Measurement and Scales of Measurement

Measurement

• Measurement involves assigning numeric values to objects or events to understand a variable.
• Units are used to define quantities of length, area, volume, temperature, pressure, and more.
• Variables are properties of objects that can take on different values.
• Precision refers to the consistency of measurements, while accuracy is the nearness to the true value.

Scales of Measurement

• Nominal Scale: Numbers are used as identifiers or names, and order doesn't matter.
  • Examples: Numbers on sports jerseys, social security numbers.
• Ordinal Scale: Represents a rank order, with order being significant but not the distance between values.
  • Examples: Ranking in a competition, Likert-type scales (e.g., strongly agree, agree, disagree).
• Interval Scale: Represents quantity with equal units, but zero is arbitrary, not absolute.
  • Examples: Fahrenheit temperature scale, measurement of sea level.
• Ratio Scale: Represents quantity with equal units, and has a true zero point.
  • Examples: Height, weight, length of wood.

Reliability and Validity

• Reliability: Consistency and reproducibility of measurements.
  • Types:
    • Test-Retest: Administering the same test twice and correlating scores.
    • Alternate: Administering two forms of a test and correlating scores.
    • Split-Half: Splitting a test into halves and correlating scores.
    • Inter-Rater: Comparing ratings from two or more raters and correlating scores.
• Validity: Ensuring that measurements truly reflect the underlying concept.
  • Types:
    • Face: Measurements appear to measure what is intended.
    • Content: Measurements are drawn from relevant course or program material.
    • Concurrent: Measurement is similar to other established measurements.
    • Construct: Measurements support a psychological concept by predicting behavior.
    • Predictive: Measurements predict some target behavior.

Measurement Errors

• Gross Errors (Mistakes): Large errors, easily identifiable, and should be eliminated before adjustment.
• Systematic Errors: Follow a mathematical function, can be checked and adjusted, tend to have a consistent sign.
  • Examples: Confounding variables or bias that artificially influence measurement in one direction.
• Random Errors: Remain after eliminating gross and systematic errors, follow probability laws.
  • Cannot be completely computed or eliminated, but can be adjusted.

Data Acquisition Parameters

• Data Acquisition: Sampling continuous real-world information to generate computer processable data.
• Sample Rate: The frequency at which data is sampled, determining the resolution of the digital representation.
• Filters: Separate desired signals from unwanted noise.
• Buffer Blocks: Portions of RAM used to store data rapidly and maintain timing accuracy.

Acquisition Types

• Polled (Asynchronous): Application determines when to sample data from the device, one sample at a time.
• Interrupt Driven (Synchronous/Buffered): Acquires data in blocks, achieving faster acquisition rates.

Acquisition Modes

• Continuous: Data acquisition at high rates, often streamed to disk in binary format.
• Burst: Even faster, limited by device speed and buffer size.

Noise

• Radiated Noise: Travels through the air as radio waves, impacting circuits or enclosures with dimensions near the wavelength.
• Conducted Noise: Enters circuits through wires, affecting signal or power supply wires.
  • Reduction strategies include shielding and filtering.