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1

ANOVA

a statistical technique for testing differences in the means of several groups

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one-way ANOVA

an analysis of variance wherein the groups are defined on only one independent variable

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assumptions in order to do ANOVAs

populations are normally distributed

populations have homogenous variances

the individual observations are independent from one another, ie, one person's score doesn't influence another person's score

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omnibus null hypothesis

the hypothesis that all population mean are equal

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sum of squares (SS)

the sum of squared deviations around some point (usually around a mean or predicted value)

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MS_between groups (MS_groups, MS_treatment):

variability among group means

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MS_within (MS_error)

variability among subjects in the same treatment group

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MS

mean square; SS/df

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F statistic

the ratio of MS_group to MS_error, or MS_between to MS_within

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η²

eta squared; SS_group/SS_total

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ω²

omega squared

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multiple comparison techniques

techniques for making comparisons between 2 or more group means subsequent to an analysis of variance

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familywise error rate

the probability that a family of comparisons contains at least one type 1 error

we want to keep this down

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3 of many post hoc procedures that control type 1 error

Protected t (fisher's least significant difference (LSD))

Bonferroni correction

Tukey procedure

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protected t (fisher's least significant difference (LSD))

a technique in which we run t tests between pairs of means only if the analysis of variance was significant

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bonferroni correction

a multiple comparison procedure in which the family-wise error rate is divided by the number of comparisons

divide α by the number of comparisons you want to make

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tukey procedure

a multiple comparison procedure for making pairwise comparisons among means while holding the familywise error rate at α

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factors

another word for IVs in the ANOVA

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factorial design

an experimental design in which every level of one variable is paired with each level of one variable

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two-way factorial design

an experimental design involving two IVs in which every level of one variable is paired with each level of one variable

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two advantages of factorial designs over using multiple one-way ANOVAs

they allow us to look the interaction of variables

they allow us to use fewer participants without sacrificing power

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interaction

a situation in a factorial design or contingency table in which the effects of one independent variable depend on the level of another independent variable

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grand mean

the mean of all observations

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main effect

the effect of one IV

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simple effect

the effect of one IV at one level of another IV

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between-subjects designs

designs in which different subjects serve under the different treatment levels

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repeated-measures designs

experimental designs in which each subject receives all levels of at least one IV

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within-subjects designs

experimental designs in which each participant produces multiple scores

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assumptions of repeated-measures designs (are the same for any other F test)

normality in the distributions

homogeneity of variance

assumption of homogeneity of covariance: correlations among DV scores must be the same

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advantages of repeated-measures designs

avoids the problem of person-to-person variability

controls for extraneous variables

requires fewer participants then between-subjects designs to have same amount of power

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disadvantages of related samples

order effect

carry-over effect

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order effect

the effect on performance attributable to the order in which treatments were administered

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carry-over effect

the effect of previous trials (conditions) on a subject's performance on subsequent trials

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counterbalancing

an arrangement of treatment conditions designed to balance out practice effects

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chi-square test

a statistical test often used for categorical data

-dv is number of observations

-is non-parametric

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one-classification variable

the chi-square goodness of fit test

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goodness-of-fit test

a test for comparing observed frequencies with theoretically predicted frequencies

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E

expected frequency

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O

observed frequency

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two classification variables

analysis of contingency tables using chi-square

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contingency table

a two-dimensional table in which each observation is classified on the basis of two variables simultaneously

ex: Walsh tested the effectiveness of using antidepressants after treatment for anorexia nervosa

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degrees of freedom for expected frequencies

(R-1)*(C-1)

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chi square tests assume

independence of observations

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you have independence if

one person's scores do not affect another person's scores

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N

must equal the total of the rows, or the total of the columns

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risk

number of occurrences on one event divided by total number of occurrences of events

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risk ratio (relative risk)

ratio of two risks

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odds

frequency of occurrence of one event divided by the frequency of occurrence of the other event

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prospective study

a study in which you select participants on the basis of some current condition and follow them into the future

ex: if you're interested in the relationship between smoking and cancer, and you find people who smoke and then track over time who develops cancer

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retrospective study

a study in which you select participants on some criterion and then look back at their behavior in the past

ex: if you study the relationship between smoking and cancer and you examine data that already exist in terms of who smoked and developed cancer

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in a prospective study

you have to collect the data and analyze it

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in a retrospective study

the data already exists and you just have to analyze it

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