IDEAS home Printed from https://ideas.repec.org/a/sae/padigm/v20y2016i2p97-112.html
   My bibliography  Save this article

Confirmatory Factor Analysis (CFA) as an Analytical Technique to Assess Measurement Error in Survey Research

Author

Listed:
  • Manit Mishra

Abstract

Common method variance (CMV), a systematic measurement error, is a key source of contamination in survey research. This article examines a potential source of CMV—socially desirable responding (SDR)—in the context of Indian culture. The statistical remedies of method variance have been critically evaluated for their suitability to capture SDR. The statistical remedy of ‘controlling for the effect of a directly measured latent method factor’ using confirmatory factor analysis (CFA) has been profoundly explained. The procedural guidelines on executing CFA using AMOS have been delineated to facilitate substantive research vulnerable to method variance.

Suggested Citation

  • Manit Mishra, 2016. "Confirmatory Factor Analysis (CFA) as an Analytical Technique to Assess Measurement Error in Survey Research," Paradigm, , vol. 20(2), pages 97-112, December.
  • Handle: RePEc:sae:padigm:v:20:y:2016:i:2:p:97-112
    DOI: 10.1177/0971890716672933
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0971890716672933
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0971890716672933?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Richard Bernardi, 2006. "Associations between Hofstede’s Cultural Constructs and Social Desirability Response Bias," Journal of Business Ethics, Springer, vol. 65(1), pages 43-53, April.
    2. Derek Dalton & Marc Ortegren, 2011. "Gender Differences in Ethics Research: The Importance of Controlling for the Social Desirability Response Bias," Journal of Business Ethics, Springer, vol. 103(1), pages 73-93, September.
    3. MacKenzie, Scott B. & Podsakoff, Philip M., 2012. "Common Method Bias in Marketing: Causes, Mechanisms, and Procedural Remedies," Journal of Retailing, Elsevier, vol. 88(4), pages 542-555.
    4. Banjo Roxas & Val Lindsay, 2012. "Social Desirability Bias in Survey Research on Sustainable Development in Small Firms: an Exploratory Analysis of Survey Mode Effect," Business Strategy and the Environment, Wiley Blackwell, vol. 21(4), pages 223-235, May.
    5. Tellis, Gerard J. & Chandrasekaran, Deepa, 2010. "Extent and impact of response biases in cross-national survey research," International Journal of Research in Marketing, Elsevier, vol. 27(4), pages 329-341.
    6. Jayson Lusk & F. Norwood, 2010. "Direct Versus Indirect Questioning: An Application to the Well-Being of Farm Animals," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 96(3), pages 551-565, May.
    7. Fisher, Robert J, 1993. "Social Desirability Bias and the Validity of Indirect Questioning," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 20(2), pages 303-315, September.
    8. Fuller, Christie M. & Simmering, Marcia J. & Atinc, Guclu & Atinc, Yasemin & Babin, Barry J., 2016. "Common methods variance detection in business research," Journal of Business Research, Elsevier, vol. 69(8), pages 3192-3198.
    9. Cote, Joseph A & Buckley, M Ronald, 1988. "Measurement Error and Theory Testing in Consumer Research: An Illustration of the Importance of Construct Validation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(4), pages 579-582, March.
    10. Sea-Jin Chang & Arjen van Witteloostuijn & Lorraine Eden, 2010. "From the Editors: Common method variance in international business research," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 41(2), pages 178-184, February.
    11. MacKenzie, Scott B, 2001. "Opportunities for Improving Consumer Research through Latent Variable Structural Equation Modeling," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 28(1), pages 159-166, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Siraphat Padthar & Phaninee Naruetharadhol & Wutthiya Aekthanate Srisathan & Chavis Ketkaew, 2024. "From Linear to Circular Economy: Embracing Digital Innovations for Sustainable Agri-Food Waste Management among Farmers and Retailers," Resources, MDPI, vol. 13(6), pages 1-29, June.
    2. Ninaus, Katharina & Diehl, Sandra & Terlutter, Ralf, 2021. "Employee perceptions of information and communication technologies in work life, perceived burnout, job satisfaction and the role of work-family balance," Journal of Business Research, Elsevier, vol. 136(C), pages 652-666.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peter J Jordan & Ashlea C Troth, 2020. "Common method bias in applied settings: The dilemma of researching in organizations," Australian Journal of Management, Australian School of Business, vol. 45(1), pages 3-14, February.
    2. Howard, Matt C. & Henderson, Jennifer, 2023. "A review of exploratory factor analysis in tourism and hospitality research: Identifying current practices and avenues for improvement," Journal of Business Research, Elsevier, vol. 154(C).
