Chapter 3 - Theory and Causality

Question 1

Two Goals of Social Science

Answer 1

1. Explain change over time

2. Explain variation

Question 2

Explanation of Variation

Answer 2

If there is variation. there MUST be an independent variable that causes it.

Question 3

Theory Definition and the Explanations of Theorists

Answer 3

A system of ideas; condenses and organizes knowledge

Theorists explain that events are related, how they are related, and why

Question 4

Concept Definition and One Example

Answer 4

Abstract representation of phenomenon

Ex.: Political interest, political knowledge

Question 5

Hypotheses Definition

Answer 5

Statement of anticipated relationship between concepts

Question 6

3 Characteristics of a Useful Hypothesis

Answer 6

1. Clear state of relationship between two variables
2. What is the effect and which comes first?
3. Falsifiable

Question 7

3 Errors in Causal Reasoning

Answer 7

1. Correlation doesn’t equal causation
2. Mixing temporal order (cause and effect mixed up)
3. Post-hoc fallacy - before doesn’t mean it is the cause

Question 8

Positive Relationship

Answer 8

Both concepts more in the same directions (more of a will result in more of b)

Question 9

Negative Relationship

Answer 9

Concepts move in opposite direction

A goes up, B goes down, vice versa

Question 10

Operationalization

Answer 10

Moving from abstract (synonymous with concept??) to concrete
Ex.: Student effort = hours studying

Question 11

Null Hypothesis

Answer 11

Any pattern between the two concepts observed in data is due to chance (default assumption)
There is no relationship between A and B.
Replication ensures a constant test of the null hypothesis.

Question 12

Alternative Hypothesis

Answer 12

Pattern between the two concepts observed in data is not due to chance

Question 13

Falsifiability

Answer 13

In order for a hypothesis to be useful, we have to imagine a scenario in which we could prove it wrong.

Question 14

Five Criteria of Causality

Answer 14

1. Correlation
2. Plausible
3. Temporal Order
4. Not spurious (both caused by a third confounding variable)
5. Consistent (reproducible)

Question 15

Confounding Variable

Answer 15

Extraneous variable that affects both of the correlated variables and makes it seem like there is a relationship between them

Question 16

The Conclusion we must come to if we can’t reproduce results

Answer 16

It’s not useful for there is an assumption that, as theory builds, we get more and more confident in the hypothesis

Question 17

Internal Validity

Answer 17

Results are reality based within the confines of the study.

Basically, we’ve met the criteria of causality.

Question 18

Observational v Experimental Data

Answer 18

Observational data researchers don’t influence the population/thing in any way, and it can only be used to establish association.
Experimental data is derived when researchers directly intervene to alter variables (give one group placebo, one real thing). Can establish causality.

Question 19

Applied v Basic Research

Answer 19

Basic research is scientific research aimed to improve scientific theories for improved understanding or prediction of natural or other phenomena whether or not the results have immediate or obvious applications.
Applied research indicates that the research addresses a topic or problem in the real world.

Question 20

Proposition

Answer 20

States that, if the hypothesis is true, the following predicate of a subject is either true or false.
(Ex.: if mental illness causes creativity, those with OCD will be creative)

Question 21

Continuum

Answer 21

Used when classifying variables that can be ordered or ranked.

Question 22

Ideal Type

Answer 22

A means of classifying concepts in which a non-existent ideal is outlined and observed cases are then compared with this ideal.

Question 23

Typology

Answer 23

Th classification of things on the basis of their characteristics (e.g. a typology of political party system based on the number of competitive parties in the system)
The classification of observations in terms of their attributes on multiple variables. Such classification is usually done on a nominal scale.

Question 24

Hypothesis-Testing

Answer 24

A method used in statistics to test the validity of a statement by comparing expected results with empirical or observed results; typically involves testing the null hypothesis and, based on the results, deciding whether to accept or reject it.

Question 25

Inductive Reasoning

Answer 25

(Theory-making based on evidence)

A type of reasoning that bases conclusions on the presence of empirical evidence; evidence used for theory development

Question 26

Deductive Reasoning

Answer 26

(Assuming theory is true, and logically concluding that theory x is the explanation for this phenomenon)
A type of reasoning that shows or attempts to show that a conclusion necessarily flows from a set of premises; in political science, often used in rational choice analysis, where individuals are assumed to be utility maximizers and their actions are assessed in relation to what they would be if this premise was true.

Question 27

Induction v Deduction

Answer 27

Deduction is finding data to support an argument, whereas induction or inductive reasoning is where an argument is made to explain existing data.

Question 28

Characteristics of a Hypothesis

Answer 28

Relationship - it states a relationship between two variables.
Comparison - it states a comparison between values of the independent variable.
Direction - it states the direction of the relationship if possible
Testability - it is empirically testable

Question 29

Prior Conditions

Answer 29

The nature or status of a situation before the inclusion of an independent effect. (Age, race sex, they proceed dependent variables like political beliefs)

Question 30

Bivariate Relationships

Answer 30

One independent variable causes a dependent variable

Question 31

Multivariate Relationship

Answer 31

Causal relationships that involve more than one independent variable.

Question 32

Spurious

Answer 32

When a relationship between two variables can be accounted for by a third (confounding?) variable.

Question 33

Control Variable

Answer 33

A third variable that we hold constant (for example by doing identical tests on men and women, we could tell whether gender was a cause)

Question 34

Intervening Variable

Answer 34

A variable that comes in between an independent and a dependent (Rain causes floods which causes death. Flood = intervening variable)

Question 35

Reinforcing Variable

Answer 35

A variable that reinforces an already causal relationship. (Women support abortion more than men, women who identify as feminists are even more supportive. Feminism is the reinforcing variable)