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Accounting Research and Common Sense

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  • James A. Ohlson

Abstract

type="main"> The paper identifies some basic shortcomings of contemporary accounting research. Aside from issues related to how researchers pick topics—they are generally too remote from pre-existing realities—the main points concern the standard paradigm: explaining a dependent variable Y in terms of X, the primary variable of interest, while controlling for Z. The paper argues that just because the t-statistic related to X is significant does not mean that X helps to explain Y. To address this issue requires a goodness-of-fit analysis that evaluates the incremental contribution of X. Such tests can show that X effectively acts as noise though X's t-statistic is significant. Incremental goodness-of-fit analyses would potentially have dramatic consequences on research because rejection of the null would now take place much less often. The paper also considers problems associated with ordinary least squares, with specific emphasis on the scaling of dollar amount variables in linear cross-sectional settings.

Suggested Citation

  • James A. Ohlson, 2015. "Accounting Research and Common Sense," Abacus, Accounting Foundation, University of Sydney, vol. 51(4), pages 525-535, December.
    • Handle: RePEc:bla:abacus:v:51:y:2015:i:4:p:525-535
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    File URL: http://hdl.handle.net/10.1111/abac.12059
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    Citations

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    Cited by:

    1. Clatworthy, Mark A. & Peel, Michael J., 2021. "Reporting accountant appointments and accounting restatements: Evidence from UK private companies," The British Accounting Review, Elsevier, vol. 53(3).
    2. Jae H. Kim & Philip I. Ji, 2024. "Testing for signal-to-noise ratio in linear regression: a test under large or massive sample," Review of Managerial Science, Springer, vol. 18(10), pages 3007-3024, October.
    3. Wai Fong Chua, 2022. "Matters of concern and engaged research," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(4), pages 4615-4627, December.
    4. Johnstone, David, 2022. "Accounting research and the significance test crisis," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 89(C).
    5. Ehalaiye, Dimu & Tippett, Mark & van Zijl, Tony, 2017. "The predictive value of bank fair values," Pacific-Basin Finance Journal, Elsevier, vol. 41(C), pages 111-127.
    6. Stewart Jones & Murray Wells, 2015. "Accounting Research: Where Now?," Abacus, Accounting Foundation, University of Sydney, vol. 51(4), pages 572-586, December.
    7. Thomas R. Dyckman & Stephen A. Zeff, 2019. "Important Issues in Statistical Testing and Recommended Improvements in Accounting Research," Econometrics, MDPI, vol. 7(2), pages 1-11, May.
    8. Rajat Deb, 2019. "Accounting Theory Coherence Revisited," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 44(1), pages 36-57, February.
    9. Chen, Xiaomeng Charlene & Jones, Stewart & Hasan, Mostafa Monzur & Zhao, Ruoyun & Alam, Nurul, 2023. "Does strategic deviation influence firms’ use of supplier finance?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
    10. Jae H. Kim, 2022. "Moving to a world beyond p-value," Review of Managerial Science, Springer, vol. 16(8), pages 2467-2493, November.
    11. Jones, Stewart & Wang, Tim, 2019. "Predicting private company failure: A multi-class analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 161-188.
    12. Jae H. Kim & Kamran Ahmed & Philip Inyeob Ji, 2018. "Significance Testing in Accounting Research: A Critical Evaluation Based on Evidence," Abacus, Accounting Foundation, University of Sydney, vol. 54(4), pages 524-546, December.
    13. James A. Ohlson, 2022. "Researchers’ data analysis choices: an excess of false positives?," Review of Accounting Studies, Springer, vol. 27(2), pages 649-667, June.
    14. Juana Aledo Martínez & Juan Manuel García Lara & María T. González Pérez & Christos A. Grambovas, 2020. "An empirical assessment of proposed solutions for resolving scale problems in value relevance accounting research," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(4), pages 3905-3933, December.
    15. Stewart Jones & Nurul Alam, 2019. "A machine learning analysis of citation impact among selected Pacific Basin journals," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(4), pages 2509-2552, December.

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