SPSS Assignment Template for Steps 2, 3, and 4

General Guidelines:

This an SPSS template for your assignment that we interpreted in class. Your assignment uses different variables and you are doing two tests for each of Steps 3 and 4.

The tables below were printed in "landscape" view. When exporting/printing the output, try to print in "landscape" view so your tables are not split. However, if you can't do this, it is no problem, but keep in mind that your t-test tables will be split into 2-3 sections and take that into account when you are looking at them.

State your research question, for each step, in words first. Then, for all three SPSS assignment steps below, follow the 5-step method suggested by Healey. Use the instructions given in class to make your decision and interpret your output accordingly.

Remember that you do not need to find the critical values from the Appendices when you are doing the tests in SPSS. You can use the probability for T or F, known as the p-value (called "Sig." on the tables), compare it to your alpha level, and make your decision according to the following rules of thumb:

if p-value < critical alpha level (.01), reject the null hypothesis (i.e. your statistic lies further out in the tail)

if p-value >alpha level, fail to reject.

In the case of the Levene test for Step 3, use these rules to decide whether to use the top or bottom row of the actual t-test: if p<α, use bottom row of t-test, and if p>α, use top row.

Assignment Step 2: Single-sample T-Test (In this case we are comparing the sample mean for hours worked, found in the first table (25.278) to a population or test value of 40 hours). In your assignment, the DV being tested is Total # of Fruits and Vegetables to a population mean of 7.

Research question: Is there a significant difference between the sample mean and the population parameter in # of hours worked per week? (State this for each step in your assignment.)

One-Sample Statistics
N / Mean / Std. Deviation / Std. Error Mean
Number of hours usually worked at all jobs in a week. / 1512 / 25.278 / 20.5113 / .5275
One-Sample Test
Test Value = 40
t / df / Sig. (2-tailed) / Mean Difference / 99% Confidence Interval of the Difference
Lower / Upper
Number of hours usually worked at all jobs in a week. / -27.907 / 1511 / .000 / -14.7222 / -16.083 / -13.362

Five Step Method:

  1. Make Assumptions and meet test requirements. Follow guidelines in powerpoint slides.
  2. State the null/alternate hypothesis: H0: μ = 40 (no difference) and H1: μ≠ 40 (there is a difference)
  3. Select the sampling distribution and establish the critical region.Use T-test. (Note that SPSS uses t-test instead of z-test)

Since n>1000, α=.01

  1. Compute the test statistic. See One-sample table above for computation.
  2. Make a decision:

t = -27.907

p (Sig.) = .000 which is less than α=.01, so reject H0

Interpretation: The sample mean = 25.278 is significantly different from the population mean in # of hours worked per week

(t = -27.907, df=1511, α=.01)

Assignment Step 3: Two-sample T-Testcomparing the sample mean for "Yes, limited in activity" (20.680) to the sample mean for "No, not limited" (27.129) in # of hours worked. You will be using Sex and Racial Origin as your groups in your own assignment with the DV being Total # of Fruits and Vegetables.

Research question: Is there a significant difference between the sample means in # of hours worked per week?

Group Statistics
Are you limited in the amount or kind of activity you can do / N / Mean / Std. Deviation / Std. Error Mean
Number of hours usually worked at all jobs in a week. / Yes / 434 / 20.680 / 21.2104 / 1.0182
No / 1078 / 27.129 / 19.9355 / .6072
Independent Samples Test
Levene's Test for Equality of Variances / t-test for Equality of Means
F / Sig. / t / df / Sig. (2-tailed) / Mean Difference / Std. Error Difference / 99% Confidence Interval of the Difference
Lower / Upper
Number of hours usually worked at all jobs in a week. / Equal variances assumed / 20.995 / .000 / -5.586 / 1510 / .000 / -6.4494 / 1.1546 / -9.4273 / -3.4715
Equal variances not assumed / -5.440 / 757.219 / .000 / -6.4494 / 1.1855 / -9.5108 / -3.3881

Five Step Method:

1. Make Assumptions and meet test requirements. Follow guidelines in powerpoint slides for two-sample tests.

2. State the null/alternate hypotheses: H0: μ1 = μ2 (no difference)H1: μ1≠μ2 (there is a difference)

3. Select the sampling distribution and establish the critical region.Use T-test. Since n>1000, α=.01 (Note that SPSS uses t-test)

4. Compute the test statistic. See Two-sample table above for computation.

5. Make a decision: a) Levene's test: Since p (Sig.) = .000< α=.01, we are going to use the bottom (unequal variances) row of the t-test

b) t = -5.440p (Sig.) = .000 which is less than α=.01, so reject H0

Interpretation: The sample mean for "Yes" = 20.680 is significantly different* from "No" = 27.167 in # of hours worked per week.

*Report your sig. stats: (t = -5.440, df=757.219, α=.01)

Assignment Step 4: Oneway ANOVA # of Hours Worked by Health Status. In your own assignment you will be using the IV's Marital Status and Education as your groups and the DV Total # of Fruits and Vegetables.

Research question: Is there a significant difference in # of hours worked per weekby Health Status?

ANOVA
Number of hours usually worked at all jobs in a week.
Sum of Squares / df / Mean Square / F / Sig.
Between Groups / 13639.472 / 4 / 3409.868 / 8.257 / .000
Within Groups / 621957.012 / 1506 / 412.986
Total / 635596.484 / 1510

Post Hoc Tests - Homogeneous Subsets

Number of hours usually worked at all jobs in a week.
Tukey B
In general, would you say your health is: / N / Subset for alpha = 0.01
1 / 2
... poor? / 33 / 11.869
... fair? / 113 / 18.566 / 18.566
... good? / 355 / 24.594
... excellent? / 440 / 26.192
... very good? / 568 / 27.133

Five Step Method:

1. Make Assumptions and meet test requirements. Follow guidelines in powerpoint slides for ANOVA.

2. State the null/alternate hypotheses: H0: μ1= μ2=μ3=μ4=μ5H1: at least 1 mean is different.

3. Select the sampling distribution and establish the critical region.F-test. Since n>1000, α=.01

4. Compute the test statistic. See ANOVA above for computation, then look at Tukey b table to see which means are different.

5. Make a decision: a) ANOVA table: F = 8.257, p (Sig.) = .000 < α=.01 therefore reject H0

b) Tukey b test: the mean for poor is significantly different from the means for good, excellent, very good (α=.01)Interpretation: There is a significant difference in the sample means of # hours worked by health status (F=8.257, df=4,1505, α=.01). The mean for poor (11.869) is not different from fair (18.566) but is significantly different from good (24.594), excellent (26.192) and very good (27.133).