An Introduction to

Scientific Research Methods in Geography-Montello and Sutton

Chapter 7: Experimental and Nonexperimental Research Designs

An Introduction to

Scientific Research Methods in Geography-Montello and Sutton

Chapter 7: Experimental and Nonexperimental Research Designs

Empirical Control in ResearchEmpirical Control in Research

Experiment refers specifically to studies that involve the manipulation of one or more variables

Physical control

Reduce potentiallydistracting influences

Assignment Control

*Create at least one variable

Statistical Control

Researcher measuresbut does not create variables

Empirical Control

Experiment refers specifically to studies that involve the manipulation of one or more variables

Physical control

Reduce potentiallydistracting influences

Assignment Control

*Create at least one variable

Statistical Control

Researcher measuresbut does not create variables

Empirical Control

Empirical Control - Any methods of increasing the ability to infer

causality from empirical data.

Empirical Control in ResearchExperimental vs. NonexperimentalEmpirical Control in ResearchExperimental 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”

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) SettingLaboratory 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.

- 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 ResearchAlternative causal patternsEmpirical Control in ResearchAlternative causal patterns

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Basic Research DesignBasic 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.

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 DesignBasic Research Design

Generally, within-case designs

a) Are more efficient

b) Lead to higher precision of estimation and power of hypothesis testing

c) Reduce confounds

Generally, within-case designs

a) Are more efficient

b) Lead to higher precision of estimation and power of hypothesis testing

c) Reduce confounds

Basic Research DesignBasic 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

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 DesignBasic 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.

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 DesignBasic 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.

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)Developmental Designs (Change over Time)

Development - systematic (nonrandom) processes of change

Developmental designs - two basic types

1) Cross-sectional design: two or more groups of cases, each at different “ages” or levels of development, are compared at the same time

2) 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.

Development - systematic (nonrandom) processes of change

Developmental designs - two basic types

1) Cross-sectional design: two or more groups of cases, each at different “ages” or levels of development, are compared at the same time

2) 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 DesignSingle-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

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 ExampleConceptual Model Example

Computational ModelingComputational 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.

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 ModelingSteps of Computational Modeling

1) Create conceptual model

2) Create computational model

3) Run the computer program

4) Compare model output to empirically obtained data

5) Accept, use, and communicate model

1) Create conceptual model

2) Create computational model

3) Run the computer program

4) Compare model output to empirically obtained data

5) Accept, use, and communicate model