Chapter 2, 3 & 15 (page 463-470)

Question 1

Deductive reasoning

Answer 1

From general info to specific

Question 2

Inductive reasoning

Answer 2

From observations to making generable info & looking back / \

Question 3

Hypotheses

Answer 3

An expected but unconfirmed relationship between two or more variables

Question 4

Explanatory variables

Answer 4

Variables that are the object of study, all other variables are extraneous

Question 5

Dependent variables (y)

Answer 5

The variable that is being explained and predicted / outcome

Question 6

Independent variables (x)

Answer 6

The variables that do the influencing and exploring / cause

Question 7

Bivariate hypothesis

Answer 7

Expected relationship between just two variables (are all possible variables and has a total effect)

Question 8

Multivariate hypothesis

Answer 8

Expected relationship betwee a dependent variable Y and multiple variables X

Question 9

Different multivariate hypothesis:

Answer 9

• Multiple causality
• Mediation (partial mediation)
• Moderating effect
• Spurious relationship

Question 10

Multiple causality

Answer 10

Relative importance of independent variables

Question 11

Mediation

Answer 11

An indirect effect (X1 –> X2 –> Y)

Question 12

Partial mediation

Answer 12

An direct + indirect effect

Question 13

Moderating effect

Answer 13

Intensifier or suppressor

Question 14

Spurious relationship

Answer 14

An observed relationship between X1 and Y is spurious because they share a common cause X2

Question 15

Common cause

Answer 15

Antecedent

Question 16

Explanatory hypothesis

Answer 16

Explanation

Question 17

Elaboration

Answer 17

Our understanding of a bivariate relationship by introducing a ‘third’ variable in contingency tables (cross/tabulations). Applies the moderation, mediation and spuriousness.

Question 18

Simpson’s paradox

Answer 18

When you are comparing two variables there seems to be a relationship, but when you are splitting the data into two groups on a third variable, it is the other way around. = So, you can draw two opposite conclusions from the same data depending about how you split things up.

Question 19

Causality, 3 necessary conditions:

Answer 19

1. Association (variables must have a statistical association)
2. Direct relationship (influence should be from cause to effect)
3. Nonspuriousness (no extranous variables are allowed to explain the relationship between the variables of interest).

Question 20

Units of analysis

Answer 20

The researched entities (objects/events)

Question 21

Nested data (multilevel)

Answer 21

Combining data from different units of observation in which individual cases constitute elements of larger groups

Question 22

External aggregation

Answer 22

Data from previous research

Question 23

Internal aggregation

Answer 23

Aggregating individual level data (data you collect)

Question 24

Logical fallacies

Answer 24

Drawing conclusions at one level while analyzing findings at another level

Question 25

Two types of Fallacies

Answer 25

Ecological fallacy & Atomistic fallacy

Question 26

Ecological fallacy

Answer 26

Drawing conclusions at the individual level while analyzing group level data

Question 27

Atomistic fallacy

Answer 27

Analysis of individual level data is used to draw conclusions on aggregate level data