Final Concepts

Question 1

Abstract

Answer 1

succinct summary of research that includes: RQ, methodology/approach, key findings, and implications of the study/contributions. Single paragraph, usually 150-200 words, mitigate stress of having to read and comprehend entire study

Question 2

Additive Relationships

Answer 2

the control variable is the cause of the DV but defines a small compositional difference across values of IV. Because relationship between IV and Z is weak, IV retains causal relationship with DV after controlling for Z.

Question 3

Ha and Rival IVs

Answer 3

Ha= mean< Ho value, mean> Ho value, or mean is different than Ho value. rivals: represented by Z, can pose a large or small threat to IV or X

Question 4

Anomalous Case

Answer 4

deviating from or inconsistent with the other cases being observed, or irregular; abnormal from the hypothesized phenomenon/theory

Question 5

Authoritative

Answer 5

official (i.e. govt. docs, press releases, publications of organizations), authored by a knowledgeable source (i.e. scholarly articles, primary accounts of history), or supremely confident (i.e. opinion articles)

Question 6

Case

Answer 6

(George and Bennett’s definition): a case is an instance of one specific phenomena or class of events

Question 7

Case Study

Answer 7

A case study is the intensive study of a single unit for the purpose of understanding a larger class of (similar) units. We do case studies in order to better understand certain classes of events or types of phenomena, not just to learn about the specific units that we study. Gerring: A case study looking to investigate causality using a single unit will involve comparing that unit’s values on variables either across time, within subunits, or across time and within subunits.
Case Study Type I: Variation in a single unit across time

Ex. The case of France in 1788 and case of France in 1789

Case Study Type II: Variation in subunits of a single unit at one time

Ex. The cases of Williamsburg in 1867, Richmond in 1867, Norfolk in 1867, Alexandria in 1867

Case Study Type III: Variation in subunits of a single unit across time

Ex. The cases of Williamsburg in 1867, Williamsburg in 1892, Norfolk in 1867, Norfolk in 1892…

(Professor Brown’s) Case Study Type IV: Counterfactuals

Ex. The case of Europe in 1914 with the assassination of FF and the counterfactual case of Europe in 1914 with
the failed assassination attempt.

Question 8

Causal Effect

Answer 8

something has happened, or is happening, based on something that has occurred or is occurring. A simple way to remember the meaning of causal effect is: B happened because of A, and the outcome of B is strong or weak depending how much of or how well A worked.

Question 9

Causal Mechanisms

Answer 9

the processes and intervening variables that link together the cause and effect.

Question 10

Counterfactuals and Counterfactual Analysis

Answer 10

reverse of proposed relationship (if x then y changes to if not x then not y), counterfactual analysis used often when we don’t have evidence of counterfactual because it hasn’t happened, parallel or alternate worlds in which key features of real world were not present/ were different values, imagine x didn’t happen, when it did, trying to prove that lack of IV leads to diff. DV to prove causal relationship b/t the two

Question 11

Content Analysis

Answer 11

Content analysis is a research tool used in document analysis to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings and relationships of such certain words, themes, or concepts. Researchers can then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time of surrounding the text.

Question 12

Control Group

Answer 12

subjects who do not receive the given treatment

Question 13

Control Variables

Answer 13

variables held constant during experimentation

Question 14

Cotenability Requirement

Answer 14

(counterfactual analysis): that scholars must also consider what else might have to change, or stay the same, in order for their counterfactual antecedent to be true and make sure they all align

Question 15

Covariation

Answer 15

(how it applies to causal analysis): as 1 variable changes, does the other variable change in some sort of systematic way?

Question 16

Deductive Reasoning

Answer 16

general to specific

Question 17

DV

Answer 17

Y, effect

Question 18

Descriptive Analysis

Answer 18

Descriptive analysis is an important first step for conducting statistical analyses. It gives you an idea of the distribution of your data, helps you detect outliers and typos, and enable you identify associations among variables, thus preparing you for conducting further statistical analyses.

Question 19

Deterministic Relationships

Answer 19

invariant/ causal relationships are always true

Question 20

Empirical Implications

Answer 20

(see process-tracing): The next step of process tracing involves looking for evidence of the empirical implications of the causal steps of your theory. Good process-tracing will also involve looking for evidence of alternative theories, you want to find evidence that other potential causal processes for the DV did not occur

Question 21

Endogeneity

Answer 21

when you think that IV causes DV but actually DV influences IV, i.e. economic inequality and democracy: some argue economic inequality diminishes democracy, however some argue that lack of democracy actually causes economic inequality

Question 22

Experiment

Answer 22

a scientific procedure undertaken to make a discovery, test a hypothesis, or demonstrate a known fact.

Question 23

Experimental Group

Answer 23

treated group

Question 24

External Validity

Answer 24

degree to which results of a study can be generalized outside of the parameters of experiment and or across diff. populations, times, and settings

Question 25

Field Experiments

Answer 25

subjects studied in natural environment, introduces possibility that other IVs will affect experiment

Question 26

Framing

Answer 26

act of communicating info in a way that promotes a particular understanding

Question 27

IV

Answer 27

X, cause

Question 28

Inductive Reasoning

Answer 28

specific to general

Question 29

Inference

Answer 29

process of using known facts to learn about unknown facts

Question 30

Interaction Relationships

Answer 30

relationship b/t IV & DV depends on value of control variable, direction of relationship + strength may change depending on value of control

Question 31

Internal Validity

Answer 31

degree to which the results represent true cause and effect relationship b/t IV & DV, did the experiment actually isolate the effect of IV on DV from other potential explanations?

