EVAL 6000: Experimental and Quasi-Experimental Designs

1. EVAL 6000:Experimental and Quasi-Experimental Designs Dr. Chris L. S. Coryn Dr. Anne Cullen Spring 2012

2. Agenda Experiments and generalized causal inference Statistical conclusion validity and internal validity

3. Experiments and Causation

4. Defining Cause, Effect, and Causal Relationships • A cause is a variable that produces an effect or result • Most causes are inus (insufficient but nonredundant part of an unnecessary but sufficient condition) • Many factors are required for an effect to occur, but they can rarely be fully known and how they relate to one another

5. Defining Cause, Effect, and Causal Relationships • An effect is the difference between what did happen and what would have happened • This reasoning generally requires a counterfactual • Knowledge of what would have happened in the absence of a suspected causal agent

6. Defining Cause, Effect, and Causal Relationships The counterfactual is physically impossible It is impossible to simultaneously receive and not receive a treatment Therefore, the central task of all cause-probing research is to approximate the physically impossible counterfactual

7. Defining Cause, Effect, and Causal Relationships • Rubin’s Causal Model • Intuitively, the causal effect of one treatment, E, over another, C

8. Defining Cause, Effect, and Causal Relationships • A causal relationship requires three conditions: • That cause preceded effect (temporal precedence) • That cause and effect covary • That no other plausible alternative explanations can account for a causal relationship

9. Defining Cause, Effect, and Causal Relationships • In experiments: • Presumed causes are manipulated to observe their effect • Variability in cause related to variation in an effect • Elements of design and extra-study knowledge are used to account for and reduce the plausibility of alternative explanations

10. Causation, Correlation, and Confounds Correlation does not prove causation Correlations do not meet the first premise of causal logic (i.e., temporal precedence) Such relationships are often due to a third variable (i.e., a confound)

11. Manipulable and Nonmanipulable Causes Experiments involve causal agents that can be manipulated Nonmanipulable causes (e.g., ethnicity, gender) cannot be causes in experiments because they cannot be deliberately varied

12. Causal Description and Causal Explanation • Causal description is identifying that a causal relationship exists between A and B • Molar causation is the overall relationship between a treatment package and its effects • Causal explanation is explaining how A causes B • Molecular causation is knowing which parts of a treatment are responsible for which parts of an effect

13. Causal Models

14. Causal Models

15. Modern Descriptions of Experiments

16. Randomized Experiment Units are assigned to conditions randomly Randomly assigned units are probabilistically equivalent based on expectancy (if certain conditions are met) Under the appropriate conditions, randomized experiments provide unbiased estimates of an effect

17. Quasi-Experiment • Shares all features of randomized experiments except assignment • Assignment to conditions occurs by self-selection • Greater emphasis on enumerating and ruling out alternative explanations • Through logic and reasoning, design, and measurement

18. Natural Experiment Naturally-occurring contrast between a treatment and comparison condition Typically concern nonmanipulable causes Requires constructing a counterfactual rather than manipulating one

19. Nonexperimental Designs Often called correlational or passive designs (i.e., cross-sectional) Statistical controls often used in place of structural design elements Generally do not support strong causal inferences

20. Experiments and the Generalization of Causal Connections

21. Most Experiments are Local but have General Aspirations Most experiments are localized Limited samples of units/persons, treatments, observations/outcomes, and settings (utos) What Campbell labeled local molar causal validity

22. Construct Validity: Causal Generalization as Representation Premised on generalizing from particular sampled instances of persons, treatments, outcomes, and settings to the abstract, higher order constructs that sampled instances represent

23. External Validity: Causal Generalization as Extrapolation Inferring a causal relationship to unsampled persons, treatments, outcomes, and settings from sampled instances Enhanced when probability sampling methods are used Broad to narrow Narrow to broad

24. Approaches to Making Causal Generalizations • Sampling • Probabilistic • Heterogeneous instances • Purposive • Grounded theory • Surface similarity • Ruling out irrelevancies • Making discrimination • Interpolation and extrapolation • Casual explanation

25. Statistical Conclusion Validity and Internal Validity

26. Validity

27. Validity Validity is the approximate truthfulness of correctness of an inference Not an all or none, either or, condition, rather a matter of degree Efforts to increase one type of validity often reduce others

