HECOL 522 Introduction to Structural Equation Modeling Wednesdays 9:00 – 11:50 AM (HEB 301)
4 September 2019 – 4 December 2019
Instructor: Matt Johnson, PhD Office: 339 Human Ecology Building E-mail: [email protected] Office Hours: by appointment
Phone: (780) 492-5008
Policy about course outlines can be found in 23.4(2) of the University Calendar.
Course Description
An introduction to the theory and practice of structural equation modeling with social science data.
Practical application in Mplus is emphasized by computing and interpreting statistical models within this framework, including path analysis, confirmatory factor analysis, and structural equation modeling.
Prerequisites
Statistics courseworks covering regression analysis.
Course Objectives
- Prepare data for SEM analysis
- Conceptually understand the specification, identification, and estimation of path models, confirmatory factor analysis models, and structural regression models
- Analyze and interpret results of path models, CFA models, and structural regression models - Develop proficiency using Mplus for data analysis
- Critically read and understand published work using SEM techniques - Construct a publishable manuscript using SEM techniques
Required Text
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York: The Guilford Press.
Required Software
Mplus Demo Version – available as a free download: http://statmodel.com/demo.shtml
Recommended Software
Mplus Student Version - http://statmodel.com/pricing.shtml
- This is a heavily discounted version of the full Mplus software package. If you do not have access to Mplus on a university computer and plan to use SEM for your thesis or dissertation, I strongly recommend purchasing this. The base program can handle all procedures covered in this course ($195 USD).
Statistics software for data management and basic analysis, such as SPSS, STATA, or SAS.
Additional Course Fees
There are no additional course fees.
Technology
Class time will be divided into lecture and lab portions. Students need to bring a laptop computer with the Mplus Demo or Student Version installed to run the class examples.
This course has an E-class page available through Moodle. This page contains course powerpoints, lab materials, and additional information.
Audio or video recording of lectures, labs, seminars or any other teaching environment by students is allowed only with the prior written consent of the instructor or as a part of the approved accommodation plan. Recorded material is to be used solely for personal study, and is not to be used or distributed for any other purpose without prior written consent from the instructor.
Email Policy
I welcome communication via email for quick questions. Please refer to the syllabus and specific
assignment instructions prior to sending an email, as the answer to your question may be located there. I will respond to all emails within 2 business days. For lengthier questions, please make an appointment.
Additionally, students need to regularly check their U of A email for communications from the professor.
If I have an emergency and need to cancel class, email will be the mechanism for communicating this information.
Late Work Policy
Late work is not accepted in this course and any assignment not completed as designated in the course outline will result in no credit for that assignment. In the case of illness or serious personal/family issue, alternate arrangements can be made.
Course Requirements
The primary requirement is to develop a publishable manuscript using a method covered in the course.
This manuscript needs to adhere to all style conventions for a journal in which students intend to submit their work. Students are encouraged to consult with their supervisors to develop their ideas for the paper and identify a source of data to test their research question, although the actual manuscript needs to be the student’s own work. Each student will present their paper on the last day of class. In addition, students will also complete a short critique of a manuscript using SEM published in their area. Assignments are as follows:
Research Question, Data Source, Intended Journal for Submission (5%): It is imperative that
students locate a source of data early in the course to test their research question so there is ample time to conduct all analyses. For this assignment, students need to submit their research question(s) that will constitute the focus of their manuscript, submit a brief description (small paragraph) of the data to be used in the paper (include a list of potential variables to test), and append the manuscript submission guidelines for the journal the student is targeting.
Introduction (5%): The introduction to a manuscript needs to accomplish three things. First, the research
Background (10%): The background section (also known as literature review or theoretical framework) should present the theoretical framework guiding the study and provide a concise review of the relevant literature related to your topic. The background section should conclude with a summative section that restates the focus of the present study, articulates the advantages of the study, and clearly articulates the contributions being made. This section, which I often title “The Present Study” needs to serve as a bridge for the reader from the literature review into the Method section. The background section of a social science journal article should typically range from five to seven pages in length.
Method (10%): The Method needs to present the details of how the data were collected, provide
information about the sample under investigation, description of all measures being used, and the analytic plan explaining how the research questions will be answered. May consider using a table to present descriptive information about the sample.
Results (15%): Clear presentation of the results, including relevant univariate and bivariate analyses conducted prior to the more advanced path model, CFA, and/or SEM analyses. Use tables and figures to display statistical details and strive to not replicate numbers depicted in tables/figures in the narrative. The narrative should clearly walk the reader through the results, making meaning of the findings along the way. In other words, the main findings should be intelligible to the (many) readers who are not well- versed in your analytic technique. Attached to the results section, include printed and clearly labeled Mplus output for your analyses (this will not be included in the full manuscript).
Discussion (10%): The discussion builds from the results to “make sense” of the findings in light of prior research and theory. This section should leave the reader knowing exactly what the findings mean and may apply the results to relevant stakeholders (e.g., stakeholders, researchers, policy makers, etc.). The Discussion also needs to address the limitations of the study and have a concluding paragraph.
Complete Manuscript (30%): The entire manuscript needs to be in “ready-to-submit” form for each student’s target journal. This means the manuscript must include a title page, abstract and reference list (elements not previously handed in for feedback) and must follow the relevant style guidelines of the journal. The entire manuscript will be marked for quality, including considerations of whether prior feedback has been incorporated into the paper.
