Scientific Research Methods in Geography An Introduction to Scientific Research Methods in Geography -Montello and Sutton Chapter 7: Experimental and Nonexperimental Research Designs

Empirical Control in Research Empirical Control - Any methods of increasing the ability to infer causality from empirical data.

Empirical Control in Research Experimental vs. Nonexperimental All true experiments have one or more manipulated variables and one or more nonmanipulated variables. Independent variables - Manipulated variable Dependent variables - nonmanipulated Spurious causality Confound - “third variable”

Laboratory vs. Field (Naturalistic) Setting - Both physical and human geographers use both field and lab settings. - The laboratory setting allows researchers to exert physical control while conducting their study. - The field setting is essentially naturalistic and phenomena are assumed to go on as they naturally do.

Empirical Control in Research Alternative causal patterns Page 115

Basic Research Design Research design must take into account: Level of variables and Design type -Between-case research design: different cases take on different levels of an independent or predictor variable -Within-case research design: over time, every case takes on each different level of an independent or predictor variable.

Basic Research Design Generally, within-case designs Are more efficient Lead to higher precision of estimation and power of hypothesis testing Reduce confounds

Basic Research Design Specific Research Designs Different research designs claim different levels of validity Nonexperimental Single measurements of a single group of cases Pretest-posttest design is favorable Multiple measurements over time, before and after Between-case study sampling two identifiable subpopulations of cases. *Ambiguous causality

Basic Research Design Specific Research Design -Experimental Designs Factorial design - experimental research design in which two or more independent variables are manipulated. Quasi-experimental - study without manipulated variables that attempts to establish causal relations more validly by applying systematic statistical control over alternative causal variables.

Basic Research Design Keep in mind… The number of variables in a study, both manipulated and measured, can be increase ad infinitum. However, there is and upper threshold where complexity and/or cost becomes to great.

Developmental Designs (Change over Time) Development - systematic (nonrandom) processes of change Developmental designs - two basic types Cross-sectional design: two or more groups of cases, each at different “ages” or levels of development, are compared at the same time Longitudinal design: one group of cases is compared to itself over time as it develops Sequential design: a hybrid of cross-sectional and longitudinal designs.

Single-Case and Multiple-Case Design Single-case design, such as a case study, is efficient and in depth but only suggestive in causality Multiple-case design provides a greater ability to understand general types of cases and can help in avoiding spurious conclusions

Conceptual Model Example

Computational Modeling Computational models are models of theoretic structures and are expressed in mathematical form Simplification is necessary in scientific work. Without it, many things would remain out of the grasp of our minds. Computational models can provide an alternative to traditional experimental designs and they can allow for the consideration of complex causal relationships. Numeric models: based on prior scientific laws: common only in physical geography and can be deterministic or stochastic Empirical models: parameters based on estimates from data: common in both human and physical geography.

Steps of Computational Modeling Create conceptual model Create computational model Run the computer program Compare model output to empirically obtained data Accept, use, and communicate model