MERM Courses

Credits: 2

Credits: 3

This course is an introduction to the process and practice of research in education. It provides an overview of a variety of educational research methods and introduces both “quantitative” and “qualitative” approaches. In this course, students are assisted to recognize research paradigms as examples of disciplined inquiry, situate various models of inquiry, such as experimental, correlational, and single-subject designs, ethnography, and case studies.  Within these models of inquiry, students will be guided to understand, interpret, and critique studies conducted using a variety of methodological approaches, and plan a study with a research design appropriate to a selected research question. The students in this course:

• examine characteristics of different educational research paradigms
• study applications of these research paradigms to different educational problems
• develop skills necessary to conduct a literature review and construct an integrated and critical summary of the literature in a particular area
• develop strategies for understanding, interpreting, and evaluating research articles conducted within a range of research traditions
• identify a research question of interest they would like to investigate
• prepare a research proposal to examine their research question

Prerequisites: Successful completion of EPSE 482 or an introductory level statistics course is a pre- or co-requisite to this course.

Credits: 3

This course provides an overview of descriptive and inferential statistics commonly used in educational and psychological research.

Students successfully completing this course should be able to comprehend the assumptions, limitations, and uses of statistical methods; compute and interpret descriptive and selected inferential statistics; comprehend research that reports frequencies, means, t-tests, F-tests, and nonparametric tests; engage in statistical thinking; and develop a positive attitude towards the use of statistical methods.

The key concepts include data displays, descriptive statistics, variance, standardized distributions, sampling, probability distributions, sampling error, hypothesis testing, t and F-tests for comparing independent and dependent means, comparing proportions, correlation, and simple linear regression.

Prerequisites: Grade 12 algebra/math. A college level course in mathematics or statistics will be a definite advantage.

Credits: 3

This course is an introductory research methods course for MEd students who are being trained as consumers rather than producers of educational research. Therefore, the course focuses on developing skills for locating, understanding, interpreting and critiquing education research. The course provides an overview of research design and process, introduces the concepts and skills involved in understanding and analyzing research in education, and provides an overview of basic, general knowledge of various research methodologies. Objectives of the course include the following:

• develop library search skills and knowledge about resources for locating research articles and reports
• understand the relationship between research questions, designs and methodologies
• understand different research designs and methods such as correlational, experimental, ethnographic
• understand and interpret statistical data and findings
• understand and critique research methodologies and analyses
• develop skills to analyze and critique articles
• understand and apply concepts of validity and validity evidence in understanding and critiquing research reports

Prerequisites: No prerequisites

May not be used as a prerequisite to EPSE 592 or EPSE 596.

This course provides an introduction to educational and psychological measurement. This is not a statistics course and it provides more in-depth coverage of measurement, reliability, validity, and theory than what is covered by typical ‘tests and measures’ courses. Four areas will be emphasized:

(a) principles of measurement theory (e.g., reliability, validity),

(b) applications of classical test theory and item response theory to real world measurement problems,

(c) historical and social context of testing and measurement,

(d) learning how to make use of measurement information when selecting and evaluating items and measures.

This course is highly recommended for anyone planning to pursue applied, clinical, or research studies/careers involving the use or development of tests or measures.

Prerequisites:Successful completion of EPSE 482 or an equivalent undergraduate statistics course.

This advanced seminar course (offered on a two-year rotation) focuses on a variety of topics and issues related to the practical development of scales and measures in psychology, education, and health. Key topics will include: principles of scale development; principles and guidelines in item writing, scaling and response formats; common method biases and how to control for them; standardization of administration and scoring; the importance and examination of factor structure; analyses used in the evaluation, selection, and revision of items; and advanced topics in reliability and validity. Other related topics (e.g., ethical issues, item weighting, equating, test adaptation, computerized/internet testing) may also be incorporated into the course as time permits and based on student interest.

The course typically centers around a major practical assignment which involves working as part of a team to develop a measure, administer it, score it, examine its psychometric properties, and make recommendations for revision. This course is strongly recommended for anyone planning to pursue applied, clinical, or research studies/careers involving the use or development of tests or measures.

Prerequisites: Successful completion of EPSE 528 or an equivalent advanced measurement course. It is expected that students will also have completed at least an undergraduate statistics course and be familiar with a statistics package such as SPSS.

Credits: 3

EPSE 581 is a special topics course with each offering focusing on a different theme or topic within measurement, evaluation, research methodology, or data analysis. The topic in 2011 is Socio-politics of Educational Assessment.

