<p> Data Analysis Project for Psyc451</p><p>The product of this project is a research report in handout form and either a poster or a powerpoint presentation of sufficient complexity and quality to: 1) demonstrate data analytic and writing skills you have acquired this semester and 2) to present at a regional or student conference.</p><p>Data Sets for the Project  Ideally you will be working on research with a faculty member or graduate student who can provide you with some data that is relevant to the work you are doing together. Be sure to tell this person why you want the data, how you will be using it, etc. If you would like, Cal will talk with this person to be sure they have a clear idea of what we are asking and what will be done with the data.  There are many public access datasets available on the web. Talk to one of the Reference Librarians about accessing the search engines designed for identifying and accessing public data sets.  If you don’t acquire a data set of your own, we have several data sets you can work with. These can be accessed from the class webpage. There are also vast numbers of datasets available on the web – frankly I’d prefer you use a data set that I or another local researcher are familiar with, but if you find something cool let me have a look at it before you invest too much energy.</p><p>Scope of the project: You may pick one of four types of analyses for your project or bargain with Cal for something different:  Nested/non-nested regression model comparisons (6-8 predictors) -- using one criterion variable & one population  Comparing a full regression model (6-8 predictors) across populations/groups -- using one criterion variable.  Comparing a full regression model (6-8 predictors) across criterion variables – using one population  Path/mediation analysis model (6-8 predictors) across no more than 4 layers – using one criterion variable in the 4th layer.</p><p> Bonuses  4% bonus for enlarged designs  more complex (multiple layers) hierarchical nested regression  more than 2 non-nested regression  more than 2 populations (multiple regression or path model)  more than 2 criterion variables (multiple regression or path model)  2% bonus for 10+ predictors  0.5% bonus for each additional reference  Up to 10% maximum total bonus The Rules</p><p> Late Penalties -- It is very important to keep things on track.  There is a single final grade for the project (which is worth 30% of your course grade)  5% deducted from your final project grade for anything not brought with you to a class/lab or that is handed in late on the day it is due  5% deducted from your final project grade for each additional day late</p><p> The final products you will produce depend…  if you are preparing a poster, you will produce …  the poster using the UNL Psyc format (Talent+ will pay for the printing if you present at a conference)  the poster will include the “important” results (there isn’t space for all of them)  an APA format paper  all results will be included in the paper – this is a show piece of your writing and analytic skills!</p><p> if you are preparing an oral presentation, you will produce …  the PowerPoint slides for the presentation (in note-augmented form)  the presentation will include the “important” results (there isn’t time for al of them)  an APA format paper  all results will be included in the paper – this is a show piece of your writing and analytic skills!</p><p> The posters, presentations and paper will have all of the components of APA format and style (same as you did in 350 Lab)  Data will be depicted in one or more well-constructed figures/tables  You may use either paragraph of "bullet" formats for the information in the method and results sections of the poster and power point slides.  The paper must be in paragraph form, with the proper section labels and formats</p><p> The introduction must include at least five (5) interrelated references that "set the stage" for the chosen data analysis, and describe why the chosen analysis will be a unique contribution to the related literature. Proposal for 451 Laboratory Project Name: ______</p><p>1. What data set will you be using (it must be approved by Cal if it is not one of the Project Datasets)?</p><p>2. What is your criterion variable(s)</p><p>Primary criterion: ______Mean = std = </p><p>3. Specify the predictor variable(s) – be sure to choose the correct type of analysis.</p><p>Nested/non-nested model comparisons (6-8 predictors) -- using one criterion variable & one population </p><p>1st model name: ______Predictor variables (include groups if qualitative): ______</p><p>2nd model name: ______Predictor variables (include groups if qualitative): ______</p><p>Why is it interesting to compare these models?</p><p>Comparing a full model (6-8 predictors) across populations/groups -- using one criterion variable.</p><p>1st population (N): ______2nd population (N): ______</p><p>Predictor variables (include groups if qualitative): ______</p><p>Why is it interesting to compare these populations on this model?</p><p>Comparing a full model (6-8 predictors) across criterion variables – using one population</p><p>2nd Criterion: ______Mean = std =</p><p>Predictors (include groups if qualitative): ______</p><p>Why is it interesting to compare these criterion variables on this model? </p><p>Path Analysis Model (6-8 predictors) – using one criterion and one population with no more than 4 layers (criterion if the 4th layer)</p><p>Predictors in layer 1 (include groups if qualitative): ______</p><p>Predictors in layer 2 (include groups if qualitative): ______</p><p>Predictors in layer 3 (include groups if qualitative): ______</p><p>Why is it interesting to investigate mediation between variables?</p><p>Something more interesting? If you plan to perform a combination or expansion of any of these, please specify what populations, criterion and models are involved and the comparisons you want to make above – add models, populations, etc. to give a full description of your plans.</p>