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SOC 3811

Basic Social Statistics

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Reminder

Hand in your assignment 6 Remember to pick up your previous

homework Hand in the extra credit assignment next

Tuesday in lecture (May 1st) No lab next Friday (May 4th) . Final exam: May 12th (Saturday), 8:00am

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Class overview

Extra credit assignment example Evaluation Assignment 5 correction General review Time for your extra credit assignment

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Extra credit example

SPSS commands Data split file organize output by groups group based on : sex

Analyze descriptive statistics crosstabs check “CHI-square” & Expected

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Extra credit examplemale

Chi-Square Testsb

23.175a 12 .02621.550 12 .0438.505 1 .0041006

Pearson Chi-SquareLikelihood RatioLinear-by-Linear AssociationN of Valid Cases

Value dfAsymp. Sig.

(2-sided)

0 cells (.0%) have expected count less than 5. The minimumexpected count is 8.42.

a.

sex RESPONDENTS SEX = 1 MALEb.

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Extra credit examplefemale

Chi-Square Testsb

56.186a 12 .00056.845 12 .00041.933 1 .000

1296

Pearson Chi-SquareLikelihood RatioLinear-by-Linear AssociationN of Valid Cases

Value dfAsymp. Sig.

(2-sided)

0 cells (.0%) have expected count less than 5. The minimumexpected count is 7.00.

a.

sex RESPONDENTS SEX = 2 FEMALEb.

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Extra credit examplemale

sexfreq FREQUENCY OF SEX DURING LAST YEAR * happy GENERAL HAPPINESS Crosstabulationa

37 97 24 15847.6 92.2 18.2 158.0

25 43 19 8726.2 50.8 10.0 87.0

31 75 10 11634.9 67.7 13.4 116.0

57 96 15 16850.6 98.0 19.4 168.0

60 122 19 20160.5 117.3 23.2 201.0

63 119 21 20361.1 118.5 23.4 203.0

30 35 8 7322.0 42.6 8.4 73.0303 587 116 1006

303.0 587.0 116.0 1006.0

CountExpected CountCountExpected CountCountExpected CountCountExpected CountCountExpected CountCountExpected CountCountExpected CountCountExpected Count

0 NOT AT ALL

1 ONCE OR TWICE

2 ONCE A MONTH

3 2-3 TIMES A MONTH

4 WEEKLY

5 2-3 PER WEEK

6 4+ PER WEEK

sexfreq FREQUENCYOF SEXDURINGLAST YEAR

Total

1 VERYHAPPY

2 PRETTYHAPPY

3 NOT TOOHAPPY

happy GENERAL HAPPINESS

Total

sex RESPONDENTS SEX = 1 MALEa.

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Extra credit examplemale

OR (4+ per week v.s not at all)= 30 (4+ weekly, very happy) / 8 (4+ weekly, not happy)

37 (not at all, very happy) / 24 (not al all, not happy)

30 (4+ weekly, very happy) / 37 (not al all, very happy)

8 (4+ weekly, not happy) / 24 (not al all, not happy

= 2.43

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Extra credit examplemale

For male, men who have more than 4 times sex per week

are 2.43 times more likely to be very happy as opposed to not too happy, relative to those who don’t have sex.

men who have more than 4 times sex per week are 143% more likely to be very happy as opposed to not too happy, relative to those who don’t have sex.

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female

sexfreq FREQUENCY OF SEX DURING LAST YEAR * happy GENERAL HAPPINESS Crosstabulationa

83 195 78 356115.4 191.7 48.9 356.0

22 50 15 8728.2 46.9 11.9 87.0

48 81 17 14647.3 78.6 20.1 146.0

64 95 25 18459.6 99.1 25.3 184.0

78 129 17 22472.6 120.6 30.8 224.0107 124 17 24880.4 133.6 34.1 248.0

18 24 9 5116.5 27.5 7.0 51.0420 698 178 1296

420.0 698.0 178.0 1296.0

CountExpected CountCountExpected CountCountExpected CountCountExpected CountCountExpected CountCountExpected CountCountExpected CountCountExpected Count

0 NOT AT ALL

1 ONCE OR TWICE

2 ONCE A MONTH

3 2-3 TIMES A MONTH

4 WEEKLY

5 2-3 PER WEEK

6 4+ PER WEEK

sexfreq FREQUENCYOF SEXDURINGLAST YEAR

Total

1 VERYHAPPY

2 PRETTYHAPPY

3 NOT TOOHAPPY

happy GENERAL HAPPINESS

Total

sex RESPONDENTS SEX = 2 FEMALEa.

