Frances K. Stage

The authors provide a basic set of guidelines and recommendations for information that should be included in any manuscript that has confirmatory factor analysis or structural equation modeling as the primary statistical analysis technique. The authors provide an introduction to both techniques, along with sample analyses, recommendations for reporting, evaluation of articles in The Journal of Educational Research using these techniques, and concluding remarks.

The objectives of this study were to review the current literature on status attainment and student college choice and to develop and test a structural model of predisposition to attend college. Family and student background characteristics, parents’ educational expectations for students, level of student involvement in school, and student achievement were cited as influences on students’ predisposition toward postsecondary education and were the chief components of the model. Data from 2,497 ninth-grade students and their parents were used to test the model using LISREL. Parents’ expectations exerted the strongest influence throughout the model. Parents’ education, student gender, high school GPA, and high school experiences also contributed significantly in explaining students’ aspirations.

The authors review the use and interpretations of path analyses in articles published in The Journal of Educational Research from 1992 to 2002 and discuss related issues. This article provides (a) a brief introduction to path analysis, (b) suggested guidelines and recommendations for reporting results, (c) a sample of a model path analysis, (d) evaluation of the JER path analysis articles, and (e) concluding remarks.

Women and minorities continue to be underrepresented in quantitatively based fields of study. Additionally, selection by all students of such majors is declining. Math/Science major choice is of concern in light of the occupational demands created by advancing technology as well as the potential gaps in occupational and economic attainment of women and minorities. This article reports the analysis of a longitudinal model of math/science major choice upon entrance to college for black and white, female and male students. The model was tested using a sample drawn from the “High School and Beyond” data base. The model included background characteristics of students, ability, and an array of high school experience factors to explain choice of quantitative major. Significant predictors of major choice for the subgroups included sophomore choice of major, mathematics attitudes, math and science completed by senior year, and various parental factors. However, there were differences across groups and the model explained nearly twice as much variance for the black male, black female, and white male subgroups compared with the white female subgroup. Recommendations include broadening our ways of researching migration into and out of the mathematics/science pipeline. Argument is made for a focus on success of students enrolled in low level college mathematics classes as a way of augmenting the mathematics/scientific pipeline.