fundamental research issues © 2012 the mcgraw-hill companies, inc
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FUNDAMENTAL RESEARCH ISSUES
© 2012 The McGraw-Hill Companies, Inc.
Define validity Define variable Describe the four categories of variables:
Situational Response participant mediating variables
Define operational definition of a variable Describe the different relationships between
variables: positive, negative, curvilinear, and no relationship
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Compare and contrast nonexperimental and experimental research methods
Distinguish between an independent variable and a dependent variable
Discuss the three elements for inferring causation: temporal order, covariation of cause and effect, and elimination of alternative explanations
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Discuss the limitations of laboratory experiments and the advantage of using multiple methods of research
Distinguish between construct validity, internal validity, and external validity
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© 2012 The McGraw-Hill Companies, Inc.
Construct Validity Internal Validity External Validity
Evaluate the adequacy of the operational definition. Is the operational definition sufficiently measuring the construct it claims to measure?
Evaluate the extent that it was the independent variable that caused the changes or differences in the dependent variable. Are there alternative explanations (confounds)?
Evaluate the extent that the results can generalize to other populations and settings. Can the results be replicated with other participants? Can the results be replicated in other settings?
Critically Evaluating Research
Four General Categories of Variables Situational variables Response variables Participant or subject variables Mediating variables
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© 2012 The McGraw-Hill Companies, Inc.
Variable is an abstract concept that must be translated into concrete forms of observation or manipulation
Studied empirically Help communicate ideas to others
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Positive Linear Relationship Increases in one variable relate to increases in another
Negative Linear Relationship Increases in one variable relative to decreases in another
Curvilinear Relationship Increases in one variable relative to both increases and decreases in
another Included U-shaped and Inverted U-shaped curves
No Relationship Correlation coefficient
Relationships and Reduction of Uncertainty
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© 2012 The McGraw-Hill Companies, Inc.
Nonexperimental Method Direction of Cause and Effect The Third-Variable or Confounding Variable
Problem Experimental Method
Experimental Control Randomization
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The casual possibilities in a non-experimental study
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COMPARISON OF NON-EXPERIMENTAL AND EXPERIMENTAL METHODS
DESCRIPTION EXAMPLES ADVANTAGES DISADVANTAGES
NON-EXPERIMENTAL
Relationships studied by making observations or measuring variables as they exist naturally
Behavior observed as it naturally occurs Asking people to describe behavior Directly observing behavior Recording physiological responses Examining public records
Allows measure of covariation between variables IV can be observed in a natural context Allows us to study participant variables that cannot be manipulated
Difficult to infer cause and effect Direction and third variable problem Difficult to control many aspects of the situation
EXPERIMENTAL
Direct manipulation and control of variables, then response or result is observed
Measuring behavior then introducing a manipulation and measuring an outcome Random assignment of participants, experimental group experiences manipulation, control group does not, outcome variable is measured
Reduces ambiguity in interpretation of results regarding cause and effect Attempts to eliminate the impact of all possible confounding third variables Permits greater experimental control Reduces the possible influence of extraneous variables through randomization
High control may create an artificial atmosphere Can be unethical or impractical
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Independent Variables The variables that are considered to be the “cause” Usually manipulated by the researcher
Dependent Variables The variables that are considered to be the “effect” Usually measured by the researcher
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Independent = Cause
Dependent = Effect
Dependent variable
y-axis
Independent variable x-axis
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Inferences of Cause and Effect Require Three Elements:
1. Temporal precedence
2. Covariation between the two variables
3. Need to eliminate plausible alternative explanations
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Artificiality of Experiments Ethical and Practical Considerations Participant Variables Description of Behavior Successful Predictions of Future Behavior Advantages of Multiple Methods
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Construct Validity Adequacy of the operational definition of variables
Internal Validity Ability to draw conclusions about causal
relationships from our data
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External Validity Extent to which the results can be generalized to
other populations and settings Conclusion Validity
Draws reasonable conclusions based upon an analysis of the data
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