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Foundations of Research
110. Basic research designs.
This is a PowerPoint Show Click “slide show” to start it.
Click through it by pressing any key.
Focus & think about each point; do not just passively click.
To print:
Click “File” then “Print…”.
Under “print what” click “handouts (6 slides per page)”.
© Dr. David J. McKirnan, 2014The University of Illinois [email protected] not use or reproduce without permission
Foundations of Research
2Basic experimental designs
This module overviews the core elements of an experimental research design.
We will discuss “pre-experimental” designs
These typically have no control group or may use existing groups
They are often used in preliminary or exploratory research
“True” experiments have several key characteristics:
A control group
Random assignment of participants to groups
Standardized or uniform procedures for each group
Foundations of Research
3Experimental designs and validity
We will discuss internal and external validity.
Internal validity In experiments we manipulate (induce…) the Independent Variable.
We then measure the Dependent Variable.
Experimental hypothesis: the outcome (the level of the Dependent Variable) is caused by – and only by – the Independent Variable.
Internal validity: How confident are we that the outcome was due only to the Independent Variable.
Confound: A variable other than the IV that caused or influenced the result.
Did the participants in the experimental v. control groups differ on something other than the IV?
Were the procedures biased in some way…?Confound
Foundations of Research
4Experimental designs and validity
External validity Experimental participants are a sample of the larger population.
The experimental manipulation attempts to accurately induce the Independent Variable.
The outcome measure represents the Dependent Variable.
The experiment is conducted in a specific physical or cultural setting.
External validity:
Does the research sample accurately represent the larger population?
Does the experimental manipulation accurately represent the concept we think causes the outcome or results?
Do the outcome measures accurately represent the phenomenon we are trying to explain?
Is the experimental setting representative of how these processes work in nature?
Foundations of Research
5External validity: summary
The study structure & context
The research
Setting:
The Dependent
Variable
The research Sample:
Is the sample representative of the larger population?
Is this typical of the natural
settings where the phenomenon
occurs?
Does the outcome measure represent
what we are trying to explain?
Does the experimental manipulation actually create the phenomenon you
are interested in?
The Independent
Variable
Foundations of Research
6Overview: Basic Designs
“Pre-experimental” designs: no control groupPost-Test Only Design Pre- Post- Test Design
Group assignment
Pre-test Experimental manipulation
Outcome
Experimental Observe2TreatmentObserve1
Foundations of Research
7Basic Designs
“Pre-experimental” designs: no control groupPost-Test Only Design Pre- Post- Test Design
Group assignment
Pre-test Experimental manipulation
Outcome
Experimental Observe2TreatmentObserve1
True (or Quasi-)experimental designs with a control group
“After only” Control group design
Pre- Post- Group Comparisons
Control Observe2ControlObserve1
Foundations of Research
8Basic Designs
“Pre-experimental” designs: no control groupPost-Test Only Design Pre- Post- Test Design
Group assignment
Pre-test Experimental manipulation
Outcome
True (or Quasi-)experimental designs with a control group
“After only” Control group design
Pre- Post- Group Comparisons
Multiple group comparison
Experimental Observe1 Observe2
Experimental
Control
Observe1
Observe1
Treatment 2 Observe2
Observe2
Treatment 1
Control
Foundations of Research
9“Pre-experimental” designsPost-Test Only Design
Treatment MeasureGroup
Only 1 “group”.
A single set of physical measures or observations
In behavioral science typically an existing group: no selection or assignment occurs.
The condition or experimental intervention (“Treatment”) may or may not be controlled by the researcher.
In Earth Sciences we may examine how a geologic formation is associated with historical water flow.
In Behavioral Sciences we may examine naturally occurring or system-wide events
• e.g., socio-economic conditions and racial conflict,
• the effect of a government policy change on foreclosure rates….
Measurement may or may not be controlled by the researcher.
E.g.; existing (archival) climate data.
A survey after an event such as 9/11
Uniform crime rates, hospital admissions, etc.
Foundations of Research
10“Pre-experimental” designs
Post-Test Only Design
Treatment MeasureGroup
Measure1Treatment Measure1
Group
Only one group;• only group
available?• naturally
occurring intervention?
Measurements from a baseline period and after an intervention or naturally occurring event.
All participants get the same treatment, which may or may not be controlled by the researcher.
