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STATPSY FINALS > CORRELATION & PEARSON > Flashcards

CORRELATION & PEARSON Flashcards

1
Q

a statistical technique that is used to measure and
describe the relationship between two variables

A

Correlation

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2
Q

The corresponding correlation for the entire population is identified
by the Greek letter

A

rho (ρ)

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3
Q

the Greek equivalent of the letter r

A

rho (ρ)

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4
Q

The sign of the correlation, positive or negative, describes the direction of the relationship

A

The Direction of the Relationship

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5
Q

Some relationships tend to have a linear form; the points in the scatter plot tend to cluster around a straight line.

A

The Form of the Relationship

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6
Q

most common use of correlation is to
measure _ relationships

A

straight-line relationships (The Form of the Relationship)

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7
Q

in correlation other forms of relationships do exist and there are _ correlations used to measure them.

A

special correlations (The Form of the Relationship)

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8
Q

The correlation measures the consistency of the relationship.

A

The Strength or Consistency of the Relationship

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9
Q

for a linear relationship the data points could fit perfectly on a _ line

A

straight line(The Strength or Consistency of the Relationship)

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10
Q

Every time X increases by one point, the value of Y also changes by a _ and predictable amount.

A

consistent (The Strength or Consistency of the Relationship)

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11
Q
  1. The Direction of the Relationship
  2. The Form of the Relationship
  3. The Strength or Consistency of the Relationship
A

3 characteristics of a relationship

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12
Q
  1. perfect negative correlation, –1.00,
  2. no linear trend, 0.00,
  3. a strong positive relationship, approximately +.90
  4. a relatively weak negative correlation,
    approximately –0.40
A

different values for linear
correlations

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13
Q

a perfect negative correlation, have a value of

A

–1.00

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14
Q

no linear trend, have a value of

A

0

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15
Q

a strong positive relationship, approximately have a value of

A

0.9

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16
Q

a relatively weak negative correlation, approximately have a value of

A

–0.40

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17
Q

measures the degree and the direction of the linear relationship between two variables

A

Pearson correlation

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18
Q

used to measure the amount of covariability between two variables.

A

The Sum of Products of Deviations

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19
Q
  1. Prediction
  2. Validity
  3. Reliability
  4. Theory Verification
A

Where and Why Correlation Are Used

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20
Q

If two variables are known to be related in some systematic way, it is possible to use one of the variables to make accurate predictions about the other.

A

Prediction

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21
Q

If two _ are known to be related in some systematic way, it is
possible to use one of the variables to make accurate predictions
about the other.

A

variables

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22
Q

One common technique for demonstrating validity is to use a
correlation.

A

Validity

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23
Q

If a test actually measures intelligence, then the scores on the test should be related to other measures of intelligence—for example, standardized IQ tests, performance on learning tasks, problem-solving ability

A

Validity

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24
Q

One way to evaluate reliability is to use correlations to determine the relationship between two sets of measurements

A

Reliability

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25
Q

When reliability is high, the correlation between two measurements
should be strong and positive

A

Reliability

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26
Q

One way to evaluate reliability is to use _ to determine the relationship between two sets of measurements

A

correlations

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27
Q

When reliability is _, the correlation between two measurements
should be strong and positive

A

high

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28
Q

The prediction of the theory could be tested by determining the correlation between the two variables.
* Well-being and positivity
* Rumination and depression

A

Theory Verification

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29
Q

The prediction of the _ could be tested by determining the
correlation between the two variables

A

Theory

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30
Q

• Well-being and positivity
• Rumination and depression

A

Theory Verification

31
Q

simply describes a relationship
between two variables

A

correlations

32
Q

It does not explain
why the two variables are related.

A

Correlation

33
Q

should not and cannot be interpreted as proof of a cause- and-effect relationship between the two variables.

A

Correlation

34
Q

the value of a correlation can be greatly by the _ of scores represented in the data.

A

range of scores

35
Q

the correlation within a restricted range could be completely _ from the correlation that would be obtained from a full range of scores

A

different

36
Q

_ , can have a dramatic effect/influence on the value of a correlation.

A

outliers

37
Q

One or two extreme data points

A

outliers

38
Q

When judging how “good” a relationship is, it is tempting to focus on the numerical value of the correlation. However, a _ should NOT be interpreted as a proportion.

