Lecture 12Psyc 300A

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).

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

Review: t-test SPSS Output

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

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

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)

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

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

Experimenter Attributes

• When characteristics of experimenter affect participants

• Examples: Age, ethnicity, attractiveness, gender, extraversion, controlling

• May limit external validity

Controlling Participant and Experimenter Effects

• Deception• Blind studies• Automation

Threats to External Validity

• Generalizing to populations• Generalizing from lab settings• Review: Replication

– Literal (exact)– Conceptual

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

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)

Order Effects and Counterbalancing

• Order Effects– Practice effects– Fatigue effects– Carryover effects

• Counterbalancing– Example: A-B-C– Complete– Partial

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

Developmental Designs

• Cross-sectional design– Between subjects design– Cohort effect

• Longitudinal design– Within subjects design

• Sequential design– Mix of cross-sectional and longitudinal