Chapter 7: An Introduction to Scientific Research Methods in Geography - Daniel R. Montello and Paul C. Sutton - Geography February 2 nd 2010

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

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

Correlation is not causality Or…. “Correlation is causality, but the specific pattern of that causality is ambiguous.” AB AB AB AB C AB CDEF (A) (B) (C) (D)

Laboratory vs. Field Settings ( ( Lab allows physical control while conducting studies. Field settings allow researcher to examine a phenomenon where it normally occurs.

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

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 A1 B2 A2 B1 A2 B2 2 variables with 2 possible options per variable

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.

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

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.

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)

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.