stats
Ann conducts a study with four groups, and finds that the estimated population variances for the groups are S2 = 12.8, 16.3, 15.1, and 19.9. What is the within- groups estimate of the population variance?
(12.8 + 16.3 + 15.1 + 19.9) / 4 = 16.03.
The mean of a distribution of differences between means is equal to
0
In the formula, Σ(M-GM) 2/dfBetween, "GM" is the
grand mean.
An interaction effect can be identified by
looking at the pattern of cell mean differences
One of the assumptions for the t test for independent means is that
the variances of the populations are the same.
If the cell means of a factorial design study are graphed, and the pattern of bars in one section of the graph is different from the pattern in the other section, the pattern indicates that
there is an interaction effect.
All of the following are advantages of factorial designs EXCEPT
they automatically provide an indicator of effect size for the measured variable.
Joe conducts an analysis of variance. If he rejected the null hypothesis, the most likely F value of the following choices is:
3.57
In an analysis of variance with a between-groups population variance estimate of S2 Between = 30 and a within-groups estimate of S2 Within = 25, the F ratio is
30 / 25 = 1.20
If an experimenter "crosses Intelligence with Attractiveness" in a factorial design in which intelligence has three levels (high, medium, and low) and attractiveness has two levels (high and low), the study will be a
3X2 factorial design
In which of the following situations would a t test for independent means be conducted?
a comparison of scores of participants in a memory study where one group is assigned to learn the words in alphabetical order and another group is assigned to learn the words in order of length of the word
The overall test of the significance of the difference among groups in an analysis of variance is called
an omnibus test.
An interaction effect in a two-way factorial design
is the effect of one variable that divides the groups, ignoring the influence of the other variable that divides the groups.
An analysis of variance (ANOVA) differs from a t test for independent means in that an analysis of variance
is usually used to compare two groups, while a t test for independent means can be used to compare two or more groups.
In an analysis of variance, you reject the null hypothesis when the F ratio is
much larger than 1.
A two-way factorial design
one interaction and two main effects.
One of the assumptions of analysis of variance (ANOVA) is that
population sizes are approximately equal.
A "distribution of differences between means" can be thought of as a distribution of
the differences obtained when a sample mean from one population is repeatedly subtracted from a sample mean from another population.
When conducting a t test for independent means, a typical research hypothesis might be
the mean of Population 1 is greater than the mean of Population 2.
If an experimenter conducts a t test for independent means and rejects the null hypothesis providing support for the research hypothesis, the correct interpretation is that
the mean of one sample is so far from the mean of the other sample that the samples must come from populations with different means.
When carrying out a t test for independent means
the null hypothesis is rejected if the computed t score is more extreme than the cutoff t score.
"S2 Within" also called "MSwithin" equals
the population variance estimate based on the variation within each of the groups.
When using a t table, the degrees of freedom used for a t test for independent means is
the sum of the degrees of freedom for the two samples.
