General Linear Model

Question 1

General Linear Model

Answer 1

Statistical model with one or more independent variable that predicts a dependent variable

Question 2

Predictor

Answer 2

Independent variable

x

Question 3

Outcome/ Criterion

Answer 3

Dependent variable

y

Question 4

GLM goal

Answer 4

Try to account for as much variability as possible in the criterion/outcome

Question 5

What kind of variable will the GLM not accept?

Answer 5

A categorical Y variable

Question 6

What kind of scale of measurement is needed for the Y variable in the GLM

Answer 6

An interval or ratio Y variable

Question 7

Nominal

Answer 7

Numbers are used to distinguish between objects
Classifying

Apples = 1, Oranges = 2

Question 8

Ordinal

Answer 8

Numbers used to put items in order and to rank them

S = 0 M= 1 A= 2
1,0,2 (Ranked by age)

Question 9

Interval

Answer 9

Equal intervals represent equal difference between items.
Differences are meaningful
A zero is not a true zero,

Ex: 0 degrees does not mean there is not any temperture

Question 10

Ratio

Answer 10

Has a true zero
Meaningful zero point

Ex: time

Question 11

Linear Regression

Answer 11

Simplest analysis in GLM
Foundation

Does length of relationship predict the degree of being upset after the breakup

Question 12

Multiple Regression

Answer 12

Multiple predictors
Single DV

Does length of relationship, amount of commitment and age impact how sad you’ll be post a breakup

Question 13

ANOVA’s

Answer 13

Analysis of categorical x variables

Look for or’s in the question

Do we tend to forgive parents, romantic partners or friends after a fight ?

Question 14

One-way ANOVA

Answer 14

One categorical x variable and one continuous y variable

Question 15

Critical factor in determining what analysis to use

Answer 15

Depends on how many x’s we have not how many levels x has

Question 16

2 or more categorical x- variables and one continuous y-variable

Answer 16

m-way ANOVA

Question 17

Mix of continuous and categorical x-variables

Answer 17

mixed-model regression

Question 18

Mixed model regression

Answer 18

Analyzes cases when there is a combination of categorical and continuous x-variables

Question 19

Difference between ANOVA’s and Regression

Answer 19

Regression: continuous x-variables

ANOVA’s: Categorical x-variables

Question 20

ANCOVA

Answer 20

Deals with both types of variables

Question 21

How can the GLM deal with multiple criterion variables

Answer 21

1. Run multiple ANOVA’s/ Regression

Question 22

Discriminant function analysis

Answer 22

Follow up on a multivariate test

Question 23

Benefits of using the GLM

Answer 23

1. Less formulas to remember
2. Simplifies the math
3. Clarifies similarities and differences between variables across a variety of tests
4. Provides conceptual framework to work with

Question 24

Data =

Answer 24

Model + Error

Question 25

Data

Answer 25

Values obtained from scientific experiments Scores on variable of interest for the researcher Actual scores on Y Y

Question 26

Goal of collecting data

Answer 26

Explains why people score on the criterion the way they do Build statistical models to explain and predict the scores on the criterion

Question 27

Model

Answer 27

Prediction is a way of demonstrating an understanding of something Combines one or more predictor variable to predict scores (y')

Question 28

Goal for the model

Answer 28

Build a statistical model that can accurately predict data

Question 29

Types of models

Answer 29

Simple model Less simple model Little more complex model Fairly complex model

Question 30

Simple model

Answer 30

predicts a constant score for everyone Everyone will get 85%

Question 31

Less simple model

Answer 31

Predicts group mean for everyone Class average was 82% after first exam so that is our prediction

Question 32

Little more complex model

Answer 32

Adding an x variable to predict the outcome Use study hours to predict grades

Question 33

Fairly complex model

Answer 33

Add multiple predictors to predict outcome Use study time, stress levels and sleep to predict grades

Question 34

Error

Answer 34

Predicted scores compared to actual scores Model's accuracy in predicting y

Question 35

Why study error?

Answer 35

To eliminate it and improve our model

Question 36

Counting error

Answer 36

Counting the number of scores you got incorrect 100% error

Question 37

Absolute error

Answer 37

Taking absolute values of the errors

Question 38

Sum of the squared errors (SSE)

Answer 38

Square the error terms and then sum them

Question 39

Why use the SSE?

Answer 39

Rewards for small error and punishes the model for large error

Question 40

What is the SSE

Answer 40

Variance your data cannot explain

Question 41

Accounted Variance + Error

Answer 41

100% variance

Question 42

A model predicts a phenomenon well means

Answer 42

We understand our model