Book: An Introduction to Scientific Research Methods in Geography (Montello & Sutton) 2006

GEOG4020-Research MethodsInstructor: Paul C. Sutton University of Denver, Dept. of Geography Prepared by: Katie Williams February 2, 2010

Chapter 7: Experimental & Nonexperimental Research Designs

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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

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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

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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

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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

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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

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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

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Specific Research DesignsNon-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

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Specific Research DesignsExperimental

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)

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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

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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

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Computational Modeling

Simplified representations of reality

Advantage of whole system representation

Detailed studies of causality, forcing, and feedbacks

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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

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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?

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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?