# Microsoft PowerPoint - experimental design b_a ch 9 by tyndale

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```									                                                                              Experiments
Creation of meaningful comparisons
Deliberate manipulation of independent variables
Take advantage of “natural” manipulations to create
Experimental Designs                               comparisons
• Ex post facto studies
• Quasi-experimental manipulations
Claudia J. Stanny                             Control of extraneous variables
PSY 6217 – Research Design                          • Random assignment of subjects to groups
• Maintain at a constant value
• Counterbalancing

Sources of Variability in Data                                       Error Variability
Ideally, error variability is distributed randomly across
Systematic Variability                                     conditions in an experiment
Variability attributed to the effects of manipulations      Error variability is evaluated & controlled within the
of an independent variable                                  statistical analysis of the data
Treatment Variability                                    Confounded Variables
Error Variability                                             Extraneous variables that are correlated with
Variability attributed to the effects of extraneous         manipulations of an independent variable
variables                                                   Problem: Confounds confuse variation due to
• Individual differences (subject variability)             treatments with variation due to these extraneous
• Error variability of measures used (when                 variables
reliability of a measure is less than 1.00)              This variability cannot be reliably isolated using
statistical procedures

Matching
Between-Subjects Designs
Costs associated with matching
Two-group versus parametric (multi-group)                   Time and expense related to finding matching subjects
designs                                                      Loss of potential subjects (unable to match)
Problem of non-equivalent groups                            Loss of generalizability of results
Problems created by attrition after matching
Matched-groups Designs
Loss of degrees of freedom in the statistical analysis
Matched pairs (precision matching)
Matched groups (frequency distribution matching)
Control of extraneous subject variables
Potential for increased sensitivity to the effects of
manipulated variables (when matching variables are
related to the dependent measure)

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Within-Subjects Designs                                  Advantages of Within-Subjects Designs

“Ultimate” Match – each subject is matched                            Efficient in the recruitment and use of subjects
with itself
Good control of individual differences as an
Each subject participates in every condition in                      extraneous variable
the design
Individual differences (subjects) are now                               Potential for increased sensitivity to effects of
treated as an additional factor in the statistical                       treatment
analysis (Repeated Measures Designs)                                     Individual differences are no longer part of error
variance, but are identified as variance attributed to
the Subjects variable

Disadvantages of Within-Subjects Designs                                      Minimizing Carryover Effects
Time required for subject participation
Subject attrition                                                       Allow subject behavior to stabilize before
Loss of data (e.g., from equipment failures) in one condition will   exposure to any data collection conditions
require discarding all data for that subject                            Create room for adaptation or habituation to occur
Carryover effects                                                      Practice trials
Practice                                                                Give enough practice before data collection begins
Fatigue                                                                 to familiarize with task and procedures
Habituation or sensitization to manipulations                         Create breaks to offset effects of boredom or
Contrast effects                                                      Counterbalance presentation of conditions
Irreversible changes

Counterbalancing                                                 Block Randomization
Each subject completes
Complete counterbalancing                                            all four conditions          Subject   Order of Conditions
All possible orders of conditions used                            4n subjects required for
Partial counterbalancing                                             the design                     1       A    C     B     D
Block Randomization                                                Controls effects of order
in the sequence
Latin Square Design                                                                              2       C    B     D     A
Practice, boredom, etc.
Randomization (requires many sequences)                           Does not control the
3       B    D     A     C
effects of unique effects
of one condition on the
following condition            4       D    A     C     B

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Choice of Design
Latin Square Design
Within-Subjects Designs
Large individual differences: Subject variables are
Each subject completes                                                    correlated with performance on the dependent measure
all four conditions & 4n     Subject    Order of Conditions
Economic use of subjects
subjects required for the
Interest in practice or order effects as manipulations
design                         1        A        B       D          C
requires use of a within-subjects design
Controls effects of order
in the sequence                2        B        C           A      D    Matched Groups Designs
Controls the unique                                                      Need to control individual differences but carryover
effects of context or          3        C        D           B      A     effects are a serious concern
contrast created by                                                      Between-Subjects Designs
experimental conditions        4        D        A           C      B     Participation in conditions requires extensive time
Concern over carryover effects

Factorial Designs                                     Three Hypotheses Tested in a Two Factor
Experiment
Two or more independent variables                                        What is the effect of
Test Hypothesis about Main Effect of each IV                             Noise Intensity?
separately (as occurs in single factor designs)                              Main Effect of Noise
Test Hypothesis about Interaction Effects                                                               Two Factor Experiment
What is the effect of
Noise Predictability?                        Noise Intensity
Single Factor Experiment:        Two Factor Experiment                        Main Effect of
Noise Intensity                                                          Predictability           Predictability   Soft    Loud
Noise Intensity
Interaction of Noise and
Soft              Loud     Predictability       Soft           Loud     Predicability                Predictable     Grp 1   Grp 2     X
Predictable        Grp 1           Grp 2       Interaction Effect
Cell Means
Unpredictable    Grp 3   Grp 4     X
Group 1            Group 2
Unpredictable       Grp 3           Grp 4
X       X

Notation for Factorial Designs                                                    Factorial Designs

Identifying the number                              Factor A             Between Subjects
of factors in the design      Factor B           A1               A2         n x n design requires n x n independent groups
Identifying the number                                                   Within Subjects (Repeated Measures)
B1            A1B1            A2B1
of levels for each factor
B2                                        n x n design requires each subject to do n x n tasks
in the design                                    A1B2            A2B2
2 x 2 design                                                           Mixed Designs
3 x 3 design
Factor A
One or more factors manipulated within-subjects
Factor
2 x 3 design                             A1          A2         A3        Other factors manipulated between subjects
B
All designs are expected
B1        A1B1 A2B1 A3B1
to be fully crossed
B2        A1B2 A2B2 A3B2
B3        A1B3 A2B3 A3B3

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Higher Order Designs

Three or more Factors
Test one main effect for each factor
Test all possible combinations of two-way
interactions
Test all possible combinations of higher-order
interactions (three-way, four-way, etc.)
Problem in interpretation of higher-order
interactions

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