Ch12 Flashcards

1
Q

There are several ways in which relationships between variables can be described. explain

A

presence
direction
strength of association
type

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2
Q

Presence

A

tells us whether a consistent and systematic relationship exists

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3
Q

direction

A

tells us whether the relationship is positive or negative

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4
Q

strength of association

A

tells us whether we have a weak or strong relationship

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5
Q

What type of relationship does strengh of association have ?

A

linear or nonlinear

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6
Q

what defines linear?

A

As long as the amount of change stays constant over the range of both variables

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7
Q

Curvilinear

A

relationships between two variables that change in strength or direction as the values of the variables change

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8
Q

What do covariation and association attempt to do?

A

they try to quantify the strength of the relationship between 2 variables

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9
Q

covariation

A

the amount of change in 1 variable that is constantly related to change in another one

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10
Q

degree of association

A

numerical measure of strength of the relationship between 2 variables

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11
Q

what type of relationship do association and covariation have?

A

linear relationship

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12
Q

Pearson correlation

A

the measure of linear association between two variables of interest

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13
Q

when should pearson correlation be used ?

A

when both variables are measured on an interval or ratio scale

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14
Q

when should spearman correlation be used ?

A

when one or more variable are measured on an ordinal scale (rank)

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15
Q

what is regression analysis useful for?

A

answering questions about the strength of a linear relationship between a dependent and 1 or more independent variables

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16
Q

What do the results of regression analysis indicate ?

A

The amount of change in the dependent variable that’s associated with a one-unit change in the independent variable

17
Q

what does structural modeling enable researchers to do?

A

analyze complex multivariate models

18
Q

what is the most appropriate structural model method ?

A

partial least squares (PLS)

19
Q

what is the PLS method

A

it’s an extension of ordinary least squares multiple regression

20
Q

what’s the goal of partial least square? PLS

A

maximize the variance explained in the dependent variable (s)

21
Q

advantage of partial least square (PLS)

A
  • ability to examine structural models with 3 or more stages
  • to include constructs measured with several questions
  • analyze data with non-normal distributions
  • obtain solutions with smaller sizes
22
Q

statistical significance

A

about whether something is real or just a fluke in data

23
Q

practical significance

A

whether the significant difference : result actually matters in real world