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Using SPSS for Chi Square UDP 520 Lab 5 Lin Lin November 8 th , 2007

Using SPSS for Chi Square UDP 520 Lab 5 Lin November 8 th, 2007

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Page 1: Using SPSS for Chi Square UDP 520 Lab 5 Lin November 8 th, 2007

Using SPSS for Chi Square

UDP 520 Lab 5Lin Lin

November 8th, 2007

Page 2: Using SPSS for Chi Square UDP 520 Lab 5 Lin November 8 th, 2007

Outline

• Dataset

• Review t-test

• Chi-square

• Exercise

Page 3: Using SPSS for Chi Square UDP 520 Lab 5 Lin November 8 th, 2007

BMI

• Body mass index (BMI) is a measure of body fat based on height and weight that applies to both adult men and women.

– Under & normal weight: BMI <25 – Overweight & obesity: BMI ≥ 25

2 2

( )BMI=703

( )

Weight lb

height in

Page 4: Using SPSS for Chi Square UDP 520 Lab 5 Lin November 8 th, 2007

Dataset – WLTP

• 1000 adults aged 18+ (males and females) were recruited to study the effectiveness of Weight Loss Training Program (WLTP)

• Variables– Sex (female=1)– BMI_1(before WLTP)– BMI_2(after WLTP)– Urban or suburban (urban=1)– Overweight_1 (overweight before WLTP) (overweight=1)– Overweight_2 (overweight after WLTP) (overweight=1)

http://courses.washington.edu/urbdp520/UDP520/WLTP.sav

Page 5: Using SPSS for Chi Square UDP 520 Lab 5 Lin November 8 th, 2007

Question 1

• Is BMI significantly different between people who live in an urban area and those who live in a suburb area before WLTP?

Independent samples t-test

Page 6: Using SPSS for Chi Square UDP 520 Lab 5 Lin November 8 th, 2007

Question 1 – Step by Step

• Step 1: Making assumptions and meeting test requirements – Sampling is random– Level of measurement is interval-ratio– Sampling distribution is normal

• Step 2: Stating the null hypothesis

• Step 3: Selecting the sampling distribution and establishing the critical region – Sampling distribution = Z distribution – Alpha = 0.05, two-tailed– Z(critical) = ±1.96

0

a

H :

H :urban suburban

urban suburban

Page 7: Using SPSS for Chi Square UDP 520 Lab 5 Lin November 8 th, 2007

Question 1 (cont.) Step 4: Computing the test statistic in SPSS

Page 8: Using SPSS for Chi Square UDP 520 Lab 5 Lin November 8 th, 2007

Question 1 (cont.)

• Step 5: Making a decision and interpreting the results of the test

Independent Samples Test

12.928 .000 6.306 998 .000 .49266 .07813 .33934 .64598

6.276 955.970 .000 .49266 .07850 .33861 .64670

Equal variancesassumed

Equal variancesnot assumed

BMI_1F Sig.

Levene's Test forEquality of Variances

t df Sig. (2-tailed)Mean

DifferenceStd. ErrorDifference Lower Upper

95% ConfidenceInterval of the

Difference

t-test for Equality of Means

Result(Z obtained)

Indicate whether result is significant or not

(based on your predetermined alpha)

Page 9: Using SPSS for Chi Square UDP 520 Lab 5 Lin November 8 th, 2007

Question 2

• Is there any relationship between living in a suburban area and being overweight before WLTP? – Under & normal weight: BMI <25 – Overweight & obese: BMI ≥ 25

Chi Square test

Page 10: Using SPSS for Chi Square UDP 520 Lab 5 Lin November 8 th, 2007

Question 2 – Step by Step

• Step 1: Making assumptions and meeting test requirements – Random sampling– Level of measurement is nominal

• Step 2: Stating the null hypothesis – H0: Living in an urban area and being overweight are

independent– Ha: Living in an urban area and being overweight are dependent

• Step 3: Selecting the sampling distribution and establishing the critical region – Sampling distribution = χ2 distribution – Alpha = 0.05– Df = (r-1)(c-1) = 1 (a 2-by-2 table)– χ2 (critical) = 3.481

Page 11: Using SPSS for Chi Square UDP 520 Lab 5 Lin November 8 th, 2007

Question 2 (cont.) Step 4: computing the test statistic in SPSS

Page 12: Using SPSS for Chi Square UDP 520 Lab 5 Lin November 8 th, 2007

Question 2 (cont.)

• Step 5: making a decision and interpreting the results of the test

overweight_1 * urban Crosstabulation

329 468 797

385.7 411.3 797.0

155 48 203

98.3 104.7 203.0

484 516 1000

484.0 516.0 1000.0

Count

Expected Count

Count

Expected Count

Count

Expected Count

0

1

overweight_1

Total

0 1

urban

Total

Chi-Square Tests

79.699b 1 .000

78.301 1 .000

82.696 1 .000

.000 .000

79.619 1 .000

1000

Pearson Chi-Square

Continuity Correctiona

Likelihood Ratio

Fisher's Exact Test

Linear-by-LinearAssociation

N of Valid Cases

Value dfAsymp. Sig.

(2-sided)Exact Sig.(2-sided)

Exact Sig.(1-sided)

Computed only for a 2x2 tablea.

0 cells (.0%) have expected count less than 5. The minimum expected count is 98.25.

b.

Result(χ2 obtained)

Page 13: Using SPSS for Chi Square UDP 520 Lab 5 Lin November 8 th, 2007

Question 2 (cont.)

Symmetric Measures

.272 .000

1000

Contingency CoefficientNominal by Nominal

N of Valid Cases

Value Approx. Sig.

Not assuming the null hypothesis.a.

Using the asymptotic standard error assuming the null hypothesis.b.

The nominal symmetric measures indicate both the strength and significance of the relationship between the row and column variables of a crosstabulation.

Page 14: Using SPSS for Chi Square UDP 520 Lab 5 Lin November 8 th, 2007

Exercise

• Does a significant relationship exist between living in a suburban area and being overweight after WLTP?

• Does a significant relationship exist between being a male and overweight before WLTP?

• Does a significant relationship exist between being a male and overweight after WLTP?