Manova, reflection, and post test
End-of-course Stats Quiz
1. *Name
2. * LO#1: Review research methods and basic statistics as they relate to planning, conducting, and interpreting inferential statistics.
In experiments the independent variable is manipulated to determine:
|
|
a. effects on the individual participantsof students |
|
|
b. effect on the dependent variable |
|
|
c. effects of certain stimuli |
|
|
d. relation to other variables |
3. * LO#2: Develop appropriate null and alternative hypotheses given a research question.
A null hypothesis:
|
|
a. states that the experimental treatment will have an effect |
|
|
b. is rarely used in experiments |
|
|
c. predicts that the experimental treatment will have no effect |
|
|
d. none of the above |
4. * LO#3: Calculate and interpret descriptive statistical analysis.
Which of the following is least affected by outliers?
|
|
a. The range |
|
|
b. The mean |
|
|
c. The median |
|
|
d. The standard deviation |
5. * LO#3: Calculate and interpret descriptive statistical analysis.
What is an outlier?
|
|
a. A set of data outside the data file |
|
|
b. A single score that is very different from others |
|
|
c. A score derived from a participant who has lied |
|
|
d. A variable that cannot be quantified |
6. *LO#4: Create and interpret visual displays of data.
A scatterplot shows:
|
|
a. the frequency with which values appear in the data |
|
|
b. the average value of groups of data |
|
|
c. scores on one variable plotted against scores on a second variable |
|
|
d. the proportion of data falling into different categories |
7. *LO#5: Apply appropriate statistical tests based on level of measurement.
Ordinal level data are characterized by:
|
|
a. data that can be meaningfully arranged by order of magnitude |
|
|
b. equal intervals between each adjacent score |
|
|
c. a fixed zero |
|
|
d. none of the above |
8. *LO#5: Apply appropriate statistical tests based on level of measurement.
Logical regression is used when you want to:
|
|
a. predict a continuous variable from dichotomous ones |
|
|
b. predict a dichotomous variable from a continuous or dichotomous variables |
|
|
c. predict any categorical variable from categorical variables |
|
|
d. predict a continuous variable from dichotomuos or continuous variables |
9. *LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
What does a significant test tell us?
|
|
a. There is an important effect |
|
|
b. The null hypothesis is false |
|
|
c. There is an effect in the population of sufficient magnitude to be scientifically interesting |
|
|
d. All of the above |
10. * LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
How much variance has been explained by a correlation of .9?
|
|
a. 81% |
|
|
b. 18% |
|
|
c. 9% |
|
|
d. None of the above |
11. * LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
When interpreting a correlation coefficient, it is important to look at:
|
|
a. the significance of the correlation coefficient |
|
|
b. the magnitude of the correlation coefficient |
|
|
c. The +/- sign of the correlation coefficient |
|
|
d. all of the above |
12. * LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
What is meant by a ‘spurious’ relationship between two variables?
|
|
a. One that is so illogical it cannot possibly be true |
|
|
b. An apparent relationship that is so curious it demands further attention |
|
|
c. A relationship that appears to be true because each variable is related to a third one |
|
|
d. One that produces a perfect negative correlation on a scatter diagram |
13. * LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
Correlational studies allow the researcher to:
|
|
a. test for differences between two variables |
|
|
b. Predict the effect of one variable upon another |
|
|
c. make casual inferences about the relationship between two variables |
|
|
d. identify the relationship between two variables |
14. * LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
R2 is:
|
|
a. the percentage of variance in the predictor accounted for by the outcome variable |
|
|
b. The proportion of variance in the outcome accounted for by the predictor variable or variables |
|
|
c. The proportion of variance in the predictor accounted for by the outcome variable |
|
|
d. The percentage of variance in the outcome accounted for by the predictor variable or variables |
15. * LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
What is multicollinearity?
|
|
a. When predictor variables correlate very highly with each other |
|
|
b. When predictor variables have a linear relationship with the outcome variable |
|
|
c. When predictor variables are correlated with variables not in the regression model |
|
|
d. When predictor variables are independent |
16. * LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
Which of the following is NOT a research question which could be investigated with logical regression?
|
|
a. Prediction of group membership |
|
|
b. Strength of association between criterion and predictors |
|
|
c. Differences between groups |
|
|
d. Interaction between predictors |
17. * LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
A researcher was interested in stress levels of lecturers during lecturers. She took the same group of 8 lecturers and measured their anxiety (out of 15) during a normal lecture and again in a lecture in which she had paid students to be disruptive and misbehave. Based on the SPSS output how would you interpret these results?