Pre- Post- Test Design
Comparing archival climate data from before & after industrialization
Examining school test scores before & after the introduction of the STEM educational approach
Foundations of Research
11“Pre-experimental” Designs (2)
Allow us to study naturally occurring interventions.
Advantage of “Post-” & “Pre- Post-” Designs:
e.g., test scores before and after some school change,
Crime rates after a policy change, etc.
Having both Pre- and Post measures allows us to examine change.
Foundations of Research
12“Pre-experimental” Designs (2)
Disadvantage of “Post-” & “Pre- Post-” Designs:
Maturation: Participants may be older / wiser by the post-test
History; Cultural, historical or physical events may occur between pre- and post-test that can represent a confound in our analysis
Mortality: Participants may non-randomly drop out of the study
Regression to baseline: Participants who are more extreme at baseline look less extreme over time as a statistical confound.
Reactive Measurement: Scores may change simply due to being measured twice, not the experimental manipulation.
No control group = many threats to internal validity.
Foundations of Research
13Experiments“After only” Control group design
Adds a control group. Either…
Observed Groups: Naturally occurring (e.g., Class 1. v. Class 2) or Self-selected (sought therapy v. did not…).
Assigned Groups:
Randomly assign participants to experimental v. control group, or
Match participants to create equivalent groups.
Measure Dependent Variable(s) only at follow-up.
Use experimental or standard measures (e.g., grades, census data, crime reports).
Experimental
Control
Treatment 2 Observe2
Observe2Control
Foundations of Research
14Advantages of experimental design
“After only” Control group design
Advantage: Lessens the likelihood of confounds or threats to internal validity.
Control group Random assignment
Disadvantage: Existing or self-selected groups may have confounds.
No baseline or pre- measure available: We cannot assess change over time. We cannot assess whether the groups are
equivalent at baseline.
Experimental
Control
Treatment 2 Observe2
Observe2Control
Foundations of Research
15Basic Designs: True experiments
Pre- Post- Group Comparisons (most common study design)
Two groups:
Observed (quasi-experiment)
orAssigned
(true experiment).
“Groups” can be Different physical
conditions or lab preparations,
Existing blocks of people
Actual experimental groups…
Baseline (“pre-test”) measure of study variables and possible confounds.
Group 1
Group 2
Measure 1
Measure 1
Foundations of Research
16Basic Designs: True experiments (2)
Pre- Post- Group Comparisons (most common study design)
The group getting the experimental condition is contrasted with a control group..
Naturally occurring
Created by experimenter
Group 1
Group 2
Measure 1
Measure 1
Treatment Measure 2
Measure2
“Post-test” follow-up of dependent variable(s);
Simple outcome Change from
baseline.
Control
Foundations of Research
17Basic Designs: True experiments (3)
Pre- Post- Group Comparisons (most common study design)
Group 1
Group 2
Measure 1
Measure 1
Treatment Measure 2
Measure2
Advantages: Pre-measure assesses baseline level of Dependent Variable
Allows researcher to assess change
Can find matched pairs of participants or physical samples and assign each to different groups (rather than random assignment).
Can assess whether groups are equivalent at baseline.
Disadvantage: Highly susceptible to confounds if using observed or self-selected groups.
Control
Foundations of Research
18More Complex Experimental DesignsMultiple group comparison
3 (or more) groups
Typically formed by Random assignment.
Multiple experimental groups, e.g. Low drug dose, High drug dose, Placebo.
or Male therapist, Female therapist, Wait list control.
Group 1
Group 2
Group 3
Measure1
Measure1
Measure1
Treatment #2
Treatment #1
Control
Foundations of Research
19More Complex Experimental Designs
Multiple group comparison
Group 1
Group 2
Group 3
Measure1
Measure1
Measure1
Treatment #2
Measure2
Measure2
Measure2
Treatment #1
Compare: Experimental group 1 from
experimental group 2. Either / both experimental
groups from the control group.
Control
Foundations of Research
20More Complex Experimental Designs
Multiple group comparison
Measure2Group 1
Group 2
Group 3
Measure1
Measure1
Measure1
Treatment #2 Measure2
Measure2
Treatment #1
Advantages: Test dose or context effects:
Drug doses, amounts of psychotherapy, levels of anxiety, etc. Increasing dose effect can be tested against no dose.
