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Psyc 411 > General Linear Model > Flashcards

General Linear Model Flashcards

1
Q

General Linear Model

A

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

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2
Q

Predictor

A

Independent variable

x

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3
Q

Outcome/ Criterion

A

Dependent variable

y

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4
Q

GLM goal

A

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

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5
Q

What kind of variable will the GLM not accept?

A

A categorical Y variable

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6
Q

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

A

An interval or ratio Y variable

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7
Q

Nominal

A

Numbers are used to distinguish between objects
Classifying

Apples = 1, Oranges = 2

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8
Q

Ordinal

A

Numbers used to put items in order and to rank them

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

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9
Q

Interval

A

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

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10
Q

Ratio

A

Has a true zero
Meaningful zero point

Ex: time

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11
Q

Linear Regression

A

Simplest analysis in GLM
Foundation

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

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12
Q

Multiple Regression

A

Multiple predictors
Single DV

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

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13
Q

ANOVA’s

A

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 ?

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14
Q

One-way ANOVA

A

One categorical x variable and one continuous y variable

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15
Q

Critical factor in determining what analysis to use

A

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

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16
Q

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

A

m-way ANOVA

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17
Q

Mix of continuous and categorical x-variables

A

mixed-model regression

18
Q

Mixed model regression

A

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

19
Q

Difference between ANOVA’s and Regression

A

Regression: continuous x-variables

ANOVA’s: Categorical x-variables

20
Q

ANCOVA

A

Deals with both types of variables

21
Q

How can the GLM deal with multiple criterion variables

A
  1. Run multiple ANOVA’s/ Regression
22
Q

Discriminant function analysis

A

Follow up on a multivariate test

23
Q

Benefits of using the GLM

A
  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
24
Q

Data =

A

Model + Error

25
Q

Data

A

Values obtained from scientific experiments

Scores on variable of interest for the researcher

Actual scores on Y

Y

26
Q

Goal of collecting data

A

Explains why people score on the criterion the way they do

Build statistical models to explain and predict the scores on the criterion

27
Q

Model

A

Prediction is a way of demonstrating an understanding of something

Combines one or more predictor variable to predict scores (y’)

28
Q

Goal for the model

A

Build a statistical model that can accurately predict data

29
Q

Types of models

A

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

30
Q

Simple model

A

predicts a constant score for everyone

Everyone will get 85%

31
Q

Less simple model

A

Predicts group mean for everyone

Class average was 82% after first exam so that is our prediction

32
Q

Little more complex model

A

Adding an x variable to predict the outcome

Use study hours to predict grades

33
Q

Fairly complex model

A

Add multiple predictors to predict outcome

Use study time, stress levels and sleep to predict grades

34
Q

Error

A

Predicted scores compared to actual scores

Model’s accuracy in predicting y

35
Q

Why study error?

A

To eliminate it and improve our model

36
Q

Counting error

A

Counting the number of scores you got incorrect

100% error

37
Q

Absolute error

A

Taking absolute values of the errors

38
Q

Sum of the squared errors (SSE)

A

Square the error terms and then sum them

39
Q

Why use the SSE?

A

Rewards for small error and punishes the model for large error

40
Q

What is the SSE

A

Variance your data cannot explain

41
Q

Accounted Variance + Error

A

100% variance

42
Q

A model predicts a phenomenon well means

A

We understand our model

Psyc 411 (2 decks)

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