Experimental Design - Wolters Kluwer Health

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Chapter 7
Experimental Design I—
Independent Variables
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The Research Process
Evidence-based practice
If found
The Body of Knowledge
• Anecdotal observations
• Scientific literature base
Search for answers
If not found
Design and development of a study
• Funding
• Human and animal use approval
• Pilot studies
• Preliminary data
If accepted
Publication of manuscript
Peer review
If rejected
Manuscript preparation
Conduct of the study
Statistical analysis of data
Collection of the data
Laboratory analysis of data
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Key Terms
Alternate/research hypothesis
Inclusion criteria
Between-group/-subject independent Independent variable
variable
Internal validity
Bias
Multivariate
Blind
Non-experimental research
Confounding or intervening variable Null hypothesis
Constant
Placebo effect
Control group
Power
Counterbalancing
Quasi-experimental research
Dependent variable
Research design
Double blind
Treatment/intervention
Exclusion criteria
Univariate
Experimental research
Variable
External validity
Within-group/-subject independent
Hawthorne effect
variable
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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
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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
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Identifying Variables
Independent Variable
Dependent Variable
Intentionally controlled or
manipulated
Outcome for which researcher
hopes to elicit an effect
Treatment or intervention
“What is measured”
May be multiple levels
May be group classification
Unknown factor
Known factor
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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
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Independent Variables: Types
• Between-group (or
between-subjects)
independent variable:
different group of subjects
for each level of the
variable
• Within-group (or withinsubject) independent
variable: each subject is
tested at each level of the
independent variable
Fig. 7-1
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Identifying Variables
Between-Group/-Subject
Within-Group/-Subject
Well suited to animal studies
Each subject serves as his/her own
control
More demanding of the
participants
Random group assignment can
reduce within-group variability;
matching might be preferred
Powerful statistical design
Reduces sample size and need for
randomization
Requires longer time period,
including accounting for wash-out
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Overview of Research Design Dimensions
Design
Primary Use
Randomized?
Degree of Retrospective or Emphasis on
Control
Prospective?
Validity
NonExperimental
Description,
examine
relationships
No
Low
Either
QuasiExperimental
Causal
inferences
No
Low/mod
erate
Either
External
True
Experimental
Causal
inferences
Yes
High
Prospective
Internal
Table 7-4
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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
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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
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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
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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
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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 realworld applicability
• Planning is key for avoiding confounding factors
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