Book: An Introduction to Scientific Research Methods in Geography (Montello & Sutton) 2006 GEOG4020-Research Methods Instructor: Paul C. Sutton University of Denver, Dept. of Geography Prepared by: Katie Williams February 2, 2010 Chapter 7: Experimental & Nonexperimental Research Designs
Chapter 7 Overview Empirical Control in Research Laboratory vs. Field Settings Basic Research Designs Specific Research Designs Developmental Designs (Change over Time) Single-Case & Multiple-Case Designs Computational Modeling Steps of Computational Modeling 2
Learning Objectives Understand the three forms of empirical control Physical, assignment, statistical Distinguish between laboratory & field setting for research Understands the differences between research designs & their implications for research Compare computational modeling with traditional experimental research 3
Experimental vs. Nonexperimental Studies Empirical control: Physical control—physically modified or restricted data collection Assignment control—creation and control of at least one variable Statistical control—explicit analysis of main variables of interest 4
Experimental Variables Independent Variable Manipulated, created Potential causal variable Dependent Variable Not manipulated, measured Potential effect variable Confounding Variable Sheds doubt on validity of casual conclusions 5
Laboratory vs. Field Lab Man-made, controlled setting Not necessarily the “chem lab” Field Natural, uncontrolled setting Affects validity of conclusions that general about other settings 6
Basic Research Designs Design choice: Level of variables Difference of variables between or within cases Between-case design Comparing data between different cases Within-case (repeated measures) design Comparing data within the same cases More efficient, higher precision, reduction of confounds 7
Specific Research Designs Non-Experimental Single Measurement Multiple Measurement Posttest-only Design- Single measurement after an event Pretest-posttest Design- Before and after measurements for comparison Multiple Pretest-Posttest Two Group Single Measurement 8
Specific Research Designs Experimental One Group, Manipulated Within Two Group, Manipulated Within Two Group, Manipulated Between Posttest Only Factorial Four Group, Manipulated Posttest Only Factorial: Manipulating two or more factors (2x2) 9
Developmental Designs Developmental process, change over time First approach: Cross-sectional (synchronic) Compare two or more groups of cases at different development stages Second approach: Longitudinal (diachronic) Group of cases at same developmental stage compared against itself over time Hybrid approach: Sequential Design Compare between and within two or more groups of cases at different development stages over time 10
Single-case vs. Multiple-case Single-case Repeated-measure design with one case Studies of single variable effects Multiple-case Generalize variance in cases Reduces potential for error See causal relationships 11
Computational Modeling Simplified representations of reality Advantage of whole system representation Detailed studies of causality, forcing, and feedbacks 12
Computational Modeling Steps 1. Create conceptual model 2. Create computational model 3. Run the program 4. Compare model output to empirical data 5. Accept, use, and communicate model 13
Discussion How would you study demographic distribution How would you ensure empirical control? What type of experiment would be best? What experimental design is most practical/cost effective? Would a computational model be useful in this case? 14
Discussion How would you design an experiment to test: Urban growth? Regional occupational gradients? Species habitat preferences? Point source contamination? Other examples? How would you ensure empirical control? What type of experiment would be best? What experimental design is most practical/cost effective? Would a computational model be useful in this case? 15