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Instruction in measurement will cover theories, principles, uses, and techniques for estimating statistical and practical significance, causation, instrument validity, reliability, and error. Principles and methods of factor analysis, structural equation modelling, hierarchical level modelling, missing value analysis, and propensity score analysis will be covered to statistically analyse educational data that are latent, nested, repeated, longitudinal, incomplete, and highly interconnected.

Instruction in measurement will cover theories, principles, uses, and techniques for estimating statistical and practical significance, causation, instrument validity, reliability, and error. Principles and methods of factor analysis, structural equation modelling, hierarchical level modelling, missing value analysis, and propensity score analysis will be covered to statistically analyse educational data that are latent, nested, repeated, longitudinal, incomplete, and highly interconnected.

Instruction in measurement will cover theories, principles, uses, and techniques for estimating statistical and practical significance, causation, instrument validity, reliability, and error. Principles and methods of factor analysis, structural equation modelling, hierarchical level modelling, missing value analysis, and propensity score analysis will be covered to statistically analyse educational data that are latent, nested, repeated, longitudinal, incomplete, and highly interconnected.

Instruction in measurement will cover theories, principles, uses, and techniques for estimating statistical and practical significance, causation, instrument validity, reliability, and error. Principles and methods of factor analysis, structural equation modelling, hierarchical level modelling, missing value analysis, and propensity score analysis will be covered to statistically analyse educational data that are latent, nested, repeated, longitudinal, incomplete, and highly interconnected.