CoChat
Sign Up
Cited by 62Online and Blended LearningEducational Technology and E-LearningInnovations in Educational Methods

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

The effect of blended courses on student learning: Evidence from introductory economics courses

Olitsky, Neal H., Cosgrove, Sarah B.·2014·International Review of Economics Education
DOI 10.1016/j.iree.2013.10.009
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1016/j.iree.2013.10.009

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 28

  1. Abadie, 2006, Large sample properties of matching estimators for average treatment effects, Econometrica, 74, 235, 10.1111/j.1468-0262.2006.00655.x · DOI · Paper page
  2. Anstine, 2005, A small sample study of traditional and online courses with sample selection adjustment, Journal of Economic Education, 36, 107
  3. Arbaugh, 2009, Research in online and blended learning in the business disciplines: key findings and possible future directions, Internet and Higher Education, 12, 71, 10.1016/j.iheduc.2009.06.006 · DOI
  4. Brown, 2002, Can web courses replace the classroom in principles of microeconomics?, American Economic Review, 92, 444, 10.1257/000282802320191778 · DOI
  5. Coates, 2004, “No significant distance” between face-to-face and online instruction: evidence from principles of economics, Economics of Education Review, 23, 533, 10.1016/j.econedurev.2004.02.002 · DOI
  6. Dehejia, 1999, Causal effects in nonexperimental studies: reevaluating the evaluation of training programs, Journal of the American Statistical Association, 94, 1053, 10.1080/01621459.1999.10473858 · DOI · Paper page
  7. Dehejia, 2002, Propensity score-matching methods for nonexperimental causal studies, Review of Economics and Statistics, 84, 151, 10.1162/003465302317331982 · DOI
  8. Garnham, 2002, Introduction to hybrid courses, Teaching with Technology Today, 8
  9. Heckman, 1997, Instrumental variables: a study of implicit behavioral assumptions used in making program evaluations, Journal of Human Resources, 32, 441, 10.2307/146178 · DOI
  10. Heckman, 1997, Matching as an econometric evaluation estimator: evidence from evaluating a job training programme, Review of Economic Studies, 64, 605, 10.2307/2971733 · DOI · Paper page
  11. Heckman, 1998, Matching as an econometric evaluation estimator, Review of Economic Studies, 65, 261, 10.1111/1467-937X.00044 · DOI · Paper page
  12. Imbens, 2003, Sensitivity to exogeneity assumptions in program evaluation, American Economic Review, 93, 126, 10.1257/000282803321946921 · DOI
  13. Imbens, 2004, Nonparametric estimation of average treatment effects under exogeneity: a review, Review of Economics and Statistics, 86, 4, 10.1162/003465304323023651 · DOI · Paper page
  14. Kelley, 1939, The selection of upper and lower groups for the validation of test items, Journal of Educational Psychology, 30, 17, 10.1037/h0057123 · DOI
  15. LaLonde, 1986, Evaluating the econometric evaluations of training programs with experimental data, American Economic Review, 76, 604
  16. Lechner, 2002, Some practical issues in the evaluation of heterogeneous labour market programmes by matching methods, Journal of the Royal Statistical Society: Series A (Statistics in Society), 165, 59, 10.1111/1467-985X.0asp2 · DOI
  17. Means, 2009, Evaluation of evidence-based practices in online learning: a meta-analysis and review of online learning studies, Structure, 15, 94
  18. Navarro, 2000, Performance and perceptions of distance learners in cyberspace, American Journal of Distance Education, 14, 15, 10.1080/08923640009527052 · DOI
  19. Ng, 2011, The effects of learning styles on course performance: a quantile regression analysis, Academy of Educational Leadership Journal, 15, 15
  20. Rosenbaum, 1983, The central role of the propensity score in observational studies for causal effects, Biometrika, 70, 41, 10.1093/biomet/70.1.41 · DOI · Paper page
  21. Rosenbaum, 1985, Constructing a control group using multivariate matched sampling methods that incorporate the propensity score, American Statistician, 39, 33, 10.1080/00031305.1985.10479383 · DOI
  22. Rubin, 1980, Bias reduction using Mahalanobis-metric matching, Biometrics, 36, 293, 10.2307/2529981 · DOI
  23. Siriopoulos, 2009, Selecting strategies to foster economists’ critical thinking skills: a quantile regression approach, International Review of Economics Education, 8, 106, 10.1016/S1477-3880(15)30076-1 · DOI
  24. Smith, 2005, Does matching overcome LaLonde's critique of nonexperimental estimators?, Journal of Econometrics, 125, 305, 10.1016/j.jeconom.2004.04.011 · DOI · Paper page
  25. Terry, 2003, Campus, online, or hybrid: an assessment of instruction modes, Journal of Economics and Economic Education Research, 4, 23
  26. Vaughan, 2007, Perspectives on blended learning in higher education, International Journal on E-Learning, 6, 81
  27. Williams, 2002, Learning on-line: a review of recent literature in a rapidly expanding field, Journal of Further and Higher Education, 26, 263, 10.1080/03098770220149620 · DOI
  28. Zhao, 2004, Using matching to estimate treatment effects: data requirements, matching metrics, and Monte Carlo evidence, Review of Economics and Statistics, 86, 91, 10.1162/003465304323023705 · DOI · Paper page

Cited by

Sorted by citations

See all 62

Metadata

Authors
Olitsky, N. H., & Cosgrove, S. B.
Affiliations
University of Massachusetts Dartmouth (US)
Corresponding author
Olitsky, Neal H.
ORCID
Venue
International Review of Economics Education
Publisher
Elsevier BV
Published
2014-01
Volume
15
Pages
17-31
ISSN
1477-3880 (ISSN-L)
DOI
10.1016/j.iree.2013.10.009
OpenAlex
W2005861674
Cited by
62
References
1
Citations by year
2026: 3 · 2025: 2 · 2024: 4 · 2023: 9 · 2022: 4 · 2021: 4 · 2020: 2 · 2019: 10 · 2018: 6 · 2017: 2 · 2016: 5 · 2015: 10 · 2014: 1
FWCI
25.81
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • International Review of Economics Education (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Education › Online and Blended Learning
  • Physical Sciences › Computer Science › Information Systems › Educational Technology and E-Learning
  • Social Sciences › Education › Innovations in Educational Methods
Keywords
blended learning, introductory economics courses, learning outcomes, face-to-face teaching, online learning, course format comparison, economics education

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review