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1
Chi-square & T-test
Comm 420.8Fall 2007
Nan Yu
2
Warming up
Please download practice 1, practice 1 answer, ChisquareData, Ttestdata
Use ChisquareData to complete the questions of practice 1.
3
Effect Sizes
Statistical Significance vs. Strength of Effect
• Strength of Effect: 0 to 10 = Least Effect1 = Maximum Effect
4
Strength of Association for Chi-Squares:
Cramér’s V
5
Effect size for Chi-square
6
Influence of Sample Size on
Statistical Significance (p-value) versus
Strength of Association (Cramer’s V)
Sample size will not affect the strength of association, only significance level.
7
Vote
Yes No
Yes
No
10 201515O
rgan
izat
ion
s
Chi-Square Value = 1.72Degrees of Freedom = 1Significant? No, p = .19Cramér’s V =.17
Chi-Square Value = 17.14Degrees of Freedom = 1Significant? Yes, p < .001Cramér’s V =.17
Yes
No 100 200
150150Org
aniz
atio
ns
Vote
Yes No
Note: The number in each cell is mean.
8
Run Chi-square to test the following question
People from different race will have a different opinion on the selection of countries that represent a great danger to the U.S.. (race, q36).
Chi-Square:_17.46_ DF _5_ p _<.01_
Cramer’s V: _.44_
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The Chi-square test showed that people from different race have a different opinion on the selection of countries that represent a great danger to the U.S., 2 (6, N=45) = 17.46, p<.01, Cramer’s V=.44.
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T-test: Testing Differences in Means
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I. The Issue of Variability
Variability withinwithin Groups Variability betweenbetween Groups
mean 1 mean 2
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Different Groups: Large Between-Group Variability Small Within-Group Variability
Similar Groups: Small Between-Group Variability Large Within-Group Variability
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Which one represents similar groups?
14
Independent Sample t-test
Independent Sample t-test is used to test differences between only 2 groups.
Ex. Female test scores will differ from male test scores.
15
T-test assumptions
IV is nominal and has two categoriesDV is interval/ratioDV is normally distributed.T-test will robust with larger samplesCases represent a random sample
(representative).Cases are independent of one another.
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Example: Males likes TV sports more than females
Put the interval orratio-level variablehere. (DV)
Put the variablerepresenting thegroups here. (IV)
Click “Define Groups"
IV? DV?
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Define your groups by the values.
In this case, it is “0” for males and “1” for females.
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Compare the means.
T-test statistic
Degrees of freedom p-value
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T-test statistic
Degrees of freedom p-value
Are these two groups similar or different (within-group) ?If p >.05, means they are similar, use the top rowIf p<.05, means they are different, use the bottom row.
20
Report: Males (M=3.04, SD=1.89) do like TV sports more than females (M=1.97, SD=.88), t(34)=2.66, p<.05.
T-test statistic
Degrees of freedom p-value
21
- Paired Sample t-test is used to test differences between 2 scores.
- Variables must be interval or ratio-level and measured on the same metric.
Ex. Aggression scores will be higher after viewing a violent film than before viewing a violent film.
Variables: Before Film Stimulus After
- Here, IV has only one level, but there are two DVs: before aggression, after aggression
Paired Sample t-test
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Paired Sample t-test
Place your “before” and “after” variables here.
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T-test statistic
Degree of freedom P-value
Report: Aggression scores after viewing the film (M=3.95, SD=.96) were significantly higher than were scores prior to viewing the film (M=2.55, SD=1.11), t(57)=6.89, p<.001.
24
Directional and non-directional hypotheses
Females and males have different levels of liking toward dramas.
Females likes to watch dramas more males.
Liking toward dramas will be different as a result of gender.
Which one is a directional hypothesis?
25
One-tailed or two-tailed test
Non-directional hypothesis: one-tailed test
Directional hypothesis: two-tailed test
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Probability Distribution
95% of chances thatwe found the two means are different.
5% of chances thatdidn’t found the difference.
Females and males have different levels of liking toward sitcom.
Mean of females is not equal to mean of males
27
Two-tailed tests—split the alpha
Females likes to watch sitcom more males.
Mean of females > Mean of males
2.5% of chances thatdidn’t found means offemales is higher thanthat of males.
2.5% of chances thatdidn’t found means offemales is lower thanthat of males.
28
In-class practice 1 (Ttestdata.sav)
H1:Males likes to watch TV reality crime more than females.
(gender, tvrcrime)
Please use t-test to test the H1 and answer the following questions:
Males: Mean _____ SD _____Females: Mean______ SD ______t(__)=____, p______
Can we reject the null here?
29
Answers to practice 1
Males: Mean _2.58_ SD _1.17_Females: Mean _3.32_ SD _1.49_t(_55_)=_2.11_, p_<.05_
Can we reject the null here?No. We proposed males > females, but we
found males < females
30
In-class practice 2
H2:Happiness scores will be higher after viewing a sad film than
before viewing a sad film.
Please use t-test to test the H2 and answer the following questions:
Before: Mean _____ SD _____
After: Mean______ SD ______
t(__)=____, p______
Can we reject the null here?
31
Answers to practice 2
Before: Mean _2.53_ SD _1.13_
After: Mean _4.02_ SD _.83_
t(_57_)=_-7.70_, p_<.001_
Can we reject the null here?
Yes