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

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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3

Notes

  1. 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. 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. 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. 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. 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. 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. 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. 8.

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

  9. 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/.

References

  1. Alspaugh, J., & Gao, R. (2003). School size as a factor in elementary school achievement. Washington: Education Resources Information Center.

    Google Scholar 

  2. Asparouhov, T., & Muthen, B. (2014). Auxiliary variables in mixture modeling: 3‑step approaches using Mplus (Version 8). Los Angeles: Muthén & Muthén. http://www.statmodel.com/examples/webnotes/webnote15.pdf

    Google Scholar 

  3. Bialosiewicz, S., Murphy, K., & Berry, T. (2013). An introduction to measurement invariance testing. American Evaluation Association Meeting, Washington DC.

    Google Scholar 

  4. Bloom, H. S., Hill, C. J., Black, A. R., & Lipsey, M. W. (2008). Performance trajectories and performance gaps as achievement effect-size benchmarks for educational interventions. Journal of Research on Educational Effectiveness, 1(4), 289–328.

    Article  Google Scholar 

  5. Bolden, R. (2011). Distributed leadership in organizations: a review of theory and research. International Journal of Management Review, 13, 251–269.

    Article  Google Scholar 

  6. Bollen, K. A., & Curran, P. J. (2006). Latent curve models: a structural equation approach. Hoboken: Wiley.

    Google Scholar 

  7. Bossert, S. T., Dwyer, D. C., Rowan, B., & Lee, G. V. (1982). The instructional management role of the principal. Educational Administration Quarterly, 18(3), 34–64.

    Article  Google Scholar 

  8. Boyd, D., Grossman, P., Lankford, H., Loeb, S., & Wyckoff, J. (2006). How changes in entry requirements alter the teacher workforce and affect student achievement. Education Finance and Policy, 1, 176–216.

    Article  Google Scholar 

  9. Bridges, E. M. (1982). Research on the school administrator: the state of the art, 1967–1980. Educational Administration Quarterly, 18, 12–33.

    Article  Google Scholar 

  10. Bryk, A. S., Sebring, P. B., Allensworth, E., Luppescu, S., & Easton, J. Q. (2010). Organizing schools for improvement: lessons from Chicago. Chicago: University of Chicago Press.

    Google Scholar 

  11. Camburn, E., Rowan, B., & Taylor, J. (2003). Distributed leadership in schools: The case of elementary schools adopting comprehensive school reform models. Educational Evaluation and Policy Analysis, 25(4), 347–373.

    Article  Google Scholar 

  12. Celeux, G., & Soromenho, G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13, 195–212.

    Article  Google Scholar 

  13. Creemers, B. P. M., & Kyriakides, L. (2008). The dynamics of educational effectiveness: a contribution to policy, practice and theory in contemporary schools. London: Routledge.

    Google Scholar 

  14. Darling Hammond, L. (2006). Securing the right to learn: policy and practice for powerful teaching and learning. Educational Researcher, 35(7), 13–24.

    Article  Google Scholar 

  15. Datnow, A., & Castellano, M. (2000). Teachers’ responses to success for all: how beliefs, experiences, and adaptations shape implementation. American Educational Research Journal, 37, 775–799.

    Article  Google Scholar 

  16. Day, C., Gu, Q., & Sammons, P. (2016). The impact of leadership on student outcomes: How successful school leaders use transformational and instructional strategies to make a difference. Educational Administration Quarterly, 52(2), 221–258.

    Article  Google Scholar 

  17. Day, C., Sammons, P., Hopkins, D., Harris, A., Leithwood, K., Gu, Q., Brown, E., Ahtaridou, E., & Kington, A. (2009). The impact of school leadership on pupil outcomes (Final report, DCSF Research Report RR108). London: Department for Children, Schools and Families (DCSF). Nottingham

    Google Scholar 

  18. Day, C., Sammons, P., Leithwood, K., Hopkins, D., Harris, A., Gu, Q., & Brown, E. (2010). Ten strong claims about successful school leadership. Nottingham: National College for School Leadership.

    Google Scholar 

  19. Dee, T. S., & Jacob, B. (2011). The impact of no Child Left Behind on student achievement. Journal of Policy Analysis and Management, 30, 418–446.

    Article  Google Scholar 

  20. Department of Education (2014). School quality survey statewide summary report. http://arch.k12.hi.us/school/sqs/sqs.html. Accessed: 12. Apr 2020.

