Research Methods IV

Question 1 of 66

1

What is the purpose of a one-way ANOVA (between or within groups)? And how is it different to a t-test?

Select one of the following:

A one way ANOVA examines the difference between four (or more) levels/groups in one variable, whereas a t-test only examines the difference between three levels/groups in one variable.
A one way ANOVA examines the difference between three (or more) levels/groups in one variable, whereas a t-test only examines the difference between two levels/groups in one variable
A one way ANOVA examines the difference between three (you can't examine more than three) levels/groups in one variable, whereas a t-test only examines the difference between two levels/groups in one variable.
A one way ANOVA examines the difference between two (or more) levels/groups in one variable, whereas a t-test only examines the difference between three levels/groups in one variable.

Explanation

Question 2 of 66

1

In an independent measures ANOVA, how is the variance split in terms of sums of squares? And which sum of squares tells us how much variance is explained by our hypothesis that the three groups are different?

Select one of the following:

First you have the residual sum of squares (SSR), which is made up of the model sum of squares (SSM) and the total sum of squares (SST). SSM tells us how much variance is explained by our hypothesis or model.
First you have the model sum of squares (SSM), which is made up of the total sum of squares (SST) and the residual sum of squares (SSR). SST tells us how much variance is explained by our hypothesis or model.
First you have the total sum of squares (SST), which is made up of the model sum of squares (SSM) and the residual sum of squares (SSR). SSM tells us how much variance is explained by our hypothesis or model.
First you have the total sum of squares (SST), which is made up of the model sum of squares (SSM) and the residual sum of squares (SSR). The none of the sum of squares values tell us how much of the variance is explained by our hypothesis.

Explanation

Question 3 of 66

1

In a repeated measures ANOVA how is the variance split in terms of sums of squares? And which sum of squares value tells us how much variance is explained by our hypothesis - that the group will be different in each condition?

Select one of the following:

First, total sum of squares (SST) shows the total variation in the data, this is made of the sum of squares for within participant variation (SSW) and the sum of squares for between participant variation (SSB). In the SSB we can further split the variation into the model sum of squares (SSM) and the residual sum of squares (SSR). SSR tells us how much variance is explained by our hypothesis.
First, total sum of squares (SST) shows the total variation in the data, this is made of the model sum of squares (SSM) and the residual sum of squares (SSR). SST tells us how much variance is explained by our hypothesis.
First, total sum of squares (SST) shows the total variation in the data, this is made of the sum of squares for between participant variation (SSB) and the sum of squares for within participant variation (SSW). SSW tells us how much variance is explained by our hypothesis.
First, total sum of squares (SST) shows the total variation in the data, this is made of the sum of squares for between participant variation (SSB) and the sum of squares for within participant variation (SSW). In the SSW we can further split the variation into the model sum of squares (SSM) and the residual sum of squares (SSR). SSM tells us how much variance is explained by our hypothesis.

Explanation

Question 4 of 66

1

Why can't we use the sum of squares to calculate the F ratio in one-way ANOVA?

Select one of the following:

Because the sum of squares are influenced by the number of scores summed.
Because the sum of squares are influenced by the degrees of freedom but mean squares are not.
This is a trick question - we do use the sum of squares, its the mean squares we don't use.
Because the sum of squares are influenced by the F ratio.

Explanation

Question 5 of 66

1

In a one way ANOVA, if our F value is more than 1 - what does this mean?

Select one of the following:

That our MSR is larger than our MSM. This means that there has been an effect of the variable between the groups/within the group - although we don't know if this is significant.
That our MSM is larger than our MSR. This means that there has been an effect of the variable between the groups/within the group - although we don't know if this is significant
That our MSM is larger than our MSR. This means that there has been an effect of the variable between the groups/within the group - and we know that this is significant.
That our MSM is larger than our MSR. This means that there has not been an effect of the variable between the groups/within the group - although we don't know if this is significant.

Explanation

Question 6 of 66

1

Which of the following is NOT an assumption of an independent samples ANOVA?

