# 1 A gender and helping study with a different outcome.

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1 A gender and helping study with a different outcome

2 Here is another set of results from the experiment on helping.

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4 A loglinear model was fitted to the data. Here is a test of its goodness-of-fit

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6 Question 1 Does this chi-square value measure the goodness-of-fit of a saturated model?

7 Answer No. When a saturated model is applied, chi-square has no degrees of freedom and has a value of zero.

8 Shortly, I shall show you a table of tests of K-way and Higher Order Effects

9 Question 2 Examine the table. Is the opposite-sex dyadic hypothesis supported by these test results?

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11 Answer No. The opposite-sex dyadic hypothesis predicts a three-way interaction of Participants Sex, Interviewers Sex and Help. The p-value for the three-way interaction (0.514) does not support this expectation.

12 Here is a table of the backward elimination statistics

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14 Question 3. How many models are described here?

15 Answer This table is difficult to follow. FOUR models are described: 1.Interviewer*Participant*Help – the saturated model. 2.Int*Part, Int*Help, Part*Help. All two-way interactions. 3.Int*Part, Int*Help. Part* Help dropped. 4.Int*Help, Part. Int * Part dropped. Opposite each model, there is a chi-square value with so-many df.

16 Answer … Remember that this chi-square refers to the RESIDUALS associated with the terms that have been LEFT OUT. Opposite the final model Int*Help, Part, is the chi-square value 2.435, with df = 3. This chi- square measures the sizes of the residuals when the terms Int*Help*Part (df = 1), Help*Part (df = 1) and Part*Int (df =1) have been removed from the model. Thats why it has 3 degrees of freedom.

17 Question 4. In the final model, where did Participant come from?

18 Answer The main effect of Participant has really been there all the time; but now it needs to be mentioned explicitly in the generating class, because all the interactions involving it have now been removed from the model.

19 The generating class In the output, we are told that the generating class is Interviewer*Help, Participant.

20 Question 5 Does the final model include a term for the main effect of the Help factor?

21 Answer It must do, according to the hierarchical principle. If there is an interaction term, all lower-order effects among the same factors must also be included in the model. The presence of the Interviewer*Help term implies the presence in the model of the main effects of Interviewer and Help.

22 Question 6 Can you write out an equation for the final loglinear model, expressing the terms verbally, rather than in algebraic symbols? The generating class of the final model is Interviewer*Help, Participant

23 The final loglinear model Theres always a constant. The model contains a main effect of Help. There is an Interviewer × Help interaction. By the hierarchical principle, there must also be main effects of Interviewer and Help. Theres a main effect of Participant.

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