MCQ Flashcards
What is multicollinearity?
When predictor variables correlate very highly with each other
When checking assumption fo regression, what does this graph tell you?
Normality of residuals
Which of the following statements about the t-statistic in regression is not true?
The t-statistic is equal to the regression coefficient divided by its standard deviation
The t-statistic tests whether the regression coefficient, b, is significantly different from 0
The t-statistic provides some idea of how well a predictor predicts the outcome variable
The t-statistic can be used to see whether a predictor variables makes a statistically significant contribution to the regression model
The t-statistic is equal to the regression coefficient divided by its standard deviation
A consumer researcher was interested in what factors influence people’s fear responses to horror films. She measured gender and how much a person is prone to believe in things that are not real (fantasy proneness). Fear responses were measured too. In this table, what does the value 847.685 represent?
The residual error in the prediction of fear scores when both gender and fantasy proneness are included as predictors in the model.
A psychologist was interested in whether the amount of news people watch predicts how depressed they are. In this table, what does the value 3.030 represent?
The improvement in the prediction of depression by fitting the model
When checking the assumption of the regression, the following graph shows (hint look at axis titles)
Regression assumptions that have been met
A consumer researcher was interested in what factors influence people’s fear responses to horror films. She measured gender (0 = female, 1 = male) and how much a person is prone to believe in things that are not real (fantasy proneness) on a scale from 0 to 4 (0 = not at all fantasy prone, 4 = very fantasy prone). Fear responses were measured on a scale from 0 (not at all scared) to 15 (the most scared I have ever felt).
Based on the information from model 2 in the table, what is the likely population value of the parameter describing the relationship between gender and fear?
Somewhere between −3.369 and −0.517
A consumer researcher was interested in what factors influence people’s fear responses to horror films. She measured gender (0 = female, 1 = male) and how much a person is prone to believe in things that are not real (fantasy proneness) on a scale from 0 to 4 (0 = not at all fantasy prone, 4 = very fantasy prone). Fear responses were measured on a scale from 0 (not at all scared) to 15 (the most scared I have ever felt).
How much variance (as a percentage) in fear is shared by gender and fantasy proneness in the population?
13.5%
Recent research has shown that lecturers are among the most stressed workers. A researcher wanted to know exactly what it was about being a lecturer that created this stress and subsequent burnout. She recruited 75 lecturers and administered several questionnaires that measured: Burnout (high score = burnt out), Perceived Control (high score = low perceived control), Coping Ability (high score = low ability to cope with stress), Stress from Teaching (high score = teaching creates a lot of stress for the person), Stress from Research (high score = research creates a lot of stress for the person), and Stress from Providing Pastoral Care (high score = providing pastoral care creates a lot of stress for the person). The outcome of interest was burnout, and Cooper’s (1988) model of stress indicates that perceived control and coping style are important predictors of this variable. The remaining predictors were measured to see the unique contribution of different aspects of a lecturer’s work to their burnout.
Which of the predictor variables does not predict burnout?
Stress from research
Using the information from model 3, how would you interpret the beta value for ‘stress from teaching’?
As stress from teaching increases by one unit, burnout decreases by 0.36 of a unit.
How much variance in burnout does the final model explain for the sample?
80.3%
A psychologist was interested in predicting how depressed people are from the amount of news they watch. Based on the output, do you think the psychologist will end up with a model that can be generalized beyond the sample?
No, because the errors show heteroscedasticity.
For the initial model the F-ratio is 99.587, which is very unlikely to
have happened by chance (p < .001). For the second model the value of F is even higher
(129.498), which is also highly significant (p < .001)
what can you interpret these results as?
An independent t-test is used to test for:
A Differences between means of groups containing different participants when the sampling distribution is normal, the groups have equal variances and data are at least interval.
B Differences between means of groups containing different participants when the data are not normally distributed or have unequal variances.
C Differences between means of groups containing the same participants when the data are normally distributed, have equal variances and data are at least interval.
D Differences between means of groups containing the same participants when the sampling distribution is not normally distributed and the data do not have unequal variances.
A differences between means of groups containing different participants when sampling distribution is normal and the groups have equal variances and data are at least interva
If you use a piared samples t-test
A The same participants take part in both experimental conditions.
BThere ought to be less unsystematic variance compared to the independent t-test.
C Other things being equal, you do not need as many participants as you would for an independent samples design.
D All of these are correct.
D All of these are correct
Which of the following statements about the t distribution is correct?