Select one of the following:

Sphericity
Interval level data
Normal distribution
Homogeneity of variance

Explanation

Question 7 of 66

1

Which of the following is NOT an assumption of a repeated measures ANOVA?

Select one of the following:

Interval level data
Independence of groups
Normal distribution
Sphericity

Explanation

Question 8 of 66

1

If the assumption of homogeneity of variance is broken - which test should you use instead?

Select one of the following:

Welch
Brown-Forsythe
Tukey
Bonferroni

Explanation

Question 9 of 66

1

If your assumption of sphericity is broken - which part of the output should you read instead?

Select one of the following:

Mauchly
Levene
Lower-bound
Greenhouse-Geisser

Explanation

Question 10 of 66

1

Can SPSS calculate an effect size for a one-way ANOVA?

Select one of the following:

Yes, but only for repeated measures - you should calculate it yourself for independent measures.
Yes, for both
Yes, but only for independent measures - you should calculate it yourself for repeated measures.
No.

Explanation

Question 11 of 66

1

I’m doing a three-way ANOVA with a 3x3x2 design. What does this tell you?

Select one of the following:

That this experiment has 3 levels. Two of them have 3 IVs and one has two.
That this experiment has 3 IVs. Three of them have 3 levels.
That this experiment has 3 IVs. Two of them have 3 levels and one has two.
That this experiment has 2 IVs. Two of them have 3 levels

Explanation

Question 12 of 66

1

What information do we get from a factorial ANOVA?

Select one of the following:

We can see the main effects of each DV and how they interact.
We can see the main effects of each DV.
We can see the main effects of each IV.
We can see the main effects of each IV and how they interact.

Explanation

Question 13 of 66

1

Within the variability explained by SSM, how can we further split the variance in an independent measures factorial ANOVA?

Select one of the following:

The variance explained by SSM is made up of only the SS for each variable.
The variance explained by SSM is made up of the SS for each variable plus the SS for the interactions.
The variance explained by SSM is made up of the MS for each variable plus the MS for the interactions.
You cannot further split the variance explained by SSM.

Explanation

Question 14 of 66

1

I have two IVs: stats knowledge (SSA) and psychology knowledge (SSB). How do we calculate SSA?

Select one of the following:

We collapse across SSB and look only at the scores split by the stats knowledge variable as compared to the participants' means
We collapse across SSB and look only at the scores split by the stats knowledge variable as compared to the grand mean.
We collapse across SSA and look only at the scores split by the psych knowledge variable as compared to the grand mean.
SSA is the same as SSM in this context.

Explanation

Question 15 of 66

1

Following from the previous question. How do I calculate SSAxB? And what does it tell me?

Select one of the following:

After calculating SSA and SSB then the remaining variance accounted for by SST is the variance from SSAxB. This is the interaction between the two variables.
After calculating SSA and SSB then the remaining variance accounted for by SSM is the variance from SSAxB. This is the interaction between the two variables
You do not get SSAxB in independent samples factorial ANOVA.
After calculating SSA and SSB then the remaining variance accounted for by SSM is the variance from SSAxB. This is the main effect of the two variables.

Explanation

Question 16 of 66

1

What is an interaction?

Select one of the following:

When both DVs have a main effect.
When both IVs have a main effect.
When the effect of one IV on the DV is dependent on another IV.
When the effect of one DV on the IV is dependent on another DV.

Explanation

Question 17 of 66

1

I’ve graphed the interaction between stats knowledge (two levels) and psychology knowledge (two levels) and their effect on a test score. What does this interaction show?

Select one of the following:

That participants that had good clinical psychology knowledge scored highly on the test, however, the converse was true for those in the good neuro psychology knowledge group.
That participants that had good clinical psychology knowledge and good non-parametric knowledge scored highly on the test, however this did not occur for those with good clinical psychology knowledge and good parametric knowledge. The converse was true for those in the good neuro psychology knowledge group.
That participants that had good clinical psychology knowledge and good parametric knowledge scored highly on the test, however this did not occur for those with good clinical psychology knowledge and good non-parametric knowledge. The converse was true for those in the good neuro psychology knowledge group.
That participants that had good clinical psychology knowledge and good parametric knowledge scored highly on the test, however this did not occur for those with good clinical psychology knowledge and good non-parametric knowledge. The same was true for those in the good neuro psychology knowledge group.