  21. Edmonds, R. (1979). Effective schools for the urban poor. Educational Leadership, 37, 15–24.

    Google Scholar 

  22. Entswile, D., & Alexander, K. (1992). Summer setback: race, poverty, school composition, and mathematics achievement in the first two years of school. American Sociological Review, 57, 72–84.

    Article  Google Scholar 

  23. Estabrook, R., & Neale, M. (2013). A Comparison of factor score estimation methods in the presence of missing data: Reliability and an application to nicotine dependence. Multivariate Behavioral Research, 48(1), 1–27.

    Article  Google Scholar 

  24. Feldhoff, T., Radisch, F., & Klieme, E. (2014). Methods in longitudinal school improvement research: state of the art. Journal of Educational Administration, 52(5), 565–736.

    Article  Google Scholar 

  25. Friedkin, N., & Thomas, S. L. (1997). Social positions in schooling. Sociology of Education, 70, 239–255.

    Article  Google Scholar 

  26. Fullan, M. (2006). Turnaround leadership. New York: Wiley & Sons.

    Google Scholar 

  27. Goff, P., & Finch, M. (2016). Challenges and opportunities for education leadership scholarship: a methodological critique. In A. J. Bowers, A. R. Shoho & B. G. Barnett (Eds.), Challenges and opportunities of educational leadership research and practice (pp. 119–145). Charlotte: Information Age Publishing.

    Google Scholar 

  28. Gold, A., Evans, J., Early, J., Halpin, D., & Collabone, P. (2002). Principled principals? Evidence from ten case studies of “outstanding school teachers.”. Annual Meeting of the American Educational Research Association, New Orleans, LA.

    Google Scholar 

  29. Goldhaber, D. D., & Brewer, D. J. (2000). Does teacher certification matter? High school teacher certification status and student achievement. Education Evaluation and Policy Analysis, 22, 129–145.

    Article  Google Scholar 

  30. Grissom, J. A., Loeb, S., & Master, B. (2013). Effective instructional time use for school leaders. Educational Researcher, 42, 433–444.

    Article  Google Scholar 

  31. Gronn, P. (2002). Distributed leadership as a unit of analysis. Leadership Quarterly, 13, 423–451.

    Article  Google Scholar 

  32. Gustafsson, J. E. (2010). Longitudinal designs. In B. P. M. Creemers, L. Kyriakides & P. Sammons (Eds.), Methodological advances in educational effectiveness research (pp. 77–101). London: Routledge.

    Google Scholar 

  33. Hall, G. E., & Hord, S. M. (2001). Implementing change: patterns, principles, and potholes. Boston: Allyn & Bacon.

    Google Scholar 

  34. Hallinger, P. (2013). Reviewing reviews of research in educational leadership: an empirical assessment. Educational Administration Quarterly, 50(4), 539–576.

    Article  Google Scholar 

  35. Hallinger, P. (2018). Bringing context out of the shadow of leadership. Educational Management, Administration & Leadership, 46(1), 5–24.

    Article  Google Scholar 

  36. Hallinger, P., & Heck, R. H. (2011). Conceptual and methodological issues in studying school leadership effects as a reciprocal process. School Effectiveness and School Improvement, 22(2), 149–173.

    Article  Google Scholar 

  37. Hallinger, P., Bickman, L., & Davis, K. (1996). School context, principal leadership and student achievement. Elementary School Journal, 96(5), 498–518.

    Article  Google Scholar 

  38. Harris, A. (2008). Distributed leadership: what we know? Journal of Educational Administration, 46(2), 172–188.

    Article  Google Scholar 

  39. Harris, A. (2013). Distributed leadership; friend or foe? Educational Management and Administration, 41(5), 545–554.

    Article  Google Scholar 

  40. Heck, R. H., & Chang, J. (2017). Examining the timing of educational changes among elementary schools after the implementation of NCLB. Educational Administration Quarterly, 53, 649–694.

  41. Hill, P. W., & Rowe, K. J. (1996). Multilevel modelling in school effectiveness research. School Effectiveness and School Improvement, 7, 1–34.

    Article  Google Scholar 

  42. Hochbein, C., & Duke, D. (2011). Crossing the line: examination of student demographic changes concomitant with declining academic performance in elementary schools. School Effectiveness and School Improvement, 22(2), 87–118.