|
|
a. Anxiety levels were significantly lower in lectures in which students misbehaved |
|
|
b. There were no significant differences between anxiety levels in normal lectures and in those in which students misbehaved |
|
|
c. Anxiety levels were significantly higher in lectures in which students misbehaved |
|
|
d. We can’t tell any of the above from the output given |
18. * LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
An independent t-test is used to test for:
|
|
a. differences between means of groups containing different people when the data are normally distributed, have equal variances and data are at least interval |
|
|
b. differences between means of groups containing different people when the data are not normally distributed or have unequal variances |
|
|
c. differences between means of groups containing the same people when the data are normally distributed, have equal variances and data are at least interval |
|
|
d. differences between means of groups containing different people when the data are not normally distributed or have unequal variances |
19. * LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
A dependent t-test is used to test for:
|
|
a. differences between means of groups containing different people when the data are normally distributed, have equal variances and data are at least interval |
|
|
b. differences between means of groups containing different people when the data are not normally distributed or have unequal variances |
|
|
c. differences between means of groups containing the same people when the data are normally distributed, have equal variances and data are at least interval |
|
|
d. differences between means of groups containing different people when the data are not normally distributed or have unequal variances |
20. * LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
Which of the following is an Indpendent Measures design?
|
|
a. All participants perform in all conditions |
|
|
b. each participant is tested twice, once in each condition |
|
|
c. different participants perform in each condition |
|
|
d. none of the above |
21. LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
A Repeated Measures design would be appropriate for which of the following situations?
|
|
a. A researcher would like to study the effect of alcohol on reaction time |
|
|
b. A researcher would like to compare individuals from at least two populations |
|
|
c. The effect of a new treatment is studied in a small group of individuals with a rare condition |
|
|
d. a and b |
22. * LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
Consider this table:
How many people took part in this experiment?
23. LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
Consider this table:
Did the independent variable have an effect?
|
|
a. Yes, there is a statistically significant effect |
|
|
b. Yes, but the effect was not statistically significant |
|
|
c. Not enough information |
|
|
d. No, it is not statistically indicant |
24. LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
Consider this table:
What was the dependent variable?
|
|
a. Variances |
|
|
b. Number correct |
|
|
c. Equal assumptions |
|
|
d. Not given in table |
25. LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
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 change in depression levels over the time of the treatment were recorded (although bear in mind only the treatment group actually got any treatment during this time). The SPSS output is below; based on this output what should the researcher conclude:
|
|
a. The treatment groups did not have a significant effect on the change in depression levels, F(2, 26.44) = 4.35 |
|
|
b. The treatment groups had a significantly effect on the change in depression levels, F(2, 35.10) = 5.11 |
|
|
c. The treatment groups had a significantly effect on the change in depression levels, F(2, 45) = 5.11 |
|
|
d. The treatment groups did not have a significant effect on the change in depression levels, F(2, 45) = 5.11 |
26.Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
The total variation in response, assuming no bias, is due to error (unexplained variation) plus differences due to treatments (known variation). If known variation is large compared to unknown variation, which of the following conclusions can be drawn?
|
|
a. There is no difference in response due to treatments |
|
|
b. There is a difference in response due to treatments |
|
|
c. The treatments are not comparable |
|
|
d. The cause of the response is due to something other than treatments |
27. LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
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 controlling for the grade of the exam, what analysis should he use?
|
|
a. Analysis of Covariance |
|
|
b. Independent Analysis of Variance |
|
|
c. Repeated Measures Analysis of Variance |
|
|
d. Mixed Analysis of Variance |
28. LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
In a factorial design, with two factors, if the effect of one factor appears to depend on the levels of the second facto, this is called:
|
|
a. a main effect |
|
|
b. An interaction effect |
|
|
c. A factorial effect |
|
|
d. An error |
29. LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
Consider the following chart; what is show in the graph?
|
|
a. There is an interaction and no main effects |
|
|
b. There is one main effect and no interaction |
|
|
c. There are two main effects and no interaction |
|
|
d. There is an interaction and two main effects |
30. LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
A nutritionist conducted an experiment on memory for dreams. She wanted to test whether it really was true that eating cheese before going to bed made you have bad dreams. Over three nights, the nutritionist fed people different foods before bed. On one night they had nothing to eat, a second night they had a big plate of cheese, and the third night they had another dairy product, Milk, before bed. All people were given all foods at some point over the three nights. The nutritionist measured heart rate during dreams as an index of distress. How should these data be analyzed?
