| Course ID |
Credit
Hours |
Course Title |
QUAN 402
QUAN 402 - Basic Statistics
3 Credit Hours
A master’s level terminal statistics course. Emphasis on descriptive statistics and graphical representation of data. Includes a brief introduction to hypothesis testing procedure.
Check Class Schedule for Availability
|
3 |
Basic Statistics |
QUAN 506
QUAN 506 - Inferential Statistics
4 Credit Hours
Covers basic descriptive techniques such as central tendency, measures of variability and graphical presentation of data. In addition, hypothesis testing, analysis of variance, nonparametrics and simple linear prediction will be covered.
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|
4 |
Inferential Statistics |
QUAN 507
QUAN 507 - Multiple Regression
4 Credit Hours
The general linear model is presented which allows for hypothesis testing including correlational analysis, analysis of variance and analysis of covariance. Non-linear relationships are presented. Emphasis is placed on testing the stated research hypotheses. Prerequisite: 506.
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|
4 |
Multiple Regression |
QUAN 508
QUAN 508 - Experimental Design
4 Credit Hours
Strategies of designing research studies and the analysis of data from studies using linear models are examined. Emphasis will be placed on internal and external validity and factors that affect power in variance designs including completely randomized designs, Latin square, repeated measures and analysis of covariance with each of the above designs. Prerequisite: 506 or equivalent.
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|
4 |
Experimental Design |
QUAN 531
QUAN 531 - Principles of Measurement
3 Credit Hours
Intended to provide theoretical principles of measurement that are applicable to both teaching and research. Part of the course will be devoted to current issues in measurement and to practical applications to these theoretical principles. Prerequisite: 506.
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|
3 |
Principles of Measurement |
QUAN 533
QUAN 533 - Survey Research Methods
3 Credit Hours
Overview of survey methods covering topics such as the purpose of survey research methods, the process of survey research, ethical considerations in survey research, questionnaire design and administration, sampling designs, data processing, and reporting of survey research. Students are expected to be familiar with basic descriptive statistics, inferential statistical procedures and principles of instrument construction/development. Prerequisite: EPSY 506 & EPSY 531 or equivalent.
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|
3 |
Survey Research Methods |
| QUAN 580a
|
4 |
Structural Equation Modeling |
QUAN 580b
QUAN 580b - Factor Analysis
4 Credit Hours
It covers exploratory and confirmatory factor analysis as it is applied in the social sciences. Factor analysis is used both for building and testing theory and is a particularly valuable technique in instrument development. It also serves as an entry point for beginning understanding of latent variable modeling methods, including structural equation modeling. Students are expected to be able to carry out and justify the decisions involved in an exploratory or confirmatory factor analysis of empirical data. Prerequisite: EPSY 507.
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|
4 |
Factor Analysis |
QUAN 580c
QUAN 580c - Multivariate Methods
3 Credit Hours
Intended to assist the student in learning to think deeply and critically about establishing research problems comprised of multiple dependent variables, and how to appropriately analyze multivariate data. It aims at understanding what is multivariate analysis, learning how to properly screen variables prior to analysis, and learning different techniques for analyzing multivariate data. Prerequisite: EPSY 507.
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|
3 |
Multivariate Methods |
QUAN 580d
QUAN 580d - Bayesian Inference
4 Credit Hours
Intended as a first introduction to Bayesian inference. The course will review relevant theoretical background and introduce the Bayesian approach to data analysis (including choice of prior distributions and calculation of posterior distributions) with an emphasis on practical applications to inference problems in social and behavioral sciences. Topics include: Bayes’ Theorem; prior distributions; inferences for discrete random variables and binomial proportions; inferences for continuous random variables and normal means; linear regression; analysis of variance; MCMC/Gibbs sampler; and model evaluation/comparison.
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|
4 |
Bayesian Inference |
QUAN 580f
QUAN 580f - Categorical Data Analysis
3 Credit Hours
This course is devoted to the analysis of data in which the response variables are categorical: either qualitative or quantitative with a limited number of values. Two approaches are discussed to the analysis of categorical data: the classical approach uses various measures of association based on chi-square statistics and odds ratios; and the generalized linear modeling approach employs methods analogous to ANOVA and regression models. Prerequisite: EPSY 507.
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|
3 |
Categorical Data Analysis |
QUAN 580f
QUAN 580f - Advanced Experimental Design
3 Credit Hours
The course focuses on statistical procedures that are extensions of analysis of variance, such as between-subjects factorial designs and within-subjects designs or repeated measures designs, and analysis of covariance (ANCOVA).
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|
3 |
Advanced Experimental Design |
QUAN 580g
QUAN 580g - Item Response Theory
3 Credit Hours
This course is designed to acquaint students with the item response theory (IRT) covering topics such as dichotomous item response models, parameter estimation, IRT software, goodness of fit, test construction, test score equating, differential item and functioning. Students are expected to be familiar with the theory, various IRT models, and have the ability to interpret and apply these models appropriately. Prerequisite: EPSY 507 and EPSY 531.
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|
3 |
Item Response Theory |
QUAN 580h
QUAN 580h - Monte Carlo Statistical Methods
3 Credit Hours
This course introduces Monte Carlo techniques that involve random number generators, Monte Carlo integration, slice sampling, and perfect sampling, with a focus on actual implementation via the use of Fortran and S-Plus.
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|
3 |
Monte Carlo Statistical Methods |
QUAN 580i
QUAN 580i - Advanced Statistics
4 Credit Hours
This higher-level course is designed for doctoral students who are interested in theoretical understanding of probability and mathematical statistics. Topic includes: special probability distributions, functions of random variables, sampling distributions, point estimation, probability and statistical inference, cumulants of sampling distributions, and order statistics.
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|
4 |
Advanced Statistics |
QUAN 580i
QUAN 580i - Computational Statistics
4 Credit Hours
This course discusses numerical methods and algorithms from a computational viewpoint, with a focus on techniques for implementing algorithms in Mathematica, Fortran, and S-Plus. Topics include: non-uniform variable generation, the conventional method of moments, the method of probability-weighted moments and L-moments, the method of Percentiles, doubling techniques, multivariate non-normal data generation, and the bootstrap.
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|
4 |
Computational Statistics |
