Chapter 8 - One-Way Analysis of Variance, Chapter 10, Chi-Square, T-test

Anova (Analysis of Variance)

testing for differences in mean scores from two or more independent samples

One-way ANOVA

one single IV (3 levels or more) on a DV (interval or ratio)

Example: IV Type of social media (3 levels: Fb, Insta, Tiktok)

DV: purchase intention - interval

Two-way ANOVA

two IVS (categorical) on a single DV (interval or ratio)

variance

a measure of spread of scores around the mean

Effect size for ANOVA

η² rules of thumb for effect size with eta-squared

• < .01 is very small, perhaps trivial

• .01 is small

• .09 is medium

• .25+ is large

df of 1 way ANOVA

Between = No. of groups - 1

Within = N (Total) - no. of groups

df of 2 way ANOVA

Main effect

• A-1

• B-1

Interaction

• (a-1)(b-1)

Within

• ab(n-1)

ANOVA statistical letter

F: used to analyze variance to test hypothesis

• bigger F: variance between group sis large and variance within groups is small

Variation between groups

use f statistic

variation within groups

used SD or variance

Chi square

IV: categorical, DV: categorical

Chi square statistic

ꭓ²

Chi Square effect size

Cramer’s V

Range:

• 0.10 small

• 0.30 medium

• 0.50 large

Chi square DF

Df (# of categories -1) * (# of

categories-1)

Type I Error

when you falsely reject null hypothesis (when you say the alternative hypothesis is true but the NULL IS)

Type II Error

when you falsely don’t reject the null hypothesis (when you say the null is true but the ALTERNATIVE is)

T Test

IV: Categorical (2 groups) DV: interval or ratio

df = n-2

effect size: d

statistic: t

Correlation

IV: interval or ration

DV: interval or ration

statistic/effect size: r

df: n-1