N580 FINAL EXAM QUESTIONS AND
ANSWERS
95th percent confidence interval - CORRECT ANSWE✅✅ - sampling mean + or
- (1.96 x SEM)
.99th percent confidence interval - CORRECT ANSWE✅✅ - sampling mean + or
- (2.58 x SEM)
.alpha - CORRECT ANSWE✅✅ - set at start of study
.analysis - CORRECT ANSWE✅✅ - clearly the problem is the driving force--the
sophistication of analysis will never compensate for an insignificant problem
always remember the research question or hypothesis always drives the analysis
look at the hypothesis or question--should be able to begin to think about the type
of analysis that would be appropriate
.analysis of variance: the logic - CORRECT ANSWE✅✅ - F statistic=between-
group variability (BGV)/within-group variability (WGV)
BGV=difference between the means of each group
WGV=variability of scores within each group
F=(effect of IV + sampling error)/sampling error
.ANCOVA - CORRECT ANSWE✅✅ - combination of multiple regression and
ANOVA to measure differences in group means
helps reduce error variance
error variance is reduced by controlling for variation in DV that comes from
extraneous variable(s)
allows you to remove the influence of that extraneous variable
Stage 1: covariate (regression piece): the variable that you want to control or the
variable that you want to remove in terms of influencing the score of the DV
Stage 2: using an ANOVA approach, the variance that remains in the DV is
explained
.ANOVA - CORRECT ANSWE✅✅ - robust test
,used to compare response variable by 2 or more groups
minimizes risk of type I error by examining differences across all groups at once
examines variance to determine if group means differ
why would you not just do multiple t tests? (see above)
sample data are used to compute a test statistic - the F ratio
F: ratio is compared to scores (F-ratio values) in a sampling distribution (table)
developed by statisticians--these of course will be reported via a statistical package
in a data run
degrees of freedom and p value are used to read the appropriate F ratio value
if the sampling distribution F ratio value is larger than your test statistic - the null
hypothesis is accepted
conversely, if your test statistic is larger than the value (falls w/in the rejection
range of the sampling distribution) the null hypothesis is rejected and the research
hypothesis accepted
.ANOVA logic - CORRECT ANSWE✅✅ - if group means are equal - there is no
between group variability - that is, the BGV=0 and the F ratio will be 0
if the group mean scores are different, the question is whether the differences are
b/c the population means are different or the difference is due to random chance
.assumptions for one-way ANOVA - CORRECT ANSWE✅✅ - robust and tends
to yield accurate results even if all of the assumptions aren't met
same as those for the t test
1. appropriate level of measurement - IV and DV
2. IV - groups are mutually exclusive
3. DV has a normal distribution
4. DV - homogeneity of variance - or the groups have equal variances (standard
deviation around those means for each group has to be relatively similar)
if they are significantly violated: you can run the Kruskall Wallis (compares by
rank and not mean scores, non-parametric--also tells you if there is a statistically
significant difference between groups and then you can run pairwise comparisons)
.assumptions of RANOVA - CORRECT ANSWE✅✅ -
.assumptions of RANOVA - CORRECT ANSWE✅✅ - similar to one-way
ANOVA except for compound symmetry
1. correlations between DVs are about the same
2. variances of DVs are equal across measures (essentially homogeneity of
variance)
, if these assumptions are violated: Friedman test (non-parametric)--looks at rank
ordering
.best test for comparing 3 or more groups - CORRECT ANSWE✅✅ - ANOVA
(parametric)
.best test for comparing two groups - CORRECT ANSWE✅✅ - t test (parametric)
data are interval or ratio
.best test for dependent groups - CORRECT ANSWE✅✅ - paired t tests
(parametric)
.BG-SS - CORRECT ANSWE✅✅ - take deviation of group mean from grand
mean
.calculating ANOVA - CORRECT ANSWE✅✅ - sums of squares for WGV
sums of square for BGV
sums of squares for total variation
.central limit theorem - CORRECT ANSWE✅✅ - means of all samples will form
a normal curve
.Chi squared assumptions - CORRECT ANSWE✅✅ - frequency data
(representing a count of the number of study participants that meet a certain
condition--expected frequency count of at least 5 participants in each cell is used)
adequate sample size
measures are independent of each other (mutually exclusive categories--an
individual can only be counted once)
theoretical basis for variable categorization (to ensure analysis will be meaningful)
.Chi squared test - CORRECT ANSWE✅✅ - non-parametric test
used to answer research questions and/or hypotheses
one of most frequently used non-parametric test, doesn't have a parametric
equivalent
.Chi squared test of independence - CORRECT ANSWE✅✅ - Pearson's Chi
