Teaching Note--Preparing Doctoral Students to Confront the Grand Challenges: Strengthening Baseline Statistical Skills

Autor/inn/en	Cronley, Courtney; Black, Beverly; Killian, Michael
Titel	Teaching Note--Preparing Doctoral Students to Confront the Grand Challenges: Strengthening Baseline Statistical Skills
Quelle	In: Journal of Social Work Education, 55 (2019) 2, S.389-395 (7 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1043-7797
DOI	10.1080/10437797.2018.1544520
Schlagwörter	Doctoral Students; Statistics; Mathematics Skills; Skill Development; Seminars; Mathematical Concepts; Social Work; Prior Learning; Computer Software; Achievement Gains; Summer Programs; Familiarity; Minimum Competencies; Introductory Courses; Public Colleges; Feedback (Response); Course Evaluation; Knowledge Level + Suchen Sie Ihr Suchwort? Doctoral studies; Doctorate studies; Student; Students; Doctoral candidate; Doktorandenprogramm; Schüler; Schülerin; Studentin; Doktorand; Doktorandin; Statistik; Mathmatics achievement; Mathematics ability; Mathematische Kompetenz; Kompetenzentwicklung; Qualifikationsentwicklung; Seminar; Soziale Arbeit; Vorkenntnisse; Achievement gain; Leistungssteigerung; Sommerkurs; Fundamentum; Mindestwissen; Einführungskurs; Wissensbasis
Abstract	We describe a statistical seminar designed to provide a common foundation in core statistical concepts among all students as they enter a social work PhD program. Student feedback surveys assessed students' statistics preparation prior to entering the program, and pre- and posttests were compared for increases in statistical knowledge. Nearly half (42.1%) reported no prior statistics training, and exposure to IBM SPSS Statistics software was low (M=1.54 out of 5, SD=1.38). Students showed significant gains in posttest knowledge. Findings suggest substantial variability in students' statistical preparedness, and for many, knowledge of basic concepts is rudimentary. Such a seminar may allow programs to begin with more advanced quantitative course work and move to increasingly more complex topics over time (e.g., multilevel modeling). (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01