Year 1

Question 1

What are the four scales of measurement?

Answer 1

Nominal, ordinal, interval, ratio

Question 2

What distinguishes ordinal and interval?

Answer 2

Ordinal has a natural ordering to the data

Question 3

What distinguishes interval and ratio?

Answer 3

Interval does not have an absolute zero point but ratio data does

Question 4

What are the 4 movements of a distribution?

Answer 4

1. Central tendency
2. Dispersion
3. Skewness
4. Kurtosis

Question 5

Negative skew?

Answer 5

Mode > mean

Question 6

Positive skew?

Answer 6

Mode < mean

Question 7

platykurtic, mesokurtic, leptokurtic?

Answer 7

Platykurtic (k<3)
Mesokurtic (k~3)
Leptokurtic (k>3)

Question 8

Parametric data analysis requirements?

Answer 8

1. continuous data
2. n>30
3. normally distributed

Question 9

Non-parametric data analysis requirements?

Answer 9

1. not continuous
2. n<30
3. non-normal

Question 10

What process allows us to begin conducting arithmetic on parametric data?

Answer 10

Normalisation or standardisation

Question 11

What happens to the mean and SD after you carry out normalisation on a distribution?

Answer 11

Mean = 0
SD = 1

Question 12

What way is a null hypothesis always phrased?

Answer 12

Negatively

Question 13

who decides the level of significance associated with hypothesis testing?

Answer 13

user based on opinion and consideration of distribution characteristics

Question 14

When is something classed as not statistically significant?

Answer 14

If the significance value falls outside the significance confidence threshold.

Question 15

What are the 2 ways of determining whether a distribution is normally distributed?

Answer 15

Q-Q plot

K-S test

Question 16

How does a q-q plot work?

Answer 16

Points should lie as closely along the line (representing a normal distribution) and be evenly distributed either side.

Question 17

How does a K-S test work?

Answer 17

Hypothesis testing - if the value returned lies outside the significance threshold then there is NOT a statistically significant difference between a normal distribution and the investigated normal distribution i.e. it is normally distributed

Question 18

What are inferential statistics?

Answer 18

Tests of difference between either samples and populations

Question 19

What are the 3 parametric inferential statistics?

Answer 19

On-sample t-test = sample and population
Two-sample t-test = sample and sample
ANOVA = 2+ samples

Question 20

What are relational statistics?

Answer 20

Testing for a relationship between variables i.e. correlation.

Question 21

What is the parametric relational statistic test?

Answer 21

Pearson’s correlation coefficient

Question 22

What are the 4 non-parametric statistical tests?

Answer 22

one-way chi = sample and population
two-way chi = 2+ sample
MWU = comparison of sample means
Kruskal Wallis = ANOVA

Question 23

What is the non-parametric relational statistic test?

Answer 23

Spearman’s Rank

Question 24

What is the ‘least squares criterion’?

Answer 24

the principle that the total difference between the points and the regression line is as small and identical either side.

Question 25

What is the f-ratio?

Answer 25

the ratio of explained variance to unexplained variance

Question 26

What is the coefficient of explanation for linear regression?

Answer 26

R squared

Question 27

What are two sources of regression error?

Answer 27

Standard error = error associated with how difficult it is to represent data that is naturally very tricky sampling error = concerned with the regression line characteristics being incorrect or poor at representing data

Question 28

What is homoscedascity?

Answer 28

When residuals from the regression line are consistently spaced either side of the regression line.

Question 29

Why is homoscedascity important?

Answer 29

because that forms one of the assumptions held regarding analysis - that there are even residuals either side of the regression line

Question 30

How do we test for homoscedascity/heteroscedascity in spss?

Answer 30

1. scatter plot needs to be well scattered with no clear patterns 2. P-plot needs to have similar amount of points either side of line and be well tied to the line 3. histogram needs to be normal in style (Gaussian)

Question 31

What is autocorrelation?

Answer 31

When each correlation between x and y values are not independent i.e. the correlation is affected by something else

Question 32

What is wrong with autocorrelation?

Answer 32

It is assumed to not occur in our parametric tests

Question 33

What test do we use for testing for autocorrelation?

Answer 33

durbin watson

Question 34

What is the range of values for significant positive autocorrelation, no autocorrelation and significant negative autocorrelation?

Answer 34

positive autocorrelation = 0-1.475 no autocorrelation = 1.566-2.434 negative autocorrelation = 2.525 - 4

Question 35

what is the difference between autocorrelation and multicollinearity?

Answer 35

autocorrelation involves the correlation between one predictor and y being affected by something else whereas multicollinearity is when different predictors are linked so that distinguishing their individual impact on y is difficult to determine