Week Four Flashcards

(15 cards)

1
Q

Demonstrate that 2 variables are
related or change together.

A

Association

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2
Q

Determine that the relationship it is
not due to variation in a third

A

Non-spuriousness

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3
Q

Demonstrate that 1 variable
occurs before another variable.

A

Temporal Ordering

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4
Q

Correlation describes a statistical association
between two or more variables, meaning they
tend to change together.
Spuriousness
 When an apparent relationship between two
variables is the result of third variable
(confounding variable) influencing both or
one of them.
 For instance, ice cream sales are
correlated with crime rates.
 Best ways to assess spuriousness: use
control variables, direct observation,
longitudinal design.

A

Non-Spuriousness

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5
Q

A variable that is held constant so that the relationship between
two (or more) other variables can be assessed.
Ways to show that the dependent variable changes due to the
independent variable.

A

Control Variable

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6
Q

Data collected from at one point in time.

A

Cross-Sectional

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7
Q

Data collected over multiple points in time

A

Longitudinal

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8
Q

 Longitudinal study in which data are collected from the same
subjects at multiple time points.
 Observes same set of individuals changing over time as
opposed to groups.

A

Panel design (fixed sample)

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9
Q

 Type of longitudinal study design in which data are collected
from different members within a particular subpopulation (cohort)
at multiple time points.

A

Cohort design (AKA event-based)

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10
Q

Relatively inexpensive
No need to recontact subjects
Can be quickly implemented to address current events
and hot-button issues

A

Cross-Sectional research pros

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11
Q

Cannot assess causal ordering

A

Cross-Sectional research cons

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12
Q

Can assess causal ordering and change over time

A

Longitudinal Research Pros

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13
Q

Attrition, or the loss of sample members over time, usually
to death or dropout, in panel studies
Costs are higher.

A

Longitudinal Research Cons

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14
Q

When a relationship has already been established, but a
third variable explaining the relationship has not been
discovered
For instance,
X—>Y
How does “Z” influence this relationship?
X->Z->Y

A

Introduce new relationships

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15
Q

What is the order of the relationship?
“Flip relationship on it’s head”
Does the independent variable cause the dependent variable, or
is it the other way around?
Often called “selection effects”

A

Reverse causation

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