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yr 2 Sem 1 > Statistics week 5 - 11 > Flashcards

Statistics week 5 - 11 Flashcards

1
Q

what are the 3 stage to interpreting SPSS data from two way factorial ANOVA

A
  1. ANOVA itself - test of between subjects
  2. if main effects are significant AND have more than 2 levels then check Post Hoc results
  3. If interaction result is significant, THEN follow up with Profile plots, interpreting main effect of IV levels and their interaction (parallel lines indicates no interaction)
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2
Q

Assumptions of two-way independant ANOVAs

A
  • normality
  • Homogeneity of variance (variance in DV should be equivalent across conditions) (tested with Levenes, no correction).
  • Independence of observations
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3
Q

non parametric equivalent for factorial ANOVAs

A

there isn’t one.

BUT they are really robust and only serious violations would be a problem

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4
Q

diff between partial eta squred and eta squared .
and why is it used in factorial two way ANOVA

A

eta squared is SSM/SST where in one way anovas, is the same as SSM/SSM+SSR

But in two way anovas this is not true because SST (total of summed squareds) involves all IV levels. BUT partial eta squared only involves one IV level.

i.e. because there are multiple IV levels in Factorial, a measure for each individual IV level is necessary

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5
Q

post hoc tests are relevent when

A

main effect of IV is significant and IV has more than 2 levels.

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6
Q

Marginal means =

A

mean score for single IV level (ignoring other IV)

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7
Q

difference in assumptions for repeated measures compared to independant. (ANOVA)
How is this assessed

A

spherecity of covariance
assessed via Mauchlys and corrected via greenhouse geisser

only when IV has more than 2 levels

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8
Q

The range within which 95% of scores in a
normally distributed population fall

formula

A

95% 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑣𝑎𝑙𝑢𝑒𝑠 𝑓𝑎𝑙𝑙:
𝜇 ± 1.96*SD

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9
Q

t formula

A

. 𝑡 =

𝑥̅𝐷/
𝐸𝑆𝐸

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10
Q

df for paired t-test

A

𝑑𝑓 = (𝑛 − 1)

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11
Q

To calculate degrees of freedom for an
independent t-test

A

𝑑𝑓 = 𝑛𝑡𝑜𝑡𝑎𝑙 − 2

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12
Q

theory behind how F is calculated

e.g. written out variance formula

A

𝐹 =
𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝐼𝑉 𝑙𝑒𝑣𝑒𝑙𝑠/

(𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 𝑤𝑖𝑡ℎ𝑖𝑛 𝐼𝑉 𝑙𝑒𝑣𝑒𝑙𝑠−𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 𝑑𝑢𝑒 𝑡𝑜 𝑖𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙 𝑑𝑖𝑓𝑓𝑠)

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13
Q

Components of the F calculation for
ANOVAs, as provided in SPSS output

A

𝑆𝑆𝑀 + 𝑆𝑆𝑅 = 𝑆𝑆𝑇

𝑆𝑆𝑀/
𝑑𝑓𝑀
= 𝑀𝑆𝑀 (mean square of model)

𝑆𝑆𝑅/
𝑑𝑓𝑅
= 𝑀𝑆𝑅 (mean square residual)

𝐹 =
𝑀𝑆𝑀/
𝑀𝑆R

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14
Q

To calculate degrees of freedom for a
bivariate correlation

A

𝑑𝑓 = 𝑁 − 2

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15
Q

R^2 Formula
(measure of effect size): the variance in
the outcome variable that is explained by the
regression model, expressed as a proportion
of total variance

A

𝑅^2 =
𝑆𝑆𝑀/
𝑆𝑆T

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16
Q

SSR =

A

sum of squares residual.

take diff between inidiv pp scores for group and that group mean. square and add them. (within groups diff)

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17
Q

SSM =

A

take diff between indiv group mean and the grand mean. square and add. (between group model)

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18
Q

MSm =

A

mean square model.

