How do we know what we know? What is science compared to other modes of knowing?

Review of Types of knowledge 1.Faith – belief without firm evidence, on faith, metaphysical 2.Investment – we know because that is what we have invested in by our past actions and beliefs 3.Logic – logic may tell us answers math can support, but based on simple assumptions that themselves may not be true. 4.Authority – figures/agents of authority tell us so! 5.Science – through repeated observation of senses (testing!). Some call this positivism

What do we mean by political science? It is a field of academic inquiry that applies theories, systematic methods, and tests to describe, explain, and predict political events and behavior. This is true of the natural sciences and typically other areas that use data analysis methods, although the substantive topic is obviously not politics.

What do we mean by systematic? Taking steps to be as objective and thorough as possible in research and typically by following principles of science. Key problems arise when one is non-systematic: –Inaccurate observations: sloppy or incomplete –Overgeneralization: Frank and Kansas? –Selective observation: typically related to one’s political biases and ideology. For example, conservatives may not recognize when they agree with liberals but hear instead what they most disagree with.

Positivism (basis for science) An objective reality exists that can be analyzed, modeled & replicated Ideas and theories are confronted with facts Regularities and patterns are present Social reality can be analyzed systematically Social reality can be quantified and measured systematically Example: poverty exists and can be measured

What is Science? 1.In some rough form, common to humans as a means of reasoning. 2.A systematic way to formulate and test research questions. Generalization! 3.A means of producing useful and reliable empirical information (based on evidence). Transmissible 4.communicable endeavor – research must be replicable, verifiable

Science 5.By necessity we make inferences about the world around us – populations or systems may be too complex to analyze in their entirety. Thus, we attempt to generalize about phenomena not directly observed or tested. 6.Conclusions are uncertain. WHAT? Can we ever know for perfect certainty that we are correct about our explanations and predictions?

Again, TESTING is very important to science. In order to make inferences about the world, we must compare our ideas and theories to reality. We bring evidence to bear when testing hypotheses through the analysis of cases in either: Case Studies Focus Groups, Interviews, etc. Statistical Analysis: Surveys or aggregate data on society, economics, legislatures, or history.

If our theories are found to properly explain our phenomena of investigation, then they can be used for prediction. Prediction is a prominent goal but very difficult in the non-natural sciences, why? Prediction is hard because unlike atoms or molecules, people have free will. People can sometimes be unpredictable even if they often follow habits and patterns.

There are at least four types of research questions and projects: 1.Description -- Who, what, where and when? (starting place for most research) 2.Explanation -- Why? (sciences, both natural and social) If we have good explanations, then prediction may be possible.

types of research questions and projects 3.Normative -- Should it be? (Philosophy/Ethics/ etc.) 4. Prescription -- What should we do? (Policy and sometimes philosophy, ideally based on empirically verified theories or other research)

As political scientists or others using scientific method, we are usually interested in explanation, but description must come first and then ideally prediction and well informed prescription. How do we do this? We explain phenomena by applying systematic methodologies based on logic and theories. Here is where the science comes in!!

What do we mean by systematic and why do you think it would be good? To be systematic means to attempt to be as objective as possible by looking at a varied amount of data and types of situations. To be systematic means to try to reduce subjectivity, opinion, bias, and prejudice, which may lurk in “conventional wisdom”, customs, legends, and myths.

An example of a widely believed assertion of conventional wisdom: War is good for a national economy What do you think? How do we know this? What types of wars should we examine, if any? What types of variables are important, or simply it is just that simple?

Through logical reasoning, it would make sense that wars lead to the demand for goods (weapons etc.) that results in a increased production, jobs, and even wages. However, what about other variables? What if wars are fought on one’s own territory? What if the economy is not fully mobilized for war? Considering that many wars are highly destructive for at least one side, if not both, war must not pay for at least half the combatants. Factories and infrastructure, or social costs, may make war too costly.

To really answer such a question we require a more systematic empirical analysis. My own research suggests that this simple assertion is untrue, despite being repeatedly echoed by politicians, the media, and others. In American history, 3 wars appear to have stimulated economic growth: Civil War (North), WWI, and WWII.

Considering that WWII was a monumental conflict that had a huge impact on many peoples lives, it tends to become an object of generalization when it is in fact somewhat anomalous. The majority of wars in American history were an economic drain or too short to have much effect: Korea, Vietnam, Gulf War I, War of 1812, Spanish-American War, War with Mexico.

Actually, my research suggests that for most countries economic growth increases the probability of war, which is then often followed by economic stagnation (inflation, destruction of infrastructure, social disorder, etc.)

Beginning Empirical Research: Reasoning It is important to start with various puzzles or research questions based on whether idea for a project is stimulated by theory or observation: 1.Inductive: Observation of phenomena lead to theory, and then testing. 2.Deductive: Theory leads to propositions that are then tested.

Social research strategies Ideas: What we think THEORY DATA Reality: What we observe DEDUCTIVE REASONING INDUCTIVE REASONING