Research Design for Political Science Exam 1

Why do scientists favor the null hypothesis?

Scientists favor the null hypothesis because it serves as a neutral starting point in hypothesis testing, suggesting no effect or no relationship between the variables under investigation. This baseline assumption helps maintain objectivity, as researchers do not presume an effect exists without evidence. It aligns with the principles of falsifiability—central to scientific inquiry—where scientists seek to disprove the null hypothesis rather than prove the alternative. By setting the null hypothesis as the default, researchers emphasize the importance of collecting evidence that can confidently reject this baseline position, as described in Kellstedt and Whitten.
Additionally, the preference for the null hypothesis allows for a systematic approach to statistical significance. When researchers test a hypothesis, they look for evidence strong enough to reject the null hypothesis in favor of the alternative hypothesis, typically at a predefined significance level (such as p < 0.05). This process helps control for false positives or Type I errors, where researchers might incorrectly conclude that a relationship exists. By requiring substantial evidence to reject the null, the scientific method ensures that any reported effects are robust and not due to random variation, maintaining the credibility and reliability of research findings.

Briefly explain what an independent variable, a dependent variable, and a theory are

Based on Kellstedt and Whitten, an independent variable is the factor that a researcher believes will influence or cause changes in another variable. It is what the researcher manipulates to observe how it affects the outcome. A dependent variable, in contrast, is the outcome that the researcher is trying to explain or predict, which is expected to change in response to variations in the independent variable. A theory is a logically consistent set of statements that explains a social phenomenon by linking independent and dependent variables through a causal mechanism. It helps to clarify how changes in the independent variable are expected to cause changes in the dependent variable.

In essence, "modernization theory" proposes that economic development leads to democratic development. Are there any potential biases in this causal proposition?

Yes, there are potential biases in the causal proposition of modernization theory, which suggests that economic development leads to democratic development. One bias is selection bias, where cases of economically developed countries that have become democratic are highlighted, while instances of economically successful but non-democratic countries are ignored. Additionally, cultural bias may arise, as the theory often assumes that Western-style economic and political pathways are universally applicable, overlooking the unique historical and cultural contexts of different countries. Endogeneity is another concern, where it might be unclear if economic development causes democracy or if democratic institutions foster economic growth, making it difficult to establish a clear causal direction. These biases can lead to overgeneralizing the relationship between economic development and democracy, as discussed in studies critiquing modernization theory.

Why do the authors recommend "pursue both generality and parsimony"?

Kellstedt and Whitten recommend pursuing both generality and parsimony to create effective theories in political science. Generality refers to the ability of a theory to apply across a wide range of cases, making its explanations and predictions relevant to many different contexts. Parsimony, on the other hand, means that a theory should be simple, relying on the fewest possible assumptions and concepts to explain a phenomenon. By balancing these two principles, researchers can develop theories that are broadly applicable while remaining clear and straightforward. This balance helps avoid overly complex explanations that may be difficult to test, while also ensuring that the theory has a broad scope and is not limited to specific cases.

Why "know local, think global" might be a good strategy for developing a theory?

The strategy of "know local, think global" is beneficial for developing a theory because it encourages researchers to deeply understand specific cases or contexts ("know local") while considering how their findings can apply to broader, more general contexts ("think global"). By starting with a detailed understanding of local or particular phenomena, researchers can identify key mechanisms and variables that may be overlooked at a more general level. Once these mechanisms are well-understood, researchers can abstract them into broader theoretical frameworks that apply to a wider range of cases, increasing the theory's generality. This approach helps to ensure that the theory is grounded in real-world observations and remains applicable across diverse situations, making it both empirically robust and widely relevant.

Mention the three strategies proposed to develop an original theory and explain how to develop one of these strategies.

Kellstedt and Whitten propose three strategies for developing an original theory: identifying a new causal relationship, challenging existing theories, and reconciling contradictory evidence.
To develop a theory by identifying a new causal relationship, researchers look for connections between variables that have not been previously explored or recognized in the literature. This involves observing patterns or phenomena that existing theories do not explain and proposing a new independent variable that might influence a known dependent variable. For instance, if a researcher notices that social media engagement influences political participation in ways that are not accounted for by existing theories, they could propose a new theory that explores the causal link between digital connectivity and civic engagement. By carefully formulating this new relationship, testing it empirically, and showing its explanatory power, a researcher can build an original theory that contributes to the field.

