Week 12 & 13 Flashcards

1
Q

Correlation

A

2 variables related to each other

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2
Q

Correlation Coefficient

A
  • Statistics used when looking for association between variables in 1 sample
  • Used in combination with p-value
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3
Q

Correlation Assumptions

A

Sample subjects should be indep & randomly selected

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4
Q

Pearson’s R

A
  • Both variables should have normal distribution & homoscedasticity
  • Must be interval/ratio
  • Magnitude between -1 to 1
  • Positive or negative direction
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5
Q

Chi-Square/Gamma

A

Nominal or ordinal

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6
Q

Spearman’s R

A

Ordinal or interval/ratio

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7
Q

Homoscedasticity

A

Having the same variance

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8
Q

Correlation Analysis

A
  • Measure strength of association (linear relationship) between 2 variables
  • No casual effect is implied
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9
Q

Direction of Relationship

A
  • Linear association: straight line
  • Either positive or negative
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10
Q

Positive Correlation

A

1 variable increases & other variable increases as well

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11
Q

Negative Correlation

A
  • 1 variable increases & other decreases
  • R is negative
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12
Q

Strength of Relationship

A
  • Determined by absolute value of r
  • Closer to +/- 1 = 1 stronger relationship
  • Closer to 0 = weaker relationship
  • 0 = no relationship
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13
Q

Direction of Relationship

A
  • Determined by sign (+/-)
  • -1 = perfect negative relationship
  • +1 = 1 perfect positive relationship
  • 0 = no relationship
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14
Q

Strength of Correlation

A
  • Weaker relationship requires larger sample size to detect
  • Sample size helps verify relationship strength
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15
Q

Key Requirements to Infer a Casual Relationship

A
  1. Time order (IV to DV)
  2. Statistical association
  3. No confounding variables that can influence IV & DV
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16
Q

Correlation vs Causation

A
  • Correlation only describes mathematical relationship between 2 variables
  • Correlation is not sufficient condition for determining causality
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17
Q

P-Value Significance

A
  • P > alpha = not significant
  • P < alpha = significant
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18
Q

Coefficient of Determination (R2)

A
  • Values between 0 and 1
  • R2 multiplied by 100 gives % of variance
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19
Q

% Variance

A

Amount of variance in DV that is explained by IV

20
Q

Correlation Coefficient Clinical Importance

A

Any value r > 0.3 (explains 9%+) often clinically important

21
Q

Regression Analysis

A

Predict value of DV based on value of at least 1 IV

22
Q

Simple Linear Regression Model

A
  • 1 IV (x)
  • Relationship between x & y is described by a linear function
  • Changes in y assumed to be caused by changes in x
23
Q

Simple Linear Regression

A

Predicting 1 DV from 1 IV

24
Q

Multiple Regression

A
  • Predicting 1 DV from multiple IVs
  • Considering multiple control variables simultaneously
25
Regression Coefficient
- How much DV is expected to increase, when IV increases by 1 - Holding all other IVs constant
26
Bivariate Analysis
- Compares 2 variables simultaneously - Looks at their association - Assess strength of their association
27
Limits of Chi-Square
- Sensitive to sample size - Can't indicate strength of the association
28
% Difference
- Provides 1 weak indicator of strength of relationship - Larger difference = stronger relationship
29
Gamma
- Nominal & ordinal variable only - Statistic that varies between 0 and +/-1 - 0= (no association) - 1 = perfect association
30
Multivariate Analysis
- Analysis of 2+ variables simultaneously - Can be discrete, continuous or both
31
Zero-Order
- Original relationship between indep & dep variables - Zero variables controlled
32
First-Order
1 control variable included in the model
33
Partial
Association of indep & dep variables for a subset of observations
34
Elaboration Model
- Introduces 3rd (control) variable into analysis - Enhance/elaborate understanding of bivariate relationship - Helps explain relationship between 2 original variables - Control variable held constant
35
Antecendent Control Variable
- Non-spurious: occurs directly before IV only - Spurious: occurs directly before both IV & DV
36
Intervening Control Variable
Occurs between IV & DV
37
Confounding Control Variable
Influences relationship of IV & DV
38
Elaboration Model Purpose
- Understanding relationship between 2 variables by controlling effects of a third - Illustrates the fundamental logic of multivariate & casual analysis
39
Replication Pattern
- Partial relationships are same as original relationship - 3rd variable does not change the original relationship - 3rd variable unrelated to original relationship - Partials remain unchanged/change insignificantly
40
Explanation Pattern - Antecedent
- Original relationship shown to be false through introduction of controlled variable - 3rd variable explains away original relationship - Original relationship no longer related - First-order partials are significantly less than zero-order relationship
41
Explanation Pattern Conditions
- Control variable must be antecedent to both IV & DV - First- order partials are significantly less than zero-order relationship
42
Interpretation Pattern - Intervening
- Control variable mediates effect of IV &DV - 3rd variable intervenes in original relationship - Original relationship no longer related - First- order partials are significantly less than zero-order relationship
43
Specification Pattern
- Partial relationship differ from one another - 3rd variable specifies conditions under which the original relationship varies - Original relationship in at least 1 partial increase/decrease/disappears but not in others
44
Suppressor Variable
- Concealing the relationship - Creates illusion of independence between IV & DV - Control variable increases association between DV & IV - Control variable associates pos with 1 variable and neg with other
45
Distorter
- Distorting the relationship - Reverse true direction of relationship between IV & DV
46
Limits to Elaboration Model
- Doesn't fit every situation - Doesn't specify statistical significance