Graduate School for Social Research Autumn 2015 Research Methodology and Methods of Social Inquiry socialinquiry.wordpress.com Causality and Causal Inference Descriptive Studies Irina Tomescu-Dubrow and Kazimierz M. Slomczynski

Causality and Causal Inference Descriptive Studies Language of Science – Terminology Theory Causality Descriptive Studies and Causal Inference

Terminology 1.Concepts & Constructs 2. Hypotheses 3. Empirical Generalizations 4. Laws 5. Theories

Theories A theory includes deductive reasoning - reasoning from one or more statements (premises) to reach a logically certain conclusion. “Top-down reasoning” vs. “Bottom-up reasoning” (from specific observations to empirical generalizations) Evaluation criteria: Parsimony Explanatory/predictive power Falsification

Empirical science Empiricism Objectivity Control

Science as Process Theories Empirical Hypotheses generalizations Observations (testing hypotheses)

Researchers questions A. Whom do we study? (Units of observation) B. Which characteristics of these units do we study? (Variables) C. What are the expected relationships between the variables? (Hypotheses) D. How do we understand the results? (Interpretation)

Hypotheses - a causal framework A hypothesis is a prediction about how variables relate to each other (i.e. what is the relationship btw. the variables). Relationships between variables: changes in the values of one variable are accompanied by systematic changes in the other variable(s). A hypothesis is usually based on theoretical expectations about how things work. At minimum, any hypothesis involves two variables: - the dependent variable (DV) measures the presumed effect/outcome; Y - the independent variable (IV) measures the presumed cause; X In addition: controls, intervening Variables; Z

Causality: David Hume ( ) Causality cannot be directly observed; it is an interpretation. Conditions: (1) A and B are contiguous in space and time (spatial and temporal contiguity) (2) A precedes B (time ordering) (3) A & B always occur together (constant conjunction)

Causality: John Stuart Mill ( ) System of Logic (1843) conditions of causality No factor can be regarded as a cause if: (1) it is present while the effect is absent (i.e. method of agreement), (2) it is absent while the effect is present (i.e. method of difference), (3) it varies while the effect does not vary (i.e. method of concomitant variation)

Causality in the social sciences (Daniel Little, Varieties of Social Explanation, 1991) 1. Causal Mechanism specifies how a cause exerts its effects. C is a cause of E if there is a series of events Ci leading from C to E, & the transition from each Ci to Ci+1 is governed by law L. 2. Inductive regularity: C is a cause of E if there is a regular association btw. events Ci and E, and Ci happens reasonably before E. 3. Necessary and sufficient conditions C is a cause of E if events Ci are necessary condition for the occurrence of E, and events Ci are jointly sufficient to give rise to E.

Deterministic and probabilistic approach to causality Deterministic approach: finding a variety of conditions C, for F causing. If all conditions C, then always F E Probabilistic approach: likelihood of the relationship F E

Statistical inference Substantive and null hypotheses A substantive hypothesis is the actual expectation about the relationship between two or more variables. (E.g.: Education has positive impact on pro-democratic attitudes) To decide if a substantive hypothesis is supported by the data, it is necessary to test a related hypothesis, called the null hypothesis (E.g.: Education has no effect on pro-democratic attitudes) Spurious Associations A statistically significant association between two variables, driven by a third variable, which affects both.

Black box Black box = mechanism through which F E E.g.: Father’s education & occupation status affect son’s outcome (income) - why? (Resource and socialization) ___________________________________________ In causality, time matters - immediate vs. lagged effects Measurement of time: calendar time (periods) and age. Cohort effects

Descriptive Studies and Causal Inferences Descriptive Studies: - specify up-front what researchers intend to do in their research - justify the need for good understanding of the complexity of context/culture of the studied phenomena - provide observable consequences/manifestations of the studied phenomena - focus on description of events in precise & systematic manner - use variety of quantitative and quantitative measures

Improvements - Inferences Inference is the process of using the facts we know to learn about facts we do not know To enhance descriptive studies: 1) select observations such that the facts that you have will allow you to infer information about unobserved facts 2) among the facts (observations) that you have, distinguish the systematic components from the non-systematic/‘random’ ones. 3) make linkages to the theory (Descriptive studies as testing cases)

Rules Data balance - more observations on the implications of a specific theory will help us evaluate that theory - avoid data selectivity problem: do not discard data - learn from preliminary data about the need for additional data - systematize data and make sure to distinct data and interpretation

Researchers questions A. Whom (what) do we study? (Units of observation) B. Which characteristics of these units do we study? (Variables) C. What are the expected relationships between the variables? (Hypotheses) D. How do we understand the results? (Interpretation)

Specific problems of descriptive studies - “path dependency” - “counterfactuals”