When conducting research, what is the main factor to be considered when posing the research question?

	a)	Whether you are examining differences between groups or predicting particular outcomes.
	b)	Whether you are exploring underlying structures.
	c)	To consider whether the measures to be implemented fit existing or suggested theoretical models.
	d)	All of the above.
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Type l errors should be avoided at all costs. How is this achieved?

	a)	By conducting a large number of separate t.tests.
	b)	To consider this when analysing data and applying more stringent statistical tests.
	c)	To apply an adjustment to the probability level (Bonferroni).
	d)	All of the above.
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In which way is the t.test associated with analysis of variance.

	a)	There is no association.
	b)	The t.test is basically a one-way ANOVA when two groups are to be compared.
	c)	The F.statistic obtained via ANOVA does not relate to the t statistic obtained via the t.test.
	d)	All of the above.
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When interpreting the output generated from the one-way ANOVA, what does the mean square (MS) column indicate?

	a)	It tells us the amount of variance accounted for by the dependent variable.
	b)	It tells us whether there are differences between the mean scores of two groups.
	c)	It tells us the level at which the data are free to vary.
	d)	All of the above.
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What is the purpose of multiple discriminant function analysis?

	a)	It allows us to discriminate between multiple groups.
	b)	It forms a composite of the dependent variables.
	c)	It applies a weighting to the dependent variables.
	d)	All of the above.
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What is the main assumption of multiple discriminant function analysis?

	a)	That the data are normally distributed.
	b)	The sample size is large enough (generally no fewer than 30 cases).
	c)	That the discriminating variables are measured at the interval level.
	d)	All of the above.
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What is the purpose of multivariate analysis of variance?

	a)	It combines the generation of composites with variance partitioning.
	b)	It is generally used when comparing scores on one variable.
	c)	It is useful because it does not assume a normal distribution of scores.
	d)	It is useful because it may be applied to small samples of data.
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