This is an introductory course on educational assessment designed for masters and doctoral students with little or no statistical training that focuses on understanding educational assessment from social and political perspectives. Technical attributes of assessment will be discussed, but this course is not a measurement course and students who are interested in learning about measurement theory and practice should take EPSE 528.

The course has three main goals:

1)  to provide a basic conceptual understanding of essential concepts in educational assessment (such as reliability, validity, and bias) so as to encourage informed critique and use of educational assessment data

2)  to explore educational assessment in relation to current issues in education policy and practice, and within socio-political contexts, including:

a) the history of educational assessment
b) the relationship between assessment and pedagogy and learning
c) the relationship between educational assessment and political ideologies and interests
d) the relationship between educational assessment and accountability

3)  to foster critical analysis of educational assessment for forward looking problem solving in educational policy and practice, including:

a) understanding and accounting for multiple interests in and perspectives on educational assessment
b) fostering good assessment at the classroom and building level
c) using large scale testing data wisely for educational planning & decision making
d) considering the differential effects of assessment in relation to gender, ethnicity, race, and special needs

Prerequisites: None

The purpose of the course is to provide an understanding of evaluation—as a discipline, as a profession, as a process and a product in a wide range of educational and social contexts. There are no prerequisites for this course and it is appropriate for all graduate students, masters or doctoral level. The primary focus of the course is program evaluation rather than the assessment of individuals (for example, the measurement of student achievement or personnel review). The course focuses on developing an understanding of the logic of evaluative thinking, the nature of evaluation as a profession and discipline, the knowledge and skills needed to be expert consumers of program evaluation and novice evaluators in contexts relevant to individual career contexts.

Upon completion of the course, students will be able to:

• Describe the logic of evaluation
• Understand and be able to use the central concepts in evaluation
• Be familiar with major approaches to evaluation
• Be aware of standards in evaluation, including ethical practices for evaluators
• Understand the social and political nature of evaluation

Because students will have different contexts of application for evaluation, course assignments are constructed so that each individual can apply evaluation concepts in a context meaningful for them.

Prerequisites: None

Cross-listed with EDST 525

Credits: 3

Most research in the social sciences (e.g., education, psychology) uses either correlation or quasi-experimental designs. Correlation designs are taught in EPSE 596. In EPSE 592, the focus is on experimental and quasi-experimental designs, how to analyze and interpret data obtained from such designs, and how to describe results from these analyses using proper format.

Researchers are frequently interested answering questions that involve comparing two or more groups (e.g., sex differences, compare age or education groups, compare control vs. intervention groups). The course will cover various analyses of variance (ANOVA) techniques designed to answer such questions (e.g., one-way ANOVA, two-way ANOVA, repeated measures ANOVA, mixed model ANOVA, ANCOVA) as well as their assumptions, nonparametric alternatives, and relevant effect size indicators. Other related topics, such as sample size and power calculations, three-way ANOVAs and MANOVA, may be included as time permits. Analyses will be conducted using SPSS.

Prerequisites: Successful completion of EPSE 482 or an equivalent course in undergraduate statistics. EPSE 483 is not an acceptable prerequisite course.

Credits: 3

Single subject research is a scientific methodology that allows researchers to conduct a true experiment with one or a small number of subjects. It has played a central role in the development of evidence-based interventions in the fields of special education, clinical psychology, school psychology, counselling psychology, rehabilitation sciences, and audiology and speech sciences. The course focuses on procedures and issues related to the design, implementation and analysis of single subject research. The course covers general methodological information as well as specific details about single subject research designs and the use of single subject methods in applied settings. Issues and applications of statistical procedures to single subject, time series data will also be introduced.

As a function of participating in the course, students will be able to: (a) design and apply single subject research procedures to address research questions and issues in special education, school psychology, counselling psychology, clinical psychology, rehabilitation sciences, or audiology and speech sciences; (b) analyze and interpret data collected with single subject research procedures; (c) discuss contexts in which statistical analysis of time series data is appropriate or necessary and describe methods for conducting such an analysis; and (d) design community-based single subject research that balances the need for scientific rigor with equal need for social relevance.

Prerequisites: Successful completion of EPSE 596 or an equivalent course or permission of the instructor.

Credits: 3

Meta-analysis is a type of systemic review that uses techniques to systematically combine and summarize the statistical results of research in any field. Summarizing existing research is a necessary endeavour in the scientific process. An understanding of how to evaluate and conduct a meta-analysis is of vital importance to today’s researchers.