Diverse conditions to test 2nd hypotheses or confounds, e.g., therapy delivered by same sex v. opposite sex therapist.
Disadvantage:
More costly and complex.
Potential ethical problem with a “no dose” (or very high dose) condition.
Control
Foundations of Research
21Core components of a research study
Participant Selection
Participant Assignment
Experimental Procedures
Experimental Treatment or Manipulation
Results
We will use this framework to think about the basic elements of an experiment.
Who or what are we studying?
How did we recruit or sample them?
We will have at least one Experimental Group and a Control Group.
How do we assign participants or samples to be in one or the other?
What instructions do we give?
What experimental tasks will participants be performing?
What measures might we be taking?
Experimental & control groups get different conditions.
We hypothesize that this manipulation “causes” the outcome.
What outcomes are we measuring?
What is the experiment trying to explain?
Foundations of Research
22Experimental design overview
Participant Selection
Participant Assignment
Experimental Procedures
Experimental Treatment or Manipulation
Results
Sample
Group A Procedure Treatment Outcome
Group B Procedure Control Outcome
(Group C) (Procedure ) (Alternate Treatment?) (Outcome)
We recruit a sample of participants from the larger population.
We randomly assign them to groups to ensure the groups are equivalent at baseline.
Procedures for all groups should be exactly the same…
…except the experimental manipulation, i.e., the Independent variable.
Hypothesis: The outcome or Dependent Variable varies only by group.
Foundations of Research
23Overview of true experimental designs
Participant Selection
Participant Assignment
Experimental Procedures
Experimental Treatment or Manipulation
Results
Sample
Group A Procedure Treatment Outcome
Group B Procedure Control Outcome
(Group C) (Procedure ) (Alternate Treatment?) (Outcome)
Experimental group
Control group
Foundations of Research
24Overview: experimental designs
Participant Recruitment
Participant Assignment
Experimental Procedures
Experimental Treatment or Manipulation
Results
Sample
Group A Procedure A Treatment Outcome
Group B Procedure A Control Outcome
Group C Procedure A Alternate Treatment (?) Outcome
Does the sample well represent
the population?
External validity
• Was recruitment biased?
• Is the sample size large enough?
What form of validity is threatened by sample
bias?
What can we do to avoid that threat?
Random selection
Foundations of Research
25Overview: experimental designs
Participant Recruitment
Participant Assignment
Experimental Procedures
Experimental Treatment or Manipulation
Results
Sample
Group A Procedure A Treatment Outcome
Group B Procedure A Control Outcome
Group C Procedure A Alternate Treatment (?) Outcome
Does the sample well
represent the population?
External validity
Random selection
Are the groups equal at
baseline?
Internal validity
Random Assignment
• Did participants Self-select (in or out) of the study?
• Did we use existing groups?
Validity Threat?
Solution?
Foundations of Research
26Overview: experimental designs
Participant Recruitment
Participant Assignment
Experimental Procedures
Experimental Treatment or Manipulation
Results
Sample
Group A Procedure A Treatment Outcome
Group B Procedure A Control Outcome
Group C Procedure A Alternate Treatment (?) Outcome
Does the sample well
represent the population?
External validity
Random selection
Are the groups equal at baseline?
Internal validity
Random Assignment
Procedures the same for all groups?
Internal validity:
Lack of confounds
• Do both groups have the same expectations?
• Are participants (and researchers) really blind?
• Do we treat both groups the same?
Validity Threat?
Solution?
Foundations of Research
27Overview: experimental designs
Participant Recruitment
Participant Assignment
Experimental Procedures
Experimental Treatment or Manipulation
Results
Sample
Group A Procedure A Treatment Outcome
Group B Procedure A Control Outcome
Group C Procedure A Alternate Treatment (?) Outcome
Does the sample well
represent the population?
External validity
Random selection
Are the groups equal at baseline?
Internal validity
Random Assignment
Procedures the same for all groups?
Internal validity:
Lack of confounds
Independent variable faithfully
reflects the construct?
External Validity
Correct IV?
• Does the operational definition really express the construct we are interested in?
• Have we given the correct dose of the IV?
Validity Threat?
Solution?
