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Psychology 202b Advanced Psychological Statistics, II

Psychology 202b Advanced Psychological Statistics, II. April 28, 2011. The Plan for Today. Review of regression assumptions. Introduce hierarchical linear models. Random effects. Acquiring software. Working with HLM. An example using High School and Beyond data. Regression assumptions.

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Psychology 202b Advanced Psychological Statistics, II

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  1. Psychology 202bAdvanced Psychological Statistics, II April 28, 2011

  2. The Plan for Today • Review of regression assumptions. • Introduce hierarchical linear models. • Random effects. • Acquiring software. • Working with HLM. • An example using High School and Beyond data.

  3. Regression assumptions • Linear relationships. • Independent errors. • Homoscedastic errors. • Normally distributed errors.

  4. Nested Data and Independence • Example: Students are nested in classrooms; classrooms are nested in schools; schools are nested in districts; districts are nested in counties… • Result: massive problems with assumption of independent errors.

  5. The Coleman Report Large, federally funded study of Equality of Educational Opportunity. Concluded that school variables (most notably, funding) did not affect student achievement. Represented school-level variables as repeated identical scores at the student level.

  6. High School and Beyond • Later, Coleman was a PI on a large, longitudinal study called High school and Beyond. • We’ll use a simple subset of that data set as an example.

  7. HSB Math Achievement • We will focus on HSB’s math achievement outcome. • Primary interest: does student SES affect math achievment? • Secondary interests: • Are public and catholic schools similar in this relationship? • Does school level SES matter?

  8. The levels • Level One: regression of Math on SES. • Level Two: regression of slope and intercept from level one on school characteristics. • We will treat slope and intercept as random effects.

  9. Random effects • Treating the slope and intercept as random effects acknowledges that there is unique variation between schools that is not captured by the model. • We will estimate a variance component for each regression parameter to represent that unique variation.

  10. Acquiring HLM (student version) • You can download a free, restricted version of HLM software here. • Limits: • No more than 5 effects at any level. • 3-level models: 8000 level-one units, 1700 level-two units, 60 level-three units. • 2-level models: 8000 level-one units, 350 level-two units.

  11. Next time • Using HLM for longitudinal analysis.

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