The focus in this course is on current methods and techniques for calculating and analyzing study effect sizes. The course covers the entire meta-analytic process: problem formulation, data collection, data evaluation, analysis and interpretation, and presentation of results. Various effect size measures are studied. Methods of combining effect sizes and the use of moderator variables are extensively examined. Students learn practical skills and complete an actual meta-analysis project that can be used as a start towards their thesis/dissertation.

Prerequisites: Successful completion of EPSE 592 and/or EPSE 596 or equivalent courses.

Credits: 3

This is an introductory research course focusing especially on interpretive and critical approaches to social science and educational research, what is often called qualitative research. There are no prerequisites for this course so it is appropriate for both masters and doctoral students who are making an initial foray into qualitative research. As an introductory course, the purpose is to explore philosophical and practical aspects of research that will help students in deciding if this research approach ‘works’ for them and to open the door to more advanced course work in interpretive and critical research.

The course begins with a brief philosophical introduction to the foundational ideas in post-positivism and interpretivism that underpin alternative research methodologies and methods. Students will be encouraged to reflect on and come to new understandings about their epistemologies as they learn about interpretive research approaches. The course will provide hands-on activities in data collection and analysis methods that are generic for many interpretive research approaches—focusing especially on participant observation, individual and group in-depth interviewing, and material culture. While the course does not focus in depth on any specific methodology, students

will be introduced to a wide range of methodological approaches. Other topics such as ethics and politics of research will discussed, particularly in relation to qualitative research. By reading exemplary examples of interpretive and critical research studies, students will be exposed to models for excellent research within this tradition.

Prerequisites: None

Credits: 3

The goal of the course is to enable students to build and evaluate statistical models for the analysis and interpretation of data in the behavioural sciences. The focus is on methods of statistical modelling of data and practical decision-making, rather than on statistical theory per se.

Simple linear and nonlinear regression, multiple regression, and logistic regression are the main topics. IBM SPSS software is used.  Regression is a highly general and very flexible data analytic framework in which to examine phenomena in the behavioural sciences. It can be used to predict or to explain relationships between an outcome variable and predictors or explanatory variables of interest. Both continuous and categorical variables of the kind typically studied in psychology and education can be accommodated.

Students successfully completing this course should be able to comprehend the assumptions, limitations, and uses of correlational and  regression analysis; compute and interpret regression solutions for non-experimental and experimental designs; conceptualize, analyze, and interpret path models including mediators and moderators; evaluate publications and compose research reports incorporating correlational and regression analyses.

Prerequisites: Successful completion of EPSE 482 is required. Successful completion of EPSE 592 is highly recommended.

Credits: 3

This course is designed for individuals who want to become familiar with the statistical techniques known collectively as “latent variable modeling”. Throughout the course, widely available, but specialized, software such as LISREL or MPlus will be used for the computation. The course focuses on the class of techniques and statistical theory that include, for example: (a) the class of models referred to as LISREL models or structural equation models (SEM), (b) unrestricted maximum likelihood factor analysis, (c) path or causal models, and (d) confirmatory factor analysis. The course will also include discussion, and examples, of measurement invariance, and rating scales – Likert response data. Both exploratory and confirmatory modeling strategies will be discussed, with an emphasis on the statistical and confirmatory approach.

Topics include:

1. Basic ideas of latent variable modeling and factor analysis.

2. Overview of “exploratory” factor models

3. General Linear Latent Variable Model

4. Confirmatory Factor Analysis (CFA)

5. Applications of CFA:

a)     Construct validity and measurement studies
b)     Multi-group CFA
c)     Test theory models of “equivalence”, sets of congeneric tests
d)     Matters of factorial and measurement invariance
e)     Models for latent growth (optional)

6. Special Problems

a)     Dealing with binary and rating scale (or Likert) data and how factor analysis is related to Item Response Theory (IRT)
b)     Incomplete data (i.e., missingness)
c)     Cautions regarding “causal” modeling; what is “causal” about causal modeling?
d)     The matter of equivalent models
e)     Model identification
f)      Methods for setting the metric of the latent variable(s)

Prerequisites: Successful completion of EPSE 592 and EPSE 596, or at least two courses (one of which must be a graduate course) in statistics and/or data analysis. It will be very helpful to have taken (or be taking) a course in regression because some of those concepts will be built on in this class. If you are unsure whether your background is sufficient, please contact the instructor.

