Matching participants involves equating experimental and control groups on extraneous variables that could influence the outcome, such as demographics or pre-existing conditions, to help control for their effects and increase the internal validity of the study. Common matching techniques include holding variables constant across groups, building extraneous variables into the factorial design, yoking controls to experimentals, and precisely equating participants on specific variables. While improving control, matching also limits random assignment and generalizability.
Research Methods: Experimental Design I (Single Factor)Brian Piper
lecture 9 from a college level research methods in psychology course taught in the spring 2012 semester by Brian J. Piper, Ph.D. (psy391@gmail.com) at Linfield College,
Potential Solutions to the Fundamental Problem of Causal Inference: An OverviewEconomic Research Forum
Ragui Assaad- University of Minnesota
Caroline Krafft- ST. Catherine University
ERF Training on Applied Micro-Econometrics and Public Policy Evaluation
Cairo, Egypt July 25-27, 2016
www.erf.org.eg
Experimental design is inferential procedure or scientific method in Statistics wherein cause and effect relationship is studied by planning an experiment. In Experimental Design methodology, proper experiments are planned in order to achieve desired objective. Copy the link given below and paste it in new browser window to get more information on Experimental Design:- www.transtutors.com/homework-help/statistics/experimental-design.aspx
Research Methods: Experimental Design I (Single Factor)Brian Piper
lecture 9 from a college level research methods in psychology course taught in the spring 2012 semester by Brian J. Piper, Ph.D. (psy391@gmail.com) at Linfield College,
Potential Solutions to the Fundamental Problem of Causal Inference: An OverviewEconomic Research Forum
Ragui Assaad- University of Minnesota
Caroline Krafft- ST. Catherine University
ERF Training on Applied Micro-Econometrics and Public Policy Evaluation
Cairo, Egypt July 25-27, 2016
www.erf.org.eg
Experimental design is inferential procedure or scientific method in Statistics wherein cause and effect relationship is studied by planning an experiment. In Experimental Design methodology, proper experiments are planned in order to achieve desired objective. Copy the link given below and paste it in new browser window to get more information on Experimental Design:- www.transtutors.com/homework-help/statistics/experimental-design.aspx
THIS IS MY PH.D., VIVA VOCE POWERPOINT. MY THESIS TITLE IS "EFFECTIVENESS OF E-LEARNING MODULES IN TEACHING MATHEMATICS AMONG SECONDARY TEACHER EDUCATION LEVEL"
Causal Comparative Research At least two different groups are compared on a dependent variable or measure of performance (called the “effect”) because the independent variable (called the “cause”) has already occurred or cannot be manipulated. Dependent variable-the change or difference occurring as a result of the independent variable. Independent variable- an activity of characteristic believed to make a difference with respect to some behavior.
This presentation is for educational purpose only. I do not own the rights to written material or pictures or illustrations used.
This is being uploaded for students who are in search of, or trying to understand how a quasi-experimental research design should look like.
This is a lecture on "Hypothesis Testing, Research Questions and Choosing a Statistical Test". It was presented at the Colombo Institute for Research and Psychology. The lecture covers key topics including the different types of data, the process of testing a hypothesis, key forms of inferential statistical tests and how to chose a test based on your research question and sample.
THIS IS MY PH.D., VIVA VOCE POWERPOINT. MY THESIS TITLE IS "EFFECTIVENESS OF E-LEARNING MODULES IN TEACHING MATHEMATICS AMONG SECONDARY TEACHER EDUCATION LEVEL"
Causal Comparative Research At least two different groups are compared on a dependent variable or measure of performance (called the “effect”) because the independent variable (called the “cause”) has already occurred or cannot be manipulated. Dependent variable-the change or difference occurring as a result of the independent variable. Independent variable- an activity of characteristic believed to make a difference with respect to some behavior.
This presentation is for educational purpose only. I do not own the rights to written material or pictures or illustrations used.
This is being uploaded for students who are in search of, or trying to understand how a quasi-experimental research design should look like.
This is a lecture on "Hypothesis Testing, Research Questions and Choosing a Statistical Test". It was presented at the Colombo Institute for Research and Psychology. The lecture covers key topics including the different types of data, the process of testing a hypothesis, key forms of inferential statistical tests and how to chose a test based on your research question and sample.
1. Factorial Designs
• So far: basic designs (one IV, one DV)
• Now: more than one IV (still one DV)
2. 2 x 3 Factorial Design
Independent Variable A
A1 A2 A3
A1 B1 A2 B1 A3 B1 B1
B1 Cell mean Marginal
mean
IV B A1 B2 A2 B2 A3 B2 B2
B2 Marginal
mean
A1 A2 A3
Marginal Marginal Marginal
mean mean mean
3. 2 types of effects
• Main Effect - The influence of one
Independent variable in a factorial design
• Interaction Effect - joint influence of two or
more IVs on the DV
– The effect of one IV depends on the level of
another IV.
