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

ContentsEmpirical ControlCorrelation and CausalityLaboratory and Field SettingsBasic / Specific Research DesignDevelopmental DesignSingle-Case and Multiple-CaseComputational Modeling

Empirical Control in ResearchEmpirical Control – Any method of increasing

the ability to infer causality from empirical data

3 ways of exercising empirical controlPhysical ControlAssignment ControlStatistical Control

Experiment - Manipulation of VariablesNonExperimental - May involved physical or

statistical control…but no variable manipulation

Correlation is not causalityOr…. “Correlation is causality, but the

specific pattern of that causality is ambiguous.”

A B

A B

A BA B

C

A B

C D E F

(A) (B)

(C) (D)

Laboratory vs. Field Settings

(http://www.nd.edu/~druccio/images/frankenstein_lab.jpg)

(http://www.spatiallyadjusted.com/2008/08/05/breaking-the-tribe-mentality/)

Lab allows physical control while conducting studies.

Field settings allow researcher to examine a phenomenon where it normally occurs.

Basic Research DesignVariables are requiredGenerally 2 or more variables so a

relationship can be examinedLevels of Variables

Between Case Sometimes unavoidable

Within Case Are more efficient Lead to higher precision Reduce confounds

Specific Research DesignsAssorted research designs (Table 7.1 pg.120)Posttest-only design vs. Pretest-posttest

designFactorial Design

Multivariable manipulationAllows 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 approachesCross-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 approachTemporal scale is important to consider at

design phase.

Single-Case and Multiple-Case DesignsSingle-case experiment – a repeated

measures design within a single case.Improve by returning to original condition

(reversal design)Nonexperimental Example: Case study

Multiple-Case DesignBetter idea of how results generalizeSignal vs Noise

Nomothetic and Idiographic approaches to knowledge

Computational ModelingComputational models are typically

instantiated as sets of equations and other logical/mathematical operations expressed in a computer programSimplified representation of realityModel 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 ModelingCreate conceptual modelCreate computational model

ID parametersRun the computer programCompare model output to empirically

obtained dataRefine model and repeat initial steps if

necessary with new insight.Accept, Use, and communicate model

(Summary of Table 7.2 p.130)

ReviewQuestions???

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.