Instructors' Guide to DDLGs
Intended Audience. The intended audience for the Data-Driven Learning Guides (DDLGs) is students in lower-division courses in the social sciences. The concepts chosen for the DDLGs were determined to be those that were consistent across textbooks commonly used in Introductory courses and sample syllabi from such courses.
Uses in Class. Analyses are set up such that the exercises can be used by students or for example purposes in class. Enough information is given so that students should be able to work through the exercises on their own, but analyses are also hyperlinked in order to make them easy to use in class. Determine which application item you want to show and simply click the link and a new window opens displaying the results. New guides will be continually added to the site.
Data-Driven Learning Guides. The DDLGs are set up to reflect lesson plans: a statement of the goal; brief description of the focal concept, its use in social science (with example research questions), and the dataset used for the exercise; an empirical application of the concept using basic statistical techniques; a guide to interpretation and a summary of the exercise; and a bibliography of related literature for further study. Students can quickly become familiar with the structure because it remains constant.
Statistical Sophistication. Students are not required to have any prior statistics background to be able to use the DDLGs. All analyses are done using the Survey Documentation and Analysis online analysis system behind the scenes so that students do not even need to interact with that interface. (This also makes it easy to show analysis examples quickly in class - no need to worry about which box to click on or where to put the variable name.) Simply clicking on a link opens a new window which displays the results. Help is given for interpreting the results in the "Interpretation Guide" found after the restatement of the application questions on the Interpretation and Summary page. Most guides make use of very basic univariate and bivariate statistical techniques (frequences, crosstabs, comparisons of means). Also, variables are simplified where necessary by collapsing categories or defining "don't know" or other unusable categories as missing. Specific notes about what was done are included in the application sections of the DDLGs so the process is transparent and instructive.
Printer-friendly versions of the DDLGs are also available which include the answers to the application questions but not the statistical analyses themselves. The option for this printer-friendly version appears at the top right corner of the white text box in each learning guide.
Flexibility. In order to try to appeal to a wider audience, DDLGs are all written to stand on their own - no guide assumes that a student has done any other guide before it. This has both benefits and drawbacks. The strongest benefit is that you don't have to worry about assigning guides in a particular order or using all of the guides in a series. One drawback, however, is that students may see the same "hints for interpretation," for example, in several consecutive guides. Given that students can learn and gain confidence from this repetition and can easily skip those hints if they choose, we felt that the benefits outweighed the potential drawbacks.
Additional flexibility is built into the DDLGs by including a link to the ICPSR Web page describing the dataset used. All datasets are chosen for their utility in the study of some aspect of the focal concept. The specific variables initially used are noted and variables created by manipulating (recoding, defining missing categories) those are also stored in the version of the dataset used for online analysis. Therefore, students can easily access the data to do follow-up analyses if you require them to do so. In this way, you can follow up on questions for "further research" noted at the end of DDLGs or other questions in which they are interested.
Coming Soon! New DDLGs will be continually added to the site to create both greater breadth of topics covered and greater depth of coverage of a single substantive area. This should allow instructors of substantive courses to include a series of empirical activities in those courses as well. In addition, we also hope to have a means for instructors to rate the guides and even contribute their own in the future. Finally, a longer-term future goal is to develop tools to assist with the assessment of student learning based on the DDLGs.