PSYC08 FINAL EXAM THEORY
QUESTIONS WITH RECENTLY UPDATED
SOLUTIONS | SOLVED ACCURATELY
ANOVA Assumptions:
DV is continuous (ratio/interval scale); the IV is categorical
The data/observations are independent.
DV is normally distributed for all groups.
Variances are homogenous across all groups.
No outliers beyond ± 4 standard deviations in all groups.
ANCOVA Assumptions:
DV is continuous & ratio/interval, IV is categorical.
Cov is linearly related to DV and independent of the IV.
Observations/data is independent.
Data is normally distributed within conditions.
No outliers beyond ±4 SDs in each group.
HOV
RBD Assumptions:
DV is continuous & ratio/interval, IV is categorical.
, Blocking variable is independent of the IV. (NEW!)
Participants must have a roughly equal chance of being assigned to each treatment condition. (NEW!)
Data is normally distributed within conditions.
HOV
Observations/data is independent.
No outliers beyond ±4 SDs in each group.
Repeated Measures Assumptions:
DV is continuous - answer ✔✔-What are the assumptions for the tests we have covered so far (ANOVA,
ANCOVA, RBD, RMANOVA)? Why are these assumptions necessary? How would violations to these
assumptions affect the interpretation of the results of these tests? Why do we test these assumptions?
If the sample size is small, it may be difficult to detect assumption violations. With small samples,
violation assumptions such as nonnormality are difficult to detect even when they are present. Also,
with small sample size(s) there is less resistance to outliers, and less protection against violation of
assumptions. but why tho...
With a small sample size, you might have problems with statistical power which is your ability to reject a
false null hypothesis. If the effect size differences between baseline and the other measures is not large
enough you may not be able to reject the null.
Outliers tend to increase the estimate of sample variance, thus decreasing the calculated F statistic for
the ANOVA and lowering the chance of rejecting the null hypothesis. - answer ✔✔-How would small
sample sizes affect your assumptions? What about outliers? What about the outcome of various
statistical tests?
holy moly! When summing across participants, the formulas for estimating the components of the
structural model remain the same. However, the random effects become population-level effects
instead of participant-specific effects. This is because the goal is to make inferences about the
population rather than individual participants. By summing across participants, we obtain estimates of
the average treatment effects and covariate effects, providing a more generalizable understanding of
the phenomena under study. - answer ✔✔-Explain the structural model for a randomized block design,
a repeated-measures ANOVA and an ANCOVA. How are the components of the structural model
QUESTIONS WITH RECENTLY UPDATED
SOLUTIONS | SOLVED ACCURATELY
ANOVA Assumptions:
DV is continuous (ratio/interval scale); the IV is categorical
The data/observations are independent.
DV is normally distributed for all groups.
Variances are homogenous across all groups.
No outliers beyond ± 4 standard deviations in all groups.
ANCOVA Assumptions:
DV is continuous & ratio/interval, IV is categorical.
Cov is linearly related to DV and independent of the IV.
Observations/data is independent.
Data is normally distributed within conditions.
No outliers beyond ±4 SDs in each group.
HOV
RBD Assumptions:
DV is continuous & ratio/interval, IV is categorical.
, Blocking variable is independent of the IV. (NEW!)
Participants must have a roughly equal chance of being assigned to each treatment condition. (NEW!)
Data is normally distributed within conditions.
HOV
Observations/data is independent.
No outliers beyond ±4 SDs in each group.
Repeated Measures Assumptions:
DV is continuous - answer ✔✔-What are the assumptions for the tests we have covered so far (ANOVA,
ANCOVA, RBD, RMANOVA)? Why are these assumptions necessary? How would violations to these
assumptions affect the interpretation of the results of these tests? Why do we test these assumptions?
If the sample size is small, it may be difficult to detect assumption violations. With small samples,
violation assumptions such as nonnormality are difficult to detect even when they are present. Also,
with small sample size(s) there is less resistance to outliers, and less protection against violation of
assumptions. but why tho...
With a small sample size, you might have problems with statistical power which is your ability to reject a
false null hypothesis. If the effect size differences between baseline and the other measures is not large
enough you may not be able to reject the null.
Outliers tend to increase the estimate of sample variance, thus decreasing the calculated F statistic for
the ANOVA and lowering the chance of rejecting the null hypothesis. - answer ✔✔-How would small
sample sizes affect your assumptions? What about outliers? What about the outcome of various
statistical tests?
holy moly! When summing across participants, the formulas for estimating the components of the
structural model remain the same. However, the random effects become population-level effects
instead of participant-specific effects. This is because the goal is to make inferences about the
population rather than individual participants. By summing across participants, we obtain estimates of
the average treatment effects and covariate effects, providing a more generalizable understanding of
the phenomena under study. - answer ✔✔-Explain the structural model for a randomized block design,
a repeated-measures ANOVA and an ANCOVA. How are the components of the structural model