    3. Xiao Zhang & Luqun Xie & Jiatao Li & Li Cheng, 2022. "“Outside in”: Global demand heterogeneity and dynamic capabilities of multinational enterprises," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 53(4), pages 709-722, June.
    4. Vendrell-Herrero, Ferran & Bustinza, Oscar F. & Opazo-Basaez, Marco, 2021. "Information technologies and product-service innovation: The moderating role of service R&D team structure," Journal of Business Research, Elsevier, vol. 128(C), pages 673-687.
    5. Aschemann-Witzel, Jessica & de Hooge, Ilona E. & Almli, Valérie L., 2021. "My style, my food, my waste! Consumer food waste-related lifestyle segments," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    6. Surajit Bag & Muhammad Sabbir Rahman & Susmi Routray & Santosh Kumar Shrivastav & Soni Agrawal, 2024. "Exploring the potential of blockchain‐enabled smart contracts for achieving net‐zero emissions: An empirical study," Business Strategy and the Environment, Wiley Blackwell, vol. 33(5), pages 3965-3985, July.
    7. Bianchi, Constanza & Abu Saleh, Md., 2020. "Investigating SME importer–foreign supplier relationship trust and commitment," Journal of Business Research, Elsevier, vol. 119(C), pages 572-584.
    8. Maestrini, Vieri & Luzzini, Davide & Caniato, Federico & Ronchi, Stefano, 2018. "Effects of monitoring and incentives on supplier performance: An agency theory perspective," International Journal of Production Economics, Elsevier, vol. 203(C), pages 322-332.
    9. Solano Acosta, Alexandra & Herrero Crespo, Ángel & Collado Agudo, Jesús, 2018. "Effect of market orientation, network capability and entrepreneurial orientation on international performance of small and medium enterprises (SMEs)," International Business Review, Elsevier, vol. 27(6), pages 1128-1140.
    10. Lai, Yufeng & Minegishi, Kota & Boaitey, Albert K., 2020. "Social Desirability Bias in Farm Animal Welfare Preference Research," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304375, Agricultural and Applied Economics Association.
    11. Raymond Fisman & Ilyana Kuziemko & Silvia Vannutelli, 2021. "Distributional Preferences in Larger Groups: Keeping up with the Joneses and Keeping Track of the Tails," Journal of the European Economic Association, European Economic Association, vol. 19(2), pages 1407-1438.
    12. Shehnaz Tehseen, T. Ramayah, Sulaiman Sajilan, 2017. "Testing and Controlling for Common Method Variance: A Review of Available Methods," Journal of Management Sciences, Geist Science, Iqra University, Faculty of Business Administration, vol. 4(2), pages 146-175, October.
    13. Adilson Carlos Yoshikuni & Rajeev Dwivedi & Ronaldo Gomes Dultra-de-Lima & Claudio Parisi & José Carlos Tiomatsu Oyadomari, 2023. "Role of Emerging Technologies in Accounting Information Systems for Achieving Strategic Flexibility through Decision-Making Performance: An Exploratory Study Based on North American and South American," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(2), pages 199-218, June.
    14. Kakkar, Shiva & Vohra, Neharika, 2021. "Self-Regulatory Effects of Performance Management System Consistency on Employee Engagement: A Moderated Mediation Model," American Business Review, Pompea College of Business, University of New Haven, vol. 24(1), pages 225-248, May.
    15. Mikalef, Patrick & Pateli, Adamantia, 2017. "Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA," Journal of Business Research, Elsevier, vol. 70(C), pages 1-16.
    16. Ipsmiller, Edith & Brouthers, Keith D. & Dikova, Desislava, 2021. "Which export channels provide real options to SMEs?," Journal of World Business, Elsevier, vol. 56(6).
    17. Ilyana Kuziemko & Michael I. Norton & Emmanuel Saez & Stefanie Stantcheva, 2015. "How Elastic Are Preferences for Redistribution? Evidence from Randomized Survey Experiments," American Economic Review, American Economic Association, vol. 105(4), pages 1478-1508, April.
    18. Tao Bai & Jialin Du & Angelo M. Solarino, 2018. "Performance of foreign subsidiaries “in” and “from” Asia: A review, synthesis and research agenda," Asia Pacific Journal of Management, Springer, vol. 35(3), pages 607-638, September.
    19. Mirza Mohammad Didarul Alam & Nor Azila Mohd Noor, 2020. "The Relationship Between Service Quality, Corporate Image, and Customer Loyalty of Generation Y: An Application of S-O-R Paradigm in the Context of Superstores in Bangladesh," SAGE Open, , vol. 10(2), pages 21582440209, May.
    20. Hakim, Adam & Klorfeld, Shira & Sela, Tal & Friedman, Doron & Shabat-Simon, Maytal & Levy, Dino J., 2021. "Machines learn neuromarketing: Improving preference prediction from self-reports using multiple EEG measures and machine learning," International Journal of Research in Marketing, Elsevier, vol. 38(3), pages 770-791.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:padigm:v:20:y:2016:i:2:p:97-112. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.