    Article  Google Scholar 

  43. Horn, J. L., & McArdle, J. J. (1992). A practical and theoretical guide to measurement invariance in aging research. Experimental Aging Research, 18(3-4), 117–144.

  44. Hopkins, D. (1996). Towards a theory for school improvement. In J. Gray, D. Reynolds, C. Fitz-Gibbon & D. Jesson (Eds.), Merging traditions. The future of research on school effectiveness and school improvement (pp. 40–60). London: Cassell.

    Google Scholar 

  45. Hu, L.-T., & Bentler, P. (1999). Cutoff criterial for fit indices in covariance structure analysis. Structural Equation Modeling, 6, 1–55.

    Article  Google Scholar 

  46. Huber, S. G. (2011). Leadership for learning—learning for leadership: the impact of professional development. In T. Townsend & J. MacBeath (Eds.), International handbook of leadership for learning (pp. 635–652). Dordrecht: Springer.

    Google Scholar 

  47. Isiordia, M., & Ferrer, E. (2018). Curve of factors model: a latent growth modeling approach for educational research. Educational and Psychological Measurement, 78(2), 203–231.

    Article  Google Scholar 

  48. Jackson, D. (2000). The school improvement journey: perspectives on leadership. School Leadership & Management, 20, 61–78.

    Article  Google Scholar 

  49. Kruger, M., Witziers, B., & Sleegers, P. (2007). The impact of school leader variables on school level factors: validation of a causal model. School Effectiveness and School Improvement, 18(1), 1–20.

    Article  Google Scholar 

  50. Lee, V. E., & Burkam, D. T. (2003). Dropping out of high school: the role of school organization and structure. American Educational Research Journal, 40(3), 353–394.

    Article  Google Scholar 

  51. Lee, V. E., & Loeb, S. (2000). School size in chicago elementary schools: effects on teachers’ attitudes and students’ achievement. American Educational Research Journal, 37(1), 3–31.

    Article  Google Scholar 

  52. Leithwood, K. (1994). Leadership for school restructuring. Educational Administration Quarterly, 30, 498–518.

    Article  Google Scholar 

  53. Leithwood, K., & Duke, D. (1999). A century’s quest to understand school leadership. In J. Murphy & K. Louis (Eds.), The handbook of research on educational administration (2nd edn., pp. 45–72). San Francisco: Jossey-Bass.

    Google Scholar 

  54. Leithwood, K., & Louis, K. S. (2012). Linking leadership to learning. San Francisco: Jossey-Bass.

    Google Scholar 

  55. Leithwood, K., & Mascall, B. (2008). Collective leadership effects on student achievement. Educational Administration Quarterly, 44(4), 529–561.

    Article  Google Scholar 

  56. Leithwood, K. A., & Montgomery, D. (1982). The role of the elementary school principal in program improvement. Review of Educational Research, 52, 309–339.

    Article  Google Scholar 

  57. Leithwood, K., Louis, K., Anderson, S., & Wahlsttom, K. (2004). Review of research: how leadership influences student learning. https://www.wallacefoundation.org/knowledge-center/Documents/How-Leadership-Influences-Student-Learning.pdf. Accessed: 19. Dec. 2007.

  58. Leithwood, K., Day, C., Sammons, P., Harris, A., & Hopkins, D. (2006). Seven strong claims about successful school leadership. Nottingham: National College of School Leadership.

    Google Scholar 

  59. Leithwood, K., Patten, S., Jantzi, D. (2010). Testing a conception of how school leadership influences student learning. Educational Administration Quarterly, 46(5), 671–706.

    Article  Google Scholar 

  60. Leithwood, K., Anderson, S., Mascall, B., & Strauss, T. (2011). School leaders’ influences on student learning: The four paths. In T. Bush, L. Bell & D. Middlewood (Eds.), The principles of educational leadership and management (pp. 13–30). Thousand Oaks: SAGE.

    Google Scholar 

  61. Leithwood, K., Harris, A., & Hopkins, D. (2020). Seven strong claims about successful school leadership revisited. School Leadership & Management, 40(1), 5–22.

    Article  Google Scholar 

  62. Louis, K. S., & Miles, M. B. (1991). Managing reform: lessons for urban high schools. School Effectiveness and School Improvement, 2(2), 75–96.

