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Title: SEM for Health, Business and Education
Resulting in 1 citation.
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Suhr, Diana D. |
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SEM for Health, Business and Education Presented: Orlando, FL, SAS Users Group International Conference, April 2002. Also: http://www2.sas.com/proceedings/sugi27/p243-27.pdf Cohort(s): Children of the NLSY79 Publisher: SAS Institute Inc. Keyword(s): Children, Academic Development; Children, School-Age; Cognitive Development; Gender Differences; Longitudinal Data Sets; Longitudinal Surveys; Methods/Methodology; Modeling; Modeling, Growth Curve/Latent Trajectory Analysis; NLS Description; Peabody Individual Achievement Test (PIAT- Reading); Statistical Analysis; Statistics; Variables, Independent - Covariate Permission to reprint the abstract has not been received from the publisher. Structural Equation Modeling (SEM) is a comprehensive statistical approach to testing hypotheses about relations among observed and latent variables (measured variables and unmeasured constructs) (Hoyle, 1995). SEM takes a confirmatory rather than an exploratory approach, specifies intervariable relations a priori, and estimates measurement errors explicity (Suhr, 1999). The purpose of this paper is to provide an introduction to the SEM statistical approach with examples from health, business, and education fields. SAS code (PROC CALIS), diagrams, and results will be discussed. In the health field, a path analysis investigates the prediction of self-perceived illness with effects of exercise participation, self-perceived fitness, stressful life experiences, and hardiness for promoting stress resistance (Kline, 1998; Roth, Wiebe, Fillingim, & Shay, 1989). Relating to the business field, this example examines the relationship between academic success and career success (e.g., ACT score, cumulative grade point average, salary) (Schumacker & Lomax, 1996). The next example compares results from a baseline latent growth curve model (LGM) of reading achievement to results from a LGM of reading achievement including a categorical variable as a covariate. Examples range from beginner to advanced levels (path analysis (regression) to LGM). |
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Bibliography Citation
Suhr, Diana D. "SEM for Health, Business and Education." Presented: Orlando, FL, SAS Users Group International Conference, April 2002. |