Deck 6: The Beast of Bias

Question 1

Which of the following transformations is most useful for correcting skewed data?

A)Log transformation
B)Tangent transformation
C)Arcsine transformation
D)Cosine transformation

Answer 1

A

Question 2

What does the assumption of independence mean?

A)This assumption means that you must use an independent design rather than a repeated-measures design.
B)This assumption means that the residuals in your model are not independent.
C)This assumption means that the errors in your model are not related to each other.
D)This assumption means that none of your independent variables are correlated.

Answer 2

C

Question 3

Which of the following is not an assumption of the general linear model?

A)Linearity
B)Normally distributed residuals
C)Dependence
D)Additivity

Answer 3

C

Question 4

Should you use significance tests of skew and kurtosis in large samples?

A)No, because they are likely to be significant even when skew and kurtosis are not too different from normal.
B)No, because they are likely to be non-significant even when skew and kurtosis are significantly different from normal.
C)Yes, because large samples produce more accurate results.
D)Yes, because large samples add power to the test.

Question 5

The assumption of homogeneity of variance is met when:

A)The variances in different groups are significantly different.
B)The variance across groups is proportional to the means of those groups.
C)The variance is the same as the interquartile range.
D)The variances in different groups are approximately equal.

Question 6

We predict an outcome variable from some kind of model. That model is described by one or more _______ variables and ________ that tell us something about the relationship between the predictor and outcome variable.

A)parameter, outcome variables
B)dependent, predictors
C)outcome, estimates
D)predictor, parameters

Question 7

The Kolmogorov-Smirnov test can be used to test:

A)Whether group means differ.
B)Whether scores are measured at the interval level.
C)Whether scores are normally distributed.
D)Whether group variances are equal.

Question 8

What is the Shapiro-Wilk test primarily used for?

A)To test for heterogeneity of variance
B)To test for homogeneity of variance
C)It is used to test for problematic outliers that could bias the results.
D)To test whether the distribution of scores deviates from a comparable normal distribution.

Question 9

A kurtosis value of -2.89 indicates:

A)There is a mistake in your calculation.
B)A flat and light-tailed distribution
C)A pointy and heavy-tailed distribution
D)A flat and heavy-tailed distribution

Question 10

Imagine you conduct a t-test using IBM SPSS and the output reveals that Levene's test for equality of variance is significant. What should you do?

A)Interpret the figures in the row labelled 'equal variances not assumed'.
B)Interpret the figures in the row labelled 'equal variances assumed'.
C)Conduct a Kruskal-Wallis test instead.
D)Collect more data.

Question 11

A standard score is:

A)A z-score
B)The standard deviation of a particular score
C)The population mean
D)The variance

Question 12

A _____ is a numerical characteristic of a sample and a _____ is a numerical characteristic of a population.

A)distribution, variable
B)statistic, parameter
C)parameter, statistic
D)variable, distribution

Question 13

Which of these variables would be considered not to have met the assumptions of parametric tests based on the normal distribution?

A)Heart rate
B)Gender
C)Reaction time
D)Temperature

Question 14

Is it possible to calculate the skewness of a set of numerical scores?

A)Yes.
B)No.
C)Only if you have a large sample size.
D)Only if you have used an independent-measures design.

Question 15

15,467 people rated how much they liked my textbook on a scale of 1 (it is rubbish) to 10 (I love it). The distribution of scores had a skew of 1.23 (SE = 0.65). Which of the following would be the best way to decide whether the skew is problematic? (Hint: Think about what you know about the central limit theorem.)

A)Use the Kolmogorov-Smirnov test.
B)None of the options, because of the large sample size.
C)See if the z-score is bigger than 1.96 or smaller than -1.96
D)See if the skew is significant at p < .05.

Question 16

What does the graph below indicate about the normality of our data?

A)The histogram reveals that the data have multivariate normality.
B)We cannot infer anything about the normality of our data from this graph.
C)The histogram reveals that the data are more or less normal.
D)The histogram reveals that the data deviate substantially from normal.

Question 17

The test statistics we use to assess a linear model are usually _______ based on the normal distribution.

A)parametric tests
B)non-parametric tests
C)robust
D)not

Question 18

In which of the following situations is the assumption of normality least important?

A)If you want only to estimate the parameters of your model.
B)If you have a small sample.
C)If you want to construct confidence intervals around the parameter estimates of your model.
D)If you want to compute significance tests relating to the parameter estimates of your model.

Question 19

Which of the following symbols does not represent a population parameter?

A) $\sigma$
B) $\mu$
C)SD
D) $\beta$

Question 20

What does the graph below indicate about the normality of our data?