Explanation

Question 18 of 66

1

If my study is a between subjects design – am I concerned about whether my data breaks the assumption of homogeneity of variance or the assumption of sphericity? And what would I expect to see if this assumption had been met?

Select one of the following:

Sphericity - the Mauchly's test should not be significant
Sphericity - the Mauchly's test should be significant.
Homogeneity of variance - the levene's test should be significant.
Homogeneity of variance - the levene's test should not be significant.

Explanation

Question 19 of 66

1

After completing our factorial ANOVA – why do we employ syntax to look at the interaction?

Select one of the following:

Because this is the best way to explore the differences between the different levels in an interaction.
We don't - multiple comparisons tells us enough.
Because we want to examine the differences between the IVs.
Trick question - this is only used to examine main effects.

Explanation

Question 20 of 66

1

Why can't we only interpret the F from the SSM line of the output?

Select one of the following:

Because we need to know how much variance is explained by the SSR output, which is part of the variance explained by SSM.
Because we don't just need to know how much variance is explained by the model but whether each individual variable is a explaining a significant amount of variance.
Because we don't just need to know how much variance is explained by the model but whether each individual variable and their interactions is a explaining a significant amount of variance.
Trick question - we only interpret the SSM line of output in factorial ANOVA.

Explanation

Question 21 of 66

1

How do we partition the variance for SSM in factorial repeated measures ANOVA?

Select one of the following:

We split it into SSA and SSB.
We don't split the variance beyond SSM.
We split it into SSA and SSAxB.
We split it into SSA, SSB and SSAxB.

Explanation

Question 22 of 66

1

How do we partition the variance for SSR in a repeated measures factorial ANOVA?

Select one of the following:

Into SSRA and SSRB.
We don't partition the variance of SSR.
Into SSRA and SSRAxB
Into SSRA, SSRB and SSRAxB.

Explanation

Question 23 of 66

1

Which formula do I use to calculate F for one of my main effects?

Select one of the following:

MSA/MSR
MSA/MSRA
MSA/MSB
MSA/dfA

Explanation

Question 24 of 66

1

In a repeated measures ANOVA are we concerned with Homogeneity of Variance or Sphericity? And what do we expect to see if the assumption has been met?

Select one of the following:

Homogeneity of Variance - Levene's test should be significant
Sphericity - Mauchly's test should not be significant
Sphericity - Mauchly's test should be significant
Homogeneity of Variance - Levene's test should not be significant

Explanation

Question 25 of 66

1

Below is my syntax for a 2 way 3x2 repeated measures ANOVA. IV1 Time of Meal (Breakfast, Lunch and Dinner), IV2 Day (Day1 Day2). I'm missing a section of the syntax - complete it.

GLM BreakfastDay1 BreakfastDay2 LunchDay1 LunchDay2 DinnerDay1 DinnerDay2

/WSFACTOR [COMPLETE THIS LINE]

/EMMEANS = TABLES(PreDuringPost*LeftRight) COMPARE(LeftRight)

Select one of the following:

Day 3 TimeMeal 2
TimeDay 6
TimeMeal 2 Day 3
TimeMeal 3 Day 2

Explanation

Question 26 of 66

1

How is an interaction different from a main effect?

Select one of the following:

For a main effect: IV 1 will have the same effect on the DV in all levels of IV 2
In an interaction: IV 1 will only effect the DV in specific levels of IV 2
Main effects and interactions are the same.
For a main effect: DV 1 will have the same effect on the IV in all levels of DV 2
In an interaction: DV 1 will only effect the IV in specific levels of DV 2
For a main effect: IV 1 will only effect the DV in specific levels of IV 2

Explanation

Question 27 of 66

1

I've conducted an experiment examining real vs sham brain stimulation on a verbal fluency experiment. I've recorded number of words produced to the letter S in one minute at Pre During and Post stimulation. The graph of my results is below. What is this interaction showing?

