Variables: Characteristics or conditions that can change and differ among individuals.
Well-defined Variables: Easily observed and measured (e.g., height, weight).
Abstract Variables: Intangible and more complex (e.g., motivation, self-esteem) require more sophisticated measurement techniques.
Independent Variable (IV): Factor manipulated by the experimenter to observe its impact.
Levels: Different values or conditions of the independent variable, at least two required.
Dependent Variable (DV): Measured outcome reflecting the effect of the independent variable.
Subject Variables: Characteristics inherent to subjects that influence outcomes.
Confounded Variables: Variables that can interfere with the relationship between IV and DV.
Extraneous Variables: Other variables that could affect the outcome if not controlled.
Frequency: Count of how often a behavior occurs.
Rate: Frequency of behavior relative to a specified time frame.
Duration: Length of time a behavior is observed.
Latency: Time taken from instruction to performance.
Topography: The specific form or pattern of behavior.
Force: Intensity or strength of the behavior.
Locus: The specific location in the environment where the behavior takes place.
To measure different behaviors (like kicking furniture or writing), the appropriate measuring methods from frequency, rate, duration, etc., must be selected.
Theory: Set of principles explaining mechanisms behind behaviors; includes concepts not directly observed.
Constructs: Hypothetical entities derived from theories; cannot be observed but help to predict behaviors (e.g., intelligence, hunger).
Defines and measures constructs through observable behavior:
Specify measurement procedures for external behaviors.
Examples: intelligence may be operationally defined as IQ scores; hunger may be measured by hours of food deprivation.
Operational definitions do not equate to the constructs themselves.
Poorly defined operational definitions may omit vital aspects or include irrelevant components.
Two key criteria: Validity and Reliability.
Good measurements are consistent across different testing scenarios.
Relationship analysis involves plotting scores to observe correlations:
Positive Relationship: Scores move in the same direction.
Negative Relationship: Scores move in opposite directions.
Correlation calculations help assess the strength and direction of relationships:
Values near +1.00 indicate a strong positive relationship.
Values near -1.00 indicate a strong negative relationship.
A value near 0 indicates no discernible relationship.
Measurement must accurately reflect what it intends to measure.
Face Validity: Superficial appearance of measurement.
Concurrent Validity: Correlation with established measures.
Predictive Validity: Ability to predict future behaviors.
Construct Validity: Alignment with theories.
Convergent Validity: Agreement across different measurement methods.
Divergent Validity: Lack of correlation between different constructs.
Consistency of measurements over time:
Measured score = True score + Error.
Sources of Error:
Observer Error: Human error in measurement.
Environmental Changes: Variability in conditions.
Participant Changes: Variations in participant states.
Test-Retest Reliability: Consistency across successive measurements.
Inter-Rater Reliability: Agreement between different observers.
Internal Consistency: Consistency of score across different parts of the same test.
Reliability is necessary for validity; without reliability, validity cannot be guaranteed.
Measurement involves classifying individuals into categories:
Components include a set of categories and procedures for assignment.
Nominal Scale: Qualitative categories.
Ordinal Scale: Ranks data and establishes order.
Interval Scale: Equal intervals without a true zero.
Ratio Scale: Includes a true zero and equal intervals, enabling various statistical methods.
Range Effect: Measurement inability to detect differences.
Ceiling Effect: Scores cluster high with no room for variance.
Floor Effect: Scores cluster low, restricting measurable decreases.
Artifact: External factors influencing data validity.
Experimenter Bias: Influences outcome expectations.
Standardization or automation of procedures increases validity.
Use of Single-Blind Studies: Researcher unaware of expected results.
Use of Double-Blind Studies: Neither researcher nor participants know the expected outcomes.
Demand Characteristics: Cues that suggest study expectations, resulting in biased responses.
Reactivity: Participants altering behavior knowing they are studied.
Subject roles: Good, Negativistic, Apprehensive, and Faithful.
Review existing literature when selecting methods;
Ensure chosen methods appropriately address the research question and sensitivity needs.