when are factorial designs needed

Answer 5

we suspect more than one iv is contributing to a dv

ignoring a dv detracts from the explanatory power of our experiments

Question 6

what do factorial designs tell us

Answer 6

allow us to explore complicated relationsips between ivs and dvs

Question 7

what is a main effect

Answer 7

how IVs factors individually affect the DV

Question 8

what is an interaction

Answer 8

how IVs combine to affect the DV

Question 9

limitations of between subjects design

Answer 9

participant variables

lots of participants required

Question 10

limitations of within subjects design

Answer 10

practice effect (lack of naeivity)
longer testing sessions

Question 11

assumptions in mixed factorial ANOVA

Answer 11

mix of between and within subject assumptions:
-interval/ ration (scale in spss)
normal distribution
homogenity of variance
sphericity of covariance

Question 12

how to test for normal distribution assumption

Answer 12

examine histogram

conduct a formal test of normality - Kolmogorov-Smirnov test

Question 13

how to test for homogenity of variance assumption

Answer 13

eyeball SDs

Levene’s test

Question 14

how to test for sphericity of covariance

Answer 14

Mauchly’s test

Question 15

rules of mixed factorial ANOVA

Answer 15

identify straight away the between and within IV
use between subject formulae for between-subject effects and within for within-subject effects
if there is a conflict (eg interactions) use the within

Question 16

how to report mixed factorial ANOVA

Answer 16

F(between group df, within or error df here)=F-value, p=

Question 17

what are tests of association

Answer 17

tests the relationships between variables

usually performed on continuous variables

Question 18

examples of tests of association (eg parametric, non-parametric etc

Answer 18

pearson's correlation (parametric)
spearman's correlation (nonparametric)
point-biserial correlation
simple linear regression
multiple regressions

Question 19

what do tests of association tell us

Answer 19

they tell us whether variables covary with other variables

Question 20

what limits us in tests of association (methodological)

Answer 20

without expreimental manipulation, we cannot infer causation

Question 21

what do scatterplots do

Answer 21

typically show relationships between pairs of variables

• data from each variable are plotted on separate axis
• each point represents one pair of observations at each measure point

Question 22

what does the direction of the cloud of points in a scatterpoint tell us

Answer 22

an indication of the direction of the relationship

Question 23

what is the spread and what does it tell us in a scatter plot

Answer 23

how close the points are to forming a line

gives an indication of the strength of a relationship

Question 24

Assumptions when running pearsons correlation

Answer 24

-we should be looking for a linear relationship between variables
check the scatterplot, if it shows a clear non-linear relationship, do not run a pearson’s correlation
-parametric tests assumes interval/ratio data
-normal distribution
-data should be free of statistical outliers

Question 25

why does data have to be normally distributed to run pearsons how to check

Answer 25

involves calculating means and SDs only appropriate if data is normally distributed plot and inspect a frequency distribution of scores for each variable can take some skew

Question 26

why must outliers be excluded from analysis in pearsons

Answer 26

outliers have a disproportionate influence on the correlation statistic or correlation coefficient r

Question 27

facts about correlation coefficients

Answer 27

range from -1 to 1 no units same for xand y as y and x positive value indicates as one value increases, so does the other negative value indicates as one variable increases, the other decreases how close a value is to -1 or +1 indicates how close the two variables are to being perfectly linearly related

Question 28

how to estimate r values

Answer 28

split scatterplot with means for each variable count number of points in each quadrant positive correlation will populate the positive quadrants more than the negative ones, and vie versa

Question 29

how to set up to calculate r values

Answer 29

plot the raw values against one another scaling problems - different means and SDs we don't care about means, SDs, units, only relationships -plot z-transformed (standardised x and y values no scalling or unit problems

Question 30

r=... in words

Answer 30

the adjusted average of the product of each standardised x-y coordinate pair

Question 31

how to report correlation

Answer 31

r(df)=r value,p=

Question 32

limitations of correlation

Answer 32

it is not the same as causation | link may be coincidental or there may be a third variable involved

Question 33

what is regression

Answer 33

a family of inferential statistical tests tests of association make prediction about data used when causal relationships are likely

Question 34

why cant we just use correlationi instead of regression

Answer 34

if interested in a causal relationship, you may be interested in how much to intervene correlation does not give you that information

Question 35

what does regression show us

Answer 35

``` unstandardised relationship between outcome (Y) and predictor (X) variables using calculations of the intercept (a) and gradient (b) expressed in the form Y=aX+b ```

Question 36

if your predictor value in regression is 0, you can expect your outcome variable to equal..

Answer 36

a

Question 37

assumptions in regression

Answer 37

``` linearity interval/ratio data normally distributed free of outliers homoscedasticity - residuals need to have the same degree of variation acorss all predictor variable scores ```

Question 38

what are residuals

Answer 38

the difference between the actual outcome score and the predicted score outcome

Question 39

opposite or homoscedasiticity

Answer 39

heteroscedasticity

Question 40

problems with predictors when carrying out regression analysis

Answer 40

predictor variables are which are highly correlated with one another (show multicollinearity) are problematic be cautious when interpretin multiple regression where predictor variable correlations >.80 (or

Question 41

talk through the three graphical tests of homoscedaticity

Answer 41

histogram - bars should approximately fit the curve scatterplot-points should follow along the diagonal regression (standardised) scatterplot - points should form a non-descript cloud

Question 42

how to report reression

Answer 42

check descriptives and correlations check that predictor and outcome variables show a linear relationship check that homoscedacity assumption is not violated report the R^2 (proportion variance explained) in the text report coefficients in a table

Question 43

multiple regressions

Answer 43

predicting one outcome variable from more than one predictor variable Y=a+b1X1+b2X2 etc

Question 44

three ways we could carry out multiple regressions

Answer 44

order predictors entered in - simultaneous = all predictors entered at the same time - hierarchical = predictors are entered in a pre-defined order. used when regressions are informed by well-defined theory - stepwise = predictors are entered in an order driven by how well they correlate with the outcome. not used as it is a relatively unstable method