How Do Psychological Constructs Differ As Interval Vs Ordinal Variables? Ever wondered how psychologists measure feelings, attitudes, or traits? In this video, we explain the difference between ordinal and interval variables and how they impact data analysis. We start by defining what ordinal variables are and how they represent rankings or categories that show order but do not specify the exact difference between each level. You’ll learn why this makes calculating averages or standard deviations tricky with ordinal data. Next, we explore interval variables, which have equal spacing between points, allowing for more precise statistical analysis. Examples like temperature scales are discussed to illustrate this concept. We also cover how researchers often treat scales like Likert scales—technically ordinal—as interval data to simplify analysis, and what risks this approach might carry. Understanding the distinction between these types of data is essential for designing accurate studies and interpreting results correctly. Whether you’re a student, researcher, or just curious about psychological measurements, this video provides clear explanations that help you grasp how data type influences analysis methods and conclusions. Join us to deepen your understanding of measurement scales in psychology and improve your data interpretation skills. Don’t forget to subscribe for more insights on data, statistics, and psychological research! ⬇️ Subscribe to our channel for more valuable insights. 🔗Subscribe: https://www.youtube.com/@TheFriendlyS... #PsychologyData #MeasurementScales #OrdinalVsInterval #DataAnalysis #PsychologicalResearch #LikertScale #Statistics #ResearchMethods #DataInterpretation #PsychologicalTraits #SurveyDesign #DataScience #StatisticsTips #ResearchSkills #EducationalVideo About Us: Welcome to The Friendly Statistician, your go-to hub for all things measurement and data! Whether you're a budding data analyst, a seasoned statistician, or just curious about the world of numbers, our channel is designed to make statistics accessible and engaging for everyone.