copyright © 2016 wolters kluwer all rights reserved chapter 7 experimental design i— independent...

Copyright © 2016 Wolters Kluwer • All Rights Reserved

Chapter 7Experimental Design I—Independent Variables

2Copyright © 2016 Wolters Kluwer • All Rights Reserved

The Research Process

Evidence-based practice

Search for answers

Design and development of a study

• Funding• Human and animal use

approval• Pilot studies

• Preliminary data

Conduct of the study

Collection of the dataLaboratory analysis of data

Statistical analysis of data

Manuscript preparation

Peer review

Publication of manuscript

The Body of Knowledge• Anecdotal observations

• Scientific literature base

If found

If not found

If rejected

If accepted


Key Terms

Alternate/research hypothesisBetween-group/-subject independent variableBiasBlindConfounding or intervening variableConstantControl groupCounterbalancingDependent variableDouble blindExclusion criteriaExperimental researchExternal validityHawthorne effect

Inclusion criteriaIndependent variableInternal validityMultivariateNon-experimental researchNull hypothesisPlacebo effectPowerQuasi-experimental researchResearch designTreatment/interventionUnivariateVariableWithin-group/-subject independent variable


Planning the Research Design

• Research design is the process by which investigators determine how to answer their research question(s)

• Flaws in research design typically cannot be overcome by editing or statistical analysis


Identifying Variables

• A variable is some characteristic or factor that can have different values and is either subject to change or can be manipulated as an intervention

• Variables may be independent, dependent, constant, or confounding



Independent Variable

Intentionally controlled or manipulated

Treatment or intervention

May be multiple levels

May be group classification

Known factor

Dependent Variable

Outcome for which researcher hopes to elicit an effect

“What is measured”

Unknown factor


Independent Variables: Levels

• Examining the effects of different doses of a drug or supplement is an example of multiple levels of a single independent variable

• How different doses affect male and female mice is an example of multiple independent variables

Fig. 7-1


Independent Variables: Types

• Between-group (or between-subjects) independent variable: different group of subjects for each level of the variable

• Within-group (or within-subject) independent variable: each subject is tested at each level of the independent variable

Fig. 7-1



Between-Group/-Subject

Well suited to animal studies

Random group assignment can reduce within-group variability;

matching might be preferred

Within-Group/-Subject

Each subject serves as his/her own control

More demanding of the participants

Powerful statistical design

Reduces sample size and need for randomization

Requires longer time period, including accounting for wash-out


Overview of Research Design Dimensions

Design Primary Use Randomized? Degree of Control

Retrospective or Prospective?

Emphasis on Validity

Non-Experimental

Description, examine relationships

No Low Either

Quasi-Experimental

Causal inferences

No Low/moderate

Either External

True Experimental

Causal inferences

Yes High Prospective Internal

Table 7-4


Experimental Design: Control Group

• Control group: measured at the same time points as the treatment group(s) but receives no treatment

• Placebo: dummy treatment that does not affect the dependent variable(s)

• Blinding: keeping participants (single blind) and ideally both participants and study personnel (double blind) naïve to the study treatment to limit bias


Experimental Design: Control Group

• Placebo effect: subjects receiving the placebo may experience a benefit even though they aren’t receiving any treatment

• Hawthorne effect: subjects may perform better due to being observed


Experimental Design: Selecting Subjects

• Identify the study population, and obtain a representative sample• Weigh the ability to improve retention (convenience sample) against

having a more representative sample• The degree to which the sample represents the population of

interest affects the power of the study• Inclusion criteria are the stated subject characteristics• Exclusion criteria restrict subject participation


Experimental Design: Controlling Variability & Bias

• Using randomization or matching to assign subjects to groups reduces other factors affecting the dependent variable

• Controlling for confounding or intervening variables reduces threats to internal validity

• Minimizing signal-to-noise ratio better enables the impact of the independent variable on the dependent variable to be observed

• Systematic errors occur when measurement error is in one direction• Random errors may occur in any direction and typically have a net

zero effect


Summary

• Research design requires balance, weighing the pros and cons of a number of experimental choices

• There is a trade-off between controlling variables and real-world applicability

• Planning is key for avoiding confounding factors

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