This paper reviews the issue of Common Method Variance (CMV) in organizational research and discusses various procedural and statistical methods to assess and control CMV. CMV occurs when systematic variance is shared among variables due to the measurement method rather than the theoretical constructs. It can lead to biased research findings if not controlled. The paper highlights the importance of using both procedural and statistical remedies to address CMV. Procedural remedies include using multiple data sources, separating measurements over time or context, and protecting respondent anonymity. Statistical remedies include Harman's single-factor test, partial correlation procedures, correlation matrix procedures, and the measured latent marker variable (MLMV) approach. These methods help detect and correct CMV in data analysis. The paper also discusses the challenges of CMV in entrepreneurship research and provides an example of a study on business owners' entrepreneurial competencies and business growth. The study used SMART PLS software to analyze the data and applied various statistical remedies to control CMV. The results showed that CMV was not a significant issue in the study. The paper concludes that researchers should use both procedural and statistical methods to control CMV and ensure the validity of their findings. Keywords: Common method variance, procedural remedies, statistical remedies, Harman's single-factor test, partial correlation procedures, correlation matrix procedure, MLMV approach.This paper reviews the issue of Common Method Variance (CMV) in organizational research and discusses various procedural and statistical methods to assess and control CMV. CMV occurs when systematic variance is shared among variables due to the measurement method rather than the theoretical constructs. It can lead to biased research findings if not controlled. The paper highlights the importance of using both procedural and statistical remedies to address CMV. Procedural remedies include using multiple data sources, separating measurements over time or context, and protecting respondent anonymity. Statistical remedies include Harman's single-factor test, partial correlation procedures, correlation matrix procedures, and the measured latent marker variable (MLMV) approach. These methods help detect and correct CMV in data analysis. The paper also discusses the challenges of CMV in entrepreneurship research and provides an example of a study on business owners' entrepreneurial competencies and business growth. The study used SMART PLS software to analyze the data and applied various statistical remedies to control CMV. The results showed that CMV was not a significant issue in the study. The paper concludes that researchers should use both procedural and statistical methods to control CMV and ensure the validity of their findings. Keywords: Common method variance, procedural remedies, statistical remedies, Harman's single-factor test, partial correlation procedures, correlation matrix procedure, MLMV approach.