Question 1 of 25 |
1.0 Points |
Effect size is a measure of:
A.the difference
between individual members of a sample |
| B.the extent to which
two populations overlap |
| C.the extent to which
two populations do not overlap |
| D.the statistical
significance of a research study |
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Question 2 of 25 |
1.0 Points |
Which of the following is NOT a correct statement about effect size of a study
finding:
A.It provides much
information about statistical significance. |
| B.It is a standardized
measure of lack of overlap between populations. |
| C.It increases with
greater differences between means. |
| D.It can be converted
to a standardized effect size. |
|
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Question 3 of 25 |
1.0 Points |
According to Cohen’s conventions, for research that compares means, a large
effect size in which only about 53% of the populations of individuals overlap
would be:
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Question 4 of 25 |
1.0 Points |
Some IQ tests have a standard deviation of 16 points. If an experimental
procedure produced an increase of 3.2 IQ points, the effect size would represent
a __________ effect size.
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Question 5 of 25 |
1.0 Points |
A standard verbal memory test is known to have a standard deviation of 10
points. If an experimental procedure produced an increase of 8 points, the
effect size would represent a __________ effect size.
A.small |
| B.medium |
| C.large |
| D.unable to determine
without additional information |
|
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Question 6 of 25 |
1.0 Points |
In what way is effect size most comparable to a Z score?
A.It can range from
1 to +1 |
| B.It provides a
direct indication of statistical significance |
| C.It provides a
standard for comparison for results across studies, even studies using different
measures |
| D.All of the
above |
|
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Question 7 of 25 |
1.0 Points |
Cohen has proposed some effect-size conventions based on the effects observed in
psychology research in general because:
A.researchers
frequently need to decide whether the effect size that they have found allows
them to reject the null hypothesis |
| B.it is usually
difficult to know how big an effect to expect from a given
experiment |
| C.Cohen originally
developed the relevant scales |
| D.they are more
accurate than figuring a minimum meaningful difference |
|
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Question 8 of 25 |
1.0 Points |
The effect size conventions proposed by Cohen are useful to researchers for:
A.predicting the
value of the measured variable to use for the experimental
condition |
| B.evaluating research
results to determine if they are statistically significant |
| C.predicting the
effect of a study on various populations |
| D.determining the
power of a planned study |
|
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Question 9 of 25 |
1.0 Points |
A statistical method for combining effect sizes from different studies is known
as:
A.combination
analysis |
| B.comparison
analysis |
| C.multivariate
analysis |
|
D.meta-analysis |
|
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Question 10 of 25 |
1.0 Points |
Reviews of a collection of studies on a particular topic that use meta-analyses
represent an alternative to traditional __________ articles. These traditional
articles describe and evaluate each study and then attempt to draw some overall
conclusion.
A.general educational
method |
| B.computer-assisted
research |
| C.engagement goal
setting |
| D.narrative
literature review |
|
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Question 11 of 25 |
1.0 Points |
It is useful to understand statistical power for which of the following reasons?
A.Determining the
number of participants to use in an experiment |
| B.Making sense of
findings in research articles |
| C.Understanding the
implications of a study that is not statistically significant |
| D.All of the
above |
|
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Question 12 of 25 |
1.0 Points |
If statistical power for a given research study is .40, one can say that:
“Assuming the researcher’s prediction is correct, the researcher has a
__________ chance of attaining statistically significant results.”
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Question 13 of 25 |
1.0 Points |
When a study has only a small chance of being significant even if the research
hypothesis is true, the study is said to have:
A.low
power |
| B.low
probability |
| C.low market
value |
| D.low sample
size |
|
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Question 14 of 25 |
1.0 Points |
Standard power tables are useful for:
A.directly
determining the power of an experiment |
| B.determining the
predicted score (but not the variance) for the group exposed to the experimental
manipulation |
| C.determining the
predicted effect size of a proposed experiment |
| D.determining the
probability of falsely accepting the research hypothesis |
|
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Question 15 of 25 |
1.0 Points |
Effect size is one of the two major factors that contribute to power. Another
factor is:
A.the sample’s
standard deviation |
| B.the minimum
meaningful difference |
| C.the sample
size |
| D.the mean of the
known population |
|
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Question 16 of 25 |
1.0 Points |
A researcher may not be able to change the effect size of a planned study to
increase power. Another aspect of a planned study that the researcher can
usually change to increase power is:
A.the sample
size |
| B.the beta
level |
| C.the population
parameters |
| D.the sample
mean |
|
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Question 17 of 25 |
1.0 Points |
In actual practice, the usual reason for determining power before conducting a
study is to:
A.eliminate the
possibility that a mistake may occur |
| B.ensure that
regardless of whether the research hypothesis is true, the experiment will yield
a significant result |
| C.determine the
number of participants needed to have a reasonable chance of getting a
significant result if the research hypothesis is true |
| D.recognize the
likelihood that the experiment will need to be repeated |
|
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Question 18 of 25 |
1.0 Points |
What effect will using a one-tailed test over a two-tailed test have on power
(presuming the true population difference is in the expected direction)?
A.it will increase
power |
| B.it will have no
effect on power |
| C.it will decrease
power |
| D.power cannot be
calculated if a one-tailed test is used |
|
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Question 19 of 25 |
1.0 Points |
Using a two-tailed test makes it __________ to get significance on any one tail.
Thus, keeping everything else the same, power __________ with a two-tailed test
than with a one-tailed test.
A.easier;
more |
| B.harder;
less |
| C.easier;
less |
| D.harder;
more |
|
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Question 20 of 25 |
1.0 Points |
If the research hypothesis is true, but the study has a low level of power:
A.there is a high
probability that the study will have a significant result |
| B.the probability of
getting a significant result is low |
| C.the null hypothesis
will almost certainly be rejected |
| D.the significance
level selected is probably too lenient (for example, .10 instead of
.05) |
|
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Question 21 of 25 |
1.0 Points |
Practical significance is a combination of statistical significance and:
A.effect
size |
| B.the level of
measurement (whether it is equal interval or ordinal) |
| C.the population
parameters |
| D.the amount over or
under that level that the sample scored |
|
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Question 22 of 25 |
1.0 Points |
In statistics, we cannot state that the research hypothesis is ever definitely
false. However, if one fails to reject the null hypothesis in a study with a
high level of power, this allows us to:
A.suspect that the
research hypothesis may still be true |
| B.conclude that the
research hypothesis is most likely false |
| C.make no statements
about the research hypothesis |
| D.reject the notion
that the effect size has anything to do with statistical
significance |
|
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Question 23 of 25 |
1.0 Points |
What is the most likely explanation for why a study with a very small effect
size came out significant?
A.the study had a
large sample size |
| B.the study had a
large population standard deviation |
| C.the researcher used
an insensitive hypothesis-testing procedure |
| D.the researcher used
a two-tailed test |
|
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Question 24 of 25 |
1.0 Points |
When judging a study’s results, there are two important questions. They are:
A.How large is the
power and how competent are the researchers? |
| B.How stringent is
the significance level and how small is the effect size? |
| C.Is the result
statistically significant and is the effect size large enough for the results to
be meaningful? |
| D.Is the study
replicable and can we draw conclusions despite not having attained statistical
significance? |
|
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Question 25 of 25 |
1.0 Points |
If the results of a study are not statistically significant and the sample size
is large, then:
A.the result is very
important |
| B.the result proves
the null hypothesis |
| C.the research
hypothesis is probably false |
| D.the result proves
the research hypothesis |
|
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