|
|
a. One-way independent ANOVA |
|
|
b. One-way repeated measures ANOVA |
|
|
c. Three-way repeated measures ANOVA |
|
|
d. Three-way independent ANOVA |
31. LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
A nutritionist conducted an experiment on memory for dreams. She wanted to test whether it really was true that eating cheese before going to bed made you have bad dreams. Over three nights, the nutritionist fed people different foods before bed. On one night they had nothing to eat, a second night they had a big plate of cheese, and the third night they had another dairy product, Milk, before bed. All people were given all foods at some point over the three nights. The nutritionist measured heart rate during dreams as an index of distress. Which statement is the correct way to report these results?
|
|
a. There was a significant effect of food on the distress caused by memories, F(2, 76) = 2.27 |
|
|
b. There was no significant effect of food on the distress caused by memories, F(2, 76) = 2.27 |
|
|
c. There was a significant effect of food on the distress caused by memories, F(1.97, 74.90) = 2.27 |
|
|
d. There was no significant effect of food on the distress caused by memories, F(1.97, 74.90) = 2.27 |
32. LO#6: Calculate, interpret, and understand the appropriate use of inferential statistical analysis.
What is NOT an advantage of MANOVA?
|
|
a. Researchers can examine several dependent measures at once |
|
|
b. It controls the overall error-rate, ensuring the risk of Type I errors is not increased |
|
|
c. It can detect dependent measures which are not theoretically sound |
|
|
d. It may be able to detect combined differences in the dependent variables not found by examining the dependent variables independently of one another |
33. LO#7: Correlate how population, sampling, and statistical power are related to inferential analysis.
A Type I error is when:
|
|
a. we conclude that there is a meaningful effect in the population when in fact there is not |
|
|
b. we conclude that there is not a meaningful effect in the population when in fact there is |
|
|
c. we conclude that the test statistic is significant when in fact it is not |
|
|
d. the data we have typed into SPSS is different to the data collected |
34. LO#7: Correlate how population, sampling, and statistical power are related to inferential analysis.
If we calculated an effect size and found it was r = .42 which expression would best describe the size of effect?
|
|
a. Small |
|
|
b. Small-to-medium |
|
|
c. Large |
|
|
d. Medium-to-large |
35. LO#7: Correlate how population, sampling, and statistical power are related to inferential analysis.
Which of these statements about statistical power is not true?
|
|
a. Power is the ability of a test to detect an effect |
|
|
b. We can use power to determine how big a sample is required to detect an effect of a certain size |
|
|
c. Power is linked to the probability of making a type I error |
|
|
d. All of the above are true |
36. LO#7: Correlate how population, sampling, and statistical power are related to inferential analysis.
A Bonferroni correction is when:
|
|
a. You apply a criterion for significance based on the usual criterion for significance (0.05) divided by the number of tests performed |
|
|
b. You divide the F-ratio by the number of tests performed |
|
|
c. The degrees of freedom are corrected to make the F-ratio less significant |
|
|
d. The error in the model is adjusted for the number of tests performed |
37. LO#8: Analyze and apply the assumptions required for valid inferential tests.
Which of the following are assumputions unerlying the use of parametric tests (based on the normal distribution)?
|
|
a. The data should be normally distributed |
|
|
b. The samples being tested should have approximately equal variances |
|
|
c. Your data should be at least interval level |
|
|
d. All of the above |
38. LO#8: Analyze and apply the assumptions required for valid inferential tests.
The assumption of homogeneity of variance is met when:
|
|
a. the variance in one group is twice as big as that of a different group |
|
|
b. variances in different groups are approximately equal |
|
|
c. the variance across groups is proportional to the means of those groups |
|
|
d. the variance is the same as the inter-quartile range |
39. LO#8: Analyze and apply the assumptions required for valid inferential tests.
If a distribution is multimodal, what does this mean?