= SSm / dfm

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19
Q

MSr

A

means sum residual

= SSr / dfr

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20
Q

diff between repeated measures and independent groups factorial ANOVA

A

no variance due to individual differences (within group variance is smaller)

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21
Q

Marginal means =

A

mean score for single IV level

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22
Q

what does a significant interaction suggest

A

effect of IVA on DV is dependant on IVB

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23
Q

strength of bivariate linear correlations

A

.1-.3 = weak
.4 - .6 = moderate
.7 . 9 = strong

24
Q

what do inferential statistics measure

A

infer probability that we have observed a relationship of this magnitude when in fact the H0 is true.

e.g. accept 5% risk of type 1 error

25
Q

Parametric assumptions of Bivariate linear relationship

A
  • Both variables must be continuous (if both ordinal (categorical) then use non-parametric) can use for likert scales if have 6 0r 7 points
  • Related pairs (each pp have x and y)
  • Absence of outliers
  • Linearity (scatterplot shows straight and not curved line
26
Q

non parametric equivalent of Bivariate linear correlation

A

Spearman’s Rho

27
Q

what is covariance

A

variance shared between x and y variable

28
Q

what does pearsons r value represent

A

ratio of covariance to separate variances

29
Q

when talking about relative strength of a relationship, you must report

A

R^2

30
Q

if R^2 is .45 , what does this mean (bivariate correlation)

A

45 % of the variance is shared by x and y variable

31
Q

partial correlation purpose

A

allows for examination of relationship without the influence of a 3rd variable

32
Q

in partial correlation:
look at diff between correlation when not partialed out and when partialed out.

if correlation had decreased but remained significant, suggests

if correlation had not decreased, would suggest

A

relationship between x and y was partially explained by z

not influenced by z. BUT may be influenced by another variable still.

33
Q

Regression model purpose

A

rel between x and y, allowing an estimate of how much y will change as a result of a change in x.

34
Q

regression model
y =
x =

A

y = outcome variable. or dependent/criterion variable

x = predictor variable or independent/explanatory variable

35
Q

why use a regression model

A
  • strength of x and y
  • can predict value of y if know x
36
Q

Assumption of regression model result

A
  • assume y is dependant on x (does not infer causality)
37
Q

what is the F ratio of the regression model comparing

A

compares simpest model (average score as a line of best fit (SST)) Vs Best model (the regression line (SSR))

the difference between the two reflects improvement in prediction

38
Q

The larger the SSM, the

A

the bigger the improvement

(in prediction model)

39
Q

assumptions of multiple regression

A
  • Sample size
  • Linearity
  • outliers
  • Multicolinearity (predictors can’t be correlated with one another)
  • normal p.p plot of regression
  • Scatterplot of regression rectangularly distributed = homoscedasticity
40
Q

what does hierarchical regression ask

A

Does adding new predictor variables allow you to explain additional variance in the outcome variable?

Examines influence of predictor variables on outcome variable after “partialing out” influence of other variables.

41
Q

in a regression model:

  • beta represents
A

standardized slope

42
Q

what are the different models in hierarchical regression?

A

model 1 (predictor to be controlled)
model 2 (all predictors)

43
Q

change statistics for model 1 of hierarchical regression

A

Compares simplest model (b=0) with model 1.
(same job as standard regression)

44
Q

change statistics for model 2 of hierarchical regression

A

compares model 1 to model 2

Tells about explanatory power of x, after effects of z are controlled for.

ΔR^2 = how much overall variance in y is explained by x, after effects of z are controlled for.
ΔF = provides a measure of how much the model has improved the prediction of y, relative to the level of inaccuracy of the model.
Δp if <.05 indicates that x explains significant proportion of y after z is partialed out

45
Q

higher risk of what error in non-parametric statistics

A

type 2

e.g.(false negative) risk of failing to reject nul when it is false

e.g. saying it’s not significant when actually it is

46
Q

Independent t-test non-parametric equivalent

A

Mann-Whitney U test

remember, man is independent of whitney

47
Q

paired t-test non-parametric equivalent

A

Wilcoxon T test

48
Q

1-way indpendent ANOVA non-parametric equivalent

A

Kruskal Wallace test

49
Q

1 way RM ANOVA non-parametric equivalent

A

Friedman test

remember Layla Friedman is repeated measured

50
Q

Factorial design non-parametric equivalent

A

non existant

51
Q

how are Repeated measures designs shown to be normally distributed

A

the DV difference scores should be normally distributed, between each paired level of the IV

52
Q

Normality assumption can be assessed with what test

A

Shapiro-Wilk test.

use to decide parametric or non-parametric

53
Q

Pearson’s correlation coefficient non-parametric equivalent

A

Spearman’s Rho: used when N > 20
Kendall’s Tau: used when N < 20

54
Q

parametric or non parametric when thinking about scale

A

if variable measured is ordinal scale, use non-parametric

e.g. if intervals between values is not constant

55
Q

partial correlation and regression non-parametric equivalent

A

non existant

56
Q

what tests analyse categorical data

A

One variable Chi Square

Chi-square Test of Independence ( two variables)

no parametric equivalents

57
Q
A

yr 2 Sem 1 (22 decks)

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