Select one of the following:

Sham stimulation increased performance as compared to Real, but only During stimulation and Post stimulation - there was no difference Pre stimulation.
Real stimulation increased performance as compared to Sham, but only Pre stimulation and Post stimulation - there was no difference During stimulation.
Real stimulation increased performance as compared to Sham, but only During stimulation and Post stimulation - there was no difference Pre stimulation.
There is no interaction between these variables.

Explanation

Question 28 of 66

1

What is a mixed factorial design?

Select one of the following:

A design which has 2+ IVs. Both of which is between subjects.
A design which has 2+ IVs. At least one of which is between subjects and one within subjects
A design which has 3+ IVs. At least one of which is between subjects and one within subjects.
A design which has 2+ IVs. Both of which is within subjects

Explanation

Question 29 of 66

1

In a mixed factorial ANOVA are we concerned with Homogeneity of Variance or Sphericity?

Select one of the following:

Only Sphericity which applies when the within subjects IV has 3+ levels as with repeated measures designs that are not mixed.
Only Sphericity which applies when the within subjects IV has 2+ levels as with repeated measures designs that are not mixed.
Both, but Sphericity only applies when the within subjects IV has 3+ levels as with repeated measures designs that are not mixed.
Only Homogeneity of Variance.

Explanation

Question 30 of 66

1

In SPSS, what process do we use to complete a mixed factorial ANOVA?

Select one of the following:

The same as for repeated measures ANOVA but we add in the between subjects variable at the 'define' stage.
The same as for repeated measures ANOVA but we add in the between subjects variable at the 'Post Hoc' stage.
Mixed ANOVA uses it's own process which is entirely different from repeated and independent measures ANOVA.
The same as for independent measures ANOVA but we add in the between subjects variable at the 'define' stage.

Explanation

Question 31 of 66

1

Assuming the data meet the assumptions of parametric tests, non-parametric tests, compared to their parametric counterparts ___________

Select one of the following:

Are more conservative
Are less likely to accept the alternative hypothesis
Have less statistical power
All of the above

Explanation

Question 32 of 66

1

A researcher measured leadership skills in police officers, doctors, and accountants. There were unequal group sizes and the data were skewed. What test should be used to analyse the data?

Select one of the following:

Wilcoxon test
Kruskal-Wallis test
One-way ANOVA
Friedman test

Explanation

Question 33 of 66

1

The results of the test used to analyse the data described in Question 32 showed a significant result. How should the researcher carry out post-hoc tests?

Select one of the following:

Compare groups using the Mann Whitney test
Compare groups using the Wilcoxon test
Compare groups using a t-test
None of the above. Post-hoc analyses cannot be carried out with non-parametric data

Explanation

Question 34 of 66

1

An advantage of non-parametric statistics is that _________

Select one of the following:

You need a computer to calculate them
They have many assumptions to meet
They are easy to calculate
They are very powerful

Explanation

Question 35 of 66

1

Which of the following tests is analogous to a standard within-subjects ANOVA?

Select one of the following:

Mann-Whitney
Friedman
Wilcoxon
Kruskal-Wallis

Explanation

Question 36 of 66

1

Tied scores (when two or more scores have the same value) may present a problem in non-parametric tests. The way to deal with them in a Kruskal-Wallis test is to _____________

Select one of the following:

Throw out the tied data
Assign any tied values the average of the ranks they would have received had they not been tied
Use a random number table to assign ranks to the tied values
Assign any tied values the lower of the ranks they would have received had they not been tied

Explanation

Question 37 of 66

1

A dataset contains the following values: 23, 28, 37, 23, 45, 49, 37, 62. Rank the values. Which rank is associated with value 37?

Select one of the following:

3.5
4
4.5
6

Explanation

Question 38 of 66

1

Researchers wanted to examine whether the ‘see-food’ diet was effective. A group of volunteers were placed on the diet and their weight (in kilograms) was measured at the beginning of the diet, after 1 month, after 2 months, then at the end of the diet after 3 months. The data were not normally distributed. Which test would be appropriate to analyse this data?

