East Carolina University
Department of Psychology

Required Research Presentation in PSYC 7433

     Students in PSYC 7433 are required to deliver, in class, a research paper in which they present the results of a multivariate analysis. The data for such analysis may be real data that have been collected by the student (such as the data for the student's thesis or dissertation), real data that have been obtained from an archive, or simulated data (obtained from a text book or similar source, or simulated by the student).

    If the data were originally obtained in association with a project for another class (such as psychometrics), that must be acknowledged and the analysis conducted for the 7433 project must represent a substantial addition to those conducted for the project in the other class -- for example, if a factor analysis was conducted for psychometrics, the same data may be used for the 7433 class, but the analysis must go beyond the factor analysis done for psychometrics.  Likewise, if the data were collected for a previously completed thesis, the analysis for the 7433 class must be different from that conducted for the thesis.

    The statistical analysis must include one or more of the following: log-linear analysis of multidimensional contingency tables, multiple regression analysis which includes testing of a moderation or mediation model, logistic regression,  canonical correlation analysis, discriminant function analysis, multiple analysis of variance, multiple analysis of covariance, the multivariate approach to repeated measures ANOVA, principal components analysis, factor analysis, path analysis, univariate factorial ANOVA (at least three factors), univariate factorial ANCOV (at least two factors, at least one covariate), or other techniques for which the student obtains prior approval.

       The presentations will be like those one sees at a scholarly convention.  Each presenter will be allocated 20 minutes, and asked to reserve the last 5 minutes for questions from the audience.  The presentation should be accompanied by a PowerPoint show.

     The presentation should include:

    Each presenter will also prepare an APA-style abstract and post it in the Research Presentation Forum in BlackBoard.  Do not prepare the abstract in Word, as text pasted from Word into BlackBoard is not properly displayed.  Instead of Word, use a plain text editor (like Notepad) or an htm editor or just type the abstract directly into BlackBoard.   Consult the APA Publication Manual for details on what should be included in the abstract.  The abstract should not exceed approximately 120 words in length.

    To post your abstract, enter the forum, click new thread, and then type your last name as the title of the thread.  Enter your abstract in the message area.  After posting the abstract in BlackBoard, you should attach supporting documents, zipped into a *.zip archiveYour abstract and the zip archive of supporting documents is due in BlackBoard not later than 24 hours before the time of your presentation.

       Prefix each file name with your last name followed by an underscore character and then indication of the type of file -- for example:

     I suggest that have a good look at the examples provided by previous students in this class.  These can be found in the Research Presentation Forum in BlackBoard.

    There are lots of data sets in archives out on the Internet. You could retrieve one of these to use for your project. Links to some sources can be found on my Data Files Page. It is not appropriate to use any data file on Dr. Wuensch's StatData Page -- those are the files that we have been using in class.  If you are going to be using a data file you found on the Internet, email all members of the class to claim that file as yours.  It is not appropriate for two or more students to use the same data file, unless Professor Karl approves a partitioning of the variables, one set for one student, a different set for the other student.

    Here are some suggestions which might help you avoid some of the mistakes that have been common in the past:

     1. Be sure to check your data regarding whether or not they meet the assumptions of the procedures you are using (for example, for parametric ANOVA, normality and homogeneity of variance). If there are serious violations of assumptions, take appropriate corrective action (transformation, adjustment of degrees of freedom, etc.). If you cannot correct the problem, at least acknowledge that the problem exists.

     2. Do not neglect to report the important descriptive statistics.

     3. In your program, name the variables in descriptive fashion ("income," "sex," "race," "education" rather than "Y," "A," "B," "C") and use PROC FORMAT or VALUE LABELS or such to provide descriptive labels to numeric codes (for example, "female" and "male" rather than "0" and "1".  Annotate your output to make it easier for Karl to read and understand it.

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Dr. Karl L. Wuensch

This page most recently revised on the 4th of January, 2015.