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PSYS 054 UVM Final
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Terms in this set (53)
One-Way ANOVA
has 1 independent variable
Factors
Independent variables in an ANOVA
Levels
each possible valve of a factor
Grand Mean
overall mean score of all participants across all groups
Omnibus Null Hypothesis
key significant test, represents the hypothesis that the population means of all groups are equal
ANOVA Assumptions
Homogeneity of variance; normality; independence of observations
ANOVA Variation
if there is a lot of between-group variation relative to within-group variation, then the ANOVA will be significant
F Ratio
if F is close to 1, retain null. if F is greater than 1, reject the null.
n
number of participants in each group
N
number of all participants
K
number of groups
Xbar
group mean
GM
grand mean
SStotal
SSgroup+SSerror
ANOVA Calculation
Calculate SStotal
- SSgroup
-SSerror
-MSgroup
-MSerror
-F ratio
Post-Hoc Tests
allows us to figure out which group means are significantly different from which other group means
Liberal
more likely to find statistically significant differences
conservative
less likely to find statistically significant differences
Bonferroni Procedure
conduct multpile follow-up t-tests but adjust your alpha level for each one. take the desired alpha level (0.05)/number of tests and use that new number for the alpha level of each
Factorial ANOVA
ANOVA with more than one IV/factor and each factor has 2 or more levels
SSa, SSb, SSab
Main effect of first factor, main effect of second factor, main effect of both factors
SSab
SScells-SSa-SSb
SSerror
SStotal-SScells
Interaction Plot
graph of interaction where x-axis is one variable and different lines are another variable
Repeated Measures ANOVA
multiple assessments of the same participants on different levels
Between-Subjects Effects
differences between participants Ex. gender or experimental groups
Within-Subjects Effects
unique to RM-ANOVA, difference in single participant's score over time
t
number of time points
ANCOVA
one or more categorical IVs and 1 continuous DV but ask 1 or more continuous covariate
covariate
variable that might be predictive of the DV whose linear influence you wish to control for
ANCOVA Assumptions
normal distribution, equal variance, independent, DV and covariate association, any IV/covariate association is equal across groups
ANCOVA weaknesses
covariates can change meaning of DV, cannot equate groups that are naturally unequal
Nominal Scale
differently named categories
Ordinal Scale
numbers represent ranks for greater than/less than comparisons
Interval Scale
equal number representing equal intervals
Ratio Scale
True zero point that can indicate absence of measurement
Positive Skew
Data Curve is higher on the left. Mode < median < mean
Negative Skew
data curve is higher on the right. Mode > median > mean
mutually exclusive
events that cannot occur at the same time
collectively exhaustive
at least one of the possible events must occur
independent trials
trials are independent of each other. The occurrence of one does not affect the probability of another
Sampling Distribution
the probability distribution under repeated sampling from the population of a given statistic
Hypothesis Testing
start with assumption that your sample comes from a "non-special" population aka a null hypothesis
Null Hypothesis (Ho)
hypothesis of no difference between groups or no relationship between variables
Type I Error
accidentally rejected Ho, convicting an innocent
Type II Error
accidentally retaining Ho, let guilty go
Central Limit Theorem
given a population with a mean u and a variance o2, the sampling distribution of the mean will have a mean equal to u, and a variance equal to o2/N
Z-Test
convert observed sample M to a z-score using central limit theorem to compare to the population M
T-Test
used for situations in which the population variance is unknown
One-Sample T-Test
comparing a sample mean to a hypothesized population value
Paired T-Test
used when we have "paired" or dependent observations
Independent Samples T-Test
most common in research, compared means with independent groups
Cohen's d
standardized mean difference for effect size
0.20 = small effect
0.50 = medium effect
0.80 = large effect
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