Select one of the following:

Mann-Whitney
Friedman
Wilcoxon
Kruskal-Wallis

Explanation

Question 39 of 66

1

In a Kruskal-Wallis test, the _________ the value of H, the more likely you are to reject the null hypothesis

Select one of the following:

Larger
Smaller
More negative
H is not relevant for the Kruskal-Wallis test

Explanation

Question 40 of 66

1

What assumption does ANCOVA have that ANOVA does not?

Select one of the following:

Homogeneity of variance
Homoscedasticity
Homogeneity of sample size
Homogeneity of regression slopes

Explanation

Question 41 of 66

1

ANCOVA is ___________

Select one of the following:

Used to correct for unmeasured variables that could affect the outcome variable
A method of portioning the results of ANOVAs to investigate where differences between independent variables lie
A robust version of ANOVA
An extension of ANOVA that partials out the effect of other measured variables

Explanation

Question 42 of 66

1

A researcher conducted a study to examine whether people’s attachment styles are associated with differences in relationship satisfaction. Participants were divided into three groups according to their attachment styles (secure, dismissing, and fearful) and their relationship satisfaction was measured. Depression levels were also measured, as it is known that relationship satisfaction covaries with depression. Which variable should the researcher use as a covariate in the data analysis?

Select one of the following:

Attachment style
Secure attachment
Depression
Relationship satisfaction

Explanation

Question 43 of 66

1

Regarding the study described in Question 42, which of the below questions would be relevant to the data analysis?

Select one of the following:

Does relationship satisfaction have a significant effect on the relationship between attachment and depression?
What would the means of the groups be on relationship satisfaction if their levels of depression were constant?
What would the mean relationship satisfaction be if levels of depression were constant?
What would the mean depression score be for the three groups of attachment styles if their levels of relationship satisfaction were constant?

Explanation

Question 44 of 66

1

What problem do you foresee with the study described in Question 42?

Select one of the following:

There could be more than three groups
It is likely that there will be a linear association between depression and relationship satisfaction
It is likely that the regression lines will be parallel
Depression might also be related to attachment style

Explanation

Question 45 of 66

1

The larger the F statistic, the larger the _____ compared to the _

Select one of the following:

Variation between groups; variation within groups
Variation within groups; variation between groups
Variation between groups; total variation
Total variation; variation between groups

Explanation

Question 46 of 66

1

A covariate is best described as __________

Select one of the following:

A variable that is related to the dependent variable
A variable that is directly influenced by the dependent variable
A variable that is related to the independent variable
A variable that is unrelated to all other variables in the study

Explanation

Question 47 of 66

1

A researcher wanted to see whether giving students caffeine would improve their memory. He gave all students a memory test, then he randomly assigned participants to two groups. One group received a caffeinated drink and the other group received a decaffeinated drink. Both groups then took another memory test (with different questions). The researcher used ANCOVA to analyse the data. Which variable was the covariate?

Select one of the following:

Caffeine
Score on the first memory test
Score on the second memory test
There was no covariate

Explanation

Question 48 of 66

1

What is the correct way to report these results?

Select one of the following:

The weight of participants did not significantly change during the diet, �2 (3) = 0.60, p = .90
The weight of participants did not significantly change during the diet, �2 (3) = 0.60, p = .89
The weight of participants significantly changed during the diet, �2 (3) = 0.60, p < .05
The weight of participants significantly changed during the diet, �2 (3) = 0.60, p > .05

Explanation

Question 49 of 66

1

The SPSS output from the ANCOVA is shown below. What is the correct way to report these results?

Select one of the following:

There was a significant effect of caffeine on memory after controlling for the effect of the covariate, F (1, 77) = 11.33, p = .001
There was no significant effect of caffeine on memory after controlling for the effect of the covariate, F (1, 77) = 11.33, p > .05
There was a significant effect of caffeine on memory after controlling for the effect of the covariate, F (1, 77) = 7.40, p = .008
There was no significant effect of caffeine on memory after controlling for the effect of the covariate, F (1, 77) = 341.48, p < .001

Explanation

Question 50 of 66

1

What would you use Box’s test for?