Explain the following sentence: "We suggest considering how a theory might work differently at varying levels of aggregation."

The sentence "We suggest considering how a theory might work differently at varying levels of aggregation" means that researchers should think about whether a theory applies in the same way across different scales or groups, such as individuals, communities, regions, or countries. A theory might produce different outcomes or have different implications when analyzed at the micro level (e.g., individual behavior) compared to the macro level (e.g., national trends). For example, a theory about how economic inequality influences voting behavior might show different patterns when looking at individuals versus entire countries. By examining how a theory functions at different levels of aggregation, researchers can refine their understanding of the scope and limitations of the theory and improve its applicability across various contexts.

Explain the relevance of this hurdle: Is there a credible causal mechanism that connects X to Y?

The hurdle "Is there a credible causal mechanism that connects X to Y?" is crucial because it ensures that a proposed relationship between an independent variable (X) and a dependent variable (Y) is based on a plausible explanation of how X causes changes in Y. This step goes beyond just observing a correlation between the two variables; it requires a logical, detailed account of the process or series of events that lead from X to Y. Without a credible causal mechanism, it is difficult to determine if the relationship is genuine or merely coincidental. Establishing such a mechanism makes the theory more convincing and allows researchers to better understand why and how the effect occurs, thus enhancing the explanatory power and reliability of their findings.

Explain the relevance of this hurdle: Have we controlled for all confounding variables Z that might make the association between X and Y spurious?

The hurdle "Have we controlled for all confounding variables Z that might make the association between X and Y spurious?" is essential for ensuring the validity of a study's conclusions. Confounding variables are external factors that can influence both the independent variable (X) and the dependent variable (Y), potentially creating a false impression of a direct causal relationship between them. If these confounding variables are not adequately controlled for, the observed association between X and Y may be misleading or spurious, suggesting that X affects Y when, in fact, the relationship could be due to the influence of Z.
By addressing this hurdle, researchers can strengthen their claims about the causal relationship by demonstrating that any observed effects are not simply the result of these external influences. This is typically achieved through various statistical controls, experimental designs, or observational methods that account for confounders, ensuring that the analysis focuses on the true relationship between X and Y. This rigor is crucial for drawing credible conclusions and advancing theoretical understanding in political science and related fields.

According to the authors, why a "substantial portion of disagreements between scholars boils down" to the fourth causal hurdle?

According to Kellstedt and Whitten, a "substantial portion of disagreements between scholars boils down" to the fourth causal hurdle—whether all relevant confounding variables have been adequately controlled. This is because researchers often have different perspectives on which variables are important and how they should be measured or controlled for in a study. Disagreements can arise from varying assumptions about causality, the selection of confounding variables, and the methods used to analyze data.
When scholars do not agree on the identification or control of confounding variables, it can lead to conflicting conclusions regarding the relationship between the independent variable (X) and the dependent variable (Y). This hurdle highlights the complexity of establishing causal relationships in social sciences, where numerous factors can influence outcomes. Consequently, addressing this hurdle effectively is critical for advancing knowledge and achieving consensus in research findings, as it directly impacts the validity of the causal inferences drawn from empirical studies.

What is an experiment and why they are useful to address relevant political science questions?

An experiment is a research method in which the researcher manipulates one or more independent variables to observe their effect on a dependent variable while controlling for other factors. This controlled setting allows researchers to establish causal relationships by systematically varying conditions and measuring outcomes.
Experiments are particularly useful in political science for several reasons. First, they can isolate the effects of specific variables, allowing researchers to draw clearer conclusions about cause and effect. For example, an experiment might investigate how different campaign strategies influence voter behavior by randomly assigning participants to different treatment groups. Second, experiments can provide robust evidence to test theories and hypotheses, enhancing the empirical foundation of political science research. Finally, they offer a level of control that is often difficult to achieve in observational studies, helping to eliminate confounding factors and biases that could otherwise distort findings. This methodological rigor makes experiments a valuable tool for addressing relevant political science questions and advancing theoretical understanding.

What is an observational study and what is the difference between a cross-sectional and a time-series observational study?

