Students will have deliverables due on the off-period between virtual sessions (see below). The instructor will have contact with and interact with the students offline (via email) on these deliverables.
Pre-class assignment:
There is one relatively straight-forward pre-class assignment for the face-to-face class on January 17th. This assignment will not be graded. It is intended simply to gauge your level of statistical capability. See below for this assignment.
Assignments:
#1: Between-class Assignments
These assignments will give you an opportunity to show that you are able to design a quantitative research project (please note that this is not about how to execute such a project). The final deliverable of these building assignments will be a fully fleshed–out topic analysis, as described in the Davis et al. book and in the Davis et al. (1979) handout for these elements. See “Handouts” file sub-directory for the latter.
Here is the outline of the elements of the typical topic analysis:
- The problem, hypothesis, or question
- The project’s importance
- Significance of prior work on the subject
- Project methodology or research approach
- Possible outcomes and the importance of each
- Timetable for each of the project phases plus targeted outlet.
Please note the appraisal questions in the Davis et al. book that will help you to fill in these various sections.
Here are the specific deliverables to be sent to straubdetmar@gmail.com:
BCI #1: January 18-27 (due January 25th at 8pm EST)
Pre-topic analysis. Come up with one or more research questions that are best studied using a quantitative paradigm. Research questions, or RQs, should express the aim of the research and are often highly similar to research goals or objectives. A simple one would be like this: “In an IT outsourcing setting, does the client’s perception of the vendor’s legitimacy make a difference in the outsourcing performance?” This example, by the way, will be used also in explaining how to respond to criticisms by reviewers about the endogeneity threats to your quantitative study.
If your RQ (or RQs) call for a short set of explanations or definitions, then by all means please provide these to the instructor
BCI #2: January 29-February 10 (due February 8th at 8pm EST)
The problem, hypothesis, or question
BCI #3: February 12-February 24 (due February 22nd at 8pm EST)
The project’s importance
BCI #4: February 26-March 9 (due March 7th at 8pm EST)
Significance of prior work on the subject. Please note that this is nothing like an exhaustive literature review. A scanning on the topic should reap more than enough references.
BCI #5: March 11-March 23 (due March 21st at 8pm EST)
Project methodology or research approach. This section will demonstrate your learning in the course more than any other section.
BCI #6: March 25-April 2 (due March 31st at 8pm EST)
Possible outcomes and the importance of each and timetable for each of the project phases plus targeted outlet
BCI #7: April 4-April 16 (due April 14th at 8pm EST)
Your completed, fully-fleshed-out topic analysis. The guidelines for length in the Davis et al. book are about 6 single-spaced pages. You can go beyond this if you like. But please create at least 6 pages.
#2: Discussion during Class and Virtual Zoom Sessions
Your class contribution grade for this course will be assessed in terms of the quality and quantity of your participation in class discussion (in both face-to-face and online sessions). Including but not limited to: your depth of analysis; the realism of your comments or analysis; the clarity of your presentation; the integration of your comments into the ongoing discussion (i.e., willingness to listen to classmates); your ability to respond to questions and to defend your arguments; and the contribution of your comments to the class’s overall learning.
- Essentially, you will be graded on the thoroughness, sophistication, persuasiveness, and logic of your critical analysis. It is important to note that a failure to meaningfully and regularly participate in class can result in a final grade a full letter lower than that received on other graded items. Therefore, attendance is important – please arrange your schedule so that you will be able to arrive on time, attend each class, and stay for the entire class period.
#3: Final Project (due 3 days before our virtual class on April 17th; thus, the due date is
April 14th at 7pm)
Please create self-selected groups of two (with special permission, groups can be different sizes, including a group of one person) and work together on this assignment.
- The purpose of this group assignment is to allow participants to analyze data and interpret results. The only conceivable way this can be done within a single semester is if you can get access to data that has already been collected.
- First, therefore, find a relatively straight-forward archival dataset. You can do this by asking your prior instructors, for example. Alternatively see if you can find interesting datasets online. Likely questionnaires (or surveys), for example. Or firm transactional data, perhaps. Some of these may be old and/or have been sanitized (i.e., “de-individualized” data). Proprietary datasets are also possible. There are publicly-available datasets. Google for these.
- Formulate a research model to be tested through the dataset. Indicate pictorially how you capture measures for each construct. (This is really about how you are mapping the measures to the construct; it is a form of content validity argumentation.) Since the point of this exercise is to utilize statistical tools to, if possible, validate your instrument(s) and then test the structural paths (causality), do not worry about whether your model is theoretically justifiable. This is a class project for learning to apply a research method, handle data, and use statistical tools and is not intended to lead you to a submittable paper to a journal or conference.
- Second, see if there are ways to examine the measurement properties of your instrument(s). If so, run the various tests to see if the instrument scales (reflective) are reliable and if they demonstrate construct validity.
- Third, analyze your data to test the causal paths using regression, ANOVA, ANCOVA, MANOVA, or MANCOVA (or PLS). Or use non-parametric tools. If you want to use other data analysis software tools, please consult with the instructor. It will likely be fine, but I would like to be able to help you with setting up the data and interpreting the results.
- Deliverables and Preparation
The oral presentation of the course project is scheduled for the last session of the course. In these presentations, you will want to present your best arguments for the importance of the hypothesis tests. Graphical aids will be most welcome. Take 20 minutes maximum for the oral presentation. The ppt-file containing the oral presentation has to be sent to the instructor via e-mail no later than 3 days before the class when it is due.