A It is skewed
BIn small samples it is narrower than the normal distribution
CAs the degrees of freedom increase, the distribution becomes closer to normal
DIt follows an exponential curve
C As the DF increase, the distribution becomes closer to normal
Which of the following sentences is an accurate description of the standard error?
AIt is the same as the standard deviation
BIt is the observed difference between sample means minus the expected difference between population means (if the null hypothesis is true)
CIt is the standard deviation of the sampling distribution of a statistic
D It is the standard deviation squared
CIt is the standard deviation of the sampling distribution of a statistic
A psychologist was interested in whether there was a gender difference in the use of email. She hypothesized that because women are generally better communicators than men, they would spend longer using email than their male counterparts. To test this hypothesis, the researcher sat by the computers in her research methods laboratory and when someone started using email, she noted whether they were male or female and then timed how long they spent using email (in minutes). Based on the output, what should she report?
(NOTE: Check for the assumption of equality of variances).
A Females spent significantly longer using email than males, t(14) = –1.90, p = .079
BFemales and males did not significantly differ in the time spent using email,t(7.18) = –1.90,p= .099
CFemales and males did not significantly differ in the time spent using email, t(7.18) = –1.90, p < .003
DFemales and males did not significantly differ in the time spent using email, t(14) = –1.90, p = .079
BFemales and males did not significantly differ in the time spent using email,t(7.18) = –1.90,p= .099
Other things being equal, compared to the paired-samples (or dependent)t-test, the independentt-test:
A Has more power to find an effect.
BHas the same amount of power, the data are just collected differently.
CHas less power to find an effect.
D Is less robust.
CHas less power to find an effect.
Differences between group means can be characterized as a regression (linear) model if:
AThe outcome variable is categorical.
BThe groups have equal sample size.
CThe experimental groups are represented by a binary variable (i.e. code 1 and 0).
DThe difference between group means cannot be characterized as a llinear model, they must be analyzed as an independent t-test.
The experimental groups are represented by a binary variable (i.e. code 1 and 0)
An experiment was done to look at whether different relaxation techniques could predict sleep quality better than nothing. A sample of 400 participants were randomly allocated to one of four groups: massage, hot bath, reading or nothing. For one month each participant received one of these relaxation techniques for 30 minutes before going to bed each night. A special device was attached to the participant’s wrist that recorded their quality of sleep, providing them with a score out of 100. The outcome was the average quality of sleep score over the course of the month.
Which test could we use to analyse these data?
A Regression only
B ANOVA only
C Regression or ANOVA
D Chi-square
C (multiple) Regression or ANOVA (independent) as regression and ANOVA is the same
Did not mention the hypothesis of prediction or it would be regression
Chi-square only used when you have one categorical predictor and outcome is categorical
A researcher testing the effects of two treatments for anxiety computed a 95% confidence interval for the difference between the mean of treatment 1 and the mean of treatment 2. If this confidence interval includes the value of zero, then she cannot conclude that there is a significant difference in the treatment means: true or false.
TRUE OR FALSE
TRUE
The student welfare office was interested in trying to enhance students’ exam performance by investigating the effects of various interventions. They took five groups of students before their statistics exams and gave them one of five interventions: (1) a control group just sat in a room contemplating the task ahead; (2) the second group had a yoga class to relax them; (3) the third group were told they would get monetary rewards contingent upon the grade they received in the exam; (4) the fourth group were given beta-blockers to calm their nerves; and (5) the fifth group were encouraged to sit around winding each other up about how much revision they had/hadn’t done (a bit like what usually happens). The final percentage obtained in the exam was the dependent variable. Using the critical values for F, how would you report the result in the table below?
AType of intervention did not have a significant effect on levels of exam performance, F(4, 29) = 12.43, p > .05.
BType of intervention had a significant effect on levels of exam performance, F(4, 29) = 12.43, p < .01.
CType of intervention did not have a significant effect on levels of exam performance, F(4, 33) = 12.43, p > .01.
DType of intervention had a significant effect on levels of exam performance, F(4, 33) = 12.43, p < .01.
Type of intervention had a significant effect on levels of exam performance, F(4, 29) = 12.43, p < .01.
Imagine you compare the effectiveness of four different types of stimulant to keep you awake while revising statistics using a one-way ANOVA. The null hypothesis would be that all four treatments have the same effect on the mean time kept awake. How would you interpret the alternative hypothesis?
