1. Foundations of Research 1 10. 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)”. 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, 2014 The University of Illinois Chicago McKirnanUIC@gmail.com Do not use or reproduce without permission

2. Foundations of Research 2 Basic 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

3. Foundations of Research 3 Experimental 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

4. Foundations of Research 4 Experimental 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?

5. Foundations of Research 5 External 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

6. Foundations of Research 6 Overview: Basic Designs “Pre-experimental” designs: no control group Post-Test Only Design Pre- Post- Test Design Group assignment Pre-test Experimental manipulation Outcome Experimental Observe 2 TreatmentObserve 1

7. Foundations of Research 7 Basic Designs “Pre-experimental” designs: no control group Post-Test Only Design Pre- Post- Test Design Group assignment Pre-test Experimental manipulation Outcome Experimental Observe 2 TreatmentObserve 1 True (or Quasi-) experimental designs with a control group “After only” Control group design Pre- Post- Group Comparisons Control Observe 2 Control Observe 1

8. Foundations of Research 8 Basic Designs “Pre-experimental” designs: no control group Post-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 Observe 1 Observe 2 Experimental Control Observe 1 Treatment 2Observe 2 Treatment 1 Control

9. Foundations of Research 9 “Pre-experimental” designs Post-Test Only Design Treatment Measure Group 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.

10. Foundations of Research 10 “Pre-experimental” designs Post-Test Only Design Treatment Measure Group Measure 1 Treatment Measure 1 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

11. 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.

12. 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.

13. Foundations of Research 13 Experiments “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 2Observe 2 Control

14. Foundations of Research 14 Advantages 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 2Observe 2 Control

15. Foundations of Research 15 Basic Designs: True experiments Pre- Post- Group Comparisons (most common study design) Two groups: Observed (quasi-experiment) or Assigned (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

16. Foundations of Research 16 Basic 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 Treatment Measure 2 “Post-test” follow-up of dependent variable(s); Simple outcome Change from baseline. Control

17. Foundations of Research 17 Basic Designs: True experiments (3) Pre- Post- Group Comparisons (most common study design) Group 1 Group 2 Measure 1 Treatment Measure 2 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

18. Foundations of Research 18 More Complex Experimental Designs Multiple 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. 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 Measure 1 Treatment #2 Treatment #1 Control

19. Foundations of Research 19 More Complex Experimental Designs Multiple group comparison Group 1 Group 2 Group 3 Measure 1 Treatment #2 Measure 2 Treatment #1 Compare: Experimental group 1 from experimental group 2. Either / both experimental groups from the control group. Control

20. Foundations of Research 20 More Complex Experimental Designs Multiple group comparison Measure 2 Group 1 Group 2 Group 3 Measure 1 Treatment #2 Measure 2 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 2 nd 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

21. Foundations of Research 21 Core 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?

22. Foundations of Research 22 Experimental 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.

23. Foundations of Research 23 Overview 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

24. Foundations of Research 24 Overview: 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

25. Foundations of Research 25 Overview: 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? Validity Threat? Solution?

26. Foundations of Research 26 Overview: 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? Validity Threat? Solution?

27. Foundations of Research 27 Overview: 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? Validity Threat? Solution?

28. Foundations of Research 28 Overview: 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.. Is any difference we see actually statistically significant (reliable & meaningful)? …or it is due to chance alone.. Validity Threat? Solution? Validity Threat? Solution?

29. Foundations of Research 29 Overview: 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?

30. Foundations of Research 30 Why 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…

31. Foundations of Research 31 Why 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.

32. 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.

33. 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

34. 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. 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.

35. 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 terms S U M M A R Y

36. Foundations of Research 36 Overview: 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 S U M M A R Y

37. Foundations of Research 37 Please go on to the Research Designs quiz.