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Understanding Pesticides and Organic Food: A Statistical Approach

This introduction explores the relationships between pesticides and organic food through statistical analysis. It discusses dependent and independent variables, the significance of estimating conditional proportions, and evaluates the types of relationships using crosstab analysis. By examining how smoking could correlate with academic performance and the potential impact of self-esteem on voting behavior, we highlight the relevance of understanding these dynamics. This foundational knowledge aids in deciphering the intricate connections within agricultural practices and health outcomes.

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Understanding Pesticides and Organic Food: A Statistical Approach

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Presentation Transcript

  1. Association An Introduction to Concept Correspondence

  2. Pesticides and Organic Food

  3. Pesticides and Organic Food • What is the dependent variable? • A. Pesticides • B. Organic food • C. There is no dependent variable • D. There is no variance

  4. Conditional proportions • Why do we estimate conditional proportions? • A. So that we can predict an outcome based on certain conditions • B. So that we can help make a causal inference between a condition and an outcome • C. So that we can estimate frequency distributions based on certain conditions • D. All of the above

  5. Independence • What does a conditional proportion look like if the condition and the outcome are independent? • A. The outcome varies across different conditions • B. The condition is independent of the outcome • C. The frequencies are the same, regardless of the condition • D. The outcome varies, but is independent of many conditions

  6. Crosstabs

  7. Types of relationships • Linear • Spurious • Intervening • Interaction effects • Suppression

  8. Linear effects

  9. Crosstabs analysis • Like a frequency table, it reports how many and what percentage fall into a particular category, but for two variables instead of one • Not suitable for continuous variables; only for discretely measured variables • It is sometimes useful to recode a variable with too many categories FOR THE PURPOSES OF ILLUSTRATION ONLY

  10. Conventions • the independent variable is arranged across the top of the table in columns • Percentages should be calculated using COLUMN • Dependent variable always in rows

  11. Simple crosstabs

  12. Simple crosstabs

  13. Simple crosstabs

  14. Simple crosstabs

  15. Simple crosstabs

  16. Simple crosstabs

  17. Simple crosstabs

  18. Simple crosstabs

  19. Simple crosstabs

  20. Identify the problems with the following crosstabs…

  21. Theoretical statement: smoking causes bad grades

  22. Theoretical statement: smoking cause bad grades

  23. Theory: Self esteem may have an impact on voting

  24. Crosstab Analysis

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