Stats I Final Pitt 3

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Anonymous
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187294
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Stats I Final Pitt 3
Updated:
2012-12-05 10:55:31
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Correlation
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Stats I Finall Pitt part 3
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  1. What is correlation?

    correlation
    measures the relationship between two variables

    there are more than one type of correlation (depending on level of measurement)
  2. Pearson product moment correlation

    correlation
    between interval/ratio variables

    most widely used measure of correlation

    has the most assumptions
  3. Speraman rank correlation

    correlation
    between ordinal variables
  4. Phi coefficient

    correlation
    between dichotomous variables
  5. point-biserial correlation

    correlation
    between a dichotomous & interval/ration variables

    ex: right or wrong of an exam item and total score
  6. biserial correlation

    correlation
    between a dichotomous (w/ underlying continuum) & interval/ratio variables

    ex:anxiety (low, high) & depression
  7. polyserial correlation

    correlation
    between a polytomous (3 or more levels) (w/ underlying continuum) & interval/ratio variables

    ex: anxiety (low, medium, high) & depression
  8. tetrachoric correlation

    correlation
    betwee dichotomous variables (w/ underlying continuum) and depression

    ex: anxiety (low, high) & depression (low, high)
  9. polychoric correlation

    correlation
    between polytomous variable (w/ underlying continuum)
  10. Pearson Product Moment Correlation Assumptions

    correlation
    No Outliers

    Linearity

    Normal Distribution
  11. Pearson Assumptions: No Outliers

    correlation
    no extreme scores

    • two types:
    • -univariate
    • -multivariate
  12. Pearson Assumption: No Outliers - Univariate

    correlation
    an extreme score that is far away from the distribution of a variable

    -only one variable
  13. Pearson Assumption: No Outliers - Multivariate

    correlation
    an extreme score that is far away from the joint distribution of variables

    it is possible for a case to be a multivariate outlier without bieng a univaritate outlier
  14. Pearson Assumption: Linearity

    correlation
    variables should NOT show any non-linear pattern
  15. Pearson Assumption: Normal Distribution

    correlation
    distribution of a variable should be univariate normal

    distribution of variables should be multivariate normal
  16. Range of Correlation

    correlation
    • all correlation range from -1 to +1
    • a measure of correlation is unitless (or standardized).

    • r = -1 perfect negative relationship
    • r = +1 perfect positive relationship
    • r = 0 no relationship
  17. Scatterplot

    correlation
    when the correlation is r= +1, the scatterplot appears in a straight line

    as the correlation approaches 0, the variability of scores increases
  18. Covariance

    correlation
    measures of relationship between two variables in raw units (unstandardized correlation)

    possible range is -infinity to +infinity
  19. Types of Research Questions

    correlation
    1. Is there a significant relationship between x and y?

    2. Is there a significant difference in the relationship of x and y between group 1 and group 2?

    3. What is the (1-alpha)%CI of the correlation?
  20. Sampling Distribution Correlation

    correlation
    • in not normal except when r=0 due to restricted range of the correlation
    • -bound between -1 & +1
  21. One-sample t-test for correlation

    correlation
    For testing H0: p = 0, Greek letter rho - population correltaion

    df = N - 2

    affected not only by N but also by r. Depends on the # of subjects & relationship
  22. When to us r-critical

    correlation
    when testing more than one correlation becomes tedious

    easier to compute r-critical value as long as there are same N
  23. Fisher's r-to-z transformation

    when to use

    correlation
    For testing a H0: p = p0

    For difference in a correlation between groups

    For confidence interval of a correlation

    Convert r to z-score

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