RUTGERS QUANTITATIVE METHODS IN PSYCHOLOGY EXAM 2 (KILIANSKI) EXAM QUESTIONS WITH COMPLETE SOLUTIONS VERIFIED AND GRADED A+ 100% GUARANTEED PASS
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RUTGERS QUANTITATIVE
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RUTGERS QUANTITATIVE
RUTGERS QUANTITATIVE METHODS IN PSYCHOLOGY EXAM 2 (KILIANSKI) EXAM QUESTIONS WITH COMPLETE SOLUTIONS VERIFIED AND GRADED A+ 100% GUARANTEED PASS
Pearson's r
finds correlation between IV and DV
values of r
values range from -1 to 1; 0 means no correlation
Pearson's r null hypothesis
Ho: r(...
RUTGERS QUANTITATIVE METHODS IN PSYCHOLOGY EXAM 2
(KILIANSKI) EXAM QUESTIONS WITH COMPLETE SOLUTIONS VERIFIED
AND GRADED A+ 100% GUARANTEED PASS
Pearson's r
finds correlation between IV and DV
values of r
values range from -1 to 1; 0 means no correlation
Pearson's r null hypothesis
Ho: r(population) = 0
In a Pearson's r test, there is no restriction between of range within the range of
possible values.
true
In a Pearson's r test, a significant correlation means there is a causal relationship
between the IV and DV.
false
Coefficient of determination
r squared; effect size in a Pearson's r test; tells you what percent of the variability in the
DV is accounted for by the IV
Paired Samples t-test
used for repeated measures in experiments; DV is measured, experimental
manipulation is administered, DV is measured again
When retesting subjects, an independent samples t-test is not acceptable
because scores within subjects are correlated.
true
Why is the SEd different in a paired samples t-test than an independent sample?
we must take within-subjects correlation into account (if significant)
one-way ANOVA test
, finding differences between three or more sample means; finds ratio of variability
between and within groups
one-way analysis of variance (ANOVA) null hypothesis
Ho: μ1 = μ2 = μ3...
In finding the difference between three or more means, you cannot use multiple t-
tests because the probability of falsely accepting the null hypothesis increases
with each test.
false; more likely to falsely reject the null hypothesis
Tukey's HSD
used when there is a significant difference to find the specific mean difference; only
used in a one-way ANOVA or a factorial ANOVA for every significant F with three or
more levels
SS(w)
find the mean for each level of the IV; subtract it from each individual score; square
those differences; sum the squares; add the values together
SS(b)
find each condition mean; subtract the grand mean from each condition mean; square
these differences; multiply them by the sample size for that condition; add those values
together
The bigger the differences between conditions are, the bigger the sum of squares
between will be.
true
factorial analysis of variance (ANOVA)
compares means across two or more IVs
"main effect" in a factorial ANOVA
effects of IVs seperately
"interaction" in a factorial ANOVA
effects of different combinations of IVs
2 x 2 ANOVA
two IVs with two levels each
2 x 3 ANOVA
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