Inclass Discussion

Political Methodology 2022, IPS-NSYSU

Author

Prof. Frank Liu

Published

December 1, 2022

TOPIC 1: Thinking about the Discipline in the Era of Data Science

the outlook of the discipline

Title Sequence Iamge of "Game of Throne"

TOPIC 2: What Do We Really Know?

sources of (scientific) ignorance

  • via epistemology stances
  • via methodological stances
  • via methods
  • via data formats

visualization of the epistemology divided world

Figure 1: author (c) Frank Liu

scholars vs. experts

  • both emphasize learning and training
  • A scholar is a person who studies an academic subject and knows a lot about it.
  • An expert is a person who is very skilled at doing something or who knows a lot about a particular subject.
  • their life styles and attitudes toward fact, truth, and knowledge may differ.

reality vs. fact vs. truth vs. knowledge

  • You use reality to refer to real things or the real nature of things rather than imagined, invented, or theoretical ideas.
  • When you refer to something as a fact or as fact, you mean that you think it is true or correct.
  • The truth about something is all the facts about it, rather than things that are imagined or invented.
  • Knowledge is information and understanding about a subject which a person has, or which all people have.

So, a scholar is

likely to be an expert in the field of choice and who is willing to make contributions to our knowledge wherever he or she will be.

How positivists think

  • The holy purpose: EXPLANATION (or using technical term, causal inference)
  • Goal of research: THEORIZATION
  • methodology: induction and deduction

methodology of positivism

induction

A method of reasoning in which you use individual ideas or facts to give you a general rule or conclusion.

deduction

The process of reaching a conclusion about something because of other things that you know to be true.

positivism the majority

the beauty of positivism

We find evidence strongly suggestive of a positive effect of plurality rules [X] on opposition harassment [Y], even after accounting for threats to causal inference” (Glynn and Ichino 2014, 1056).

This sentence includes almost the most critical key words that reflect the positivist way of thinking.

challenges embedded in positivism

  • measurement issues: validity and reliability (Merom and John 2019)
  • overstatement: “When causal effects are heterogeneous, randomized experiments identify average effects but not individual effects” (Glynn 2012, 258).

for more discussion

  1. Why some people believe they could measure anything (e.g., Hubbard 2014)
  2. How accurate is accurate enough when you like to measure something?
  3. What would be the best (acceptable) method you could choose to study relationships between concepts?
  4. What do scholars in your APSA section of choice say about positivism? (students, join the intellectural clubs for free)

TOPIC 4: Scientific Realism and the Discipline’s Evolutionary Epistemology

a positivist mind map is like this

graph LR

  X1[ X1 ] -->|Hypothesis 1| Y(Y)
  X2[ X2 ] --> |H2| Y(Y)

subgraph Someone's Model
X1 & X2 & Y
end

competition between models as a positivist way to accumulate knowledge

graph LR
  X1[ X1 ] -->|H1| Y(Y)
  X2[ X2 ] -.-> |H2 ?| Y(Y)
  X3[ X3 ] -.-> |+ H3| Y(Y)
  
subgraph Your Model
X1 & X2 & X3 & Y
end

a scientific realist’s mindmap is like this

graph LR
  X1[X1] --> Y[Y]
  X1[X1] --> A[A]
  X1[X1] --> B[B]
  B[B] --> A[A]
  A[A]-->Y[Y]
  B[B]-->X2[X2]
  A[A]--> X3[X3]
  X3[X3]-->Y[Y]
  X2[X2]-->X3[X3]
  X2[X2]  --> A[A]
  X2[X2]  --> Y[Y]
  X2[X2]-->X1[X1]
  X2[X2]-->?{?}
  ?{?}-->X3[X3]
  B[B]-->!(!)
  A[A]-->!(!)

Scientific realism is committed to the mind-independent existence of the reality, independent of our thoughts or theoretical commitments. (See also Elster 1998, pp. 62)

an example

graph LR
  X1[Democracy] --> A[Religion]
  A[Religion]-->B[Desires]
  B[Desires]-->Y[Action]
  X1[Democracy] --> X2[Opportunities]
  X2[Opportunities]-->Y[Action]
  X1[Democracy] --> X3[Irreligion]

See Elster (1998, 62–63)

differences between the two stances

lThe great war is not between qualitative and quantitative paradigm but between the replication and meaning-making, and in very ontic distinctions of being and becoming (Khanna 2019, 167).

positivists search for the best theory/law; vs. scientific realists search for the best explanation

four roles of scholars that you could choose to play

  • A positivist likes to be a theory checker;
  • A scientific/critical realist likes to be a mechanism/theory builder;
  • A pragmatism likes to be a solution seeker; and
  • A interpretist is devoted to be a sense maker.

How sould scientific realists defend themselves?

  • ontological conflation?

  • testing a lot of hypotheses and do better theorization?

  • providing a wanted ansswer?

  • using a subjective way of drawing a objective mechanism?

  • None of the above!

