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Exploring Multi-Level Models and Exam Data Analysis Techniques

This guide covers essential methods in multi-level analysis, including assumption checking and variance calculations for cross-classified models. Key areas of focus include verifying standard regression assumptions, evaluating normality in dummy sets, and understanding the correlation of multiple predictors. We revisit the THKS dataset and encourage familiarization with the exam data, emphasizing independent practice without do-files. Finally, we highlight the importance of narrative in statistical reporting, underscoring that the story behind data is crucial for effective communication and analysis.

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Exploring Multi-Level Models and Exam Data Analysis Techniques

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  1. Today’sleftovers • Assumptionchecking in multi-level • Cross-classifiedmulti-level models • The THKS data revisited • Ourexam data • It’s the story thatcounts ...

  2. 1. Assumptioncheckingin multi-level analysis • Check the standard regressionassumptionsusing “reg” (instead of xtreg or xtmixed) • Check the normality of the dummy set • Ifyou have “more thanoneGreek letter”, check theircorrelation

  3. 2. Cross-classifiedmulti-level models Variance at type: about 87% = 8587745/(8587745+206153+1104296) Variance at Dealer: About 2% = 206153/(8587745+206153+1104296)

  4. 3. The THKS data revisited <See the do-file online>

  5. 4. Ourexam data • Make surethatyouunderstandits content. • Practicewith the data at home. • You are NOTallowedtobring do-files withyou. • Last year’sexam is online (justto get a feel for the kinds of questions) <go toStatahere>

  6. 5. It’s the story thatcounts

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