Single-Subject and Correlational Research

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Single-Subject and Correlational Research

• Bring Schraw et al.

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Scientific America
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Single-Subject Research

• Chapter Fourteen

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Single-subject Research

• Chapter Fourteen

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Essential Characteristics of Single-subject
Research

• There are reasons why single subject research is
  selected instead of the study of groups.
• Instruments can be inappropriate at times and
  intense data collection on a few individuals can
  make more sense.
• Single-subject designs are adaptations of the
  basic time-series design where data is collected
  and analyzed for only one subject at a time.

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Single-subject Designs

• Single-subject designs use line graphs to present
  their data and to illustrate the effects of a
  particular intervention or treatment on an
  individual.
• The first condition is usually the baseline,
  followed by the intervention (independent
  variable).
• Condition lines show if the condition has changed
  or separated.
• Data points represent when the data was collected
  during the study.

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Single-Subject Graph
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Types of Single-subject Designs

• The A-B design.
• Exposes the same subject, operating under his or
  her own control, to two conditions or phases,
  after establishing a baseline.
• The A-B-A design.
• Called a reverse design, researchers add another
  baseline period to the A-B design.
• The A-B-A-B design.
• Two baseline periods are combined with two
  treatment periods.
• The B-A-B design.
• Used when an individuals behavior is so severe
  that a researcher cannot wait for a baseline to
  be established.
• The A-B-C-B design.
• The C condition refers to a variation on the
  intervention in the B condition. The
  intervention is changed during the C phase to
  control for any extra attention the subject may
  have received during the B phase.

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An A-B Design
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An A-B-A Design
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Illustrations of the Results of a Study Involving
an A-B-A-B Design
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A B-A-B Design
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An A-B-C-B Design
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Multiple-Baseline Designs

• This is considered an alternative to the A-B-A-B
  design.
• Multiple-baseline designs are typically used when
  it is not possible or ethical to withdraw a
  treatment and return to the baseline condition.
• Researchers collect data on several behaviors
  compared to focusing on just one per subject,
  obtaining a baseline for each during the same
  period of time.
• The researcher applies the treatment at different
  times for each behavior until all of them are
  undergoing the treatment.
• If behavior changes in each case only after the
  treatment has been applied, the treatment is
  judged to be the cause of the change.

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Multiple-Baseline Design
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Illustration of a Multiple-Baseline Design
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A Multiple-Baseline Design Applied to Different
Settings
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Variations in Baseline Stability
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Threats to Internal Validity in Single-Subject
Research
The following threats can affect the Internal
Validity in Single-Subject Studies

• Condition length (how long the baseline and
  intervention conditions are in effect)
• Number of variables changed when moving from one
  condition to another (it is important that one
  variable be changed at a time, when moving from
  one condition to another)
• Degree and speed of change (magnitude with which
  the data change at the time the intervention
  condition is implemented)

• Return to baseline level (level should quickly
  return if the intervention was the causal factor)
• Independence of behaviors (are behaviors that are
  being measured dependent upon one another, or
  related?)
• Number of baselines (did an extraneous event
  cause the change during the introduction times?)

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Differences in Degree and Speed of Change
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Differences in Return to Baseline Conditions
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Controlling Threats in a Single-subject Study

• Single subject designs are most effective in
  controlling for the following
• Subject characteristics
• Mortality
• Testing
• History
• They are less effective with the following
• Location
• Data collector characteristics
• Maturation
• Regression
• They are even weaker with the following
• Collector bias
• Attitude
• Implementation

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External Validity and Single-Subject Research

• Single-subject studies are weak when it comes to
  external validity (i.e., generalizability).
• Treatment on one subject would not be
  appropriate.
• As a result, these studies must rely on
  replications, across individuals rather than
  groups, if such results are to be found worthy of
  generalizability.

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Correlational Research

• Chapter Fifteen

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Correlational Research

• Chapter Fifteen

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The Nature of Correlational Research

• Correlational Research is also known as
  Associational Research.
• Relationships among two or more variables are
  studied without any attempt to influence them.
• Investigates the possibility of relationships
  between two variables.
• There is no manipulation of variables in
  Correlational Research.

Correlational studies describe the variable
relationship via a correlation coefficient
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Three Sets of Data Showing Different Directions
and Degrees of Correlation (Table 15.1)
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Purpose of Correlational Research

• Correlational studies are carried out to explain
  important human behavior or to predict likely
  outcomes (identify relationships among
  variables).
• If a relationship of sufficient magnitude exists
  between two variables, it becomes possible to
  predict a score on either variable if a score on
  the other variable is known (Prediction Studies).
• The variable that is used to make the prediction
  is called the predictor variable.

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Purpose of Correlational Research(cont.)

• The variable about which the prediction is made
  is called the criterion variable.
• Both scatterplots and regression lines are used
  in correlational studies to predict a score on a
  criterion variable
• A predicted score is never exact. Through a
  prediction equation (see p. 585), researchers use
  a predicted score and an index of prediction
  error (standard error of estimate) to conclude if
  the score is likely to be incorrect.

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Scatterplot Illustrating a Correlation of 1.00
(Figure 15.1)
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Prediction Using a Scatterplot (Figure 15.2)
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More Complex Correlational Techniques

• Multiple Regression
• Technique that enables researchers to determine a
  correlation between a criterion variable and the
  best combination of two or more predictor
  variables
• Coefficient of multiple correlation (R)
• Indicates the strength of the correlation between
  the combination of the predictor variables and
  the criterion variable
• Coefficient of Determination
• Indicates the percentage of the variability among
  the criterion scores that can be attributed to
  differences in the scores on the predictor
  variable
• Discriminant Function Analysis
• Rather than using multiple regression, this
  technique is used when the criterion value is
  categorical

• Factor Analysis
• Allows the researcher to determine whether many
  variables can be described by a few factors
• Path Analysis
• Used to test the likelihood of a causal
  connection among three or more variables
• Structural Modeling
• Sophisticated method for exploring and possibly
  confirming causation among several variables

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Scatterplot Illustrating a Correlation of 1.00
(Figure 15.3)
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Prediction Using a Scatterplot (Figure 15.4)
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Path Analysis Diagram (Figure 15.5)
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Partial Correlation (Figure 15.6)
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Scatterplots Illustrating How a Factor (C) May
Not be a Threat to Internal Validity (Figure
15.7)
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Circle Diagrams Illustrating Relationships Among
Variables(Figure 15.8)
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Basic Steps in Correlational Research

• Problem selection
• Choosing a sample
• Selecting or choosing proper instruments

• Determining design and procedures
• Collecting and analyzing data
• Interpreting results

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What Do Correlational Coefficients Tell Us?

• The meaning of a given correlation coefficient
  depends on how it is applied.
• Correlation coefficients below .35 show only a
  slight relationship between variables.
• Correlations between .40 and .60 may have
  theoretical and/or practical value depending on
  the context.
• Only when a correlation of .65 or higher is
  obtained, can one reasonably assume an accurate
  prediction.
• Correlations over .85 indicate a very strong
  relationship between the variables correlated.

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Threats to Internal Validityin Correlational
Research

• Subject characteristics
• Mortality
• Location
• Instrument decay

• Testing
• History
• Data collector characteristics
• Data collector bias

The following must be controlled to reduce
threats to internal validity