Course
Applied Statistics for Educational Researchers (DUH165)
Facts
Course code DUH165
Credits (ECTS) 5
Semester tution start Spring
Language of instruction English
Number of semesters 1
Exam semester Spring
Time table View course schedule
Literature The syllabus can be found in Leganto
Introduction
This seminar offers a comprehensive introduction to latent variable modeling, with a strong focus on practical applications in quantitative research. Core topics include confirmatory factor analysis, structural equation modeling, and the underlying principles of psychometric theory.
Participants will gain hands-on experience by working with real-world datasets, learning how to visualize, describe, and analyze data using advanced statistical techniques. The course emphasizes both conceptual understanding and practical implementation, equipping students with the skills to apply latent variable models effectively in their own research.
Content
Learning outcome
By the end of this course, PhD candidates will have acquired the following knowledge:
Knowledge
- An understanding of basic measurement theory.
- A solid grasp of multiple regression and factor analysis within the structural equation modeling (SEM) framework.
- A clear comprehension of hierarchical structures in data and methods to analyze them effectively.
- A good understanding of SEM and latent growth curve (LGC) models in the context of complex survey data.
Skills
- Conducting preliminary analyses to assess the validity and reliability of data.
- Performing SEM and LGC analyses using Mplus or R.
- Preparing the results of these analyses for publication.
General Competencies:
- Ability to choose and apply the appropriate analyses for the given design and data.
- Development of advanced strategies for future quantitative research.
Required prerequisite knowledge
Recommended prerequisites
The students are expected to have the software package Mplus or R installed on their personal computers. It is beneficial to have some familiarity with the data structure of one's own projects and the potential research questions. It is advantage to get acquainted with Mplus or R before the course in order to master preparation of the data for import (for Mplus syntax, please see chapters 1 + 2 in Muthén & Muthén).
Exam
Fakta
Weight 1/1
Marks Passed / Not Passed
Evaluation will be based on a brief individual paper. The paper should contain a short thematic conceptualization, research questions, a section presenting sample and procedure, analytic strategies, and results, and be no longer than 4000 words +/-10% (references excluded). Coursework requirements: Active participation in lectures and seminars at the workshop. Self-study. The students’ workload will be approximately 150 hours of work.
Coursework requirements
80 % attendance
At least 80 % attendance in lectures and seminars.
Method of work
In this week-long seminar, we will introduce confirmatory factor analysis (CFA) structural equation modeling (SEM), and multilevel modeling work within the SEM framework. The seminar also demonstrates how Latent Growth Curve Modelling can be understood as an extension of SEM with intercepts and/or slopes being modeled as latent variables, first as an unconditional latent curve model. We will then look at conditional Latent Growth Curve models (including mediation models, and a comparison of (latent) groups with different approaches to testing measurement invariance. The latter will also be replicated and shown in R.
The working format is a blending of lectures, group discussions, and hands-on analyses in Mplus/R.
Overlapping courses
| Course | Reduction (SP) |
|---|---|
| Advanced Statistics for Educational Researchers: Analyzing Structural Equation Models and Latent Growth Curves w/ MPlus (DSP165_1) , Applied Statistics for Educational Researchers (DUH165_1) | 5 |