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Author: Suhr, Diana D.

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1.	Suhr, Diana D.
1.	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.

2.	Suhr, Diana D.
2.	Investigation of Mathematics and Reading Achievement of 5- through 14-Year Olds Using Latent Growth Curve Methodology Ph.D. Dissertation, Northern Colorado University, 1999 Cohort(s): Children of the NLSY79 Publisher: UMI - University Microfilms, Bell and Howell Information and Learning Keyword(s): Children, Academic Development; Children, School-Age; Cognitive Ability; Cognitive Development; Education; Ethnic Differences; Gender Differences; Growth Curves; Hispanics; Modeling, Growth Curve/Latent Trajectory Analysis; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Racial Differences The development of literacy and numeracy (i.e., reading and mathematics) for 5-through 14-year old children was investigated using a cohort-sequential design. A fundamental assumption motivating the study was that knowledge about the pattern of growth of individual students' mathematics and reading skills, differences in the pattern of growth among groups of students (i.e., gender and ethnic groups), and variation within groups of students provides a foundation for developing effective educational policy. The purpose of the research was to advance a methodology, latent growth curve models (LGM), for analyzing individual and group differences. Latent growth curve models estimate mean initial level of achievement, mean rate of change in achievement, variances of initial level and rate of change, covariance of initial level with mean rate of change, variances of test measurements, and growth scores (i.e., structural slopes). Findings indicate the form of achievement growth curves for 5- through 14-year old children is a negatively accelerating function of age. In the sample, no differences were found in mathematics achievement between boys (n = 561) and girls (n = 573). Differences in reading recognition achievement between boys (n = 581) and girls (n = 607) were found with respect to variances in initial level of achievement and test measurements at ages 10, 11, and 12. Girls (n = 525) had a faster growth rate than boys (n = 488) in reading comprehension achievement at ages 8 and 12, whereas boys had a faster growth at age 11. In mathematics, reading recognition, and reading comprehension achievement, differences between White and Black/Hispanic children in levels of achievement increased over time. Rates of change in mathematics, reading recognition, and recognition achievement were faster for White children than for Black and Hispanic children.
	Bibliography Citation Suhr, Diana D. Investigation of Mathematics and Reading Achievement of 5- through 14-Year Olds Using Latent Growth Curve Methodology. Ph.D. Dissertation, Northern Colorado University, 1999.

3.	Suhr, Diana D.
3.	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.

4.	Suhr, Diana D.
4.	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.

5.	Suhr, Diana D.
5.	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.

6.	Suhr, Diana D.
6.	The Basics of Structural Equation Modeling Presented: Irvine, CA, SAS User Group of the Western Region of the United States (WUSS), September 2006. Also: http://www.lexjansen.com/wuss/2006/tutorials/TUT-Suhr.pdf Cohort(s): Children of the NLSY79 Publisher: SAS Institute Inc. Keyword(s): Methods/Methodology; Modeling; Modeling, Growth Curve/Latent Trajectory Analysis; Statistical Analysis; Statistics Permission to reprint the abstract has not been received from the publisher. Structural equation modeling (SEM) is a methodology for representing, estimating, and testing a network of relationships between variables (measured variables and latent constructs). This tutorial provides an introduction to SEM including comparisons between “traditional statistical” and SEM analyses. Examples include path analysis/ regression, repeated measures analysis/latent growth curve modeling, and confirmatory factor analysis. Participants will learn basic skills to analyze data with structural equation modeling. (This is designed as a tutorial.)
	Bibliography Citation Suhr, Diana D. "The Basics of Structural Equation Modeling." Presented: Irvine, CA, SAS User Group of the Western Region of the United States (WUSS), September 2006.

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