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Introduction to Measurement, Psychometrics, History of Testing and Measurement, Probability Review, Expected Value Mean, Expectation Rules, Variance, Covariance are some points from this helpful lecture notes.
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Ch. 1. Introduction I. Definition A Measurement: systematic assignment of numbers to the characteristics of objects. B. Psychometrics: theories concerning psychological measurement.
II. History of testing and measurement ( A History of Psychological Testing , by Philip H. Du Bois (1970)) A. About 1000 B.C. China had a testing system for civil-service officers (music, horsemanship, civil law, writing, Confucian principles, and knowledge of public and private ceremonies). In 1905, China eliminated the system. B. Francis Galton (1822-1911): Anthropometric Lab had Galton's test which contained various sensory and motor skills. Regression (1885). C. Karl Pearson (1857-1936): Pearson product moment correlation coefficient and chi-square test, r (1895). D. Alfred Binet (1857-1911): First individual intelligence test (1905). E. William Stern (1871-1938): developed the concept of Intelligence Quotient (IQ) which was the ratio of mental (measured) age to chronological (actual) age. F. Charles Spearman (1863-1945): modern concept of reliability and factor analysis. G. Many journals were founded: Psychometrika (1935), Educational and Psychological Measurement (1941), British Journal of Mathematical and Statistical Psychology (1947), Multivariate Behavioral Research (1966).
III. Probability Review A. Expected value (Mean): Sum [pro(x)*value(x)]
E (X) = Σp(X = xi)xi = μX
E (XY) = ΣΣp(X = xi, Y = yj)xiyj = E (X) E (Y)
E (X²) = Σp(X = xi)xi²
B. Expectation Rules (X and Y are independent)
C. Variance σ²X = E (X - μX)² = E (X² - 2XμX + μX²) = then, a miracle happened. = E (X²) - 2μX E (X) + E (μX²) = E (X²) - 2μXμX + μX² = E (X²) - μX² = E (X²) – [ E (X)]^2 D. Covariance σXY = E (X - μX)(Y - μY) = E (XY) - μXμY = E (XY) - E (X) E (Y) = 0 if X and Y are independent.
σ²(X+Y) = σ²X + σ²Y + 2σXY = σ²X + σ²Y if X and Y are independent. E. Correlation σXY ρXY = ───── σXσY