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Quantitative Methodologies

Quantitative Methodologies. Matthew Schwarz and Valerie Dao Fulbright Research Mentorship Program Ho Chi Minh City, Vietnam. Introduction. Quantitative research… Quantify variation What factors influence athletic ability? Predict relationships When is it most likely to rain?

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Quantitative Methodologies

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  1. Quantitative Methodologies Matthew Schwarz and Valerie Dao Fulbright Research Mentorship Program Ho Chi Minh City, Vietnam

  2. Introduction • Quantitative research… • Quantify variation • What factors influence athletic ability? • Predict relationships • When is it most likely to rain? • Describe characteristics • What is the average height of an FRMP student?

  3. Bridging the Gap Qualitative: Understand a certain phenomenon Is my understanding generalizable? Qualitative: Determine whether your understanding can be generalized

  4. Review • Review • What is a variable? • Independent variable? • Dependent variable? • How are independent and dependent variables related? • What does it mean to operationalize a variable? • Bonus Questions • Can you have multiple independent variables? • Can you have multiple dependent variables?

  5. What is the Operational Definition of Pho?

  6. Steps • Define your research question • Why are there more foreign businesses in HCMC than Hanoi? • Define your variables • Independent variable: Location (HCMC or Hanoi) • Dependent variable: Factors influencing decision. • Operationalize your dependent variable • Labor supply • Business-friendly authorities • Others? • Collect data • Survey • Databases • Analyze relationship using statistical methods

  7. Correlation and Causation • What is correlation? • Correlation means that there is a relationship between two variables. • What is causation? • Causation means that one variable causes another variable to occur.

  8. Correlation and Causation • If there is correlation… isn’t there automatically causation? • Let’s look at some examples and see if we can answer this question.

  9. Finance • Research question • What factors cause stock prices to increase? • Variables • Independent: Price • Dependent (1): Earnings • Hypothesis • “When a company reports strong earnings, it’s stock price tends to increase.” • Correlation? • Causation?

  10. Politics • Research question • What was the most important reason why Barack Obama won the 2008 election? • Variables • Independent: Vote for Obama? • Dependent (1): Democrat • Dependent (2): Intelligent • Hypothesis • “The most important reason why Barack Obama won the 2008 election was his status as a Democrat.” • Correlation? • Causation?

  11. Medicine • Research question • Does this pill improve health? • Variables • Independent: Took pill? • Dependent (1): Health • Hypothesis • “Taking this pill makes people healthier.” • Correlation? • Causation?

  12. “Correlation does not imply causation” • By now, you should understand that correlation does not imply causation. • There are two main reasons why we cannot assume causation even when we observe correlation: • Coincidence • Intervening variables

  13. Coincidence • Even if we observe a strong correlation between two variables, we cannot be sure that it’s not a coincidence. • Always ask yourself this question: • “Am I confident that the dependent variable is changing because of changes in the independent variable?”

  14. Intervening Variables • Intervening or confounding variables prevent us from credibly providing causality. • An intervening variable is a variable that influences both the dependent and independent variable.

  15. Which one is the Intervening Variable?

  16. Spurious Relationships The intervening variables cause spurious relationships. Spurious relationships are when are when two variables seem to have a connection due to a third unknown or unseen (intervening) variable.

  17. Spotting Intervening Variables • Let’s revisit our examples to see if we notice any intervening variables.

  18. Finance • In the Finance example, we saw a correlation between a company’s earnings and its stock price. • Are there any intervening variables?

  19. Politics • In the Politics example, we saw a correlation between voting for Obama and status as a Democrat. • Are there any intervening variables?

  20. Medicine • In the Medicine example, we saw a correlation between taking the pill and feeling healthier. • Are there any intervening variables?

  21. Dealing with Intervening Variables • The way to move from correlation to causation is by controlling for intervening variables. • This requires the researcher to modify his/her statistical model.

  22. Survey Design

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