Quiz Over Chapter 9 of the
Statistical Inferences Concerning
Bivariate Correlation Coefficients
- (T/F) The notions of "sample" & "population"
are irrelevant when dealing inferentially with correlation.
- (T/F) Researchers usually build CIs around their sample values
of r rather than deal with null hypotheses.
Statistical Tests Involving a Single Correlation Coefficient
- Do researchers very often make inferences based upon a single correlation
that comes from a single sample?
- (T/F) The "direction" of the researcher's inference
moves from the population to the sample.
- What pinpoint number is usually in the null hypothesis?
- Does the typical researcher indicate, either in words or symbols,
the null hypothesis that he/she tested?
- Look at Excerpt 9.5. Express in symbols the null hypothesis that
most likely was tested . . . and rejected.
- (T/F) The sample value of r, computed from the collected data,
typically serves as the calculated value.
- If r is compared against a critical value, Ho will be
rejected if the former is __ (smaller/larger) than the latter.
- (Yes/No) Do researchers usually include the critical value
in their research report?
- Can tests on correlation coefficients be conducted in a one-tailed
- (T/F) Inferential tests can be done on Pearson & Spearman
correlations but not on biserial or point-biserial correlations.
- (T/F) If a researcher tests a correlation coefficient but does not
indicate the type of correlation, you should guess that it was
Tests on Many Correlation Coefficients (Each of Which is Treated Separately)
- Do researchers very often set up and test more than 1 correlational
null hypothesis in the same study?
- (T/F) If two or more rs are tested in the same study, the null
hypothesis will likely be the same in all tests.
- Look at Excerpt 9.12. If there had been 8 coping strategies rather than 6,
how many null hypotheses would have been tested in this excerpt's table?
- If a researcher computes all possible bivariate correlations among
5 variables, what would the Bonferroni-corrected alpha level be if
the researcher wants to keep Type I error risk at 5% for the set of
tests being conducted?
Tests on Reliability and Validity Coefficients
- Can a researcher set up and test a null hypothesis concerning a reliability
or validity coefficient?
- Look at Excerpt 9.16. In order for the GMAT to have accounted for 80% of the variability
among the final MBA GPAs, how high would the correlation have needed to be?
Statistically Comparing Two Correlation Coefficients
- (T/F) If a researcher inferentially compares two correlations, there
might be 1 group involved or 2 groups.
- If the correlation between height and weight for a sample of men
is compared with the correlation between height and weight in a sample
of women, how many inferences would be made to the 2 populations?
The Use of Confidence Intervals Around Correlation Coefficients
- Which is more popular: setting up and testing a correlational Ho
or building a CI around the sample value of r?
- If a researcher discovered that r = .13 and that CI.95 = .06 to .20,
would he/she claim p < .05?
- Can a researcher's r turn out to be close to zero and yet still be
significantly different from zero?
- Which of these would constitute better evidence that there is, in
the population, a strong relationship between the two variables
that a researcher has measured and found to be significantly related:
- p < .0001
- r2 = .70
- n = 10,000
- Do many researchers concern themselves with the notions of "power"
and "effect size" when testing their correlations?
- If a correlation coefficient is found to be statistically significant,
and if a power analysis is then conducted, is it fair to assume that
a "strong" relationship exists if the statistical test is shown to have
had "high" power?
- Do many folks concerns themselves with the notions of "linearity"
& "homoscedasticity" when testing their correlations?
- Which of these terms is a fairly good synonym for the term "homoscedasticity"?
- Equal means
- Equal variances
- Equal correlations
- Equal variables
- (T/F) If a bivariate correlation coefficient has been found
to be statistically significant with p<.05 (or better yet, with p<.01),
the researcher can legitimately infer that a causal relationship exists
between the 2 variables.
- Attenuation causes r to ____ (underestimate/overestimate) r,
the magnitude of the correlation in the population.
- What causes attenuation?
- an n that's too small
- measurement errors
- inadequate statistical power
- Who is connected to the procedure that goes by the name "r-to-z transformation"?
These Questions are Supposed to be a Bit More Challenging
- Look at Excerpt 9.18. If the value of the second r had
turned out equal to .26 (rather than equal to -.22), how many of the
3 results would have been significant?
- It's a good guess to think that the study from which Excerpt 9.3
involved a ___ (small/large) sample size.
- In Excerpt 9.4, an r of .13 turned out to be nonsignificant. In
Excerpt 9.12, an r of .14 turned
out to be significant (with p < .001). Assuming that both of these
rs were of the Pearson product-moment variety, how could the results be so
different when the sample correlation coefficients are almost identical?
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