Admission Requirements
Statistics and probability.
Description
In behavioral sciences, life sciences, and official statistics it is customary to measure properties of individuals rather than populations. Many properties of individual subjects, such as extroversion, verbal intelligence, the quality of life after an eye operation, or the tendency to avoid taxes, cannot be measured directly. These attributes are latent and can only be gauged via the measurement of manifest variables which are contingent upon them. Latent variable models make this possible. This course will provide you with in-depth knowledge of latent variable models, and you will learn to work with them. During the course, you will work on the analysis of empirical and simulated data and make exercises about the theory. Substantive issues are only cursorily discussed; this is primarily an applied statistics course.
In this course, we work mainly with test and scale data, although other data sources, such as capture-recapture data to estimate latent prevalence of attributes, have been analyzed with latent variable models too. A test consists of a number of separate items--- questions to be answered or problems to be solved. The responses to these items are used to obtain a score that approximates the subject's level on a latent variable. Similarly, scale scores attempt to measure some latent dimension (e.g., depression, extraversion). Researchers are interested in various aspects of these scores. In particular, one may want to know something about its meaning, reliability, validity, and the best way to obtain them. To this end, latent variable models for tests and item responses have been developed.
An important topic in psychometrics is the study of (causal) relations between these latent variables. These relations are depicted by vertices in a directed graph and modeled by regression equations. Structural equation models (SEMs) allow the researcher to specify this relation structure on a set of manifest or latent variables. The parameters of such SEMs can be estimated and the fit model fit tested. SEM’s are frequently used in disciplines like behavioral genetics (twin studies), sociology, and econometrics.
The course has three parts: Part I deals with classical test theory, Part II with modern test theory, item response theory, and factor analysis. The first is most often used practice, but the second is more statistically sound and has a usefulness that goes far beyond that of traditional test theory. Advanced applications of modern test theory, such as adaptive testing, differential item-functioning, and equating the scores of different tests, are discussed at the end of Part II. Part III concerns structural equations models, and extensions of measurement models to questions such as population heterogeneity and change-over-time. These topics combines latent variables in a system of regression equations. Both classical and modern measurement models can be integrated into SEM’s. All computations and simulations will be performed with R.
Course objectives
Understand the mathematics of latent variable models and be able to derive some of their properties from basic assumptions.
Being able to analyze real empirical data with latent variable models and interpret their outcomes. *Some knowledge of reasons why models fail and how to deal with it.
Timetable
You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.
MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).
For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.
Mode of Instruction
Studying a textbook, following lectures, completing data assignments. Analyzing empirical data. This work may be done in small groups of two or three students. During practicals, lecturers may be consulted.
Assessment Method
The final grade depends on three data assignments, final presentation, and a written exam. Access to the exam depends on the successful execution of assignments, one for each of the three parts of the course. These assignments are completed and submitted no later than at the end of each part. In the final week, a presentation is given over the material of the whole course. Course credits will be obtained when the exam is graded by at least a 6. The assignments are reports on the analysis of test data with: I classical test theory, II modern test theory, III structural equation models. The exam tests insight into the theory gained by executing exercises and studying the book.
Empirical data is provided by the lecturer. You may also analyze your own data if they are appropriate for this course.
Reading List
Given the breadth of the course across several methodological areas, readings (open-access) will be provided per-topic.
Registration
Every student must register for courses with the new enrollment tool MyStudymap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.
Please note that it is compulsory to register for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam.
Extensive FAQ on MyStudymap can be found here.
Contact
Dr. E.M. McCormick: e.m.mccormick@fsw.leidenuniv.nl