4. Example
• Study examining gender (M-F) and
intervention to improve test-taking skills
• 3 IV levels
– control (no intervention)
– reading material (instructional booklet)
– personalized tutoring
5. Intervention
Control Booklet Tutoring
A1 B1 A2 B1 A3 B1
Male
A1 B2 A2 B2 A3 B2
Female
6. What would a main effect of gender
look like?
100
90
80
70
60
Male
50
Female
40
30
20
10
0
Control Reading Tutoring
7. What would a main effect of
intervention look like?
120
100
80
Male
60
Female
40
20
0
Control Reading Tutoring
8. What would an interaction look like?
120
100
80
Male
60
Female
40
20
0
Control Reading Tutoring
9. What should we interpret?
• If one main effect - report it
• 2 main effects – report both
• BUT if there’s an interaction…
– Only interpret/report the interaction
– Because the effect of test-taking intervention
depends on gender
11. Combining Between and Within
Participant Designs
• Factorial design based on a mixed model
• -or- mixed model design
• IVs can be either between-groups
(e.g., gender) or within-groups (a.k.a.
repeated)
12. Advantages of Factorial Designs
• Can test more than 1 hypothesis at a time
• Able to deal with extraneous variables
– Build into design and test outright
• Increases precision b/c it evaluates more variables at
once
• Allows researcher to understand interactive effects
of variables
13. Disadvantages of Factorial Designs
• Gets messy with more than 2 IVs
• Requires more participants (N per cell)
• More difficult to simultaneously manipulate
all IVs when you have more of them
14. Choosing an Experimental Design
• Depends on…
• Research question
• Nature of variables you are investigating
• We have discussed design building blocks
• Page 255: guiding questions
16. Overview
• Control at the beginning of experiment
– Random assignment Create equivalent
– Matching experimental groups
• Control during the experiment
– Counterbalancing Treat groups
the same
– Controlling for participant effects during the
– Controlling for experimenter effects experiment
17. Random Assignment
• Not to be confused with random sampling!
• In reality, random sampling is rarely used in
experimental research
• Generalize on the basis of multiple studies
• With different kinds of samples/settings
18. Random Assignment
• a.k.a randomization
– Most important of all control methods
– Only technique for controlling both known
and unknown extraneous variables
19. Random Assignment
• Quiz time:
• How does randomization eliminate systematic
bias in experiments (produce control)?
– All variables distributed in approximately the same
manner in all groups
– Influence of extraneous variables is held constant
20. Random Assignment
• Sample Size
– It is possible for random assignment to fail
– rare with a large enough sample size (N > 30)
21. Random Assignment
• Ways of achieving randomization
– Table of random numbers
Text pp. 203-207
– Randomizer.org
– Draw out of a hat
– Be creative – flip a coin/lottery/etc
• www.Randomizer.org
22. Matching
• Equate participants on one or more selected
variables
• Matching Variable: The extraneous variable
used in matching
• Useful when random assignment is not
possible
23. Methods for Matching Participants
• Holding variables constant
• Building the extraneous variable into the
design
• Yoked control
• Equating participants
24. Matching by Holding Variables
Constant
• Hold extraneous variable constant for all
groups in the experiment
• All participants in each treatment group will
have same degree or type of extraneous
variable
• Requires selection criteria for participant
sample
25. Build Extraneous Variable into the
Research Design
• Especially useful if you are interested in:
– Differences produced by the levels of the
extraneous variable
– Interaction between levels of IV and levels of
extraneous variable
• Sound familiar?
– What kind of research design would this be?
26. Example: Effect of a study skills intervention
on college grades in a Quantitative Methods
course…
Intensive tutoring program Study packets (usual)
But the literature suggests that learning style may affect how students
respond to different study skills training methods.
Learning style is a potential confounding extraneous variable….but we can
build it in to the design!
Learning Style
Visual Auditory Kinesthetic
Intervention
Intensive
tutoring
program
Study packets
27. Matching by Yoked Control
• Match participants on the basis of the
sequence of administering an event
• Each control participant is “yoked” to an
experimental participant
• Controls for the possible influence of
participant-controlled events
• Example: Sklar & Anisan (1979)
– stress and immune response
28. Matching by Equating Participants
Precision control
• Match each participant in experimental group
with a participant in control group on
variable(s) of concern
• Example: Scholtz (1973) compared defense
styles in suicide attempt vs. no attempt
29. Matching by Equating Participants
• Precision Control Advantage
– Groups are equated on matching variables
• Precision Control Disadvantages
– How do you know which variables are critical?
– Difficulty of finding matched participants increases
exponentially as number of matching variables increases
– Matching limits generalizability of results
– Some variables are difficult to match
• Example: prior psychotherapy
– Matching can only be as accurate as the available
measurement of the matching variable