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PS2002 > Stats > Flashcards

Stats Flashcards

1
Q

between experimental designs

A

different participants in each condition

so difference between groups

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2
Q

within subjects

A

the same participants in each condition

difference between treatments

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3
Q

similarities of how experiments are ran both between and within

A

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

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4
Q

what are factorial designs

A

designs with

  • one dv
  • two or more independent variables (unlike t-tests and one way ANOVA)
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5
Q

when are factorial designs needed

A

we suspect more than one iv is contributing to a dv

ignoring a dv detracts from the explanatory power of our experiments

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6
Q

what do factorial designs tell us

A

allow us to explore complicated relationsips between ivs and dvs

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7
Q

what is a main effect

A

how IVs factors individually affect the DV

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8
Q

what is an interaction

A

how IVs combine to affect the DV

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9
Q

limitations of between subjects design

A

participant variables

lots of participants required

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10
Q

limitations of within subjects design

A 
practice effect (lack of naeivity)
longer testing sessions
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11
Q

assumptions in mixed factorial ANOVA

A 
mix of between and within subject assumptions:
-interval/ ration (scale in spss)
normal distribution
homogenity of variance
sphericity of covariance
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12
Q

how to test for normal distribution assumption

A

examine histogram

conduct a formal test of normality - Kolmogorov-Smirnov test

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13
Q

how to test for homogenity of variance assumption

A

eyeball SDs

Levene’s test

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14
Q

how to test for sphericity of covariance

A

Mauchly’s test

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15
Q

rules of mixed factorial ANOVA

A

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

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16
Q

how to report mixed factorial ANOVA

A

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

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17
Q

what are tests of association

A

tests the relationships between variables

usually performed on continuous variables

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18
Q

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

A 
pearson's correlation (parametric)
spearman's correlation (nonparametric)
point-biserial correlation
simple linear regression
multiple regressions
19
Q

what do tests of association tell us

A

they tell us whether variables covary with other variables

20
Q

what limits us in tests of association (methodological)

A

without expreimental manipulation, we cannot infer causation

21
Q

what do scatterplots do

A

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
22
Q

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

A

an indication of the direction of the relationship

23
Q

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

A

how close the points are to forming a line

gives an indication of the strength of a relationship

24
Q

Assumptions when running pearsons correlation

A

-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

25
Q

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

A

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

26
Q

why must outliers be excluded from analysis in pearsons

A

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

27
Q

facts about correlation coefficients

A

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

28
Q

how to estimate r values

A

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

29
Q

how to set up to calculate r values

A

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

30
Q

r=… in words

A

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

31
Q

how to report correlation

A

r(df)=r value,p=

32
Q

limitations of correlation

A

it is not the same as causation

link may be coincidental or there may be a third variable involved

33
Q

what is regression

A

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

34
Q

why cant we just use correlationi instead of regression

A

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

35
Q

what does regression show us

A 
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
36
Q

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

A

a

37
Q

assumptions in regression

A 
linearity
interval/ratio data
normally distributed
free of outliers
homoscedasticity - residuals need to have the same degree of variation acorss all predictor variable scores
38
Q

what are residuals

A

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

39
Q

opposite or homoscedasiticity

A

heteroscedasticity

40
Q

problems with predictors when carrying out regression analysis

A

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

41
Q

talk through the three graphical tests of homoscedaticity

A

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

42
Q

how to report reression

A

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

43
Q

multiple regressions

A

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

44
Q

three ways we could carry out multiple regressions

A

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

PS2002 (4 decks)

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