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PEELS Training Modules

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1Module 1: Introduction, Overview of PEELS Design, and Contents of Restricted-Use CD-Rom
Module 1 describes the PEELS study design and provides an overview of the restricted-use CD-ROM dataset. 32:04

2Module 2: Sampling, Weighting and Imputation
This module describes the PEELS sampling procedures, provides information on the development and use of weights, and presents some information on procedures used for imputing missing values. 16:47

3Module 3: Child Assessment Data Collection
Module 3 describes the direct and alternate child assessments, the specific subtests used, and assessment administration. 20:20

4Module 4: Parent Interview Data Collection
This module provides an introduction to the assessment and data collection procedures for the parent interviews. 15:13

5Module 5: Teacher Questionnaire Data Collection
Module 5 discusses the content and administration of the teacher questionnaires. 11:24

6Module 6: State, Local Education Agency, and Principal/Program Director Questionnaire Data Collection
This module explains data collection procedures and analysis issues for four PEELS questionnaires: SEA Policies and Practices Questionnaire; LEA Policies and Practices Questionnaire; Early Childhood Program Director Questionnaire; and the Elementary School Principal Questionnaire. 11:18

7Module 7: Using the Electronic Codebook (ECB)
This module introduces the PEELs electronic code book and describes how to install and use it to view information about variables, create taglists, and generate code to extract data into multiple software programs. 17:56

8Module 8: Statistical Software for Analyzing PEELS Data
Module 8 describes packages and software that can be used to analyze PEELS data. 7:30

9Module 9: Creating WesVar Files, Running Descriptive Statistics and Cross-Tabulations in WesVar
Module 9 describes how to create WesVar files using SAS and SPSS datasets; open a WesVar workbook; run descriptive statistics in WesVar including frequencies, percentages, means, medians, and interquartile ranges; and run cross-tabulations. 21:51

10Module 10: Applying Filters and Creating Derived Variables in WesVar
This module helps users apply filters or subset files and create and recode variables in WesVar. 10:38

11Module 11: Testing for Significant Differences in Wes Var
Module 11 shows how to use WesVar to analyze data for statistical significance. 16:23

12Module 12: Creating Combined Files and Selecting Weights
This module discusses combining data files and selecting appropriate weights for analysis. 8:30

13Module 13: Conducting Longitudinal Analyses
This module describes deriving variables that maximize the longitudinal nature of PEELs data and discusses important statistical issues to consider when conducting longitudinal analyses. 4:27

14Module 14: Running Regressions in WesVar
This module demonstrates specifying a regression model, determining the type of model based on a dependent variable, setting up dummy variables, running a linear regression in WesVar, and reviewing analysis output. 8:19

15Module 15: Analyzing PEELS Data with SPSS Part 1: Weights, Descriptive Statistics, Cross-Tabulations
Module 15 focuses on SPSS and demonstrates creating an analysis file that appropriately incorporates weights and running descriptive statistics and cross-tabulations. 10:56

16Module 16: Analyzing PEELS Data with SPSS Part 2: Testing for Significant Differences and Conducting Regressions
This module demonstrates how to test for significant differences in SPSS, including t test, ANOVAs, and chi-squares. 12:32

17Module 17: Multilevel Analysis with PEELS Data
This module introduces multilevel models and demonstrates how to create multilevel weights and datasets and run analyses using hierarchical linear modeling. 31:26

18Module 18: PEELS Datalab: Running Descriptive Statistics
Module 18 focuses on running simple descriptive statistics in Datalab. 09:58

19Module 19: PEELS Datalab: Running Complex Descriptive Statistics and Regressions
This module presents how to run complex descriptive statistics and regressions in Datalab. 16:24