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Source: SAS Users Group International (SUGI)

Resulting in 6 citations.

1.	Allison, Paul D.
1.	Fixed Effects Regression Methods In SAS® Presented: San Francisco, CA, SAS Users Group International Conference (SUGI31), March 2006 Cohort(s): Children of the NLSY79 Publisher: SAS Institute Inc. Keyword(s): Longitudinal Data Sets; Methods/Methodology; Modeling, Fixed Effects; Statistical Analysis; Statistics Permission to reprint the abstract has not been received from the publisher. Fixed effects regression methods are used to analyze longitudinal data with repeated measures on both independent and dependent variables. They have the attractive feature of controlling for all stable characteristics of the individuals, whether measured or not. This is accomplished by using only within-individual variation to estimate the regression coefficients. This paper surveys the wide variety of fixed effects methods and their implementation in SAS, specifically, linear models with PROC GLM, logistic regression models with PROC LOGISTIC, models for count data with PROC GENMOD, and survival models with PROC PHREG.
	Bibliography Citation Allison, Paul D. "Fixed Effects Regression Methods In SAS®." Presented: San Francisco, CA, SAS Users Group International Conference (SUGI31), March 2006.

2.	Dirmyer, Richard Schley, Sara
2.	Dummy Variables and the Relationship of Deaf and Hearing Growth Using SAS/GRAPH® Presented: Pittsburgh, PA , Northeast SAS Users Group Conference, September 2008. Also: http://www.nesug.org/proceedings/nesug08/sa/sa08.pdf Cohort(s): Children of the NLSY79 Publisher: SAS Institute Inc. Keyword(s): Child Health; Children, Academic Development; Disability; Modeling, Growth Curve/Latent Trajectory Analysis Permission to reprint the abstract has not been received from the publisher. Historically, deaf education in the United States has achieved poor results. An oft-quoted statistic is that on average, deaf students graduating from high school (at age 18-21) perform at the level of hearing 8-10 year olds in terms of reading and writing skills (Allen, 1994; Traxler, 2000). In this research, deaf children and their hearing siblings from a longitudinal database are tracked during their elementary and secondary school years. This presentation will focus on graphic displays of the longitudinal data, focusing on presenting individual data points and sub-group regression lines on a single graph (here, one regression line for the deaf sample, and one for the hearing siblings), tracking their school progress from K-12. These displays inform statistical data analysis. Detailed examples of code will be shared, as well as insights into the value of combining graphic displays with data analysis.
	Bibliography Citation Dirmyer, Richard and Sara Schley. "Dummy Variables and the Relationship of Deaf and Hearing Growth Using SAS/GRAPH®." Presented: Pittsburgh, PA , Northeast SAS Users Group Conference, September 2008.

3.	Suhr, Diana D.
3.	Exploratory or Confirmatory Factor Analysis? Presented: San Francisco, CA, SAS Users Group International Conference (SUGI31), March 2006 Cohort(s): Children of the NLSY79 Publisher: SAS Institute Inc. Keyword(s): Children, Academic Development; Children, School-Age; Cognitive Development; Longitudinal Data Sets; Longitudinal Surveys; Methods/Methodology; Modeling; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Statistical Analysis; Statistics Permission to reprint the abstract has not been received from the publisher. Exploratory factor analysis (EFA) could be described as orderly simplification of interrelated measures. EFA, traditionally, has been used to explore the possible underlying factor structure of a set of observed variables without imposing a preconceived structure on the outcome (Child, 1990). By performing EFA, the underlying factor structure is identified. Confirmatory factor analysis (CFA) is a statistical technique used to verify the factor structure of a set of observed variables. CFA allows the researcher to test the hypothesis that a relationship between observed variables and their underlying latent constructs exists. The researcher uses knowledge of the theory, empirical research, or both, postulates the relationship pattern a priori and then tests the hypothesis statistically. The process of data analysis with EFA and CFA will be explained. Examples with FACTOR and CALIS procedures will illustrate EFA and CFA statistical techniques.
	Bibliography Citation Suhr, Diana D. "Exploratory or Confirmatory Factor Analysis?" Presented: San Francisco, CA, SAS Users Group International Conference (SUGI31), March 2006.

4.	Suhr, Diana D.
4.	Principal Component Analysis vs. Exploratory Factor Analysis Presented: Philadelphia, PA, Paper 203-30, SAS® Users Group International Conference (SUGI 30), Proceedings of the Thirtieth Annual, April 10-13, 2005. Also: http://www2.sas.com/proceedings/sugi30/203-30.pdf#search=%22how%20to%20compute%20the%20PIAT%20math%20score%22 Cohort(s): Children of the NLSY79 Publisher: SAS Institute Inc. Keyword(s): Behavior; Data Analysis; Economics of Minorities; Ethnic Differences; Home Environment; Labor Market Outcomes; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Psychological Effects; Racial Differences; Statistical Analysis Permission to reprint the abstract has not been received from the publisher. Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) are both variable reduction techniques and sometimes mistaken as the same statistical method. However, there are distinct differences between PCA and EFA. Similarities and differences between PCA and EFA will be examined. Examples of PCA and EFA with PRINCOMP and FACTOR will be illustrated and discussed. Copyright © 2005 by SAS Institute Inc., Cary, NC, USA.
	Bibliography Citation Suhr, Diana D. "Principal Component Analysis vs. Exploratory Factor Analysis." Presented: Philadelphia, PA, Paper 203-30, SAS® Users Group International Conference (SUGI 30), Proceedings of the Thirtieth Annual, April 10-13, 2005.

5.	Suhr, Diana D.
5.	PROC GLM or PROC CALIS Presented: Long Beach, CA, SAS Users Group International Conference, April 2001. Also: http://www.sas.com/usergroups/sugi/sugi26/grid.wedam.html 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; NLS Description; Peabody Individual Achievement Test (PIAT- Reading); Statistical Analysis; Statistics Permission to reprint the abstract has not been received from the publisher. Traditional statistical approaches to data analysis use PROC GLM, whereas Structural Equation Modeling (SEM) techniques use PROC CALIS. Regression, analysis of variance (anova), or repeated measures anova are traditional methods using PROC GLM. SEM with PROC CALIS is a comprehensive and flexible approach to multivariate analysis using observed (measured) and unobserved (latent) variables (Hoyle, 1995). Research hypothesis typically tested by traditional methods may be tested using SEM techniques. Similarities and differences between traditional and SEM approaches will be discussed. A repeated measures analysis of variances example illustrates traditional and SEM methodologies. The repeated measures anova hypothesizes differences between the means and linear trends. The SEM analysis estimates nonlinear trends, variances of measured and latent variables, relationship between latent variable variances, means of latent variables (initial level and rate of change). The audience could be beginner through advanced SAS programmers.
	Bibliography Citation Suhr, Diana D. "PROC GLM or PROC CALIS." Presented: Long Beach, CA, SAS Users Group International Conference, April 2001.

6.	Suhr, Diana D.
6.	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).
	Bibliography Citation Suhr, Diana D. "SEM for Health, Business and Education." Presented: Orlando, FL, SAS Users Group International Conference, April 2002.

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