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Measurement 1 (2)

Measurement in Psychology

  • Definition: Measurement assigns scores to individuals reflecting specific characteristics.

    • Examples of measured characteristics include:

      • Height

      • Weight

      • Running Speed

      • Vertical Leap

      • Intelligence

      • Personality

      • Depression

      • Anxiety

Types of Variables

Observable vs. Latent Variables

  • Observable Variables: Directly measurable traits.*

    • Examples: Height, weight

  • Latent Variables: Constructs inferred from observable symptoms.

    • Example of Depression Symptoms:

      • Feeling dejected or down

      • Taking less interest in previously enjoyable activities

      • Changes in appetite or sleep patterns

      • Fatigue or agitation

      • Suicidal thoughts

Assessment and Measurement Variables

  • Assessments are formed based on conceptual importance.

  • Key Terms:

    • Variable: Quantity assumed to vary in value.

    • Conceptual Variable: Theoretical definition.

    • Operational Definition: Specific means to measure a conceptual variable.

    • Measured Variable: Resulting value from an operational definition.

Psychometrics

  • Field Overview: Study of psychological measurements including attributes like attitudes, traits, and abilities.

  • Reliability: Consistency of assessments.

    • Importance: Reliable tests yield dependable results.

  • Validity: Accuracy of measurement; does it truly measure what it’s supposed to?

    • Note: A reliable test may still lack validity.

Levels of Measurement

Four Levels

  1. Nominal: Categorical variables with no inherent order.

  2. Ordinal: Ranked categories indicating an order without precise differences.

  3. Interval: Numeric scales with equal intervals but no true zero.

  4. Ratio: Numeric scales with a true zero, allowing for meaningful comparisons.

Ordinal Data

  • Characteristics: Classifies variables in a natural order.

    • Examples include:

      • School grades (A, B, C)

      • Education levels (Bachelor’s, Master’s, PhD)

  • Analysis: Non-parametric statistics, frequency distribution, mode, median, and range.

Validity Types

Types of Validity Characteristics

  • Face Validity: Layperson’s assessment of content relevance.

  • Content Validity: Expert assessment of theoretical content.

  • Criterion Validity: Measurement accuracy against established criteria.

  • Concurrent Validity: Agreement between two measures at the same time.

  • Discriminant Validity: Ability to differentiate between unrelated constructs.

  • Predictive Validity: Forecasts significant differences in groups.

  • Construct Validity: Measurement of what it is supposed to assess.

Internal Validity

  • Focuses on accurate causal relationships in a study.

  • Threats:

    • Sample Attrition: Loss of participants skewing results.

    • Confounding Variables: External variables affecting dependent variables, leading to unclear results.

External Validity

  • Definition: Generalizability of study findings to broader circumstances.

  • Sub-types:

    1. Population Validity: Applicability to diverse demographics.

    2. Temporal Validity: Relevance over time.

    3. Ecological Validity: Context of application.

    4. Cross-Cultural Validity: Applicability across cultures.

    5. Task Validity: Appropriate measurements for specific tasks.

    6. Measurement Validity: Assessments are accurate in context.

Reliability Types

Measurement Types

  • Test-Retest Reliability: Stability of test results over time.

  • Interrater Reliability: Consistency among different observers.

  • Parallel Forms Reliability: Consistency between different versions of a test.

  • Internal Consistency: Measures how well test items correlate with each other.