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Lecture Notes

Seven of the lecture presentations are posted below. You will need Adobe Reader to view these files. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this site.

Module Number: Title

> Lecture 1: Introduction to Measurement (180 KB)

Lecturer: William Eaton,JHSPH Department of Mental Health

After this class students will be able to (1) briefly describe the concept of reliability in both intuitive and statistical terms, and (2) identify the key assumptions of classical test theory.

> Lecture 3: Principles of Psychometrics: Reliability I (632 KB)

Lecturer: William Eaton, JHSPH Department of Mental Health

After this class students will be able to (1) describe two definitions of the concept of reliability, (2) predict how long a scale should be, and (3) estimate reliability for continuous and categorical measures

> Lecture 4: Principles of Psychometrics: Reliability II (744 KB)

Lecturer: Jeannie-Marie Leoutsakos, JHSPH Department of Mental Health

After this class students will be able to (1) describe the relationship of the intraclass correlation coefficient to other measures of reliability, and (2) correctly identify which intraclass correlation to use for different research designs

> Lecture 5: Principles of Psychometrics: Validity I (273 KB)

Lecturer: William Eaton, JHSPH Department of Mental Health

After this class students will be able to (1) distinguish four different types of validity, (2) describe the conceptual and quantitative relationship of reliability to validity, (3) estimate a true correlation from an observed correlation

> Lecture 6: Principles of Psychometrics: Validity II (638 KB)

Lecturer: Jeannie-Marie Leoutsakos, JHSPH Department of Mental Health

After this class students will be able to evaluate the relative utility of different cutoffs for a measure in relation to a gold standard

> Lecture 8: Factor Analysis I (393 KB)

Lecturer: Elizabeth Garrett-Mayer, JHSPH Department of Biostatistics

After this class students will be able to (1) identify when a factor analysis is appropriate and when it is not, (2) run a one-factor and multi-factor analysis, (3) interpret the results from a factor analysis

> Lecture 9: Factor Analysis II (488 KB)

Lecturer: Elizabeth Garrett-Mayer, JHSPH Department of Biostatistics

After this class students will be able to (1) use the statistical procedure of rotation to aid in the interpretation of results from a factor analysis, (2) be able to apply both orthogonal and oblique rotations and identify the assumptions underlying each, and (3) apply the appropriate method of estimation for factor analysis