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

Autor/in	Coleman, Stephanie L.
Titel	Common Factors and Common Elements: Use of Data Science-Derived Innovations to Improve School-Based Counseling
Quelle	In: Contemporary School Psychology, 22 (2018) 4, S.512-524 (13 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2159-2020
DOI	10.1007/s40688-018-0192-z
Schlagwörter	School Counseling; Data Analysis; Innovation; Counseling Psychology; Feedback (Response); Psychotherapy; Counseling Effectiveness; Clinical Psychology; School Psychology + Suchen Sie Ihr Suchwort? School counselling; Pädagogische Beratung; Auswertung; Counselling psychology; Beratungspsychologie; Psychotherapie; Klinische Psychologie; Schulpsychologie
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01