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geog 109 LE 3 > Lesson 13 > Flashcards

Lesson 13 Flashcards

1
Q

How variable is related to some other variable

A

Measures of Association and correlation

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2
Q
  • Answers the question “Is there some sort of relationship?”
  • Also called bivariate tables/RxC tables
A

Contingency Table

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3
Q

Elements of Contingency Table

A
  1. Title (variables, year, source)
  2. Column variables
  3. Row variables
  4. Column marginals - subtotals
  5. Row marginals - subtotals of row variables
  6. Grand total
  7. Source
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4
Q

Values of ___? variables can be explained by the variables of independent variables

A

Dependent

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5
Q

For ____? variables, the order of categories depends on personal preference

A

Nominal

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6
Q

Modelling Relationships between 2 Variables

A
  1. One-Way Direct Relationship
  2. Two-Way Direct Relationship
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7
Q

The variable is “controlled for” when we take into account its effect on variables in the bivariate relationship

A

Control Variable

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8
Q

If there’s a change in direction between the relationship of 2 variables, there exist a different type of variable (T or F)

A

T

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9
Q

Possible conclusions when a 3rd variable is introduced?

A
  • A direct relationship still exist (the third effect has no effect
  • A spurious or intervening relationship exists
  • A condition relationship exists
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10
Q

Types of Relationship

A
  1. Direct Relationship
  2. Spurious Relationship
  3. Intervening Relationship
  4. Conditional Relationship
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11
Q

Type of relationship where when we introduce a variable, there is no direct relationship between IV and DV

A

Spurious Relationship

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12
Q

Type of relationship where we cannot directly explain the other variable and must be defined first

A

Conditional Relationship

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13
Q

Summary measure that reflects the strength of the relationship between two variables (how strong the relationship between two variables)

A

Measures of Association

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14
Q

The closer the measure to 1, the ??? the association

A

Stronger

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15
Q

If measures equals 0, there is ??? between the two variables

A

No relationship

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16
Q

The sign of the measure indicates whether the relationship is positive or negative.

A

Direction

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17
Q

Types of Measures of Association where the value of the measure is the same regardless of which variable is the IV or DV

A

Symmetric Measure

18
Q

Types of Measures of Association where the value of the measure may vary depending on which variable is assigned as the IV or the DV

A

Asymmetric Measure

19
Q

Different Methods of Association for Nominal Variables

A
  1. Phi
  2. Cramer’s V
  3. Lambda
  4. T^2
  5. Contingency Coefficient, C
  6. Tetrachoric Correlation, rtet
  7. Rank Biserial Correlation, rrb
20
Q
  • Used to measure association between nominal variables in 2x2 Contingency Tables.
  • Adjusts the chi-square statistic by the sample size
  • Both IV and DV have 2 categories
  • For 2x2 tables, the Value ranges from 0 (No association) to 1 (Perfect Association)
  • For larger tables, the generated value exceeds 1, making it harder to interpret
A

Phi

21
Q
  • Appropriate for testing the relationship between nominal variables in table of any size
  • Easy to derive once the x^2 test statistic has already been computed
  • Has an upper limit of 1, regardless of the size of the table
  • Has a major issue like Phi where there is no meaningful interpretation for values between the extreme values of 0 and 1.
  • Not sensitive to sample size
A

Cramer’s V

22
Q

Different Methods of Association for Ordinal Variables

A
  1. Spearman’s Rho
  2. Gamma
  3. Somer’s d
  4. Kendall’s tau (tau-b)
23
Q
  • Appropriate for ordinal variables that are continuous in form and have a broad range of different scores and few ties between cases on either variables.
  • Shows the level of agreement or consistency in the rankings between the two variables.
A

Spearman’s Rho

24
Q

Property of Spearman’s Rho that states there are no disagreement in ranks between the two variables (Rs = +1)

A

Perfect Positive Association

25
Q

Property of Spearman’s Rho that states there are perfect disagreement in ranks between the two variables (Rs = -1)

A

Perfect Negative Association

26
Q

Rs^2 indicates the ___? of prediction when predicting the rank of one variable based on the range of another variable

A

Proportional Reduction in Errors

27
Q

Use this method when the level of measurement is nominal and have two dichotomous variables

A

Phi

28
Q

Use this method when the level of measurement is nominal and have two variables with any number of categories

A

Cramer’s V

29
Q

Use this method when the level of measurement is nominal and have two variables with any number of categories (but IV and DV must be specified)

A

Lambda

30
Q

Use this method when the level of measurement is ordinal and have ranked data (If one is inherently ordinal and the other is interval/ratio, both must be expressed into ranks prior to the computation)

A

Spearman’s Rho

31
Q

Use this method when the level of measurement is ordinal and have data with several tied observations

A

Gamma

32
Q

Use this method when the level of measurement is ordinal and have IV and DV that have equal number of categories

A

Kendall’s Tau

33
Q

Use this method when the level of measurement is interval/ratio (both scales are interval/ratio)

A

Pearson’s R

34
Q

Refer to categorical/nominal variables or ordinal (shows the strength or if related)

A

Measures of Association

35
Q

Use of measures to evaluate whether variables are contemporaneous, covary, or coexist in space/ or time.

A

Correlation

36
Q

Correlation = Causation (T or F)

A

F whahaha

37
Q

Cases obtaining high scores on one variable tend to obtain high scores on the second variable (High-High)

A

Positive Relationship

38
Q

Cases obtaining high scores on one variable tend to obtain low scores on the second variable (High-Low)

A

Negative Relationship

39
Q

___? Correlation does not indicate a cause and effect relationship between variables

A

High

40
Q

Failure to find relationship between the variables may be due to:

A
  • The variables are in fact, not related
  • The variables are related but in a non-linear manner
  • The range of values is one or both variables is restricted (truncated)

geog 109 LE 3 (3 decks)

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