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From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez
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From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

Dec 28, 2015

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Page 1: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

From GLM to HLMWorking with

Continuous Outcomes

EPSY 5245Michael C. Rodriguez

Page 2: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

Continuous Variables

• Review statistical procedures for continuous variables

• Consider options on Variables Chart• Generalize options under the GLM approach

Page 3: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

Statistical Paradigm

• Model Building • Estimation of Parameters• Testing Fit of the Model

Page 4: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

Model Building

• Theory• Model Specification• Measurement• Data Collection

Page 5: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

Estimation of Parameters

• Conceptualizing Relevant Factors• A General Approach to Data Analysis• GLM Model Assumptions

Page 6: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

General Approach to Data Analysis

• Univariate & bivariate descriptive analyses• Specifying the model• Testing interaction terms• Removing insignificant terms• Examining outliers• Checking assumptions

Page 7: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

General Linear Model Assumptions

• STOCHASTIC Independence Normality Mean of zero Homogeneity of variance Independence from

explanatory variables

• STRUCTURAL Independent observations Linear relationships Variable independence Errorless measurement Correct specification

Page 8: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

Testing Model-Data Fit

• Parsimony• Indicators

– Correlation– Simple Regression– Multiple Regression

• Controlling Type-I error

Page 9: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

Common Problemsin the analysis of clustered (nested) data

• The “unit of analysis” problem – misestimated precision

• Testing hypotheses about effects occurring at each level and across levels

• Problems related to measurement of change or growth

Page 10: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

Estimation of parameters requires some distributional assumptions. One requires the error term (the part of the outcome that is not explained by observed factors) to be independent and identically distributed.

This is in contrast with the idea that people exist within meaningful relationships in organizations.

Frank, K. (1998). Quantitative methods for studying social contexts. Review of Research in Education, 23, 171-216.

Estimation Requirements

Page 11: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

Handout: Elements of the Regression Line

The Notation of Regression

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Page 12: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

What’s in a name…

• Sociology: Multilevel Models• Biometrics: Mixed-Effects Models or

Random-Effects Models• Econometrics: Random-Coefficient

Regression Models• Statistics: Covariance Components

Models

Page 13: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

When to use HLM

• Nested data: Dependent observations• Do children of different gender, race, or

exposure to different reading programs grow at the same rate in reading?

• Is the relationship between social status and achievement the same in schools of different size or sector (public v. catholic)?

Page 14: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

Building Models in HLM

• Level One– Within Units

• Level Two– Between Units

Page 15: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

Examples of Multiple Levels

Level 1 Level 2 Level 3Students Classrooms SchoolsTeachers Schools School DistrictsChildren Families NeighborhoodsFamilies Neighborhoods CitiesNurses Wards/Units HospitalsWorkers US-Based Firms Multinational FirmsJuvenile Delinquents Social Workers Social Service AgenciesLongitudinal Scores Students Teachers

Page 16: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

Advantages of HLM

• Adjusting for nonindependence of observations within subjects

• Larger framework for real-life problems• Unbalanced designs and missing data are

accommodated

Page 17: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

Alternatives to HLM

• Individual level example• Group level example• Just use regression

Page 18: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

What do we gain through HLM?

Improved estimation of effects within individual units.

Example: Developing an improved estimate of a regression model for an individual school by borrowing strength from the fact that similar relationships exist for other schools.

Page 19: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

What do we gain through HLM?

Formulation and testing of hypotheses about cross-level effects.

Example: How school size might be related to the magnitude of the relationship between social class and academic achievement within schools.

Page 20: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

What do we gain through HLM?

Partitioning variance and covariance components among levels.

Example: Decomposing the correlation among a set of student-level variables into within- and between-school components. How much of the variance is within or between schools?

Page 21: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

A relationship between 8th grade and 11th grade performance?

5

10

15

20

25

30

35

40

45

0 10 20 30 40 50

8th Grade Math Score

11thGrade Math Score

Goldstein (1999). http://www.arnoldpublishers.com/support/goldstein.htm

Page 22: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

Accounting for school

5

10

15

20

25

30

35

40

45

0 10 20 30 40 50

8th Grade Math Score

11thGradeMathScore

Goldstein (1999). http://www.arnoldpublishers.com/support/goldstein.htm

Page 23: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

Socioeconomic Status

Ach

ieve

men

t

When school is a meaningful organizational unit, relations may be a

function of the unit.

Page 24: From GLM to HLM Working with Continuous Outcomes EPSY 5245 Michael C. Rodriguez.

Example Model

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