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Contemporary School Psychology

, Volume 22, Issue 4, pp 512–524 | Cite as

Common Factors and Common Elements: Use of Data Science-Derived Innovations to Improve School-Based Counseling

  • Stephanie L. Coleman
Tools for Practice
  • 43 Downloads

Abstract

Innovations in data science like predictive analytics, data mining, and data reduction have improved a variety of fields. Innovations in data science have also enabled the growth of two data-driven movements primarily used in clinical and counseling psychology: the common factors and common elements approaches. Each of these movements contains practical applications, such as the use of feedback within psychotherapy and the use of modular treatment. This paper describes the rationale of these movements, evidence on their effectiveness, and how these methods could potentially benefit school psychology practice within the context of multi-tiered systems of support (MTSS). Finally, the paper discusses the need for more research on these methods within school-based settings.

Keywords

Data science School psychology Evidence-based practice School-based counseling 

Notes

Compliance with ethical standards

Conflict of interest

The author states that there is no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by the author.

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Copyright information

© California Association of School Psychologists 2018

Authors and Affiliations

  1. 1.Department of Social SciencesUniversity of Houston—DowntownHoustonUSA

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