Applied Structural Equation Modeling A Step-By-Step Guide to Questionnaire Validation

Preface  1. Overview of the nature of the constructs: Latent variables and composites  Part I. Theoretical foundations and practical guidelines  2. Developing instrument items and pre-data collection validity  3. Data characteristics and SEM estimations  4. The common factor model and composite model; post-data collection validity  5. Empirical example of the common factor model: Validating the Second Language Trait Emotional Intelligence scale (L2-TEI)  Part II. Empirical applications of common factor and composite models  6. Empirical example of the composite model: Validating the Language Classroom Engagement Inventory (LCEI)

Biography

Abdullah Alamer is an Associate Professor at King Faisal University, Saudi Arabia. He holds a Ph.D. from the University of New South Wales (UNSW), Australia. His primary research interest lies in the psychology of language learning, with structural equation modeling (SEM) being the dominant method in his work. He is an editorial board member in Innovation in Language Learning and Teaching among other journals.

"Alamer offers a clear, rigorous, and much-needed guide to construct validation using structural equation modeling. By distinguishing latent variables from composites and emphasizing theory-driven modeling, this book brings conceptual clarity to validation practices that are often taken for granted in applied research. With accessible explanations and practical examples, it is an essential resource for researchers and graduate students in applied linguistics and language psychology."

Herb Marsh, University of Oxford, UK

"This guide demystifies Structural Equation Modeling (SEM) for researchers in language acquisition and related fields. While covering essential SEM fundamentals, the book stands out for its practical, discipline-specific approach to questionnaire validation—bridging the gap between statistical theory and real-world application. Particularly innovative is the author's thoughtful integration of generative AI tools into the research workflow. Rather than ignoring or prohibiting these emerging technologies, the book provides explicit, productive guidance on leveraging AI to enhance—not replace—rigorous methodological practice."

Benjamin Domingue, Stanford University, USA

"This book offers an impressively coherent and authoritative integration of classical and contemporary approaches to structural equation modeling for construct validation. By guiding readers from established factor-based methods to newer developments such as ESEM and composite-based SEM, the author demonstrates both deep technical mastery and a profound pedagogical clarity. Throughout the volume, a strong commitment to improving the rigor, transparency, and theoretical grounding of empirical research in second language studies is evident. As such, this book will be an essential resource not only for applied linguists, but also for social science researchers seeking to strengthen the quality and credibility of their quantitative work."

 Jörg Henseler, University of Twente, Netherlands

"This is a remarkably clear and well-organized account of crucial measurement and analysis issues for questionnaires and similar methods that seldom receive the attention they deserve. The challenges of aligning measurement and analysis are outlined, decision procedures provided, and some new and much better methods for dealing with them are introduced with carefully explained examples."

Philip Dale, University of New Mexico, USA.

"If I had one wish for L2 research, I think it would be better measurement. This book—full of examples, practical guidance, open-science supports, and the conceptual backing needed for researchers to validate their instruments with confidence—helps make that wish come true."

Luke Plonsky, Northern Arizona University, USA.