A. All four stimulants have different effects on the mean time spent awake
B, All stimulants will increase mean time spent awake compared to taking nothing
C. At least two of the stimulants will have different effects on the mean time spent awake
D, None of the above
C. At least two of the stimulants will have different effects on the mean time spent awake
A psychologist was looking at the effects of an intervention on depression levels. Three groups were used: waiting list control, treatment and post-treatment (a group who had had the treatment 6 months before). The SPSS output is below. Based on this output, what should the researcher report?
A. The treatment groups had a significant effect on depression levels,F(2, 45) = 5.11.
B. The treatment groups did not have a significant effect on the change in depression levels,F(2, 35.10) = 5.11.
C. The treatment groups did not have a significant effect on depression levels,F(2, 26.44) = 4.35.
D. The treatment groups had a significant effect on the depression levels,F(2, 26.44) = 4.35.
A. The treatment groups had a significant effect on depression levels,F(2, 45) = 5.11
The student welfare office was interested in trying to enhance students’ exam performance by investigating the effects of various interventions.
They took five groups of students before their statistics exams and gave them one of five interventions: (1) a control group just sat in a room contemplating the task ahead (Control); (2) the second group had a yoga class to relax them (Yoga); (3) the third group were told they would get monetary rewards contingent upon the grade they received in the exam (Bribes); (4) the fourth group were given beta-blockers to calm their nerves (Beta-Blockers); and (5) the fifth group were encouraged to sit around winding each other up about how much revision they had/hadn’t done (You’re all going to fail).
The student welfare office made four predictions: (1) all interventions should be different from the control; (2) yoga, bribery and beta-blockers should lead to higher exam scores than panic; (3) yoga and bribery should have different effects than the beta-blocker drugs; and (4) yoga and bribery should also differ.
Which of the following planned contrasts (with the appropriate group codings) are correct to test these hypotheses?
ANSWER 1
ANSWER 2
ANSWER 3
ANSWER 4
ANSWER 3
What is the correct interpretation of the results?
Participants who read Women are from Bras… reported significantly higher relationship satisfaction than those who read Marie Claire. There was a medium effect size.
Participants who read Women are from Bras… reported significantly lower relationship satisfaction than those who read Marie Claire. There was a large effect size.
Participants who read Women are from Bras… reported significantly lower relationship satisfaction than those who read Marie Claire. There was a small effect size.
Participants who read Women are from Bras… reported lower relationship satisfaction than those who read Marie Claire, however this difference was not significant.
Participants who read Women are from Bras… reported significantly lower relationship satisfaction than those who read Marie Claire. There was a large effect size.
Calcukate the Cohen’s D from it
Is it 0.15, 0.17 or 0;12?
- 20.0180 – 18.4900 divided by smallest SD gives 0.17 (correct answer)
What is the correct interpretation of the data?
Participants who read Women are from Bras… reported significantly higher relationship satisfaction than those who read Andy Field. There was a small effect size.
Participants who read Women are from Bras… reported significantly higher relationship satisfaction than those who read Andy Field. There was a large effect size.
Participants who read Women are from Bras… reported significantly lower relationship satisfaction than those who read Andy Field. There was a medium effect size.
Participants who read Women are from Bras… reported higher relationship satisfaction than those who read Andy Field, however this difference was not significant.
Participants who read Women are from Bras… reported significantly higher relationship satisfaction than those who read Andy Field. There was a small effect size.
What is the eta-sqaured for this one-way ANOVA output?
0.50, 0.61 or 0.70? and how large is the effect? - (2)
between groups/total = 1205.067/1979.467 = 0.6087 = 0.61 = 61% of variance in exam score is accounted for by the model
Large effect
What are these Bonferroni correction post hoc tests show?
The difference between the punish and the reward groups was …
The difference between the punish and indifferent groups was ….
