lecture 12 psyc 300a. review: inferential statistics we test our sample recognizing that differences...
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![Page 1: Lecture 12 Psyc 300A. Review: Inferential Statistics We test our sample recognizing that differences we observe may be simply due to chance. Significance](https://reader030.vdocuments.us/reader030/viewer/2022032522/56649d615503460f94a431b9/html5/thumbnails/1.jpg)
Lecture 12Psyc 300A
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Review: Inferential Statistics
• We test our sample recognizing that differences we observe may be simply due to chance.
• Significance level (alpha) is the risk we are willing to assume that we will say there is a relationship between variables when in fact there isn’t (it’s due to chance).
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Review: Type I and Type II Errors
Accept the Null Hypothesis
Reject the Null Hypothesis
Null is really True
Correct Decision
Type I Error
Null is really False
Type II Error
Correct Decision
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Review: t-test SPSS Output
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Experimenter and Participant Effects
• Participant Effects– Participants are not passive– Participant reactivity: behavior
changes when participants know they are watched(may respond with cooperation, antagonism, social desirability)
– Respond to demand characteristics
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Social Desirability
• The pressure that participants feel to respond as they think they should, not as they actually feel or believe.
• The acceptable or PC response
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Demand Characteristics
• These are cues that come from the experimenter or the experimental situation that tell a participant about the purpose of the experiment
• Participants may be right or wrong in their guesses
• Participants may change their behavior (e.g., become more or less cooperative)
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Experimenter Effects
• Experimenter bias occurs when the behavior of the experimenter in some way affects the results of the study– Experimenter expectancy effects– Experimenter attributes
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Experimenter Expectancy Effects
• When experimenter’s knowledge or belief about participants cause participants to act different from normal
• May involve– Demand characteristics (leaking)– Interpretation of responses– Subtle differences in behavior toward
participants
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Experimenter Attributes
• When characteristics of experimenter affect participants
• Examples: Age, ethnicity, attractiveness, gender, extraversion, controlling
• May limit external validity
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Controlling Participant and Experimenter Effects
• Deception• Blind studies• Automation
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Threats to External Validity
• Generalizing to populations• Generalizing from lab settings• Review: Replication
– Literal (exact)– Conceptual
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Review: Types of Experiments
• Between-Subjects (or Between-Participants) Design– Different subjects are assigned to each level of
the IV– Random assignment to conditions – Difference between random assignment and
random sampling
• Within-Subjects (or Within-Participants, or Repeated Measures) Design– Same subjects in all levels of the IV
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Within-Subjects Designs
• Advantages– Participants serve as own controls
• Groups are equivalent at beginning• Reduced variability among participants
– Need fewer participants– More powerful (more likely to detect
relationships when present)
• Limitations– Order effects– Can’t use when participant variables are IV– Sometimes long time lags (esp. longitudinal
designs)
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Order Effects and Counterbalancing
• Order Effects– Practice effects– Fatigue effects– Carryover effects
• Counterbalancing– Example: A-B-C– Complete– Partial
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Matched-Participants Design
• Match participants on an important variable
• Different participants in different conditions (like between), but analyzed like a within-subjects
• Advantages: reduce demand characteristics, eliminate order effects, reduce variability
• Disadvantages: Finding matches, only matches for some characteristics
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Developmental Designs
• Cross-sectional design– Between subjects design– Cohort effect
• Longitudinal design– Within subjects design
• Sequential design– Mix of cross-sectional and longitudinal