# 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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 Author: Vcrawford ID: 223618 Filename: Chapter 9 Updated: 2013-06-12 22:03:37 Tags: McMillan Folders: Description: Research class Show Answers:

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