An observational study is a research method in which the researcher observes and records behavior, events, or conditions without manipulating any variables. This approach is often used when experiments are impractical or unethical. Observational studies aim to identify patterns, correlations, and associations between variables in natural settings.
The main difference between cross-sectional and time-series observational studies lies in their design and focus on data collection:
Cross-Sectional Study: This type of study collects data at a single point in time from a population or a representative subset. It provides a snapshot of a particular moment, allowing researchers to analyze the relationships between variables across different subjects or groups. For instance, a cross-sectional study might assess the voting preferences of various demographic groups in a single election.
Time-Series Study: In contrast, a time-series study collects data at multiple points over time. This design enables researchers to analyze trends, patterns, and changes in variables across different time periods. For example, a time-series study might track changes in public opinion about a political issue over several years, helping to identify shifts and potential causal influences over time.
Overall, the choice between cross-sectional and time-series observational studies depends on the research question and the nature of the variables being examined.

Explains two drawbacks to experimental research designs.

Based on Kellstedt and Whitten, two drawbacks to experimental research designs are external validity and ethical concerns.
External Validity: Experimental designs often take place in controlled environments that may not accurately reflect real-world conditions. As a result, the findings from an experiment may have limited generalizability to broader populations or different contexts. The specific sample used in the experiment may not represent the larger population, and the artificial nature of the setting can affect participants' behavior, leading to results that do not translate well to real-life situations.
Ethical Concerns: Experimental research can raise ethical issues, particularly when it involves manipulating variables that affect participants' well-being or decision-making. For example, researchers may face dilemmas when designing experiments that require deception or when the manipulation of an independent variable could potentially harm participants. Such ethical considerations can limit the types of experiments that can be conducted, making it challenging to explore certain political science questions through experimental methods.

Briefly explain stratified, purposive, snowball, and quota sampling.

Based on Knott, here are brief explanations of four sampling methods used in social science research:
Stratified Sampling: This technique divides the population into distinct subgroups or strata based on specific characteristics (e.g., age, gender). Researchers then randomly select participants from each stratum in proportion to their representation in the overall population. Stratified sampling ensures that all relevant subgroups are represented, enhancing the generalizability of findings.
Purposive Sampling: Also known as judgmental sampling, this method involves selecting participants based on specific criteria relevant to the research question. Researchers use their judgment to identify individuals who can provide rich, informative data, making it useful for studying specialized populations.
Snowball Sampling: This non-probability technique is often used to study hard-to-reach or hidden populations. Researchers start with a small group of initial participants who then refer others, creating a "snowball" effect that helps access individuals not easily identifiable through traditional methods.
Quota Sampling: This method divides the population into subgroups and selects a specific number of participants (quotas) from each subgroup to ensure representation. Unlike stratified sampling, quota sampling is non-random and may involve purposive selection within subgroups.

Provide one example each of a probing question, a specifying question, and an indirect question.

Here are examples of different types of questions used in interviews:
Probing Question: "Can you tell me more about how that experience affected your views on community engagement?"
Specifying Question: "What specific actions did you take to address the challenges you faced during that project?"
Indirect Question: "Some people believe that social media influences political opinions. What are your thoughts on that?"

Discuss two strengths and two limitations of interviews as a research method.

Interviews as a research method have distinct strengths and limitations:
Strengths
In-Depth Understanding: Interviews allow researchers to gather rich, qualitative data by exploring participants' thoughts, feelings, and experiences in detail. This depth of understanding can reveal insights that quantitative methods might overlook, providing context and nuance to the research findings.
Flexibility: Interviews can be adapted in real-time, enabling researchers to probe further into interesting or unexpected topics that arise during the conversation. This flexibility allows for a more dynamic interaction and can lead to the discovery of new information relevant to the research question.
Limitations
Subjectivity and Bias: The interviewer's biases and preconceptions can influence how questions are asked and how responses are interpreted. Additionally, participants may provide socially desirable answers or alter their responses based on perceived expectations, which can compromise the validity of the data.
Time-Consuming and Resource-Intensive: Conducting interviews requires significant time for both the researcher and participants, from scheduling and conducting the interviews to transcribing and analyzing the data. This can limit the sample size and scope of the research, making it challenging to generalize findings to a larger population.

Why building rapport with the interviewees is important, and how it can be achieved?