|
|
a. It will not be a normal distribution |
|
|
b. The data has been entered incorrectly |
|
|
c. It will be a normal distribution |
|
|
d. It will have to be checked with a Levene’s test |
40. LO#8: Analyze and apply the assumptions required for valid inferential tests.
The following graph shows:
|
|
a. Heterscedasticity |
|
|
b. Non-linearity |
|
|
c. Heteroscedasticity and non-linearity |
|
|
d. Regression assumptions that have been met |
41. LO#8: Analyze and apply the assumptions required for valid inferential tests.
Levene’s tests whether:
|
|
a. Data are normally-distributed |
|
|
b. The variances in different groups are equal |
|
|
c. The assumption of sphericity has been met |
|
|
d. Group means differ |
42. LO#8: Analyze and apply the assumptions required for valid inferential tests.
A music teacher had noticed that some students were very anxious 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 and measured their 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 controlling the grade ofthe exam. The first part of the SPSS output is below. What does this part of the output tell us?
|
|
a. The type of instrument played did not have a significant effect on anxiety |
|
|
b. The grade of exam taken did not have a significant effect on anxiety |
|
|
c. The variances of anxiety scores were roughly the same in the different groups of musicians |
|
|
d. The variances of anxiety scores were different in the different groups of musicians |
43. LO#8: Analyze and apply the assumptions required for valid inferential tests.
Sphericity is:
|
|
a. an assumption that means the data distribution must be round |
|
|
b. the critical value area of the graph is round |
|
|
c. a way of rounding up the decimal points |
|
|
d. an assumption that means the data in each level should uncorrelated |
44. LO#9: Evaluate the difference between parametric and non-parametric data analysis and how to apply the correct statistical procedure.
Another term for non-parametric tests is:
|
|
a. non-normal tests |
|
|
b. data-free tests |
|
|
c. non-continuous tests |
|
|
d. distribution-free tests |
45. LO#9: Evaluate the difference between parametric and non-parametric data analysis and how to apply the correct statistical procedure.
What might be a reason to choose parametric tests over non-parametric tests even if some parametric assumptions are violated?
|
|
a. Non-parametric tests are harder to carry out |
|
|
b. Non-parametric tests are less powerful |
|
|
c. Parametric tests are less robust |
|
|
d. Parametric tests are more likely to show causal effects |
46. LO#9: Evaluate the difference between parametric and non-parametric data analysis and how to apply the correct statistical procedure.
In SPSS, what is the ‘Data Viewer’?
|
|
a. A table summarizing the frequencies of data for one variable |
|
|
b. A spreadsheet into which data can be entered |
|
|
c. A dialog box that allows you to choose a statistical test |
|
|
d. A screen in which variables can be defined and labeled |
47. LO#9: Evaluate the difference between parametric and non-parametric data analysis and how to apply the correct statistical procedure.
How is a variable name different from a variable label?
|
|
a. It is shorter and less detailed |
|
|
b. It is longer and more detailed |
|
|
c. It is abstract and unspecific |
|
|
d. It refers to codes rather than variables |
48. LO#9: Evaluate the difference between parametric and non-parametric data analysis and how to apply the correct statistical procedure.
What does the operation ‘Recode Into Different Variables’ do to the data?
|
|
a. Replaces missing data with some random scores |
|
|
b. Reverses the position of the independent and dependent variable on a graph |
|
|
c. Redistributes a range of values into a new set of categories and creates a new variable |
|
|
d. Represents the data in the form of a pie chart |
49. LO#9: Evaluate the difference between parametric and non-parametric data analysis and how to apply the correct statistical procedure.
To generate a correlation coefficient between two variables with ordinal data, which set of instructions should you give SPSS?
|
|
a. Analyze → Crosstabs → Descriptive Statistics → Spearman → ok |
|
|
b. Graphs → Frequencies → [select variables]→ Spearman → ok |
|
|
c. Analyze → Compare Means → Anova table → First layer → Spearman → ok |
|
|
d. Analyze → Correlate → Bivariate →[select variables] → Spearman → ok |
50. LO#9: Evaluate the difference between parametric and non-parametric data analysis and how to apply the correct statistical procedure.
What is the correct way to record non-numerical values?
|
|
a. You can’t, SPSS only uses numbers |
|
|
b. Define the variable as “string” |
|
|
c. Recode all the values as numbers |
|
|
d. Define the variable as “date” |
51. LO#9: Evaluate the difference between parametric and non-parametric data analysis and how to apply the correct statistical procedure.
How would you request a logistic regression analysis in SPSS?
|
|
a. Analyze – non-parametric – binomial |
|
|
b. Analyze – regression- ordinal |
|
|
c. Analyze – Regression – Binary Logistic |
|
|
d. Analyze – loglinear – general |