Foundations of Research
28Overview: experimental designs
Participant Recruitment
Participant Assignment
Experimental Procedures
Experimental Treatment or Manipulation
Results
Sample
Group A Procedure A Treatment Outcome
Group B Procedure A Control Outcome
Group C Procedure A Alternate Treatment (?) Outcome
Does the sample well
represent the population?
External validity
Random selection
Are the groups equal at baseline?
Internal validity
Random Assignment
Procedures the same for all groups?
Internal validity:
Lack of confounds
Independent variable faithfully
reflects the construct?
External Validity
Correct IV?
Internal Validity:
Statistical testing
Groups really
different at outcome?
• Is any difference we see actually statistically significant (reliable & meaningful)?
• …or it is due to chance alone..
Validity Threat?
Solution?
Foundations of Research
29Overview: experimental designs
Participant Recruitment
Participant Assignment
Experimental Procedures
Experimental Treatment or Manipulation
Results
Sample
Group A Procedure A Treatment Outcome
Group B Procedure A Control Outcome
Group C Procedure A Alternate Treatment (?) Outcome
Does the sample well
represent the population?
External validity
Random selection
Are the groups equal at baseline?
Internal validity
Random Assignment
Procedures the same for all groups?
Internal validity:
Lack of confounds
Independent variable faithfully
reflects the construct?
External Validity
Correct IV?
Internal Validity:
Statistical testing
Groups really different at outcome?
Foundations of Research
30Why are research methods so important?A case study.
Siyan, S. et al., (2014). The Relationship Between Return on Investment and Quality of Study Methodology
in Workplace Health Promotion Programs. American Journal of Health Promotion, Vol. 28 (6), Pp. 347-363.
Do workplace health programs actually save money?
Over the past 20+ years there has been considerable interest in workplace health promotion:
…dietary, “lifestyle” and exercise advice & resources; …smoking cessation, weight loss programs…
Foundations of Research
31Why are research methods so important?A case study.
Do workplace health programs actually save money?
The hypothesis is that healthier employees will save the company money, via lower absenteeism, health insurance costs, etc.
Evidence appears to support that claim,
Slyan et al. took published studies and divided them into four categories:
Randomized Controlled Trials; “true experiments”, the gold standard of research.
Quasi-experimental designs; where participants were able to choose whether to get the health program or not (self-selection into groups).*
Non-experiments; basically anecdotal or observational studies.
Modeling studies; predicting outcomes based on extant data on the general effects of healthier behavior.
or does it?
* We will discuss quasi-experiments next module.
Foundations of Research
32
Do workplace health programs actually save money?
Why are research methods so important?A case study.
The results showed clearly that “Return On Investment” (ROI; actual savings) was higher as methodological quality went down.
High quality studies
showed a very modest ROI
Whereas low quality studies
showed substantial
ROI
In low quality studies, companies appeared save more than twice the money they invested in health promotion.
Foundations of Research
33
Do workplace health programs actually save money?
Why are research methods so important?A case study.
Comparing Randomly Controlled Trials (RCTs) to others was particularly damning for the hypothesis….
RCTs showed companies to actually lose
money through health
promotion.
Non-experimental and modeling
studies showed significant ROI
Lower-quality research lead to very misleading results
Foundations of Research
34
Do workplace health programs actually save money?
Why are research methods so important?A case study.
Why this huge difference between randomized controlled trials and non-experiments?
In the non-randomized trials employees were able to choose (self-select) which group they wanted to be in.
It is completely plausible that healthier or more motivated employees would join the health group, not the control group.
Studies with self-selection may be simply showing us that healthier people stay healthy and cost less, not that the actual programs did anything.
Foundations of Research
35
Experimental design key elements Control group v. non-controlled designs Threats to internal validity:
Maturation History Mortality Regression to baseline Reactive Measurement
“Pre-experimental” designs Pre-post designs Multiple group comparisons
Overview: key termsS
U M
M A
R Y
Foundations of Research
36Overview: experimental designs
Participant Recruitment
Participant Assignment
Experimental Procedures
Exp. Treatment or Manipulation
Results
Does the sample well represent
the population?
External validity
Are the groups equal at
baseline?
Internal validity
Procedures the same for all groups?
Independent variable faithfully
reflects the construct?
Groups really
different at outcome?
External validity
Internal validity
Internal validity
S U
M M
A R
Y
Foundations of Research
37
Please go on to the Research Designs quiz.