This paper reviews various procedural and statistical methods to assess and control Common Method Variance (CMV) in organizational research, particularly in entrepreneurship studies. CMV, which refers to the systematic variance shared among variables due to measurement techniques, can significantly influence research findings if not properly addressed. The authors highlight the importance of both procedural and statistical remedies to minimize the impact of CMV. Procedural remedies include using multiple sources for data collection, temporal or methodological separation of measurements, protecting respondent anonymity, and counterbalancing the order of measurements. Statistical remedies, such as Harman’s Single-Factor Test, partial correlation procedures (partialling out of general factor, marker variable, or unrelated variable), correlation matrix procedure, and the Measured Latent Marker Variable (MLMV) approach, are also discussed. The paper emphasizes the need for researchers to use a combination of these methods to ensure the validity and reliability of their findings. An empirical example is provided to illustrate the application of these methods in a study on business owners' entrepreneurial competencies and business growth. The study concludes that while CMV can be challenging to control, a combination of procedural and statistical remedies can effectively mitigate its impact.This paper reviews various procedural and statistical methods to assess and control Common Method Variance (CMV) in organizational research, particularly in entrepreneurship studies. CMV, which refers to the systematic variance shared among variables due to measurement techniques, can significantly influence research findings if not properly addressed. The authors highlight the importance of both procedural and statistical remedies to minimize the impact of CMV. Procedural remedies include using multiple sources for data collection, temporal or methodological separation of measurements, protecting respondent anonymity, and counterbalancing the order of measurements. Statistical remedies, such as Harman’s Single-Factor Test, partial correlation procedures (partialling out of general factor, marker variable, or unrelated variable), correlation matrix procedure, and the Measured Latent Marker Variable (MLMV) approach, are also discussed. The paper emphasizes the need for researchers to use a combination of these methods to ensure the validity and reliability of their findings. An empirical example is provided to illustrate the application of these methods in a study on business owners' entrepreneurial competencies and business growth. The study concludes that while CMV can be challenging to control, a combination of procedural and statistical remedies can effectively mitigate its impact.