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School leadership and school organization: investigating their effects on school improvement in reading and math

Führung an Schulen und Schulorganisation: Eine Untersuchung ihrer Effekte auf die Schulentwicklung in den Domänen Lesen und Mathematik

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Abstract

In this study, we explore patterns of improvement among a large set of elementary schools over four years. We use as a starting point the premise that school improvement, by definition, entails a change in the state of the organization over some period of time. We first examine whether changes in school leadership and school organizational processes impact growth in student reading and math outcomes. We next identify four latent classes of schools with contrasting growth trajectories and determine whether or not these empirically-derived latent classes are associated with differences in schools’ contextual conditions and specific malleable school leadership and school organization constructs. Our results provide some initial steps that link these different achievement classifications to varied patterns of school leadership and school organization practices.

Zusammenfassung

In dieser Studie untersuchen wir Schulentwicklungsmuster anhand einer großen Anzahl von Grundschulen über einen Zeitraum von vier Jahren. Dabei legen wir die Annahme zugrunde, dass die Entwicklung einer Schule im Laufe der Zeit eine Veränderung der Schulorganisation mit sich bringt. Wir untersuchen zunächst, ob Veränderungen in der Führung von Schulen und in den schulischen Organisationsprozessen das Wachstum in der Lese- und Mathematikleistungen von Schülerinnen und Schüler beeinflussen. Als nächstes identifizieren wir vier latente Klassen von Schulen mit unterschiedlichen Wachstumsverläufen und untersuchen, ob diese empirisch abgeleiteten latenten Klassen mit den Kontextbedingungen der Schulen und beeinflussbaren Schulleitungs- und Schulorganisationsmerkmalen zusammen hängen oder nicht. Unsere Befunde liefern erste Aussagen dazu, wie diese unterschiedlichen Lernentwicklungsmuster wiederum mit Mustern der Schulleitungs- und Schulorganisationspraxis zusammen hängen.

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Notes

  1. We made this choice to provide a little more time between teachers’ initial responses regarding school processes and our final assessment of student progress (i.e., covering approximately four years) based on previous a longitudinal study of monitoring school improvement in responding to mandatory restructuring (Heck and Chang 2017).

  2. We investigated possible nonlinear growth between schools by freeing the last factor loading (0.1,*). We found the final loading was 1.97 for reading and 1.86 for math. This suggested the final growth factor loadings could be fixed to 2.0. We also adopted this linear coding for our latent growth mixture model, as it facilitated final model convergence due to the complexity of the proposed parallel growth model and the differences in latent class sizes.

  3. At the first step, the latent classes are defined independent from any covariates. In the second step, the most likely class variable is created (i.e., a nominal variable consisting of the identified classes), using the latent class posterior distribution obtained during the latent class formulation step. In the third step, the most likely class variable is regressed on the covariates, which are included as auxiliary variables, so that they will not affect the measurement of the latent class variable, C, while considering the misclassification encountered in the second step. This is important, as including the covariates in the initial steps could lead to distorted results in determining the number and size of the latent classes and thus diminish their stability.

  4. Standardized factor loadings (which defined are invariant across groups in the scalar invariance solution) are slightly different at each occasion due to differences in the standard deviations. Stronger standardized factor loadings indicate better, more discriminating item. The standardized loadings ranged from 0.8 to well above 0.9 for all subscales defining the leadership and school organization factors. Twelve of the 18 estimates over the three occasions were 0.90 to 0.96, and the other 6 were (0.80 to 0.87). This provides evidence the observed subscales were strong measures of the underlying factors.

  5. The lower bound of model fit for the Standardized Root Mean Square is often considered as SRMR = 0.05 (Hu and Bentler 1999). By this guideline, only the estimate of the between-school SRMR was larger than suggested guidelines.

  6. We note Bryk et al. (2010) observed similar significant effects (0.08–0.11) of instructional leadership, program coherence, collective responsibility, and orientation toward innovation on school academic improvement.

  7. We noted some multicollinearity in preliminary investigations of our set of predictors used to predict latent class membership (i.e. several tolerance coefficients below 0.4). We therefore constructed an initial status latent factor (i.e. a weighted factor of leadership and school organization) and a change latent factor (i.e. a weighted factor of change in leadership and school organization) for this part of our analysis. We found these new constructs were more satisfactory indicators of class membership in our predictive models (given that they took in all the information regarding the initial status and change estimates but were only weakly correlated), with tolerance coefficients above 0.9.

  8. Odds ratios have nonsymmetrical confidence intervals (Asparouhov and Muthén 2014).

  9. The Reading section of the SAT-10 received an alpha reliability rating of 0.87; the Math section was 0.80–0.87. (Harcourt Assessment, Inc.). Downloaded from https://www.statisticssolutions.com/stanford-achievement-test-10-sat-10/.

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    Ronald H. Heck & Tingting Reid

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Heck, R.H., Reid, T. School leadership and school organization: investigating their effects on school improvement in reading and math. Z Erziehungswiss 23, 925–954 (2020). https://doi.org/10.1007/s11618-020-00969-3

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