Building rapport with interviewees is crucial in the interview process because it fosters trust and openness, which can lead to more candid and insightful responses. When participants feel comfortable and respected, they are more likely to share their thoughts and experiences honestly, enhancing the quality and richness of the data collected. A strong rapport can also help reduce anxiety, making the interview experience more positive for both the researcher and the participant.
According to Leech, rapport can be achieved through several techniques:
Establishing a Comfortable Environment: Conducting interviews in a relaxed and neutral setting can help interviewees feel at ease. This includes choosing a quiet location and ensuring privacy, which can encourage openness.
Active Listening and Empathy: Demonstrating genuine interest in the interviewee's responses through active listening and empathetic engagement can strengthen rapport. This involves nodding, maintaining eye contact, and acknowledging feelings or experiences shared by the participant.
Building a Personal Connection: Finding common ground or shared interests can create a sense of connection. Small talk before the interview begins or discussing relevant topics can help establish a more personal relationship.
Clear Communication: Being transparent about the purpose of the interview and how the information will be used can instill confidence in the participant. Ensuring that they understand the process helps build trust and rapport.
By employing these techniques, researchers can create a conducive atmosphere for meaningful dialogue, ultimately enriching the data collection process.

What are grand tour questions, example questions, and prompts, and when should you use each?

Grand tour questions, example questions, and prompts are different types of inquiries used in qualitative interviews to elicit detailed information from participants. Here's a breakdown of each:
Grand Tour Questions
Definition: Grand tour questions are broad, open-ended inquiries that aim to encourage participants to describe their experiences, perspectives, or contexts in a comprehensive way.
Example: "Can you walk me through your experience with [specific topic] from start to finish?"
When to Use: These questions are useful at the beginning of an interview or when exploring a new topic. They help set the stage for the discussion and allow participants to share their narratives without constraints.
Example Questions
Definition: Example questions are more specific and ask participants to provide particular instances or illustrations related to broader topics.
Example: "Can you give me an example of a time when you faced challenges in [specific context]?"
When to Use: Use example questions after establishing the broader context with grand tour questions. They help to clarify and deepen the discussion by prompting participants to share concrete experiences that highlight key themes or concepts.
Prompts
Definition: Prompts are follow-up statements or questions that encourage participants to elaborate on their responses, providing more detail or clarification.
Example: "Could you tell me more about that?" or "What did you feel when that happened?"
When to Use: Prompts are helpful throughout the interview whenever a participant provides a vague or brief response. They allow the researcher to delve deeper into specific areas of interest and obtain richer data.

What are the pros and cons of open-ended questions?

Leech highlights that open-ended questions in semi-structured interviews allow respondents to provide more detailed, nuanced answers, which can lead to deeper insights and uncover unexpected information. The flexibility of these questions is beneficial when exploring complex topics or issues where the interviewer's knowledge is incomplete. However, the drawbacks include the risk of receiving irrelevant or off-topic information, which can make data harder to analyze. Additionally, open-ended questions can lead to lengthy responses, potentially making the interview process more time-consuming and challenging to manage.

Why do the authors praise using a systematic coding procedure for their elite interviews?

Aberbach and Rockman emphasize the importance of using a systematic coding procedure in elite interviews to ensure consistency and reliability in analyzing data. Elite interviews often involve complex and detailed responses, and without a structured coding system, the analysis could become subjective and prone to bias. A systematic approach helps in organizing the data, identifying patterns, and drawing meaningful conclusions. It also allows researchers to compare responses across different interviews and ensures that the research findings are transparent and replicable, thus enhancing the credibility of the study.

The author says that interviewees have no obligation to be objective and tell the truth. What suggestions does he provide to address this problem?

In Berry acknowledges that interviewees, particularly elites, may not feel obligated to provide objective or truthful responses. To address this problem, Berry suggests several strategies. First, he advises triangulating interview data with other sources, such as documents or interviews with other individuals, to verify the accuracy of statements. Second, he recommends building rapport with the interviewees to encourage more candid responses. Finally, he emphasizes the importance of critical evaluation, where researchers must remain skeptical and cross-check answers against known facts or patterns to detect inconsistencies or biases.

According to the author, when interviewers have reasons to probe interviewees?

According to Berry, interviewers should probe interviewees when responses are vague, incomplete, or when they suspect that the interviewee is being evasive or holding back important information. Probing is also necessary when the interviewee's answers seem inconsistent with other data or known facts, as this can reveal deeper insights or clarify ambiguities. Additionally, probing can help uncover nuances or details that the interviewee might not initially provide, ensuring that the interviewer fully understands the subject matter and gathers richer, more complete data for analysis.

Discuss two reasons the author provides for conducting elite interviews.

