This paper presents validation guidelines for IS positivist research, emphasizing the importance of rigorous validation of research instruments. The authors argue that despite previous studies highlighting the need for better validation practices, IS researchers still face major barriers in instrument, statistical, and other forms of validation. The paper offers specific heuristics for research practice in the validities of content validity, construct validity, reliability, manipulation validity, and statistical conclusion validity. These heuristics are based on a thorough analysis of the current state of research validities in IS. The authors suggest that the IS academic community should bring these issues into open debate to improve the quality of research. The paper also discusses various types of validity, including content validity, construct validity, predictive validity, reliability, manipulation validity, and statistical conclusion validity. It provides examples of how these validities can be assessed and offers guidelines for developing instruments that capture the essence of the constructs being measured. The authors conclude that validation is a critical aspect of IS research and that researchers should strive to ensure that their instruments are valid and reliable. The paper also discusses the importance of distinguishing between formative and reflective measures and the use of structural equation modeling techniques such as LISREL and PLS to assess validity. The authors emphasize the need for rigorous validation practices in IS research to ensure the scientific validity of findings and interpretations.This paper presents validation guidelines for IS positivist research, emphasizing the importance of rigorous validation of research instruments. The authors argue that despite previous studies highlighting the need for better validation practices, IS researchers still face major barriers in instrument, statistical, and other forms of validation. The paper offers specific heuristics for research practice in the validities of content validity, construct validity, reliability, manipulation validity, and statistical conclusion validity. These heuristics are based on a thorough analysis of the current state of research validities in IS. The authors suggest that the IS academic community should bring these issues into open debate to improve the quality of research. The paper also discusses various types of validity, including content validity, construct validity, predictive validity, reliability, manipulation validity, and statistical conclusion validity. It provides examples of how these validities can be assessed and offers guidelines for developing instruments that capture the essence of the constructs being measured. The authors conclude that validation is a critical aspect of IS research and that researchers should strive to ensure that their instruments are valid and reliable. The paper also discusses the importance of distinguishing between formative and reflective measures and the use of structural equation modeling techniques such as LISREL and PLS to assess validity. The authors emphasize the need for rigorous validation practices in IS research to ensure the scientific validity of findings and interpretations.