square
ANSWERS
95th percent confidence interval - CORRECT ANSWE✅✅ - sampling mean + or
- (1.96 x SEM)
.99th percent confidence interval - CORRECT ANSWE✅✅ - sampling mean + or
- (2.58 x SEM)
.alpha - CORRECT ANSWE✅✅ - set at start of study
.analysis - CORRECT ANSWE✅✅ - clearly the problem is the driving force--the
sophistication of analysis will never compensate for an insignificant problem
always remember the research question or hypothesis always drives the analysis
look at the hypothesis or question--should be able to begin to think about the type
of analysis that would be appropriate
.analysis of variance: the logic - CORRECT ANSWE✅✅ - F statistic=between-
group variability (BGV)/within-group variability (WGV)
BGV=difference between the means of each group
WGV=variability of scores within each group
F=(effect of IV + sampling error)/sampling error
.ANCOVA - CORRECT ANSWE✅✅ - combination of multiple regression and
ANOVA to measure differences in group means
helps reduce error variance
error variance is reduced by controlling for variation in DV that comes from
extraneous variable(s)
allows you to remove the influence of that extraneous variable
Stage 1: covariate (regression piece): the variable that you want to control or the
variable that you want to remove in terms of influencing the score of the DV
Stage 2: using an ANOVA approach, the variance that remains in the DV is
explained
.ANOVA - CORRECT ANSWE✅✅ - robust test
,used to compare response variable by 2 or more groups
minimizes risk of type I error by examining differences across all groups at once
examines variance to determine if group means differ
why would you not just do multiple t tests? (see above)
sample data are used to compute a test statistic - the F ratio
F: ratio is compared to scores (F-ratio values) in a sampling distribution (table)
developed by statisticians--these of course will be reported via a statistical package
in a data run
degrees of freedom and p value are used to read the appropriate F ratio value
if the sampling distribution F ratio value is larger than your test statistic - the null
hypothesis is accepted
conversely, if your test statistic is larger than the value (falls w/in the rejection
range of the sampling distribution) the null hypothesis is rejected and the research
hypothesis accepted
.ANOVA logic - CORRECT ANSWE✅✅ - if group means are equal - there is no
between group variability - that is, the BGV=0 and the F ratio will be 0
if the group mean scores are different, the question is whether the differences are
b/c the population means are different or the difference is due to random chance
.assumptions for one-way ANOVA - CORRECT ANSWE✅✅ - robust and tends
to yield accurate results even if all of the assumptions aren't met
same as those for the t test
1. appropriate level of measurement - IV and DV
2. IV - groups are mutually exclusive
3. DV has a normal distribution
4. DV - homogeneity of variance - or the groups have equal variances (standard
deviation around those means for each group has to be relatively similar)
if they are significantly violated: you can run the Kruskall Wallis (compares by
rank and not mean scores, non-parametric--also tells you if there is a statistically
significant difference between groups and then you can run pairwise comparisons)
.assumptions of RANOVA - CORRECT ANSWE✅✅ -
.assumptions of RANOVA - CORRECT ANSWE✅✅ - similar to one-way
ANOVA except for compound symmetry
1. correlations between DVs are about the same
2. variances of DVs are equal across measures (essentially homogeneity of
variance)
, if these assumptions are violated: Friedman test (non-parametric)--looks at rank
ordering
.best test for comparing 3 or more groups - CORRECT ANSWE✅✅ - ANOVA
(parametric)
.best test for comparing two groups - CORRECT ANSWE✅✅ - t test (parametric)
data are interval or ratio
.best test for dependent groups - CORRECT ANSWE✅✅ - paired t tests
(parametric)
.BG-SS - CORRECT ANSWE✅✅ - take deviation of group mean from grand
mean
.calculating ANOVA - CORRECT ANSWE✅✅ - sums of squares for WGV
sums of square for BGV
sums of squares for total variation
.central limit theorem - CORRECT ANSWE✅✅ - means of all samples will form
a normal curve
.Chi squared assumptions - CORRECT ANSWE✅✅ - frequency data
(representing a count of the number of study participants that meet a certain
condition--expected frequency count of at least 5 participants in each cell is used)
adequate sample size
measures are independent of each other (mutually exclusive categories--an
individual can only be counted once)
theoretical basis for variable categorization (to ensure analysis will be meaningful)
.Chi squared test - CORRECT ANSWE✅✅ - non-parametric test
used to answer research questions and/or hypotheses
one of most frequently used non-parametric test, doesn't have a parametric
equivalent
.Chi squared test of independence - CORRECT ANSWE✅✅ - Pearson's Chi
square
