STUDY GUIDE ON POS3713 FINAL EVERYTHING YOU NEED TO KNOW UNIT 1 Epistemology the study of what do we know and how do we know it How we believe something is true Knowing does not equal truth Inference the goal of scientific research is inference Descriptive inference is using observations about the world to learn about other unobserved facts Causal inference is cause and effect assumption We want to infer a relationship between two concepts this requires quantitative and qualitative judgments X causing Y WE cannot observe causation we don t see x make y happen We observe correlation in which we see x and y move together We come up with a causal story to explain why the correlation takes places x and y occurs together because x causes y and the story is the why and the how x causes y Deterministic Relationship If some cause occurs then the effect will occur with certainty physical sciences world Cause X always lead to effect Y when x occurs y will also occur with certainty E g Force mass x acceleration The effect is all the time guaranteed Probabilistic or Stochastic Relationship cause X usually leads to affect Y When X occurs Y will tend to occur but not with certainty Increases in X are associated with increases or decreases in the probability of Y occurring but those probabilities are not certainties world of human interactions Stochastic in meaning the randomness in the world the social world is stochastic IN our probabilistic models the best we can do is say that x appears to increase or decrease the probability of y occurring or changing These models cannot prove causality The key to inferring causality is research design Previous Research and Theories without scientific method we can only describe political phenomena the who and the what The scientific method allows for causal inference the why and the how Political science is rooted in observation Duberger s Law A principle which asserts that a majority voting election system naturally leads to a two party system Hotelling s Law In many markets it is rational for producers to make their products as similar as possible When examing previous research must have a skeptical view of research and theory Must think about these questions what if any other causes of the dependent variable did the previous miss Can their theory be applied elsewhere If we believe their findings are there further implications How might this theory work at different levels of aggregation micro macro Conceptual Definitions the meanings we assign to terms Concepts are a shared understanding To have a clear sense of what the concept is that we are trying to measure and we are all using the same definition for all language Independent and Dependent Variable independent variable is the single cause Dependent variable is the single effect The value of the dependent variable depends on the value of the independent variable According to our theory a change in the value of the independent variable causes change in the value of the dependent variable Correlation and Causation Correlation 2 variables seem to move together or apart Causation changes in one variable lead to changes in another variable this would require correlation correlation does not equal causation Correlation is necessary but not sufficient for inferring causation Correlation can be called covary It is possible for a causal relationship to exist between X and Y even if there is not bivariate association between X and Y 4 hurdles when establishing causal relationships 1 Credible causal mechanism connect X and Y The how and why of relationship 2 Ask whether it is actually possible or even likely that Y might cause X 3 Consider whether X and Y are correlated simple bivariate relationship 4 Consider a confounding variable Z that is related to both X and Y and makes the association between X and Y spurious The Fallacy of Affirming the Consequent We have evidence and reason in our favor therefore we have proved our theory but in reality all we can say is we have yet to falsify our theory Formal fallacy always a bad argument it is affirming the consequent Example 1 If I am lecturing then I am standing If A then B 2 I am standing B 3 Therefore I am lecturing Therefore A Time Series vs Cross Sectional Time series Temporal variation time dimension identifies the point or points in time at which we would like to measure our variable The spatial dimension in a time series measure is the same for all cases and the dependent variable is measured at multiple points in time Cross Sectional Spatial dimension identifies the units that we want to measure Cross sectional measures is when the time dimension is the same for all cases and the dependent variable is measured for multiple spatial units Measuring our dependent variable such that one of these 2 dimensions will be static or consistent This means that our measures of our dependent variable will be of one of two types A time series example includes the average monthly level of U S presidential approval displayed from 1995 2005 Spatial unit is same US but variable has been measured at multiple points in time each month Example of time series Cross sectional example military spending as a percentage of gross domestic product GDP in 2005 for 24 randomly selected nations Starting with a puzzle 2 cases expected to have similar outcomes but they don t OR 2 cases expected to have different outcomes but they do Means of Evaluating Theories Strengthening Theories Generalization falsifiable parsimony 1 Falsification we observe something x influencing something else y we may also observe when x does not influence y falsifiable theory there has to be some way the theory can be disproven i e evidence 2 Generalization theory should pursue generality meaning we want our theories to be applied to as general a class of phenomena as possible We take an observation and then generalize to a broader population Example theory explaining phenomena across multiple countries is stronger than theory explaining in only one country Being able to use similarities to apply theories to other conditions or subjects 3 Parsimony meaning theory should be simpler which causes it to be more appealing to make a theory more generally we need to add qualifications and the more qualifications that we add to a theory the less parsimonious it becomes Method for choosing the simplest explanation among a variety of possible explanations for phenomena when decisive evidence is unavoidable CONFLICT
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