An effect size measure for one-way analysis of variance.

Familywise error rate

The probability of committing at least one type 1 error across multiple significance tests.

Multiple comparison test

control type I error rate when multiple significance tests are applied to the same data set

Multiple comparison test examples

Bonferroni-Dunn test, Tukey’s HSD test, the Newman-Keuls method, Dunnett's test, and the Scheffe method.

Repeated Measures One-Way ANOVA (Within Subjects)

tests differences of means across a single factor in which the same participants provide measurements for each factor level

Repeated Measures One-Way ANOVA (Within Subjects) Assumptions:

Interval- or ratio-level data. Random sampling, independence of subjects. normally distributed, sphericity of k populations

repeated-measures analysis of variance assumption that requires variances of the differences between all pairs of groups be approximately equal.

repeated-measures ANOVA effect size is commonly measured by η2=

(SS condition)/(SS total- SS participant)

compares means when the experimental design includes two factors.

Two-way ANOVA assumptions:

between-subjects one-way ANOVA and all cells have the same number of observations

The pattern of means across the different levels of a single factor in an analysis of variance design, averaging over any other factors

Interaction effect: A measure of the extent to which the effect of

one factor depends on the levels of the other factor

COMPARING COUNTS/FREQUENCIES

use nominal/categorical variables

first transform the counts to percentages

A measure of association between two variables, each of which has only two possible values

computed as ad/bc (ratio of the product of the diagonal cells)

An odds ratio of 1 would indicate that men and women are

equally likely to prefer candidate A to candidate B

The χ² (chi) test is

commonly applied to contingency tables

χ² (chi) test assumptions

Random sampling a nd independence of observations. 2 X 2 tables, all expected frequencies at least 5. For larger tables, 80% of expected cell frequencies should be at least 5, and none should be 0

A common effect size measure for RxC contingency tables is Cramer’s V

(also called Cramer's φ), which ranges from 0 to 1

Help

Options

focusNode

Didn't know it?
click below

Knew it?
click below

Don't Know

Remaining cards (0)

Know

retry

shuffle

restart

0:00

Embed Code - If you would like this activity on your web page, copy the script below and paste it into your web page.

Normal Size Small Size show me how

psy400ch14p348-357

omnibus null hypothesis

Term	Definition
Omnibus null hypothesis	In analysis of variance, the null hypothesis that all group means are equal (Ho=mu1=mu2=mu3)
Eta squared ( η2)	An effect size measure for one-way analysis of variance.
η2=	SSbetween/SStotal
Familywise error rate	The probability of committing at least one type 1 error across multiple significance tests.
Multiple comparison test	control type I error rate when multiple significance tests are applied to the same data set
Multiple comparison test examples	Bonferroni-Dunn test, Tukey’s HSD test, the Newman-Keuls method, Dunnett's test, and the Scheffe method.
Repeated Measures One-Way ANOVA (Within Subjects)	tests differences of means across a single factor in which the same participants provide measurements for each factor level
Repeated Measures One-Way ANOVA (Within Subjects) Assumptions:	Interval- or ratio-level data. Random sampling, independence of subjects. normally distributed, sphericity of k populations
Repeated Measures One-Way ANOVA (Within Subjects) Nonparametric alternative:	Friedman test
Sphericity	repeated-measures analysis of variance assumption that requires variances of the differences between all pairs of groups be approximately equal.
repeated-measures ANOVA effect size is commonly measured by η2=	(SS condition)/(SS total- SS participant)
Two-way ANOVA	compares means when the experimental design includes two factors.
Two-way ANOVA assumptions:	between-subjects one-way ANOVA and all cells have the same number of observations
Main effect	The pattern of means across the different levels of a single factor in an analysis of variance design, averaging over any other factors
Interaction effect: A measure of the extent to which the effect of	one factor depends on the levels of the other factor
COMPARING COUNTS/FREQUENCIES	use nominal/categorical variables
2x2 Tables	first transform the counts to percentages
For 2X2 contingency tables, the most common measure of effect size is	the coefficient φ (phi)
Odds ratio	A measure of association between two variables, each of which has only two possible values
the odds ratio is	computed as ad/bc (ratio of the product of the diagonal cells)
An odds ratio of 1 would indicate that men and women are	equally likely to prefer candidate A to candidate B
The χ² (chi) test is	commonly applied to contingency tables
χ² (chi) test assumptions	Random sampling a nd independence of observations. 2 X 2 tables, all expected frequencies at least 5. For larger tables, 80% of expected cell frequencies should be at least 5, and none should be 0
A common effect size measure for RxC contingency tables is Cramer’s V	(also called Cramer's φ), which ranges from 0 to 1

Created by: james22222222

"Know" box contains:
Time elapsed:
Retries: