I am a typical old-academic fortunately trapped in doing things they like. In my graduate-student days I learned some structural equation modeling – only to find most people did not understand it. So I wrote the first textbook-length introduction to SEM with latent variables, and the trap sprung. My next book dealt with SEM misunderstandings, disagreements, and some fun ideas. The bulk of my subsequent work appeared as articles striving to improve general structural equation modeling. I participate in SEMNET – a free listserve dedicated to structural equation modeling – and have disagreed with many people about many things. Those discussions can get quite heated, and my publications have tended to address the substance of the disagreements by illustrating or clarifying contentious points.
Over the years I encountered more than my fair share of cleanly fitting and informative structural models, and I count myself fortunate for having assisted several researchers who can justifiably make the same claim. Along the way I encountered students and colleagues stumbling unnecessarily over things for which I could not find any literature-remedy, or ameliorative examples. Fortuitously I recently found myself struggling with some failing structural equation models – and feeling good about the struggle; and also feeling optimistic about employing these models to instruct others. Hence, I am currently working on a book addressing basic structural equation modeling diagnostics. This is not about statistics, or disagreements – it focuses on thinking about structural equation models.
My substantive interests reside in social psychology, and social psychology’s connections to physiology, neurons, genetics, and neuroscience. The academic edges here require sharpening if they are to become cutting-edges, so I have become the social psychological equivalent of a barber’s strop. Here, as in structural equation modeling, I function as a variance-maximizer. Any given audience is bifurcated into those greatly appreciating or disliking my approach and comments. It produces an enlivening academic ride.
Structural Equation Modeling
Statistical reasoning and techniques used by sociologists to summarize data and test hypotheses. Topics include describing distributions, cross-tabulations, scaling, probability, correlation/regression and non-parametric tests. Prerequisite: SOC 100 or consent of instructor. Note: This course is intended primarily for students concentrating in SociologyFall Term 2020 Winter Term 2021
An introduction to the study of individual and group behaviour observed in social processes. Prerequisites: SOC 100, or PSYCO 104 or 105, or consent of instructor. Note: SOC 241 and PSYCO 241 may not both be taken for credit.Fall Term 2020 Winter Term 2021
Hayduk, L. A. (2018). Review essay on Rex B. Kline's Principles and Practice of Structural Equation Modeling: Encouraging a fifth edition. Canadian Studies in Population, 45(3-4):154-178.
Hayduk, L. A. (2016). Improving measurement-invariance assessments: Correcting entrenched testing deficiencies. ( BMC: Medical Research Methodology, 16:130) DOI 10.1186/s12874-016-0230-3
Hayduk, L. A. (2014a). Seeing perfectly-fitting factor models that are causally misspecified: Understanding that close-fitting models can be worse. Educational and Psychological Measurement, 74(6): 905-926. (doi: 10.1177/0013164414527449)
Hayduk, L. A. (2014b). Shame for disrespecting evidence: The personal consequences of insufficient respect for structural equation model testing. ( BMC: Medical Research Methodology, 14:124) DOI 10.1186/1471-2288-14-124 http://www.biomedcentral.com/1471-2288/14/124
Hayduk, L. A., & Littvay, L. (2012). Should researchers use single indicators, best indicators, or multiple indicators in structural equation models? BMC Medical Research Methodology, 12:159, 1-17. (Open Web Access).
Hayduk, L. A., Pazderka-Robinson, H., Cummings, G .G., Levers, M-J. D., & Beres, M. A. (2005). Structural equation model testing and the quality of natural killer cell activity measurements. BMC Medical Research Methodology, 5(1), 1-9. (Open Web Access). (doi: 10.1186/1471-2288-5-1) Note the correction of .922 to .992, and the correction of .944 to .994 in Table 1.
Hayduk, L. A., & Glaser, D. N. (2000a). Jiving the four-step, waltzing around factor analysis, and other serious fun. Structural Equation Modeling, 7(1), 1-35.Hayduk, L. A., & Glaser, D. N. (2000b). Doing the four-step, right-2-3, wrong-2-3: A brief reply to Mulaik and Millsap; Bollen; Bentler; and Herting and Costner. Structural Equation Modeling, 7(1), 111-123.
Hayduk, L. A. (2006). Blocked-Error-R2: A conceptually improved definition of the proportion of explained variance in models containing loops or correlated residuals. Quality and Quantity, 40, 629-649.
Hayduk, L. A., Cummings, G. Boadu, K., Pazderka-Robinson, H., & Boulianne, S. (2007). Testing! testing! one, two, three – Testing the theory in structural equation models! Personality and Individual Differences, 42(5), 841-850.
Hayduk, L. A. (2009). Finite feedback cycling in structural equation models. Structural Equation Modeling, 16, 658-675.
Hayduk, L. & Pazderka-Robinson, H. (2007) Fighting to understand the world causally: Three battles connected to the causal implications of structural equation models. Pp 147-171 in W. Outhwaite and S. Turner (eds.) Sage Handbook of Social Science Methodology. London: Sage Publications.
Hayduk, L. A. (1994). Personal Space: Understanding the Simplex Model. Journal of Nonverbal Behavior, 18(3), 245-260.
Hayduk, L. A. & Avakme, E.F. (1990). Modeling the deterrent effect of sanctions on family violence: Some variations on the deterrence theme. Criminometrica 6/7:19-37.
Hayduk, L. A. (1981).The Permeability of Personal Space. Canadian Journal of Behavioural Science, 13(3), 274-287.
Hayduk, L. A. (1987). Structural Equation Modeling with LISREL: Essentials and Advances. Baltimore: Johns Hopkins University Press.
Hayduk, L. A. (1996). LISREL Issues, Debates, and Strategies. Baltimore: Johns Hopkins University Press.
Entwisle, D. R., Hayduk, L. A., & Reilly, T. W. (1982). Early Schooling: Cognitive and Affective Outcomes. Baltimore: Johns Hopkins University Press.
Hayduk, L., Mah, X., and Carter-Snell, C. (editors) (2002). Structural Equation Modeling and Hierarchical Linear Modeling: Communicating Across Disciplines. Population Research Laboratory, University of Alberta: Edmonton, Canada T6G 2H4.