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A gender and helping study with a different outcome

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

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A loglinear model was fitted to the data. Here is a test of its

goodness-of-fit

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Question 1

• Does this chi-square value measure the goodness-of-fit of a saturated model?

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

Effects

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Question 2

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

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Answer

• No. The opposite-sex dyadic hypothesis predicts a three-way interaction of Participant’s Sex, Interviewer’s Sex and Help. The p-value for the three-way interaction (0.514) does not support this expectation.

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Here is a table of the backward elimination statistics

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Question 3. How many models are described

here?

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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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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. That’s why it has 3 degrees of freedom.

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Question 4.

In the final model, where did Participant come from?

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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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The generating class

• In the output, we are told that the generating class is

• Interviewer*Help, Participant.

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Question 5

• Does the final model include a term for the main effect of the Help factor?

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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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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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The final loglinear model

• There’s 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. • There’s a main effect of Participant.

tParticipan

of

effectmain

ninteractio

Help r Interviewe

Help

of

effect main

rInterviewe

of

effectmain

constant )ln(E