Select one of the following:

To test for multivariate normality
To test for a plausible linear combination of the dependent variables
To test for homogeneity of variance
To test for homogeneity of covariance matrices

Explanation

Question 51 of 66

1

If your MANOVA is statistically significant:

Select one of the following:

You could conduct separate Bonferroni-corrected ANOVAs on each dependent variable
There is no added value in performing discriminant function analysis
You could conclude that all groups differ significantly
None of the answers

Explanation

Question 52 of 66

1

How does MANOVA handle the dependent variables (DVs) in the analysis?

Select one of the following:

The DVs are compiled into a linear combination
The DVs are entered sequentially into a model
The DVs are entered stepwise into an analysis
The DVs are standardized and summed

Explanation

Question 53 of 66

1

Which one of these might you consider a violation in MANOVA, suggesting it may be better to use an alternative analysis?

Select one of the following:

You have 30 participants per group in your between-participants design
You have equal numbers of participants and it is a large sample size
You have normally distributed dependent variables and all linear combinations of the dependent variables
Box's M has an associated p-value of < 0.05 and you have unequal sample sizes

Explanation

Question 54 of 66

1

The difference between MANOVA and ANOVA is that MANOVA has the ability to handle _______

Select one of the following:

Several dependent variables
Several independent variables
Nonmetric independent variables
Data with large error terms

Explanation

Question 55 of 66

1

Which of the following are true of MANOVA?

Select one of the following:

Dependent variables are combined into discriminant function variates
It involves the computation of F values
Calculations are performed on matrices which contain cross-products
All of the options

Explanation

Question 56 of 66

1

A researcher wanted to examine the effects of a new antidepressant drug. She recruited 60 clinically depressed participants and randomly allocated them to three conditions: no dose (placebo), low dose and medium dose of the drug. She administered the drugs to participants for one month, then measured their mood levels by asking them to complete a self-report mood measure. However, she decided that mood should not just be measured by self-report, so she also asked the participants’ psychiatrists to give their ratings of each person’s mood. All mood ratings were measured on a 1-50 scale, with 1 indicating the lowest possible mood and 50 indicating the highest possible mood. The SPSS output from the data analysis is shown to the left. Are all the assumptions of MANOVA met?

Select one of the following:

Yes
No, the assumption of equality of covariance matrices is violated
No, the assumption of multivariate normality is violated
It is not possible to tell from this output

Explanation

Question 57 of 66

1

Which of the following statements about the output shown in the previous question is true?

Select one of the following:

There is a significant multivariate difference between the drug groups
Significant univariate differences are only present for psychiatrist ratings of mood
Self-report ratings of mood are significantly higher than psychiatrist ratings of mood
All of the answers are true

Explanation

Question 58 of 66

1

Which of the following might be a problem with the study described two questions ago?

Select one of the following:

There may have been individual differences in mood at the beginning of the study
There was only one independent variable
The dependent variables were measured on different scales
All of the answers

Explanation

Question 59 of 66

1

In the study described in Question 56, which test statistic gives the largest effect size for the effect of the drug?

Select one of the following:

Pillai’s Trace
Wilks’ Lambda
Roy’s Largest Root
They are all equal

Explanation

Question 60 of 66

1

A dataset contains the following values: 45, 67, 32, 54, 32, 61, 54, 49. Rank the values. Which rank is associated with value 54?

Select one of the following:

6
5.5
4.5
4

Explanation

Question 61 of 66

1

A researcher conducted a study to examine whether people’s level of daily exercise is associated with differences in their mood. Participants were divided into three groups according to their exercise level (low, medium, and high) and their mood was measured using a questionnaire. Amount of sleep per night was also measured, as it was thought that mood might also be related to sleep. Which variable could the researcher use as a covariate in the data analysis?

Select one of the following:

Sleep
Exercise levels
Mood
All of the answers

Explanation

Question 62 of 66

1

What does 'N' represent in this Kruskal-Wallis equation?