Tansey provides two key reasons for conducting elite interviews. First, elite interviews allow researchers to gain access to privileged information and insider perspectives that may not be publicly available, helping to clarify decision-making processes and the motivations of key actors. This is particularly valuable in process tracing, where understanding causal mechanisms is essential. Second, elite interviews can help researchers verify or challenge existing evidence, offering a way to triangulate data and ensure the validity of conclusions drawn from other sources such as documents or public statements.

The author advocates for using a non-probability sample in elite interviews. What are the advantages and disadvantages of his proposition?

Tansey advocates for using non-probability sampling in elite interviews, emphasizing that it offers several advantages. One key benefit is that non-probability sampling allows researchers to target individuals who possess unique, specialized knowledge relevant to the research question, making the data more focused and contextually rich. This method is particularly useful in process tracing, where the goal is to explore causal mechanisms rather than generalize to a larger population. However, there are also disadvantages. Non-probability samples lack representativeness, meaning the findings may not be generalizable to the broader population. This introduces the risk of selection bias, as the chosen elites might have perspectives that differ from those who were not interviewed, potentially skewing the conclusions drawn from the research.

When is it more appropriate for a researcher to conduct a case study? Justify your answer, including comparisons to other social science research methods.

Yin argues that a case study is most appropriate when the researcher seeks to explore "how" or "why" questions, particularly when investigating contemporary phenomena within real-life contexts where the boundaries between the phenomenon and the context are not clearly defined. Case studies are ideal for situations where the researcher has little control over events and aims to understand complex processes or causal relationships. Compared to other methods, like experiments, which manipulate variables in controlled settings, or surveys, which capture broad patterns but may lack depth, case studies provide detailed, in-depth insights into specific cases. They allow for the exploration of unique instances, which can uncover new variables or processes that other methods might overlook.

How does the author define case studies? Do not provide a verbatim answer, but address the "twofold definition."

Yin defines case studies through a twofold approach. First, he describes them as an empirical method that investigates contemporary phenomena within their real-world context, especially when the boundaries between the phenomenon and its context are not clear. Second, he emphasizes that case studies rely on multiple sources of evidence, which allows for triangulation and a more comprehensive understanding of the subject. This dual focus distinguishes case studies from other methods by integrating context and complexity into the research, while also grounding findings in diverse forms of data.

Discuss the four major types of case studies.

Yin outlines four major types of case studies: exploratory, descriptive, explanatory, and multiple-case studies. Exploratory case studies are used when researchers seek to define the questions or hypotheses for further study. Descriptive case studies provide a detailed account of a particular phenomenon within its context. Explanatory case studies aim to explore causal relationships, investigating how or why certain events occur. Lastly, multiple-case studies involve studying several cases simultaneously or sequentially, allowing for comparisons and the development of broader insights.

The author says case studies can be critical, extreme, common, revelatory, or longitudinal. Briefly explain three.

Critical case studies are selected because they provide a crucial test of a theory or framework. If a theory holds true in a critical case, it is likely to hold true in others, making these cases vital for testing hypotheses.
Extreme case studies focus on unusual or outlier cases that are either highly successful or have experienced significant failures. These cases offer valuable insights due to their distinctiveness and can challenge existing assumptions or theories.
Revelatory case studies are conducted when a researcher gains access to a phenomenon that was previously inaccessible or understudied, offering new insights into processes or dynamics that were not previously observed.

Some people believe that the Apollo 11 moon landing in 1969 was faked and filmed on a movie set.

How could this belief pass a hoop test and a smoking-gun test? Explain both tests first, then discuss

what kind of evidence would be needed to pass each of them.

Hoop Test: A hypothesis must pass this test to remain viable, but passing it does not confirm the hypothesis. Failure results in rejecting the hypothesis, but passing only shows that the hypothesis is still possible. For the claim about the Moon Landing, a hoop test would require evidence showing that there was an opportunity or context for such a substitution to occur, such as historical records indicating secrecy or unusual behavior surrounding the moon landing. If such records existed, the hypothesis could continue being considered, but passing alone wouldn't prove the claim.
Smoking Gun Test: This test provides stronger evidence. If the hypothesis passes, it gives high confidence that the hypothesis is true. If it fails, the hypothesis can still survive. To pass a smoking gun test for the claim that the moon landing was fake, conclusive evidence would be required—such as physical records proving a fake video, movie set, and communications surrounding this event. Without such definitive evidence, the hypothesis would not pass the smoking gun test.
Thus, the belief about the moon landing might pass a hoop test if there are historical ambiguities or irregularities surrounding the landing, but without clear, irrefutable proof of a movie set that filmed the landing, it would fail the smoking gun test.