A

correlation

39
Q

A correlation that have numerical value of .5 DOES NOT MEAN that you can make predictions with 50% _

A

accuracy

40
Q

To describe how accurately one variable predicts the other, you
must _ the correlation.

A

square (r²)

41
Q

a correlation of r = .5 means that one variable partially predicts the other, but the predictable portion is only _ % of the total variability.

A

.25 or 25%

42
Q

a correlation of r = .5 means that one variable _ predicts the other, but the predictable portion is only r² = 0.25 (or 25%) of the total variability.

A

partially

43
Q

the predictable portion of the total variability

A

the value r²

44
Q

The value r² is called

A

coefficient of determination

45
Q

it measures the proportion of variability in one variable that can be determined from the relationship with the other variable.

A

the value r²

46
Q

A correlation of r = 0.80 (or –0.80) means that r² = 0.64
or 64% of the _ in the Y scores can be predicted from the
relationship with X.

A

variability

47
Q

high or low scores are?

A

extreme scores

48
Q

score that are more toward the mean are what scores?

A

less extreme scores

49
Q

When there is a less-than-perfect correlation between two variables, extreme scores (high or low) for one variable tend to be paired with the less extreme scores (more toward the mean) on the second variable.

A

Regression toward the Mean

50
Q

less-than-perfect correlation between two variables, extreme scores (high or low) for one variable tend to be paired with the less extreme scores (more toward the mean) on the second variable

are _ for any situation

A

TRUE

51
Q

if you get a extreme high/low score most likely the next time you’ll get a _ extreme scores

A

less extreme scores

52
Q

statistical hypothesis, no direction is specified, two tailed test

A

Non-directional Hypotheses

53
Q

statistical hypothesis, either an increase or decrease in population mean score (located in the critical region), one tailed test

A

Directional Hypotheses

54
Q

Non-directional Hypotheses
Ho : ρ = 0

A

There is no population correlation

55
Q

Non-directional Hypotheses
H1 : ρ ≠ 0

A

There is a real correlation

56
Q

Directional Hypotheses
Ho : ρ ≤ 0

A

The population correlation is not positive.

57
Q

Directional Hypotheses
H1 : ρ > 0

A

The population correlation is positive

58
Q

The hypothesis test evaluating the significance of a correlation can be conducted using either a _ or an _ (Regression)

A

t statistic or an F-ratio

59
Q

An alternative method for measuring effect size is to determine how much of the variability in the scores is explained by the _ effect

A

treatment effect

60
Q

the concept behind this measure is that the treatment causes the scores to increase (or decrease), which means that the treatment is causing the scores to vary

A

Measuring the Percentage of Variance Explained, r²

61
Q

in correlation the _ causes the scores to increase (or decrease), which means that the treatment is causing the scores to vary

A

treatment

62
Q

how much of the variability is explained by the treatment

A

percentage of variance accounted for by the
treatment and is identified as r²

63
Q

we can measure how much of the variability is explained by the
treatment, we will obtain a measure of the size of the treatment
_

A

the size of the treatment effect

64
Q

_ (1988) also proposed criteria for evaluating the size of a
treatment effect that is measured by r²

A

Cohen

65
Q

r² = 0.01

A

small effect

66
Q

r² = 0.25

A

large effect

67
Q

r² = 0.09

A

medium effect

68
Q

r (degrees of freedom, n-2) = calculated value for the correlation, probability level, and the type of test used (one- or two-tailed)

A

APA standart format of pearson correlation

69
Q

A correlation for the data revealed a significant relationship between amount of education and annual income, r (28)= +.65, p <.01, two tailed.

A

r (degrees of freedom, n-2) = calculated value for the correlation, probability level, and the type of test used (one- or two-tailed)

70
Q

In a study with several variables, correlations between all possible variable _ are computed.

A

all possible variable pairings

71
Q

a study measured people’s annual income, amount of education, age, and intelligence, how many possible pairing are theres?

A

6 possible pairings leading to 6 different correlations

72
Q

number of possible pairings = number of different correlations

A

a study with several/multiple variables

73
Q

results from multiple correlations are most easily reported in a table called a correlation _, using footnotes to indicate which correlations are significant.

A

correlation matrix

74
Q

The analysis examined the relationships among
income, amount of education, age, and intelligence
for n = 30 participants. The correlations between
pairs of variables are reported in Table 1. Significant
correlations are noted in the table.

A

sample report in correlation matrix

STATPSY FINALS (9 decks)

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