    Article  Google Scholar 

  63. Louis, K. S., Mayrowetz, D., Murphy, J., & Smylie, M. (2013). Making sense of distributed leadership: how secondary school educators look at job redesign. International Journal of Educational Leadership and Management, 1(1), 33–68.

    Google Scholar 

  64. Luyten, H., Visscher, A., & Witziers, B. (2005). School effectiveness research: from a review of the criticism to recommendations for further development. School Effectiveness and School Improvement, 16(3), 249–279.

    Article  Google Scholar 

  65. MacBeath, J., Frost, D., & Swaffield, S. (2008). Editorial. School Leadership & Management, 28(4), 301–306.

    Article  Google Scholar 

  66. Malone, N., & Kekahio, W. (2011). Research review and analysis of the Hawaii department of education’s school quality survey. Honolulu: McREL.

    Google Scholar 

  67. Marks, H. M., & Printy, S. M. (2003). Principal leadership and school performance: an integration of transformational and instructional leadership. Educational Administration Quarterly, 39, 370–397.

    Article  Google Scholar 

  68. Marsh, H. W., & Craven, R. G. (2006). Reciprocal effects of self-concept and performance from a multidimensional perspective: beyond seductive pleasure and unidimensional perspectives. Perspectives on Psychological Science, 1, 133–163.

    Article  Google Scholar 

  69. Mayrowetz, D. (2008). Making sense of distributed leadership: exploring the multiple usages of the concept in the field. Educational Administration Quarterly, 44(3), 424–435.

    Article  Google Scholar 

  70. McNeish, D., & Matta, T. (2018). Differentiating between mixed-effects and latent-curve approaches to growth modeling. Behavioral Research Methods, 50(4), 1298–1414.

    Article  Google Scholar 

  71. Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58, 525–543.

    Article  Google Scholar 

  72. Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107–122.

    Article  Google Scholar 

  73. Mintrop, H., & Sunderman, G. L. (2009). Predictable failure of federal sanctions driven accountability for school improvement: and why we may retain it anyway. Educational Researcher, 38, 353–364.

    Article  Google Scholar 

  74. Mintrop, H., & Trijillo, T. (2007). The practical relevance of accountability systems for school improvement: a descriptive analysis of California schools. Educational Evaluation and Policy Analysis, 29, 319–352.

    Article  Google Scholar 

  75. Mulford, B., & Silins, H. (2003). Leadership for organizational learning and improved student outcomes—What do we know? Cambridge Journal of Education, 22, 175–195.

    Article  Google Scholar 

  76. Mulford, B., & Silins, H. (2011). Revised models and conceptualisation of successful school principalship for improved student outcomes. International Journal of Educational Management, 25(1), 61–82.

    Google Scholar 

  77. Murphy, J. (2013). The architecture of school improvement. Journal of Educational Administration, 51(3), 252–263.

    Article  Google Scholar 

  78. Muthén, B., & Asparouhov, T. (2009). Growth mixture modeling: Analysis with non-Gaussian random effects. In G. Fitzmaurice, M. Davidian, G. Verbeke & G. Molenberghs (Eds.), Longitudinal data analysis (pp. 143–165). Boca Raton: CRC Press.

    Google Scholar 

  79. Muthén, L., & Muthén, B. O. (2017). Mplus user’s guide (8th edn.). Los Angeles: Muthén & Muthén. (1998–2017)

    Google Scholar 

  80. Nonaka, I., & Toyama, R. (2002). Firm as a dialectic being: toward the dynamic theory of the firm. Industrial and Corporate Change, 11, 995–1109.

    Article  Google Scholar 

  81. Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535–564.

    Article  Google Scholar 

  82. Nylund-Gibson, K., Grimm, R., Quirk, R. M., & Furlong, M. (2014). A latent transition mixture model using the three-step specification. Structural Equation Modeling: A Multidisciplinary Journal, 21, 1–16.

    Article  Google Scholar 

  83. Oakes, J. (2005). Keeping track: how schools structure inequality (2nd edn.). New Haven: Yale University Press.

    Google Scholar 

  84. Ogawa, R. T., & Bossert, S. T. (1995). Leadership as an organizational quality. Educational Administration Quarterly, 31, 224–243.

    Article  Google Scholar 

  85. Opdenakker, M. C., & Van Damme, J. (2007). Do school context, student composition and school leadership affect school practice and outcomes in secondary education? British Educational Research Journal, 33, 179–206.

