Question

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

Select one:

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) = 0.01, p = .93, indicating that the assumption of homogenity of variance had been violated.

c. Levene’s test was significant, F(1, 118) = 0.93, p = .007, indicating that the assumption of homogenity of variance had been met.

d. Levene’s test was non-significant, F(1, 118) = 0.01, p = .93, indicating that the assumption of homogenity of variance had been violated.

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

Select one:

a. When you are conducting a two-tailed test.

b. When you have equal group sizes.

c. When you have a small sample.

d. When you have unequal group sizes.

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.

Select one:

a. dependent, predictors

b. outcome, estimates

c. predictor, parameters

d. parameter, outcome variables

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:

Select one:

a. Significance testing

b. Bootstrapping

c. Sampling

d. The standard error

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

Select one:

a. It is non-significant

b. It is significant at p ≤ .001

c. It is significant at p ≤ .05

d. It is significant at p ≤ .01

The central limit theorem tells us:

Select one:

a. In small samples, the assumptions of parametric tests matter less.

b. If the sample is large enough we can assume homogeneity of variance.

c. If the sample is large enough, the sampling distribution of a parameter will be normal.

d. In small samples, significance tests can’t be trusted.

Answer 1