Bigfoot is real. How can this belief pass a straw-in-the-wind test and a doubly decisive test? Again, explain both tests before discussing the evidence needed to pass them.

Straw-in-the-Wind Test: This test provides weak evidence for or against a hypothesis. Passing this test slightly strengthens the hypothesis, while failing it slightly weakens it, but neither outcome decisively proves or disproves the hypothesis. For Bigfoot, a straw-in-the-wind test could be anecdotal evidence, such as witness accounts or blurry photographs. These accounts wouldn't prove Bigfoot's existence, but if many credible witnesses consistently describe similar features, the hypothesis gains minor support. However, passing this test alone does not provide strong confirmation, and failing it doesn't fully discredit the belief.
Doubly Decisive Test: This test is much more powerful—passing it can both confirm the hypothesis and rule out alternatives, while failing it can disprove the hypothesis. To pass a doubly decisive test for Bigfoot's existence, conclusive evidence is required, such as a confirmed biological specimen (like a body or DNA) or clear, scientifically verified footage. This would definitively prove Bigfoot's existence while ruling out hoaxes or misidentifications as explanations. Without such undeniable evidence, the belief would fail the doubly decisive test.

Explain: "I have tried to show that any nonexperimental research that makes causal claims, be it of the large-N or small-N variety, must confront counterfactuals in the form of key assumptions or in the use of hypothetical comparison cases.

In this statement, Fearon emphasizes that any research aiming to make causal claims—whether involving large-N studies (which analyze data from many cases) or small-N studies (which focus on a few specific cases)—must address counterfactuals, referring to the consideration of alternative scenarios or outcomes that could have occurred under different conditions. Researchers cannot simply observe outcomes and claim causality without contemplating what might have happened if key factors had changed. For instance, in a small-N study, a researcher might examine a specific historical event and analyze how different decisions might have led to different outcomes, while in a large-N study, researchers make assumptions about relationships and influences across many cases, requiring hypothetical comparisons to test their theories. By confronting counterfactuals, researchers clarify the assumptions underlying their causal claims, strengthening their analysis and ensuring that conclusions drawn from nonexperimental data are robust and systematically examined. Ultimately, addressing counterfactuals is essential for rigorous hypothesis testing and making valid causal inferences in political science research.

Why "Counterfactual analysis is often seen as a tool to support a contingent and anti-determinist world view"?

Levy argues that counterfactual analysis is often viewed as a tool that supports a contingent and anti-determinist worldview because it emphasizes the role of human agency, choices, and unpredictable events in shaping historical outcomes. By exploring what might have happened under different circumstances, counterfactual analysis highlights the contingent nature of historical events, suggesting that outcomes are not predetermined but rather the result of specific decisions and actions taken by individuals or groups. This approach counters deterministic views that see history as unfolding according to fixed laws or inevitable trends. Instead, counterfactuals reveal the potential for multiple outcomes based on varying choices, underscoring the complexity of causal relationships in history. Ultimately, this perspective allows for a more nuanced understanding of historical events, acknowledging that they can be influenced by a variety of factors, including chance and human agency.

What criteria does the author propose to evaluate counterfactuals? Explain three.

Levy proposes several criteria for evaluating counterfactuals to ensure they are credible and meaningful, Plausibility, Relevance, Specificity, Feasibility, Consistency, & Causal Mechanisms.
Plausibility: Counterfactuals must be grounded in plausible scenarios based on historical context and available evidence. This means that the alternative outcomes considered should be consistent with what is known about the events, actors, and conditions of the time. If a counterfactual scenario is highly implausible or disconnected from the historical reality, it diminishes its usefulness for analysis.
Relevance: The counterfactuals examined should be relevant to the causal question being investigated. This means they must directly address the factors or decisions that are believed to influence the outcome in question. Irrelevant counterfactuals, while interesting, do not contribute meaningfully to understanding the causal mechanisms at play and can lead to confusion or distraction from the core analysis.
Specificity: Counterfactuals should be specific enough to allow for clear comparisons between the actual historical outcomes and the proposed alternatives. This involves detailing the conditions and variables involved in the counterfactual scenario, which enables researchers to assess how different choices or circumstances might have led to different outcomes. Vague or overly broad counterfactuals fail to provide the necessary clarity for meaningful causal inference.