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3340 Data Analysis > Week 12 & 13 > Flashcards

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
Q

Regression Coefficient

A
  • How much DV is expected to increase, when IV increases by 1
  • Holding all other IVs constant
26
Q

Bivariate Analysis

A
  • Compares 2 variables simultaneously
  • Looks at their association
  • Assess strength of their association
27
Q

Limits of Chi-Square

A
  • Sensitive to sample size
  • Can’t indicate strength of the association
28
Q

% Difference

A
  • Provides 1 weak indicator of strength of relationship
  • Larger difference = stronger relationship
29
Q

Gamma

A
  • Nominal & ordinal variable only
  • Statistic that varies between 0 and +/-1
  • 0= (no association)
  • 1 = perfect association
30
Q

Multivariate Analysis

A
  • Analysis of 2+ variables simultaneously
  • Can be discrete, continuous or both
31
Q

Zero-Order

A
  • Original relationship between indep & dep variables
  • Zero variables controlled
32
Q

First-Order

A

1 control variable included in the model

33
Q

Partial

A

Association of indep & dep variables for a subset of observations

34
Q

Elaboration Model

A
  • Introduces 3rd (control) variable into analysis
  • Enhance/elaborate understanding of bivariate relationship
  • Helps explain relationship between 2 original variables
  • Control variable held constant
35
Q

Antecendent Control Variable

A
  • Non-spurious: occurs directly before IV only
  • Spurious: occurs directly before both IV & DV
36
Q

Intervening Control Variable

A

Occurs between IV & DV

37
Q

Confounding Control Variable

A

Influences relationship of IV & DV

38
Q

Elaboration Model Purpose

A
  • Understanding relationship between 2 variables by controlling effects of a third
  • Illustrates the fundamental logic of multivariate & casual analysis
39
Q

Replication Pattern

A
  • 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
Q

Explanation Pattern - Antecedent

A
  • 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
Q

Explanation Pattern Conditions

A
  • Control variable must be antecedent to both IV & DV
  • First- order partials are significantly less than zero-order relationship
42
Q

Interpretation Pattern - Intervening

A
  • 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
Q

Specification Pattern

A
  • 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
Q

Suppressor Variable

A
  • 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
Q

Distorter

A
  • Distorting the relationship
  • Reverse true direction of relationship between IV & DV
46
Q

Limits to Elaboration Model

A
  • Doesn’t fit every situation
  • Doesn’t specify statistical significance

3340 Data Analysis (3 decks)

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