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1 A gender and helping study with a different outcome

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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?

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

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

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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.

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12 Here is a table of the backward elimination statistics

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14 Question 3. How many models are described here?

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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.

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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.

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

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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.

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

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

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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.

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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

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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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