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Extra credit examplefemale

OR (4+ per week v.s not at all)= 18 (4+ weekly, very happy) / 9 (4+ weekly, not happy)

83 (not at all, very happy) / 78 (not al all, not happy)

18 (4+ weekly, very happy) /83 (not al all, very happy)

9 (4+ weekly, not happy) / 78 (not al all, not happy

= 1.88

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Extra credit examplefemale

For female, women who have more than 4 times sex per week

are 1.88 times more likely to be very happy as opposed to not too happy, relative to those who don’t have sex.

women who have more than 4 times sex per week are 88% more likely to be very happy as opposed to not too happy, relative to those who don’t have sex.

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Extra credit examplecompare men and women

Take the odds ratio of odds ratios

2.43

1.88

= 1.29 Interpretation?

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Extra credit examplecompare men and women

Start your description from the denominator group, then describe how the relationship is stronger (or weaker) for the numerator group

Women who have sex more than 4 times per week are 1.88 times more likely to be very happy as opposed to not too happy, relative to women who don’t have sex. For men, the relationship is 1.29 times stronger.

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Extra credit examplecompare men and women

Women who have sex more than 4 times per week are 1.88 times more likely to be very happy as opposed to not too happy, relative to women who don’t have sex. For men, the relationship is 29% stronger.

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Evaluation

Yu-Ju Chien Spring 2007 Sociology 3811 Sec 6 (Friday morning) Sec 7 (Friday afternoon)

16. Cultural difference is a problem for working with Yu-Ju. 17. Language is a problem for working with Yu-Ju.

1 2 3 4 5 6 7 strong agree strong disagree

I’ll be back in 10 mins

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Assignment 5

F-test: gate keeper test T-test: compare means

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F-test

Ho: variances are equal Ha: variances are not equal

If Sig. (p value)>.05 → can’t reject Ho (variances are equal) If Sig. (p value)≤.05 → reject Ho

(variances are not equal)

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ˆ

ˆ

F

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T-test (two-tail)

Ho:

Ha:

Calculating z/t score:

(note: the formula is different for different type of cases)

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021

021

}{**

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yyse

yytz

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T-test

Independent Samples Test

4.693 .030 -2.545 1237 .011 -.394 .155 -.698 -.090

-2.561 1205.187 .011 -.394 .154 -.696 -.092

Equal variances assumed

Equal variances not assumed

relactiv HOW OFTENDOES R TAKE PARTIN RELIG ACTIVITIES

F Sig.

Levene's Test for Equalityof Variances

t df Sig. (2-tailed)Mean

DifferenceStd. ErrorDifference Lower Upper

95% Confidence Intervalof the Difference

t-test for Equality of Means

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Review

Inferential statistics :

Regression modelsT-test + F testPearson’s Chi-Square test + Odds ratio

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Review

Inference review Two steps in inference:

1. Use a sample to develop population estimates.

2. Use inference to see if estimate is significant (Is our estimate far enough away from a predetermined value to be sure that it is different). (Reject the null. )

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Inference in regression

Regression models:

test if the effects of independent variables on dependent variables are statistically significant.

eXXYdependent 22110

eXYdependent 110

eXXXXYdependent )( 21322110

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Inference in regression

most often we are comparing our estimate to zero.

In regression if the slope is 0, there is no relationship.

In regression if the slope is not 0, there is some relationship. (then, go further to explain the relationship: positive/negative.)

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Dummy regression model

T-test compare means of two groups (if independent, it is same as a dummy regression model)

gate keeper test: the F test test if the variances are equal

eXY dummydependent 10

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Reminder

Hand in your assignment 6 Remember to pick up your previous

homework Hand in the extra credit assignment next

Tuesday in lecture (May 1st) No lab next Friday (May 4th) . Final exam: May 12th (Saturday), 8:00am