Sample DDLG: Is Love Really Blind?
ICPSR offers Data-Driven Learning Guides (DDLG) to help you get started exploring ICPSR datasets. Each DDLG is focused on a specific theme and has the same elements.
You can browse DDLGs by title, analysis method, or subject term. When choosing a DDLG to work with, you may wish to choose one on a subject that interests you, and/or an analysis method with which you are comfortable. The most basic DDLGs are those using cross-tabulation and frequency as analysis methods.
You will be asked to log in to your MyData account to view DDLGs, if you are not logged in already. Once you click on the title of a DDLG, you will be brought to an introductory page; click the green Go to Resource button to view the whole guide:
Every DDLG has a number of sections:
Goal & Concept: Explains the data analysis concept (for example, comparison of means) which the DDLG is designed to teach you. It also includes important questions that you might ask about this type of data/study in order to better understand it.
Dataset: Tells you about the dataset that the DDLG is based on and the background of the study.
Variables: A list of important variables in the study that will be brought into the DDLG. This section also explains any changes that have been made to the variables (for example, sometimes variables have been combined) to make them more manageable for this assignment.
Application: This is the key section of the DDLG! It walks you through various (pre-compiled) analyses of the data and questions that you might ask.
Interpretation: The Interpretation section reiterates, in a single list, the questions asked in the Application section. After you have answered the questions, click to view the Interpretation Guide and see some of the questions/considerations that ICPSR staff suggest you think about to understand this study.
Summary: The "elevator speech" -- a review of the purpose of the DDLG and important conclusions from the study.
Bibliography: A selected list of research articles that are related to the study -- either because they use the dataset, or because they deal with related topics.