    Article  Google Scholar 

  86. Oud, J. H. L. (2002). Continuous time modeling of the cross-lagged panel design. Kwantitatieve Methoden, 69, 1–26.

    Google Scholar 

  87. Pounder, D. G., Ogawa, R. T., & Adams, E. A. (1995). Leadership as an organization-wide phenomena: its impact on school performance. Educational Administration Quarterly, 31(4), 564–588.

    Article  Google Scholar 

  88. Reinecke, J. (2006). Longitudinal analysis of adolescents’ deviant and delinquent behavior. Methodology, 2, 100–112.

    Article  Google Scholar 

  89. Ribbins, P., & Gunter, H. (2002). Mapping leadership studies in education: Towards a typology of knowledge domains. Educational Management and Administration, 30(4), 359–86.

    Article  Google Scholar 

  90. Robinson, V. M. J., Lloyd, C. A., & Rowe, K. J. (2008). The impact of leadership on student outcomes. Educational Administration Quarterly, 44, 635–674.

    Article  Google Scholar 

  91. Rowan, B., & Miller, R. J. (2007). Organizational strategies for promoting instructional change: implementation dynamics in schools working with comprehensive school reform providers. American Educational Research Journal, 44, 252–297.

    Article  Google Scholar 

  92. Schwartz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–464.

    Article  Google Scholar 

  93. Sebring, P. B., Allensworth, E., Bryk, A. S., Easton, J. Q., & Luppescu, S. (2006). The essential supports for school improvement. Chicago: Consortium on Chicago School Research, University of Chicago.

  94. Selig, J. P., & Preacher, K. P. (2009). Mediation models for longitudinal data in developmental research. Research in Human Development, 6, 144–164.

    Article  Google Scholar 

  95. Silins, H., & Mulford, W. (2002). Leadership and school results. In K. Leithwood & P. Hallinger (Eds.), Second international handbook of educational leadership and administration (pp. 561–612). Dordrecht: Kluwer.

    Google Scholar 

  96. Sleegers, P. J. C., Thoonen, E. E. J., Oort, F. J., & Peetsma, T. T. D. (2014). Changing classroom practices: the role of school-wide capacity for sustainable improvement. Journal of Educational Administration, 52(5), 617–652.

    Article  Google Scholar 

  97. Southworth, G. (2002). Instructional leadership in schools: reflections and empirical evidence. School Leadership & Management, 22, 73–91.

    Article  Google Scholar 

  98. Spillane, J. (2006). Distributed leadership. San Francisco: Jossey-Bass.

    Google Scholar 

  99. Spillane, J. P., & Camburn, E. (2006). The practice of leading and managing: the distribution of responsibility for leadership and management in the schoolhouse. American Educational Research Association, San Francisco.

    Google Scholar 

  100. Spillane, J. P., Halverson, R., & Drummond, J. B. (2001). Investigating school leadership practice: a distributed perspective. Educational Researcher, 30(3), 23–28.

    Article  Google Scholar 

  101. Stoll, L., & Fink, D. (1996). Changing our schools: linking school effectiveness and school improvement. Buckingham: Open University Press.

    Google Scholar 

  102. Thoonen, E., Sleegers, P., Oorta, F., & Peetsmaa, T. (2012). Building school-wide capacity for improvement: the role of leadership, school organizational conditions, and teacher factors. School Effectiveness and School Improvement, 23(4), 441–460.

    Article  Google Scholar 

  103. Vermunt, J. K. (2008). Latent class and finite mixture models for multilevel data sets. Statistical Methods in Medical Research, 17(1), 33–51.

    Article  Google Scholar 

  104. Wayne, A. J., & Youngs, P. (2003). Teacher characteristics and student achievement gains: a review. Review of Educational Research, 73, 89–122.

    Article  Google Scholar 

  105. Willower, D. J., & Forsyth, P. B. (1999). A brief history of scholarship in educational administration. In J. Murphy & K. Louis (Eds.), The handbook of research on educational administration (2nd edn., pp. 1–23). San Francisco: Jossey-Bass.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Prof. Dr. Ronald H. Heck.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

  • Collaborative leadership
  • School change
  • School improvement
  • School leadership

Schlüsselwörter

  • Schulführung
  • Kollaborative Führung
  • Schulentwicklung
  • Schulveränderung