Measurement Scales Overview

Measurement Definitions

• Measurement involves assigning numbers or labels to objects, events, or characteristics according to specific rules.
• A measurement scale represents the relationship between numbers and the properties they represent.
• Different types of measurement scales exist (nominal, ordinal, interval, ratio) with varying properties.

Nominal Scales

• Nominal scales categorize data into distinct groups or categories.
• No inherent order or ranking exists among the categories.
• Examples include gender (male/female), eye color, or type of vehicle.
• Statistical analysis is very limited to frequencies and modes.

Ordinal Scales

• Ordinal scales categorize data into distinct groups, and the categories have a natural order.
• The difference between categories is not necessarily equal.
• Examples include customer satisfaction levels (e.g., dissatisfied, neutral, satisfied), education levels (e.g., high school, bachelor's), or rankings in a competition.
• Statistical analysis includes median, percentiles, and non-parametric tests.

Interval Scales

• Interval scales have ordered categories with equal intervals between them, but no true zero point.
• The difference between values are meaningful, but the ratio between values is not meaningful.
• Examples include temperature measured in Celsius or Fahrenheit (0°C doesn't represent an absence of temperature) or standardized test scores like IQ.
• Statistical analysis includes mean, standard deviation, correlation, and t-tests.

Ratio Scales

• Ratio scales have ordered categories with equal intervals between them and a true zero point.
• Ratios between values are meaningful.
• Examples include height, weight, age, income, or number of items sold.
• Statistical analyses can include all of the above plus geometric or harmonic means.

Reliability and Validity of Measurements

• Reliability refers to the consistency or repeatability of a measurement.
• A reliable measure produces consistent results on repeated administrations or by different raters.
• Validity refers to the accuracy or truthfulness of a measurement, whether it measures what it claims to measure.
• A valid measure accurately reflects the underlying construct.

Different types of Validity

• Content validity: Extent to which a measurement reflects all important aspects of a construct.
• Criterion-related validity: Degree to which a measurement predicts or correlates with an external criterion.
• Construct validity: Degree to which a measurement represents the theoretical construct it is intended to measure.

Evaluating Measurement Instruments

• Test-retest reliability method assesses the stability of a measure over time.
• Inter-rater reliability assesses the consistency between different raters.
• Internal consistency reliability evaluates the homogeneity of items within a measure.

Measurement Errors

• Measurement errors represent the difference between the observed value and the true value.
• Systematic errors are predictable and consistent inaccuracies, while random errors are unpredictable and fluctuate around the true value.

Evaluation Methodologies

• Quantitative methods involve numerical data and statistical analysis to determine relationships.
• Qualitative methods use descriptive data (interviews, observations) to understand experiences and perspectives.
• Mixed methods use both quantitative and qualitative approaches to gain a broader understanding.

Ethical Considerations in Measurement

• Participant privacy and confidentiality are essential.
• Informed consent must be obtained before collecting data from participants.
• Researchers must ensure data accuracy and integrity.