Jason W. Osborne, Miami University
Jason W. Osborne, Miami University
Provost and Executive Vice President (2019-22), Professor of Statistics, Miami (Ohio)
Verified email at - Homepage
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Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis
AB Costello, JW Osborne
Practical Assessment, Research & Evaluation 10 (7), 1-9, 2005
Best practices in quantitative methods
JW Osborne
Sage Publications, Inc, 2008
Best Practices in Exploratory Factor Analysis
JW Osborne
CreateSpace Independent Publishing Platform, 2014
The power of outliers (and why researchers should always check for them)
JW Osborne, A Overbay
Practical assessment, research & evaluation 9 (6), 1-12, 2004
Four Assumptions Of Multiple Regression That Researchers Should Always Test
JW Osborne, E Waters
Practical Assessment, Research, and Evaulation 8 (2), 1-5, 2002
Sample size and subject to item ratio in principal components analysis
JW Osborne, AB Costello
Practical Assessment, Research & Evaluation 9 (11), 8, 2004
Improving your data transformations: Applying the Box-Cox transformation
JW Osborne
Practical Assessment, Research & Evaluation 15 (12), 2, 2010
Best practices in data cleaning: A complete guide to everything you need to do before and after collecting your data
JW Osborne
Sage publications, 2012
Notes on the use of data transformations
J Osborne
Practical assessment, research, and evaluation 8 (1), 2002
Race and academic disidentification.
JW Osborne
Journal of Educational Psychology 89 (4), 728, 1997
Is inquiry possible in light of accountability?: A quantitative comparison of the relative effectiveness of guided inquiry and verification laboratory instruction
MR Blanchard, SA Southerland, JW Osborne, VD Sampson, LA Annetta, ...
Science education 94 (4), 577-616, 2010
What is rotating in exploratory factor analysis?
JW Osborne
Practical Assessment, Research, and Evaluation 20 (1), 2015
Testing stereotype threat: Does anxiety explain race and sex differences in achievement?
JW Osborne
Contemporary Educational Psychology 26 (3), 291-310, 2001
Advantages of hierarchical linear modeling
JW Osborne
Practical Assessment, Research & Evaluation 7 (1), 1-3, 2000
The development of the STEM career interest survey (STEM-CIS)
MW Kier, MR Blanchard, JW Osborne, JL Albert
Research in Science Education 44, 461-481, 2014
Best practices in logistic regression
JW Osborne
Sage Publications, 2014
Academics, self-esteem, and race: A look at the underlying assumptions of the disidentification hypothesis
JW Osborne
Personality and Social Psychology Bulletin 21 (5), 449, 1995
Metacognitive monitoring accuracy and student performance in the postsecondary classroom
JL Nietfeld, L Cao, JW Osborne
The Journal of Experimental Educational, 7-28, 2005
Replication analysis in exploratory factor analysis: What it is and why it makes your analysis better
JW Osborne, DC Fitzpatrick
Practical Assessment, Research & Evaluation 17 (15), 1-8, 2012
Stereotype threat, identification with academics, and withdrawal from school: Why the most successful students of colour might be most likely to withdraw
JW Osborne, C Walker
Educational Psychology 26 (4), 563-577, 2006
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