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AUS Research Methods > Year 1 > Flashcards

Year 1 Flashcards

1
Q

What are the four scales of measurement?

A

Nominal, ordinal, interval, ratio

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2
Q

What distinguishes ordinal and interval?

A

Ordinal has a natural ordering to the data

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3
Q

What distinguishes interval and ratio?

A

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

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4
Q

What are the 4 movements of a distribution?

A
  1. Central tendency
  2. Dispersion
  3. Skewness
  4. Kurtosis
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5
Q

Negative skew?

A

Mode > mean

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6
Q

Positive skew?

A

Mode < mean

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7
Q

platykurtic, mesokurtic, leptokurtic?

A

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

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8
Q

Parametric data analysis requirements?

A
  1. continuous data
  2. n>30
  3. normally distributed
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9
Q

Non-parametric data analysis requirements?

A
  1. not continuous
  2. n<30
  3. non-normal
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10
Q

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

A

Normalisation or standardisation

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11
Q

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

A 
Mean = 0
SD = 1
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12
Q

What way is a null hypothesis always phrased?

A

Negatively

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13
Q

who decides the level of significance associated with hypothesis testing?

A

user based on opinion and consideration of distribution characteristics

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14
Q

When is something classed as not statistically significant?

A

If the significance value falls outside the significance confidence threshold.

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15
Q

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

A

Q-Q plot

K-S test

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16
Q

How does a q-q plot work?

A

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

17
Q

How does a K-S test work?

A

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

18
Q

What are inferential statistics?

A

Tests of difference between either samples and populations

19
Q

What are the 3 parametric inferential statistics?

A

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

20
Q

What are relational statistics?

A

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

21
Q

What is the parametric relational statistic test?

A

Pearson’s correlation coefficient

22
Q

What are the 4 non-parametric statistical tests?

A

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

23
Q

What is the non-parametric relational statistic test?

A

Spearman’s Rank

24
Q

What is the ‘least squares criterion’?

A

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

25
Q

What is the f-ratio?

A

the ratio of explained variance to unexplained variance

26
Q

What is the coefficient of explanation for linear regression?

A

R squared

27
Q

What are two sources of regression error?

A

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

28
Q

What is homoscedascity?

A

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

29
Q

Why is homoscedascity important?

A

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

30
Q

How do we test for homoscedascity/heteroscedascity in spss?

A
  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)
31
Q

What is autocorrelation?

A

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

32
Q

What is wrong with autocorrelation?

A

It is assumed to not occur in our parametric tests

33
Q

What test do we use for testing for autocorrelation?

A

durbin watson

34
Q

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

A

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

35
Q

what is the difference between autocorrelation and multicollinearity?

A

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

AUS Research Methods (4 decks)

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