Experimental and Nonexperimental Research Design

1. Experimental and Nonexperimental Research Design Chapter 7: An Introduction to Scientific Research Methods in Geography Daniel R. Montello and Paul C. Sutton Geography 4020 February 2nd 2010

2. Contents • Empirical Control • Correlation and Causality • Laboratory and Field Settings • Basic / Specific Research Design • Developmental Design • Single-Case and Multiple-Case • Computational Modeling

3. Empirical Control in Research • Empirical Control – Any method of increasing the ability to infer causality from empirical data • 3 ways of exercising empirical control • Physical Control • Assignment Control • Statistical Control • Experiment - Manipulation of Variables • NonExperimental - May involved physical or statistical control…but no variable manipulation

4. Correlation is not causality • Or…. “Correlation is causality, but the specific pattern of that causality is ambiguous.” A B A B A B (A) (B) A B A B C C D E F (C) (D)

5. Laboratory vs. Field Settings Lab allows physical control while conducting studies. (http://www.spatiallyadjusted.com/2008/08/05/breaking-the-tribe-mentality/) Field settings allow researcher to examine a phenomenon where it normally occurs. (http://www.nd.edu/~druccio/images/frankenstein_lab.jpg)

6. Basic Research Design • Variables are required • Generally 2 or more variables so a relationship can be examined • Levels of Variables • Between Case • Sometimes unavoidable • Within Case • Are more efficient • Lead to higher precision • Reduce confounds

7. Specific Research Designs • Assorted research designs (Table 7.1 pg.120) • Posttest-only design vs. Pretest-posttest design • Factorial Design • Multivariable manipulation • Allows investigation of factor interactions A1 B1 A2 B1 A1 B2 A2 B2 2 variables with 2 possible options per variable

8. Developmental Designs (Δ/Time) • Developmental Designs – studies designed to conduct research on developmental processes. • 2 basic approaches • Cross-sectional – comparing 2 or more groups(cohorts) at different stages of development. • Longitudinal – a ground of cases at one level compared to itself over time • Sequential Design – a hybrid approach • Temporal scale is important to consider at design phase.

9. Single-Case and Multiple-Case Designs • Single-case experiment – a repeated measures design within a single case. • Improve by returning to original condition (reversal design) • Nonexperimental Example: Case study • Multiple-Case Design • Better idea of how results generalize • Signal vs Noise • Nomothetic and Idiographic approaches to knowledge

10. Computational Modeling • Computational models are typically instantiated as sets of equations and other logical/mathematical operations expressed in a computer program • Simplified representation of reality • Model output can be considered “Simulated data” and are typically compared to standard empirical measurements. • Gives empirical access to events that would be otherwise very difficult or impossible to study. • Example of complex climate modeling.

11. Steps of Computational Modeling • Create conceptual model • Create computational model • ID parameters • Run the computer program • Compare model output to empirically obtained data • Refine model and repeat initial steps if necessary with new insight. • Accept, Use, and communicate model (Summary of Table 7.2 p.130)

12. Review • Questions??? • What are the 3 forms of empirical control in research? • What are Confounds? • Describe the difference between within-case and between case design. Pro/Con of approaches? • Discuss how computational modeling might help you better understand or design real world empirical research.