Select one of the following:

Sum of ranks
Number of conditions
Each condition
Total number of participants

Explanation

Question 63 of 66

1

How would you write out the interaction for this between-subjects factorial ANOVA?

Select one of the following:

F(1,50) = .22, p = .64, np2 = .004
F(1,50) = 6.04, p = .02, np2 = .11
F(1,50) = .41, p = .53, np2 = .008
F(1,50) = .41, p = .526, np2 =.008

Explanation

Question 64 of 66

1

What does partial eta squared (η2p) show?

Select one of the following:

Partial eta squared (η2p) reflects the proportion of variance that a variable explains that is not explained by other variables in the analysis
Means of two groups
Statistical power of a test and it's ability to find an effect, if one exists
The estimation of the effect size in the population

Explanation

Question 65 of 66

1

Which of the following is NOT true in relation to P-values?

Select one of the following:

Does not tell us how important the effect is
Arbitrary, but encourages all or nothing thinking
Greatly influenced by sample size (as sample increases, the critical value decreases)
Is an objective and usually standardised measure of the magnitude of the observed effect

Explanation

Question 66 of 66

1

Which of the following is NOT a cross-product relating to the sums of squares used in univariate ANOVA?

Select one of the following:

Total cross-product
Cross-product due to the mode
Residual cross-product
Reverse Product

	Created by Ben Armstrong almost 8 years ago

Degree Psychology Quiz on Research Methods IV, created by Ben Armstrong on 15/02/2017.

Research Methods IV

Question 1 of 66

What is the purpose of a one-way ANOVA (between or within groups)? And how is it different to a t-test?

Select one of the following:

Explanation

Question 2 of 66

In an independent measures ANOVA, how is the variance split in terms of sums of squares? And which sum of squares tells us how much variance is explained by our hypothesis that the three groups are different?

Select one of the following:

Explanation

Question 3 of 66

In a repeated measures ANOVA how is the variance split in terms of sums of squares? And which sum of squares value tells us how much variance is explained by our hypothesis - that the group will be different in each condition?

Select one of the following:

Explanation

Question 4 of 66

Why can't we use the sum of squares to calculate the F ratio in one-way ANOVA?

Select one of the following:

Explanation

Question 5 of 66

In a one way ANOVA, if our F value is more than 1 - what does this mean?

Select one of the following:

Explanation

Question 6 of 66

Which of the following is NOT an assumption of an independent samples ANOVA?

Select one of the following:

Explanation

Question 7 of 66

Which of the following is NOT an assumption of a repeated measures ANOVA?

Select one of the following:

Explanation

Question 8 of 66

If the assumption of homogeneity of variance is broken - which test should you use instead?

Select one of the following:

Explanation

Question 9 of 66

If your assumption of sphericity is broken - which part of the output should you read instead?

Select one of the following:

Explanation

Question 10 of 66

Can SPSS calculate an effect size for a one-way ANOVA?

Select one of the following:

Explanation

Question 11 of 66

I’m doing a three-way ANOVA with a 3x3x2 design. What does this tell you?

Select one of the following:

Explanation

Question 12 of 66

What information do we get from a factorial ANOVA?

Select one of the following:

Explanation

Question 13 of 66

Within the variability explained by SSM, how can we further split the variance in an independent measures factorial ANOVA?

Select one of the following:

Explanation

Question 14 of 66

I have two IVs: stats knowledge (SSA) and psychology knowledge (SSB). How do we calculate SSA?

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Explanation

Question 15 of 66

Following from the previous question. How do I calculate SSAxB? And what does it tell me?

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Question 16 of 66

What is an interaction?

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Question 17 of 66

I’ve graphed the interaction between stats knowledge (two levels) and psychology knowledge (two levels) and their effect on a test score. What does this interaction show?

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Question 18 of 66

If my study is a between subjects design – am I concerned about whether my data breaks the assumption of homogeneity of variance or the assumption of sphericity? And what would I expect to see if this assumption had been met?

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Explanation

Question 19 of 66

After completing our factorial ANOVA – why do we employ syntax to look at the interaction?

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Question 20 of 66

Why can't we only interpret the F from the SSM line of the output?