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Influencing the Probability for Graduation at Four-Year Institutions: A Multi-Model Analysis

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Abstract

The purpose of this study is to identify student and institutional characteristics that influence the probability for graduation. The study delves further into the probability for graduation by examining how far the student deviates from the institutional mean with respect to academics and affordability; this concept is referred to as the “match.” The validity of the matching model is tested using a multivariate analysis with select variables from the Beginning Postsecondary Study: 1996/2001 (BPS:96/01) and the Integrated Postsecondary Education Data System (IPEDS). Traditional multivariate models examine the importance of both student and institutional characteristics but assume the two are independent of one another. This study is different in that it uses the matching model to examine the relationship between student and institutional characteristics. The results are compared to more frequently used models and show that the relationship between students and their institutions plays an important role in understanding the probability for graduation.

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Notes

  1. The Armed forces qualifying test.

  2. Stop-out refers to a student who takes time off from college, but returns at a later date.

  3. A list of all variables, along with their source and description, is included in Table 1.

  4. Titus (2003) utilized the BPS: 96/01 dataset.

  5. Based on a cut-off value of 0.8 (Hensher et al. 2005).

  6. Traditional model refers to a traditional multivariate model with multiple independent variables, but no interaction variables. An example of the traditional model equation can be seen in Eq. 1.

  7. Academic match and affordability match are the only two “Match” variables because these are the only variables available in the BPS: 96/01 dataset and IPEDS where both a student value and an average institutional value are available to measure deviation from the institutional mean. These two measures (academic quality and affordability) do capture Tinto’s notion of fit and are among the most critical elements in higher education. However, the author recognizes that additional match variables would be desirable, but are not essential. Other match variables such as social fit or fit within one’s major may be able to be accomplished with an analysis of one or two institutions, but these match variables are not afforded in the BPS: 96/01 dataset. Since a match is defined as deviation from the mean the notion of the institution’s average social fit does not seem appropriate. Further, the BPS: 96/01 dataset is a representative sample, but not for all variables, such as student major. Therefore, it would be statistically inappropriate to measure fit with a major when a representative sample is not collected for each major. It may be possible to investigate fit within one’s major if the study is modified and replicated for one to two institutions.

  8. Absolute value of the “match” is not appropriate to use in this model because having $5,000 in excess is not equivalent as having $5,000 too little.

  9. Work-study aid is included in the models, but is not included in the affordability match calculations. This is because work-study money is earned over the course of the academic year; it is not available at the beginning of the semester when the cost of attendance is due to the institution. As such, the affordability match includes grants, loans, other aid, and expected family contribution. This notion is supported by McPherson et al. (1993) as well as Altbach et al. (1999).

  10. ACT scores were converted to SAT score.

  11. Two outcomes were considered for this study: Option 1: Y = 1 when a student began at and graduated from the same four-year institution within six years. Y = 0 when a student did not graduate from the first institution of attendance within six years. Option 2: Y = 1 when a student began at and graduated from the same four-year institution within six years. Y = 0 when a student began and ended his or her studies at the first institution of attendance, but did not graduate within 6 years. The first option was chosen for the study because it focuses on all students who began college at a four-year college or university. The second outcome focuses on a smaller segment of the sample. Since both students and institutions waste time and money when students do not graduate from the first institution of attendance, the first outcome includes the group of students of greatest interest. As a sensitivity analysis, identical logistic regression analyses were performed on both outcomes to confirm that there were no differences that would warrant additional analysis. No significant differences emerged.

  12. “Standard statistical packages such as SPSS and SAS are inappropriate for complex survey data, because they are based on the assumption of independent, identically distributed observations, or simple random sampling with replacement” (U.S. Department of Education National Center for Education Statistics 2000, p. 1). A limited number of software programs, such as Stata can properly apply the assigned weights. Failure to use appropriate software or ignore the sampling weights leads to incorrect standard errors, and biased beta coefficients, which increases the chance of committing a Type I error, determining that there is a difference, when that is not true, and misinterpretation of results (Thomas and Heck 2001).

  13. Student involvement and engagement variables in the BPS dataset provide information regarding whether or not the student participated in an activity (e.g., clubs, arts, intramurals, talk with faculty). Unfortunately, the dataset does not include more granular involvement/engagement variables that allow researchers to drill down to another level (e.g., math club, political science club, student government association, etc.).

  14. To ensure confidentiality, NCES requires that restricted-use data users adhere to specific statistical standards. All sample size numbers must be rounded to the nearest 10 (e.g., 788 should be rounded to 790), and minimum and maximum values of a variable may not be released. Consequently, they are not included in this study.

  15. A term used by Mortenson in 2007. The gated communities of higher education: 50 most exclusive public and private four-year institutions, 2003–2004. Retrieved May 1, 2007, from www.postsecondary.org.

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Cragg, K.M. Influencing the Probability for Graduation at Four-Year Institutions: A Multi-Model Analysis. Res High Educ 50, 394–413 (2009). https://doi.org/10.1007/s11162-009-9122-2

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