Question 32

Intervening Variables

Answer 32

mediators b/t cause and effect

Question 33

Journalism v. Scholarship

Answer 33

both should share goal of truth, but non-scholarly audiences may have other goals such as furthering political debate to fuel coverage. Some misreport, others don’t report at all because research might suggest that what’s in the news isn’t actually important. Scholars can aid journalists by providing structural context for daily news, identifying stories worthy of coverage, countering elite spin, identifying historical comparisons, and identifying q’s that scholars seek to answer but cannot.

Question 34

Lab Experiments

Answer 34

subjects studied in environment created wholly by investigator, great for max control, may not approximate real world conditions, subjects are aware they are being experimented on

Question 35

Large-N v. Small-n

Answer 35

n=sample size, large sample size, small sample size/ number of cases

Question 36

Latent Events

Answer 36

In statistics, latent variables, as opposed to observable variables, are variables that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed (directly measured).

Question 37

Least-Likely Case

Answer 37

A least likely case, con- versely, is a tough test for a theory because it is a case in which the theory is least likely to hold true.

Question 38

Method of Difference

Answer 38

(Most similar cases): two similar cases in all relevant ways except for value on IV, if they also differ in DV, conclude IV causes DV

Question 39

Method of Similarity

Answer 39

Most different cases): causality addressed by comparing two cases that differ in all ways but one: key IV, if cases take on same Dv, conclude that common IV causes DV

Question 40

Mixed-Methods Research

Answer 40

involves at least 2 diff. types of research methods (qualitative, quantitative, formal modeling)

Question 41

Most-Likely Case

Answer 41

A most likely case is one that is almost certain to ‹t a theory if the theory is true for any cases at all. The failure of a theory to explain a most likely case greatly undermines our confidence in the theory.

Question 42

Necessary Conditions

Answer 42

this IV must happen in order to see the DV

Question 43

No-Variance Designs

Answer 43

where cases under study all have same value on DV or IV, scholars use this design with anomalous cases

Question 44

Post-Testing

Answer 44

measures subject’s values on DV after treatment, makes potential causal relationships clear and easy to measure

Question 45

Pre-Testing

Answer 45

measures subject’s values on DV prior to treatment

Question 46

Primary Sources

Answer 46

original source material on an event, including all evidence contemporary to the event

Question 47

Probabilistic Relationships

Answer 47

aren’t always true, case studies aren’t good for assessing these relationships, can study cases where relationship doesn’t occur and discount probabilistic as deterministic

Question 48

Process-Tracing

Answer 48

identify and follow causal mechanisms through analysis of how a causal process plays out in an actual case, ID and find empirical evidence for the steps that lead the IV to cause/influence DV w/in that case, often in conjunction with case studies. good for understanding causal mechanisms (process through which IV interferes with DV), can help identify intervening variables, start with a theory, look for steps between IV and DV, need really well defined theories, usually only used for small-n, can be good for refining theory

Question 49

Qualitative v. Quantitive Methods

Answer 49

qual: testing causal mechanisms (ideas, thought processes), quant: estimating causal effects in same study (structural variables) (also consider how this relates to case selection)

Question 50

Scope Conditions

Answer 50

conditions under which a given theory applies or the universe of cases to which a theory applies

Question 51

Secondary Sources

Answer 51

written about an event after it occurred (i.e. work by historians)

Question 52

Selecting on DV

Answer 52

selecting cases based on their values on DV rather than IV, biases the study, limits variation

Question 53

Spurious Relationships

Answer 53

in terms of Z, spurious relationships b/t IV and DV can mean: control defines large compositional diff. across values of IV or dist. of scores for Z is diff. for each value of IV, Z causes IV and Z causes DV, not Z causes IV which causes DV (intervening variable)

Question 54

Sufficient Conditions

Answer 54

doesn’t have to happen but if it does, its enough to cause the DV

Question 55

Theory Building

Answer 55

clearly specify the causal mechanisms and causal process

Question 56

Theory Testing

Answer 56

Once the researcher has theorized the causal process linking the IV(s) and DV, the next task is figuring out what the empirical implications of each step in the process will be, “If my theory is correct, then what outcomes should I see along the way?”

Question 57

Treatment

Answer 57

condition researcher applies to participants

Question 58

Triangulation

Answer 58

1) diversification of sources to bring more info, 2) double-checking one source against another to reduce uncertainty, important for establishing causality and describing intent and motivation

Question 59

Truth Decay

Answer 59

“Where basic facts and well-supported analyses of these facts were once generally accepted, disagreement about even objective facts and well-supported analyses has swelled in recent years.”

elements: blurred line b/t opinion and fact, increased volume and value of opinion, diminished trust in formerly respected institutions
causes: cognitive biases, changes in info system, polarization, competing demands on educational system

Question 60

Type 1 Error

Answer 60

rejection of a true null hypothesis. Usually a type I error leads to the conclusion that a supposed effect or relationship exists when in fact it does not. Examples of type I errors include a test that shows a patient to have a disease when in fact the patient does not have the disease, a fire alarm going on indicating a fire when in fact there is no fire, or an experiment indicating that a medical treatment should cure a disease when in fact it does not.

Question 61

Type 2 Error

Answer 61

failure to reject a false null hypothesis. Some examples of type II errors are a blood test failing to detect the disease it was designed to detect, in a patient who really has the disease; a fire breaking out and the fire alarm does not ring; or a clinical trial of a medical treatment failing to show that the treatment works when really it does.

Question 62

Universe of Cases

Answer 62

All cases that comprise a class of events or phenomena