A)We cannot infer anything about the normality of our data from this type of graph.
B)The P-P plot reveals that the data deviate mildly from normal.
C)The P-P plot reveals that the data are normal.
D)The P-P plot reveals that the data deviate substantially from normal.

Question 21

In a small data sample (N = 20), what can we say about a z-score of 2.37?

A)It is significant at p < .05
B)It is significant at p < .001
C)It is significant at p < .01
D)It is non-significant

Question 22

The central limit theorem tells us:

A)In small samples, significance tests can't be trusted.
B)In small samples, the assumptions of parametric tests matter less.
C)If the sample is large enough we can assume homogeneity of variance.
D)If the sample is large enough, the sampling distribution of a parameter will be normal.

Question 23

Looking at the table below, which of the following statements is the most accurate?

A)For the number of hours spent practising, the data are fairly positively skewed.
B)For the number of hours spent practising, there is not an issue with kurtosis.
C)For the level of musical skill, the data are heavily negatively skewed.
D)For the number of hours spent practising, there is an issue with kurtosis.

Question 24

A researcher investigating 'Pygmalion in the classroom' measured teachers' perceptions of male and female students' mathematical abilities. She wanted to test whether teachers perceived males as being better at maths by comparing teachers' mean ratings of 97 male and female students. Based on the output, what action should the researcher take before testing whether the mean teacher perceptions are different for male and female students?

A)Use bootstrapping.
B)Automatically delete extreme scores.
C)Take no action.
D)Log-transform the data.

Question 25

Levene's test can be used to measure:

A)Whether scores are normally distributed.
B)Whether scores are independent.
C)Whether group means are equal.
D)Whether group variances differ.

Question 26

Which of the following is not a way to deal with bias in the observed data?

A)Watsonize the scores.
B)Transform the data.
C)Use bootstrapping.
D)Trim the data.

Question 27

A confidence interval (and p-value) associated with a parameter (e.g., the mean, or a b in regression) assumes:

A)The residuals are normally distributed.
B)The sampling distribution of the parameter estimate is normal.
C)The data are normally distributed.
D)The data have heterogeneity of variance.

Question 28

A researcher investigating 'Pygmalion in the classroom' measured teachers' perceptions of male and female students' mathematical abilities. She collected teacher ratings of 97 male and female students. Based on the output, what can you say about the data? $\quad$$\quad$$\quad$$\quad$$\quad$$\quad$$\quad$$\quad$$\quad$$\quad$$\quad$$\quad$$\quad$$\quad$$\quad$$\text { Test of Homogeneity of Variance }$
$\begin{array}{l}\begin{array}{|ll|r|r|r|r|}\hline &&{\text { Levene }} & & & \\& &\text { Statistic } &{\text { df1 }} & {\text { df2 }} & {\text { Sig. }} \\\hline \text { Teachers perception of } & \text { Based on Mean } & 4.875 & 1 & 95 & .030 \\\text { mathematical ability } & \text { Based on Median } & 4.616 & 1 & 95 & .034 \\& \begin{array}{l}\text { Based on Median and } \\\text { with adjusted df }\end{array} & 4.616 & 1 & 92.069 & .034 \\& \text { Based on trimmed mean } & 4.975 & 1 & 95 & .028 \\\hline\end{array}\end{array}$

A)The variances of ratings were significantly different for males and females.
B)Homogeneity of variance can be assumed.
C)Mean perceptions of male and female students were significantly different.
D)Mean and median ratings were similar for males and females

Question 29

To get a sample of a certain size, scores are taken one-by-one from the observed data and each time replaced. The parameter of interest (e.g., the mean or b in regression) is computed within the sample. This process is repeated numerous times. The resulting parameter estimates are used to compute a confidence interval. The process I am describing is:

A)Significance testing
B)Bootstrapping
C)Sampling
D)The standard error

Question 30

When it is not necessary to use Levene's test?

A)When you have unequal group sizes.
B)When you have a small sample.
C)When you are conducting a two-tailed test.
D)When you have equal group sizes.

Question 31

When we talk about the assumption of normality, what do we mean?

A)The sampling distribution of the parameter estimate must be normally distributed.
B)Observed scores must be normally distributed.
C)The Kolmogorov-Smirnov test must be significant.
D)Residuals should not have a funnel shape.

Question 32

Looking at the table below, which of the following statements is correct?

A)Levene's test was non-significant, F(1, 118) = 0.01, p = .93, indicating that the assumption of homogenity of variance had been met.
B)Levene's test was significant, F(1, 118) = 0.93, p = .007, indicating that the assumption of homogenity of variance had been met.
C)Levene's test was non-significant, F(1, 118) = 0.01, p = .93, indicating that the assumption of homogenity of variance had been violated.
D)Levene's test was significant, F(1, 118) = 0.01, p = .93, indicating that the assumption of homogenity of variance had been violated.