Chapter 9

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  1. Inferential statistics
    infers the characteristics of a population
  2. null hypothesis
    Hypothesis that concludes there is no relationship or difference in a measure
  3. level of significance
    probability of being wrong in rejecting the Ho

    reported as p = x; typically level of significance is <.05
  4. Type 1 Error
    Reject Ho but it turns out there is no difference or relationship in the population
  5. Type II Error
    Fail to reject the Ho when there IS a difference/relationship in population
  6. alpha level
    level of significance set prior to data collection as a criterion for rejecting Ho
  7. level of significance affected by 3 factors
    • 1. groups being compared - greater the difference, smaller the p value
    • 2. degree of sampling and measurement error (SD) lower the error, smaller the p value
    • 3. size of the sample (N) - large sample, p will have smaller value than in small sample
  8. confidence intervals
    provide a range of values in which the population or "real" trait value lies with specific probabilities
  9. confidence intervals measured by:
    • 1. using sample data, calculate standard error of the mean Sx
    • 2. this value used to create intervals around sample mean that correspond to the probability of obtaining a population value in that interval
    • ex - sample mean is 60; researcher has 95% confidence interval of 48-72 - 95% chance that population or true mean is in that interval
  10. Effect size
    way of quantifying the degree of difference b/t two groups; also coefficient of determination

    X1-X2/SD = cohen's D
  11. parametric tests
    used when certain assumptions can be made about the data - normally distributed, equal variance, interval level measures
  12. t-test (parametric)
    • tests null hypothesis
    • 1. independent-samples t-test: different subjects in each group
    • 2. paired dependent-samples/correlated/matched : subjects in the groups are paired or matched in the same way
  13. degrees of freedom (df)
    • used to calculate the level of significance
    • approximately equal to # of subjects in the study
  14. ANOVA
    • simple analysis of variance
    • compares group means to determine the probability of being wrong in rejecting Ho (like t-test)
    • independent variable has multiple levels
  15. Simple/One-Way ANOVA
    • single independent variable analyzed w/single dependent variable
    • ex: study 3 types of students and means... students from SES h/m/l. 1x3 ANOVA
    • F statistic calculated from variance of the groups
  16. Two-Way Anova
    • factorial analysis of variance
    • 2 or more i.v.s are analyzed together
    • test for each i.v. 
    • ex: one i.v. has 2 levels, one has 3, 2x3ANOVA
  17. ANCOVA
    • analysis of covariance
    • adjusts for pretest differences b/t groups
    • pretest is the covariate
    • ex: 1 grp has mean of 15 and other has mean of 18 on a pretest; ANCOVA used to adjust posttest scores statistically to compensate for 3 pt difference
  18. Multivariate Statistics
    • two or more dependent variables are analyzed together
    • MANCOVA 
    • Hotelling's T
  19. Chi Square
    • x2, c2
    • used when researchers are interested in # of responses or cases in different categories
    • results reported in a contingency table
    • ex: relationship b/t gender and book choice
    • m/f, 4 book types to choose from, 2x4 table

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Chapter 9
2013-06-12 22:03:37

Research class
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