The difference between the reward and indifferent groups was …. - (3)
significant
non significant
significant
A psychologist was interested in the effects of different fear information on children’s beliefs about an animal. Three groups of children were shown a picture of an animal that they had never seen before (a quoll). Then one group was told a negative story (in which the quoll is described as a vicious, disease-ridden bundle of nastiness that eats children’s brains), one group a positive story (in which the quoll is described as a harmless, docile creature who likes nothing more than to be stroked), and a final group weren’t told a story at all. After the story children rated how scared they would be if they met a quoll, on a scale ranging from 1 (not at all scared) to 5 (very scared indeed). To account for the natural anxiousness of each child, a questionnaire measure of trait anxiety was given to the children and used in the analysis
what analysis has been used -
Independent analysis of variance
Repeated-measures analysis of variance
Mixed analysis of variance
Analysis of covariance
Analysis of covariance (ANCOVA)
A psychologist was interested in the effects of different fear information on children’s beliefs about an animal. Three groups of children were shown a picture of an animal that they had never seen before (a quoll). Then one group was told a negative story (in which the quoll is described as a vicious, disease-ridden bundle of nastiness that eats children’s brains), one group a positive story (in which the quoll is described as a harmless, docile creature who likes nothing more than to be stroked), and a final group weren’t told a story at all. After the story children rated how scared they would be if they met a quoll, on a scale ranging from 1 (not at all scared) to 5 (very scared indeed). To account for the natural anxiousness of each child, a questionnaire measure of trait anxiety was given to the children and used in the analysis
what is covariate?
Natural Fear Level
Whic graphs show an interaction effect?
A or B?\
B
In these outputs is there a effect of gender, education or interaction level?
- Is there an effect of gender overall?
No, F(1,54) = 1.63, p = .207
Is there an effect of education level?
Yes, F(2,54) = 147.52, p < .001
Is there an interaction effect?
Yes, F(2,54) = 4.64, p = .014
How to interpret these findings?
- Main effect of Aspirin: Aspirin reduces heart attackes compard to placebo (1)
- Main effect of carotene: Beta carotene reduces heart attack (2)
- Interaction effect: Yes, bigger effect when aspirin and beta carotene taken together (3) - also lines drawn more its an interaction
A music teacher had noticed that some students went to pieces during exams. He wanted to test whether this performance anxiety was different for people playing different instruments. He took groups of guitarists, drummers and pianists (variable = ‘Instru’) and measured their anxiety (variable = ‘Anxiety’) during the exam. He also noted the type of exam they were performing (in the UK, musical instrument exams are known as ‘grades’ and range from 1 to 8). He wanted to see whether the type of instrument played affected performance anxiety when accounting for the grade of the exam. Which of the following statements best reflects what the effect of ‘Instru’ in the output table below tells us?
(Hint: ANCOVA looks at the relationship between an independent and dependent variable, takinginto account the effect of a covariate.)
AThe type of instrument played in the exam did not have a significant effect on the level of anxiety experienced.
BThe type of instrument played in the exam had a significant effect on the level of anxiety experienced.
CThe type of instrument played in the exam had a significant effect on the level of anxiety experienced, even after the effect of the grade of the exam had been accounted for.
DThe type of instrument played in the exam did not have a significant effect on the level of anxiety experienced, even after the effect of the grade of the exam had been accounted for.
CThe type of instrument played in the exam had a significant effect on the level of anxiety experienced, even after the effect of the grade of the exam had been accounted for.
A study was conducted to look at whether caffeine improves productivity at work in different conditions. There were two independent variables. The first independent variable was facebook, whereby participants either had access to facebook or not (2 levels: ‘facebook access’ and ‘no facebook access’). The second independent variable was caffeine, whereby participants either drank a strong caffeinated drink or not (2 levels: ‘caffeinated drink’ and ‘decaffeinated drink’). Different participants took part in each condition. Productivity was recorded at the end of the day on a scale of 0 (I may as well have stayed in bed) to 20 (wow! I got enough work done today to last all year). Looking at the group means in the table below, which of the following statements best describes the data?
AThere is likely to be a significant main effect of caffeine
BThe effect of facebook is relatively unaffected by whether the drink was caffeinated
CThe effect of caffeine is about the same regardless of whether the person had facebook access
DA significant interaction effect is likely to be present between caffeine consumption and facebook access
D - A significant interaction effect is likely to be present between caffeine consumption and facebook access because little difference betweenemail and no email, but for caffeinateddrinks there is
A: There is likely a significant main effect of caeffine = wrong as mean fordecaffeinated drinks is (12.08 + 11.98)/2 =12.03, for caffeinated it is (19.83 + 5.49)/2= 12.66. A main effect of caffeine reflects a difference between these two means(12.03 and 12.66), so it’s unlikely to be significant
B: The effect of facebook is relatively unaffected by whether the drink was caeffinated - This is incorrect: for decaffeinated drinksthere is little difference between FB and no FB, but for caffeinated drinks there is a huge difference
C: The effect of caeffine is about the same regardless if a person has facebook access or not - This is incorrect: when there is no FB, caffeine enhances productivity, but when there is FB it lowers it
A psychologist was interested in the effects of different fear information on children’s beliefs about an animal. Three groups of children were shown a picture of an animal that they had never seen before (a quoll). Then one group was told a negative story (in which the quoll is described as a vicious, disease-ridden bundle of nastiness that eats children’s brains), one group a positive story (in which the quoll is described as a harmless, docile creature who likes nothing more than to be stroked), and a final group weren’t told a story at all. After the story children rated how scared they would be if they met a quoll, on a scale ranging from 1 (not at all scared) to 5 (very scared indeed). To account for the natural anxiousness of each child, a questionnaire measure of trait anxiety was given to the children and used in the analysis. Which of the following statements best reflects what the ‘pairwise comparisons’ tell us?
