Gender Equality in Education: Looking beyond Parity OUTCOME ...
1 A gender and helping study with a different outcome.
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Transcript of 1 A gender and helping study with a different outcome.
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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