28. Validity Typology Statistical conclusion validity is the validity of inferences about the covation between treatment (cause) and outcome (effect) Internal validity is the validity of inferences about whether observed covariation between A (treatment/cause) and B (outcome/effect) reflects a causal relationship from A to B as those variables were manipulated or measured

29. Validity Typology Construct validity is the validity of inferences about the higher order constructs that represent sampling particulars External validity is the validity of inferences about whether a cause-effect relationship holds over variations in persons, settings, treatments, and outcomes

30. Threats to Validity Threats to validity are reasons why an inference may be partly or wholly incorrect Design controls can be used to reduce many validity threats, but not in all instances Threats to validity are generally context-dependent

31. Statistical Conclusion Validity

32. Statistical Conclusion Validity Whether a presumed cause and effect covary The magnitude of covariation Concerned with Type I and Type II errors

33. Reporting Results of Statistical Tests of Covariation Emphasis should be placed on effect sizes and confidence intervals rather than null hypothesis statistical significance testing (NHST) Confidence intervals provide all of the information provided by NHST but focus attention on the magnitude of an effect and the precision of the effect estimate

34. Threats to Statistical Conclusion Validity Low statistical power. An insufficiently powered experiment may incorrectly conclude that the relationship between cause and effect is not statistically significant Violated assumptions of statistical test. Violations of statistical test assumptions can lead to either overestimating or underestimating the size and significance of an effect Fishing and the error rate problem. Repeated tests for significant relationships, if uncorrected for the number of tests, can artificially inflate statistical significance Unreliability of measures. Measurement error weakens the relationship between two variables Restriction of range. Reduced range on a variable usually weakens the relationship between it and another variable

35. Threats to Statistical Conclusion Validity Unreliability of treatment implementation. If a treatment is intended to implemented in a standardized manner is implemented only partially for some respondents, effects may be underestimated Extraneous variance in experimental setting. Some features of an experimental setting may inflate error, making detection of an effect more difficult Heterogeneity of units. Increased variability on the outcome variable within conditions increases error variance, making detection of a relationship more difficult Inaccurate effect size estimation. Some statistics systematically overestimate or underestimate the size of an effect

36. Internal Validity

37. Internal Validity Inferences about whether the observed covariation between A and B reflects a causal relationship from A to B in the form in which the variables were manipulated or measured In most cause-probing studies, internal validity is the primary focus

38. Threats to Internal Validity Ambiguous temporal precedence. Lack of clarity about which variable occurred first may yield confusion about which variable is the cause and which is the effect Selection. Systematic differences over conditions in respondent characteristics that could also cause the observed effect History. Events occurring concurrently with treatment that could cause the observed effect Maturation. Naturally occurring changes over time that could be confused with a treatment effect Regression. When units are selected for their extreme scores, they will often have less extreme scores on other variables, an occurrence that can be confused with a treatment effect

39. Threats to Internal Validity Attrition. Loss of respondents to treatment or measurement can produce artifactual effects if that loss is systematically correlated with conditions Testing. Exposure to a test can affect test scores on subsequent exposures to that test, an occurrence that can be confused with a treatment effect Instrumentation. The nature of a measure may change over time or conditions in a way that could be confused with a treatment effect Additive and interactive threats. The impact of a threat can be added to that of another threat or may depend on the level of another threat

40. Estimating Internal Validity in Experiments and Quasi-Experiments By definition randomized experiments eliminate selection through random assignment to conditions Most other threats are (should be) probabilistically distributed as well

41. Estimating Internal Validity in Experiments and Quasi-Experiments • Only two likely validity threats (typically) arise from experiments • Attrition • Testing

42. Estimating Internal Validity in Experiments and Quasi-Experiments In quasi-experiments differences between groups tend to be more systematic than random All threats should be made explicit and then ruled out one by one Once identified, threats can be systematically examined

43. The Relationship Between Statistical Conclusion Validity and Internal Validity

44. Similarities Both statistical conclusion validity and internal validity are concerned with study operations (rather than the constructs those operations are intended to represent) and with the relationship between treatment and outcome