Correlation and Distribution Flashcards

1
Q

what are scatterplots used to display?

A

relations between two quantitative variables

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2
Q

positive relationship

A

both variables increase

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3
Q

negative relationship

A

variables change in opposite directions

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4
Q

strong relationship

A

points lie close to a line

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5
Q

weak relationship

A

points are widely scattered

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6
Q

perfect relationship

A

often appears when cheating has occurred, or when the same thing is being measured

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7
Q

what is the purpose of correlational analysis?

A
  • determine whether there is a linear relationship between variables
  • the direction and strength of relationships
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8
Q

pearson correlation coefficient

A

r

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9
Q

spearman correlation coefficient

A

rs

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10
Q

what do correlations make no distinction between?

A

the IV and DV

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11
Q

how can linear relationships be measured?

A

by correlations

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12
Q

correlations and non-linear relationships

A

correlations cannot be used, and data may need to be transformed

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13
Q

pearson correlation

A
  • calculated directly from the raw score
  • suitable for interval or ratio data
  • highly affected by outliers
  • not suitable for skewed data
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14
Q

spearman correlation

A
  • calculated from the ranking of the raw scores
  • suitable for ordinal data
  • marginally affected by outliers
  • suitable for skewed data
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15
Q

issues with sample size

A

small samples can indicate a pattern when there is no real relationship

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16
Q

when are density curves useful?

A

when dealing with lots of data, that can be distributed normally and generalised to the population

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17
Q

what do density curves use?

A

a mathematical model to describe a histogram distribution of scores

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18
Q

_______ and _______ ______ should be ignored

A

outliers, extreme values

19
Q

normal distribution

A

perfectly skewed data

20
Q

positively skewed data

21
Q

negatively skewed data

22
Q

what do density curves display?

A

the overall pattern of a distribution, as all the data is under the curve

23
Q

when can predictions be made for distribution?

A

if certain values of the model are known, e.g., mean or SD

24
Q

what can normal distributions be used to describe?

A

lots of naturally occuring data that is distributed similarly around a single measure of central tendency

25
what can observed data be fit to?
the normal curve
26
how can distributions be described?
using different parameters of mean and SD
27
mean of the sample
28
mean of the population
μ
29
SD of the sample
S
30
SD of the population
σ
31
what is normal distribution described by?
a normal curve - symmetrical, single-peaked, and the tail meets at the x-axis at infinity - the higher the mean, the further along the axis it is - shape is determined by SD
32
what do statistical tests assume?
data is normally distributed
33
what happens if data is not normally distributed?
parametric tests must be used
34
what do standard scores allow us to do?
compare values from different datasets
35
how can different datasets by translated into a standard normal distribution?
they can be standardised by calculating z-scores
36
z-score
(deviation of x from mean) / standard deviation (x - x̄) / S
37
what is the z-score
the number of standard deviation that the observation deviates from mean
38
what is created when z-scores are plotted?
a normal distribution
39
mean and SD of z-score
mean = 0 SD = 1
40
mean and SD of standard normal distribution
μ = 0 σ = 1
41
how are normal datasets standardised into standard normal distributions?
by calculating the z-score
42
what does the area under the standard normal distribution curve represent?
the percentage of participants (100%) and equals 1
43
what does the table entry indicate?
the area under the curve to the left of the line