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- Unit 4 And Final Study Guide

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Null hypothesis for a repeated measures ANOVA

The mean of the groups are equal

Alternative hypothesis for a repeated measures ANOVA

The mean in *at least one *of the groups is different from the others

When to use a repeated measures design:

To compare the means on a **quantitative variable**, from each participant, who is in **all** of the conditions if the qualitative variable

- You are investigating
__differences over time__of the same group

Repeated measures ANOVA vs. Dependent t-test

Similarity:

- Both a t-test and a repeated measures ANOVA can be used when collecting data from a
**group of participants that were in two conditions**

- A
**t-test is only for 2 conditions**, while an**ANOVA is for 2 or more conditions/ levels**

Repeated measures ANOVA vs. One-way ANOVA

Similarity:

- Both a repeated measures ANOVA and a one-way ANOVA can be used
**in a study with 2 or more conditions/ levels**

- A
**RM ANOVA is for participants in all conditions** - A
**one-way ANOVA is for participants in only one of the conditions**

Process of Statistical Analysis for Multiple IV Condition Designs

- Perform the Omnibus
*F*test - Compute all Pairwise comparisons - if your
*F*test was significant - Reject the null: mean differences > minimum mean
- Check that the significant mean difference is in the hypothesized condition
- SPSS computes the math for you

Repeated measures ANOVA - reporting results format

Benefits of Factorial Analysis of Variance

Multivariate Research

3 variable is a 2x2 factorial design

3 effects involves in a 2x2 factorial design

Interaction

Main Effects

5 important terms involves in a factorial design

Cell means

Marginal mean

Simple effects

Why do we need to be careful when we interpret main effects?

5 basic patterns of results for a 2x2 factorial design

What patterns are potentially misleading?

Factorial hypotheses: main effect

Factorial hypotheses: interaction

Main effect research hypothesis: descriptive

Main effect research hypothesis: misleading

Main effect research hypothesis: associative

Main effect research hypothesis: causal

Interaction research hypothesis: associative

Interaction research hypothesis: causal

Describe how each "effect" in a 2x2 factorial design is statistically examined

Main effects *F *tests

The corresponding significance test

Interaction *F *tests

LSD pairwise comparisons

4 things you need to know to compute the LSD pairwise comparisons

How do you compare cell means?

How do you compare marginal means?

3 type of factorial designs

Between-groups factorial design

Within-groups factorial design

Mixed factorial design

Main effects and the interaction are 3 separate effects and...

How is an interaction different than main effects?

You cannot reject the null if...

Order of Operations

Write up your results - Factorial ANOVA (interaction - part 1)

Write up your results - Factorial ANOVA (main effect - part 2)

How do we know if we can causally interpret the effects?

Definition of correlation (r)

Range of correlation

Type of variables for correlation

Definition of chi square (X^{2})

Range of chi square

Type of variables for chi square

How do we visually display correlation data?

How do we visually display chi square data?

Contingency table

Types of relationships for correlation

Types of direction for correlation

Types of strength for correlation

Correlation research hypothesis must specify:

Correlation null hypothesis must specify:

When you retain the null for a correlation you are concluding that...

When you reject the null for a correlation you are concluding that...

Chi square research hypothesis must specify:

Chi square null hypothesis must specify:

When you retain the null for chi square you are concluding that...

When you reject the null for chi square you are concluding that...

Can we causally interpret a correlation?

Parametric tests

Non-parametric tests

To eyeball what the pattern of differences show in a chi square you are going to have to look where?

Write up your results: Chi square

Describe the pattern of a correlation

One sample t-test: chart

Independent t-test: chart

Dependent t-test

One-way ANOVA

Repeated measures ANOVA

Factorial ANOVA

Correlation

Chi square

Attributive hypothesis

Associative hypothesis

Causal hypothesis

Population sampling frame

Sampling frame

Selected sample

Data sample

Independent variable

Common approaches to manipulating the IV include...

Dependent variable

Control

Controlling extraneous variables:

Internal validity

True experiments

Non-experiment

Quasi experiment

Natural groups design

Between-groups experiment

Advantages to a between-groups experiment

Disadvantages to a between-groups experiment

Within-groups experiment

Advantages to within-groups experiment

Disadvantages to within-groups experiment

Types of data collection procedures

Self report

Naturalistic observation

Undisguised participant observation

Disguised participant observation

Types of data collection setting

Laboratory setting

Structures setting

Field setting

Type I error

Type II error

Type III error

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