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Chapter 7: Scale Reliability and Validity, Chapter 8: Sampling - Coggle…

- - - - Rank Ordering of Categories (e.g., class ranking)
    - - Equal Intervals Between Values, No True Zero (e.g., temperature in Celsius)
    - - True Zero Point and Equal Intervals (e.g., weight, height)
    - - Classification into Categories (e.g., gender, ethnicity)
  - - - Unpredictable, Unsystematic Error (e.g., fluctuations in a person's mood)
    - - Consistent, Predictable Error (e.g., a scale that always adds 5 pounds)
- - - - Consistency Over Time
      - Importance of Time Interval
    - - Consistency Between Two Halves of a Measure
      - Tendency to Overestimate Reliability of Longer Instruments
    - - Categorical Measures: Percentage of Agreement
      - Interval/Ratio Measures: Correlation Between Raters
      - Consistency Between Independent Raters
    - - Average Inter-item Correlation
      - Cronbach’s Alpha: Formula and Calculation
      - Consistency Between Different Items of the Same Construct
- - - - Face Validity
        
        Reasonableness of an Indicator as a Measure
      - Content Validity
        
        Match Between Scale Items and Content Domain
    - - Discriminant Validity
        
        Distinction Between Different Constructs
      - Concurrent Validity
        
        Agreement with Other Measures of the Same Construct
      - Convergent Validity
        
        Closeness of Measure to the Construct
      - Predictive Validity
        
        Ability to Predict Related Outcomes
    - - Theoretical Approaches
        
        Panel of Expert Judges
        
        Experts rate each item for relevance and representation
        
        Q-sort Technique
        
        Sorting items into categories that represent different levels of the construct
      - Empirical Approaches
        
        Factor Analysis
        
        Principal Components Analysis
        
        Identifying the underlying structure of the data
        
        Factor Loadings for Convergent and Discriminant Validity
        
        Assessing which items load on which factors
        
        Rotated Factor Matrix
        
        Clarifying the structure by rotating the factors
        
        Multi-trait Multi-method (MTMM) Approach
        
        Assessing the validity of a measure by comparing it with multiple traits and methods
        
        Correlational Analysis
        
        Examining the relationship between the measure and other relevant measures
- - - - Ensuring the sample represents certain characteristics of the population.
      - Example: Ensuring equal numbers of males and females in a study.
    - - Selecting based on the researcher’s judgment about who is most useful.
      - Example: Choosing experts in a field for a study.
    - - Selecting samples that are easiest to access.
      - Example: Surveying people at a mall.
    - - Existing study subjects recruit future subjects from among their acquaintances.
      - Example: Studying a hidden population like drug users.