W2/3: Practice Questions for Associations Flashcards

1
Q

What does the term “association” mean in the content of a RQ

A

A particular kind of relationship between two constructs.

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2
Q

What if the measurements of 2 constructs are categorical. Talk about how the relationship

A

Contingency Table.

Occurrence of one construct is contingent with particular categories in the other construct

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3
Q

What if the measurements of 2 constructs are continous. Talk about how the relationship

A

Correlation/Correlation.

Values of one construct measure vary SYSTEMATICALLY with values on a second construct

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4
Q

What is the difference between a covariance and a correlation? What do the values reflect?

A

Covariance: Unstandardized measure of strength and direction of association between scores on two continuous variables

Correlation: Standardized measure of strength and direction of association between scores on two continuous variables

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5
Q

What do the values of covariance and correlation reflect?

A

Covariance: Values reflect scaling of 2 sets of scores for 2 variables.

Correlation: Z scores.

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6
Q

How are covariance, a correlation, and a variance similar to each other?

A
  1. ) Calculated using sum of products of deviation scores of one form or other
  2. ) An average measure of covariability (i.e., covariance and correlation) or variability (i.e., variance).
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7
Q

If a 95% confidence interval for a correlation does not capture zero, what can be inferred from this result?

A

95% indicate 0 (i.e., no linear association) is not a plausible value for population correlation

a) 95% confident that a non-zero association is occurring at the population level.

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8
Q

If a 95% confidence interval for a correlation capture zero, what can be inferred from this result?

A

0 is one of the plausible values for the population correlation parameter.

a) Cannot rule out no association (i.e., a correlation of 0) as being a likely results based on sample data.
b) Lower and upper bound values different in sign = Cannot reasonably infer what the direction of the association might be, even if one of the non-zero values within the interval were the actual population parameter value.

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9
Q

What can we use to quantify the precision of a sample correlation estimate? Give an example.

A

Precision of a sample correlation = Width of 95% confidence interval (the range of correlation values between the lower bound and the upper bound).

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10
Q

How can the degree of precision for a sample correlation be clearly indicated when using this method? Give an example.

A

Main determinant of precision: Sample size

Larger = Narrow interval (all other aspect equal)

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11
Q

How is a contingency table constructed?

A

Cross-classifying each category in one variable with all categories in a second variable

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12
Q

What are the typical units of observation in a contingency table?

A

Contingency table = I x J cells (I = categories in row; J = categories in column)

Values in each cell (frequency) = No. of people who are members in both row and column.

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13
Q

Give an example of an imprecise CI for an association vs precise CI

A

0.20 to 0.90 is imprecise because it covers nearly 70 percent of the possible range. 0.20 is 0.30 is much more precise because it covers only 10 percent of the possible range.

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14
Q

What are the observed frequencies in a contingency table.

A

Number of people uniquely defined by one of the row categories and by one of the column categories,

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15
Q

What are the expected frequencies in a contingency table?

A

Number of people who would have been uniquely defined by one of the row categories and by one of the column categories if there actually was INDEPENDENCE between the row and column categories. (No association)

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16
Q

How do expected frequencies differ from observed frequencies?

A

Observed: No. of people measures in each cells defined by joint categories from 2 variables making up the table
Expected: From observed marginal row and column frequencies under assumption of independence

17
Q

What is a joint cell in a contingency table? How many joint cells does an I x J contingency table have

A

A joint cell in a contingency table is the cell in the table uniquely defined by one of the row categories and by one of the column categories. An I x J contingency table will have I x J joint cells.

18
Q

What do sets of all observed frequency in IxJ cells sum to?

A

Set of all observed frequencies in the I x J cells in the contingency table summing up to the sample size.

19
Q

What do the subscripts mean in the general notation f_o_ij? How is the concept of a joint cell related to this kind of notation?

A

The subscripts ij in the notation f_o_ij mean the joint cell defined by i and j, where i = 1, 2,…,I and j = 1, 2,…,J .

20
Q

What is the meaning of the terms odds?

A

Probability occurring = P
Probability no occurring = 1 - P

Ratio of the probability of event occurring to it not occurring is given by P / (1-P) = Odds = Expressing probability of any event occurring

21
Q

What is the meaning of the terms odds ratio?

A

Ratio of two sets of odds: Relative odds of two events jointly occurring relative to them not occurring.

Each set of odds is the ratio of the probability of one category in a variable being present to the probability of it not occurring within each of two categories of a second variable.

22
Q

What is the range of possible values for an odds ratio? What does a value of 1 for an odds ratio signify?

A

An odds ratio can range in value from 0 to +infinity , and it can never be negative (Frequency counts cannot be negative).

Population/sample odds ratio of 1 indicates independence between the row and column variables in a contingency table.

23
Q

If we calculate the reciprocal of an odds ratio value that is < 1 (i.e., we invert the value), what is the resultant value of the odds ratio now

A

The reciprocal of an odds ratio value < 1 will be a value that is > 1