A SR scholar will…

  • take interpretation as a means, but not the ends or goals, for the creation of mechanism (avoid claiming SR as an ontology)

  • provide a theoretical statement in the beginning of the research

  • provide a wanted theory or mechanism (not an answer or hypotheses)

  • using both a subjective and an objective way to draw a objective mechanism

Keywords for identifying SR scholars and works

  • mechanism (not enough, since positivists are using it quite often)

  • process

  • complexity

  • emergence

  • patterns

  • enduring??

    See the video here to get a better sense of the beauty of SR.

TOPIC 5: Why Do We Stop Telling Good Stories-Interpretivists’ Way of Thinking

The World of Interpretivism

  • The goal of interpretists: discover and (re)defining concepts and their meaning

  • subjective perception about the world is proximate truth.

abductive reasoning as a methodological approach

  • induction vs. deduction vs. abduction

    • Comparison (Schwartz-Shea and Yanow 2011, 26–34)


      		 induction deduction abduction
      theory loading medium heavy zero to light
      beginning of inquiry events general laws puzzles or surprise
      pattern of seeking answers linear linear circular-spiral
      way of asking questions to gain knowledge stranger-ness

the abductive way of thinking

  • Abduction is a pragmatist’s methodology that has been adopted by interpretivism (Schwartz-Shea and Yanow 2011, ch2)

  • In abductive reasoning, the researcher’s thinking is led, or, more actively, directed, in n inferential process, from the surprise toward its possible explanation(s) (p.28) .

  • the process of sense-making:

    of coming up with an interpretation that makes sense of the surprise, the tension, and anomaly (p.28).

  • beginning with surprise is where abduction differs from induction.

  • puzzles or surprises come from “a misfit between experience and expectations.”

    Strangers are constantly violating norms, often meeting strong reactions from those who know the unwritten rules. The surprises that emerge out of such encounters are often the sources of puzzles that spark an abductive reasoning process. Yet at the same time, approximating ever more closely the “familiarity” with which situated knowers navigate their physical and cognitive settings is important for generating understanding of what is puzzling only to a stranger. Striking and maintaining a balence between being a stranger and being a familiar, as difficult as it is to achieve, lies at the heart of generating research-relevant knowledge. (p.29)

abductive reasoning for interpretivism

abductive reasoning on its own does not require that one search for meaning, or that that meaning be context-specific (Schwartz-Shea and Yanow 2011, 32)),

following the abductive reasoning logic, however, an interpretative will emphasize in their research

  • interactive-recursive processing

  • focus on contextual meaning

Interpretivist Research Design

Schwartz-Shea, P., & Yanow, D. (2011). Interpretive Research Design: Concepts and Processes (1 edition). Routledge. p.127

interpretivists’ toolbox of methods

  • ethnography 民族誌

  • participatory observation 參與式觀察

  • phenomenological analysis 現象分析

  • case study

  • in-depth interviews

  • story-telling

  • field research

comparison between fact, theory, and concept

to an interpretist

  • a fact is a real, certain, proven, and crystallized concepts.

  • a theory is often a conjecture (i.e., an opinion or conclusion formed on the basis of incomplete information)

  • a concept stands between fact and theory; researchers use concepts to

    explain their distinctive treatments in interpretive and logics of inquiry. (p.40)

Pragmatism and its core beliefs

  • actions, experiences, and languages compose the world of knowledge
  • so called knowledge IS what you gained from the interaction between your mind and the world, which involves your perception of empirical problems, the updates of experiences, and lessons learned from actions.
  • theories are MEANS not ends for problem-driven inquiries

Reflections on big data

  • ask yourself the PURPOSE of a research project

  • what is the contribution of the “data-driven” paradigm

  • let data talk (?)

  • how data-driven research finds its home in epistemology?

References

Elster, Jon. 1998. “A Plea for Causal Mechanisms.” In, edited by Peter Hedstrom and Richard Swedberg, 45–73. New York: Cambridge University Press.
Glynn, Adam N. 2012. “The Product and Difference Fallacies for Indirect Effects.” American Journal of Political Science 56 (1): 257–69. https://doi.org/10.1111/j.1540-5907.2011.00543.x.
Glynn, Adam N., and Nahomi Ichino. 2014. “Using Qualitative Information to Improve Causal Inference.” American Journal of Political Science, 1055–71. https://doi.org/10.1111/ajps.12154.
Hubbard, Douglas W. 2014. How to Measure Anything: Finding the Value of Intangibles in Business. 3 edition. Hoboken, New Jersey: Wiley.
Khanna, Priya. 2019. “Positivism and Realism.” Handbook of Research Methods in Health Social Sciences, 151–68. https://doi.org/10.1007/978-981-10-5251-4_59.
Merom, Dafna, and James Rufus John. 2019. “Measurement Issues in Quantitative Research.” Handbook of Research Methods in Health Social Sciences, 663–79. https://doi.org/10.1007/978-981-10-5251-4_95.
Schwartz-Shea, Peregrine, and Dvora Yanow. 2011. Interpretive Research Design: Concepts and Processes. 1 edition. New York, NY: Routledge.