Presentation of Manuscript (10%): Each student will deliver an oral presentation over their manuscript during the final class period. The presentation must be 10-12 minutes in length (this is the typical length of a paper presentation at an academic conference) and cover all sections of the manuscript. Of course, significant portions from the manuscript will have to be excised from the presentation.
Critique of SEM Results in a Journal Article (5%): Students need to locate an article of interest from a peer-reviewed journal that uses one of the analytic techniques covered in this course. Complete a short (2- 3 page) critique of how the results are presented in this manuscript. Are any important details omitted, are tables and figures well-used, is the narrative coherent and easy to understand? Please point out positive elements of the presentation, as well. Chapter 18 in the text might be helpful to formulate a critique.
Summary of all assignments, weight, and the due dates.
Assignment Weight Due Date
Research Question, Data Source, Intended Journal for Submission 5% September 11
Introduction 5% September 18
Background 10% October 2
Method 10% October 16
Critique 5% October 30
Results 15% November 20
Discussion 10% November 27
Complete Manuscript 30% December 4
Presentation 10% December 4
Grading Scale
Evaluation of assignments is expressed in raw marks throughout the term. A final, cumulative score is translated into a letter grade based on the university four-point grading system (see below). Assignment of final grades is based on a combination of absolute achievement and relative performance in this course. The following descriptions will be used to guide the determination of final grades:
Excellent:
The student has demonstrated excellent understanding of course
content.
A+ Outstanding: The student has demonstrated an extraordinary grasp of the course content and performance reflects creativity and innovation, in addition to a high
level of analytical ability.
A Excellent: The student has demonstrated superior understanding of the course content and a high level of analytical ability.
A- The student has demonstrated superior understanding of the course content, but has not shown the same level of analytical ability as students receiving an A.
Good:
The student has demonstrated a sound understanding of course
content.
B+ The student has demonstrated a sound understanding of course content in terms of scope, depth, and breadth, with superior understanding being evident in some
topics.
B The student has demonstrated a uniformly sound understanding of course material.
Satisfactory/Adequate:
The student has demonstrated adequate
awareness of course content.
B- The student has demonstrated adequate awareness of course content with sound understanding of some topics.
Unsatisfactory/Fail C+ The student has demonstrated inadequate awareness of central dimensions of course content with superficial understanding of some topics.
C or lower
The student has demonstrated a limited understanding of the course content.
Performance is characterized by a lack of knowledge of the majority of the
University in this respect. Students are particularly urged to familiarize themselves with the provisions of the Code of Student Behaviour (online at www.ualberta.ca/secretariat/appeals.htm) and avoid any behaviour which could potentially result in suspicions of cheating, plagiarism, misrepresentation of facts and/or participation in an offence.
Academic dishonesty is a serious offence and can result in suspension or expulsion from the University.” (GFC 2003)
Code of Student Behaviour
“All students at the University of Alberta are subject to the Code of Student Behaviour, as outlined at:
http://www.governance.ualberta.ca/en/CodesofConductandResidenceCommunityStandards/CodeofStudentBehavio ur.aspx. Please familiarize yourself with it and ensure that you do not participate in any inappropriate behavior as defined by the Code. Key components of the code include the following statements.
30.3.2(1) No Student shall submit the words, ideas, images or data of another person as the Student’s own in any academic writing, essay, thesis, project, assignment, presentation or poster in a course or program of study.
30.3.2(2) c. No Student shall represent another’s substantial editorial or compositional assistance on an assignment as the Student’s own work.”
Course Schedule
Day Topic Readings Lab Assignments
Sept. 4 Syllabus Review
Intro to SEM
Kline Chapter 1 Huinink et al. (2011) –
on eClass
Intro to the Mplus interface
Sept. 11 Regression Review
Significance Testing & Bootstrapping
Kline Chapter 2 & 3 Importing data into Mplus Regression analysis
Research Question, Data Source, Intended
Journal Sept. 18 Data Preparation and Psychometrics
Review, Missing Data
Kline Chapter 4 Data Preparation/Missing Data Introduction Sept. 25 Specification and Identification of Path
Models
Kline Chapter 6 & 7 Skim Chapter 8
Path Model Oct. 2 Specification and Identification of CFA
and Structural Regression Models
Kline Chapter 9 CFA Model Background
Oct. 9 Specification and Identification of Structural Regression Models and
Covariates
Kline Chapter 10 Workshop Your Models Structural Regression Models
Oct. 16 Model Estimation and Local Fit Testing Kline Chapter 11 Path Analysis Method
Oct. 23 Global Fit Testing Kline Chapter 12 Model Comparisons
Model Constraints
Oct. 30 Analysis of CFA Models Kline Chapter 13 CFA Analysis
Parceling
Critique Nov. 6 Analysis of Structural Regression Models Kline Chapter 14 Analysis of Student Models
Nov. 13 No Class - Reading Week Nov. 20 Multiple Group Analysis and
Measurement Invariance
Kline Chapter 16 Multiple Group Analysis Peer Feedback on Manuscript
Results
Nov. 27 Choose Your Own Adventure TBD TBD Discussion
Dec. 4 Student Presentations Presentations & Full
Manuscript
*This schedule is subject to changes.*