AFear beliefs were significantly lower after positive information compared to negative information and no information; fear beliefs were not significantly different after negative information compared to no information.
BFear beliefs were significantly higher after negative information compared to positive information and no information, and fear beliefs were not significantly different after positive information compared to no information.
CFear beliefs were significantly higher after negative information compared to positive information; fear beliefs were significantly lower after positive information compared to no information.
DFear beliefs were all about the same after different types of information
Fear beliefs were significantly higher after negative information compared to positive information and no information, and fear beliefs were not significantly different after positive information compared to no information.
An experiment was done to look at whether there is an effect of the number of hours spent practising a musical instrument and gender on the level of musical ability. A sample of 30 (15 men and 15 women) participants who had never learnt to play a musical instrument before were recruited. Participants were randomly allocated to one of three groups that varied in the number of hours they would spend practising every day for 1 year (0 hours, 1 hours, 2 hours). Men and women were divided equally across groups. All participants had a one-hour lesson each week over the course of the year, after which their level of musical skill was measured on a 10-point scale ranging from 0 (you can’t play for toffee) to 10 (‘Are you Mozart reincarnated?’). An ANOVA was conducted on the data from the experiment. Which of the following sentences best describes the pattern of results shown in the graph?
A The graph shows that the relationship between musical skill and time spent practising was the same for men and women
B The graph shows that the relationship between musical skill and time spent practising was different for men and women
C The graph indicates that men and women were most musically skilled when they practised for 2 hours per day
D Women were more musically skilled than men
B The graph shows that the relationship between musical skill and time spent practising was different for men and women
What is the correct interpretation of this data of two-way IV ANOVA?
AOverall, children with inattentive type ADHD had higher concentration levels than children with hyperactive type ADHD. There was no effect of drug on concentration levels.
BThere was no effect of ADHD subtype or of drug on concentration levels, but a significant interaction effect.
C Overall, children with inattentive type ADHD had higher concentration levels than children with hyperactive type ADHD. The effect of drug on concentration levels was dependent on ADHD subtype.
C Overall, children with inattentive type ADHD had higher concentration levels than children with hyperactive type ADHD. The effect of drug on concentration levels was dependent on ADHD subtype.
Using the output from the contrasts and looking at the line graph, which of the following conclusions can be drawn?
AThe amount of caffeine taken had no effect on video game performance.
BHigher doses of caffeine led to increases in performance compared to having no caffeine, but this was not significant for the no caffeine-moderate caffeine comparison.
CTaking a low amount of caffeine led to a significant decrease in video game performance compared to having no caffeine. However, higher doses of caffiene led to an increase in performance, and a high dose of caffeine led to a significant increase in performance compared to having no caffeine.
DTaking a low amount of caffeine led to a significant increase in video game performance compared to having no caffeine. However, higher doses of caffiene led to a decline in performance, and a high dose of caffeine led to a significant decrease in performance compared to having no caffeine.
No caeffine (1)
Low caeffine (2)
Moderate caeffine (3)
High caeffine (4)
D Taking a low amount of caffeine led to a significant increase in video game performance compared to having no caffeine. However, higher doses of caffiene led to a decline in performance, and a high dose of caffeine led to a significant decrease in performance compared to having no caffeine.
Based on SPSS statistics and plot, how would you interpret these results?
A.Although the main effect for time of day was not significant, the significant interaction effect suggests that time of day may still affect video game performance depending on how much caffeine the participant had.
B.When participants took part in the afternoon, caffeine had little effect on their performance.
C.Participants who took part in the morning and had no caffeine performed the worst on the video game.
D. All of the above.
D. All of the above