Credits: 6

EPSE 681 is an advanced topics course in research and measurement, with each offering focusing on a different theme or topic within measurement, evaluation, research methodology, or data analysis. The topic in 2011 is Advanced Qualitative Research in Psychology.

This advanced level course is offered for doctoral students wishing to extend their knowledge of qualitative methodologies. The course content will focus on in-depth understandings of the philosophy of science and how various epistemological perspectives underpin how we conceptualize methods of inquiry. The advanced methods covered will focus on narrative inquiry, critical narrative methods, critical ethnographic methods, and various forms of discourse analysis as applied within psychology.

Prerequisites: Successful completion of EPSE 595 or an equivalent course or permission of the instructor.

Credits: 3

This course focuses on multivariate research design, statistical methods and data analysis. Emphasis will be placed on providing a fundamental understanding of the multivariate quantitative methodological techniques used in the empirical social, behavioural, and educational sciences. Emphasis is placed on encouraging you to learn to communicate in the language of multivariate data analysis. Communication is at the core of this course. Computation is meant to aid in communication.

Emphasis will be placed on data analysis techniques. An awareness of the common pitfalls and misconceptions of the various techniques and the fundamental assumptions you need to make to apply these methods will be emphasized. The multivariate general linear model and other multivariate methods will be discussed both from a geometric and matrix algebraic formulation.

Topics covered include:

• Why do we need multivariate statistics in our research repertoire — or do we? Along the way, a brief review of some univariate and bivariate statistics, including multiple regression modeling.
• Some rudiments of matrix algebra in the context of statistical analysis and some fundamental statistical theorems and results for multivariate analysis.
• Analysis of experimental data for designs involving completely between or within factors, mixed or split-plot designs, nested designs, and fixed versus random factors. Repeated measures ANOVA.
• Analysis of covariance.
• Brief discussion of canonical correlation.
• Multivariate analysis of variance and covariance (MANOVA/MANCOVA), step-down analysis, post-hocs to MANOVA.
• Discriminant Function analysis.
• Brief discussion of principal components and factor analysis.

Prerequisites:  Successful completion of EPSE 592 and EPSE 596 or equivalent graduate-level courses involving regression and the analysis of experimental data. It will be very helpful to have taken (or be taking) a course in regression because some of those concepts will be built on in this class. If you are unsure whether your background is sufficient, please contact the instructor.

Change over time is a fundamental concept in the social, behavioural, and health sciences. For some areas such as human development or areas involving program evaluation, change is a central aspect of study. In other areas, change may not be the central aspect of study, but it can still be of concern. Research in educational, cognitive, school, clinical, counselling, or social psychology examines change whenever treatments are compared to a control group or to a base rate. Researchers are frequently interested in answering questions requiring the use of pre- and post-measures and longitudinal (multi-wave) data.  This course will cover the conceptual measurement, design and data analysis issues surrounding change and growth. Where possible, practical applications will be brought to class and be the focus of discussion.

Objectives: This course focuses on: (i) issues in the use of change or difference scores in two-wave data, (ii) HLM and other multi-level models for the trajectories resulting from multi-wave data that collected over more than two time points. Although the focus of the multi-wave analyses will be on HLM or multilevel models, if time permits, the use of structural equation models for change and growth modeling will be briefly described. Another way of describing this course is that you are learning about HLM modeling in the context of growth and change.

Prerequisites: Successful completion of a graduate course in statistics, data analysis, and research design (in the social, educational, and health sciences). It would be an asset for you to have completed a course covering basic topics in measurement (e.g., reliability and validity) and having covered regression and ANOVA. It would be a real asset for you if have covered MANOVA, repeated measures, and factor analysis, but these are not necessary. Students without measurement and regression will struggle with most of the material.

EPSE 684 is an advanced level course on educational and psychological measurement which focuses primarily on item response theory (IRT). The course focuses on providing a foundation for understanding modeling item responses using IRT, applying IRT to different measurement problems, and examining its uses in research literature.  Using software packages is an integral part of studying and using IRT models and the course dedicates portions of class times to learning and using an IRT estimation package. The students in this course will:

• examine fundamentals of item response theory
• study a variety of IRT models
• study applications of these models to different measurement problems
• develop skills necessary to model responses for dichotomous and polytomous items, to calibrate responses from such items, and interpret item and examinee parameter estimates
• identify and examine current issues and publications in IRT
• identify a research question of interest they would like to investigate
• conduct analyses and prepare a research paper to examine their research question

Prerequisites: Successful completion of EPSE 528 or an introductory level measurement course