Reference no: EM133027772

PSY 5993-41 Directed Research - Evaluating Evidence in the Psychology of Culture, Race, and Ethnicity - University of Minnesota

For your semester project you will develop an idea based on your own interests that involves evaluating evidence on a topic related to the psychology of culture, race, and ethnicity. You will then carry out your own small empirical meta-science study, which will allow you to say something about the state of evidence on a particular topic.

The goals of this project are to 1) learn more about a topic in the psychology of culture, race, and ethnicity that interests you, 2) devise a small empirical project to explore that topic, 3) get experience retrieving research articles from a database, 4) get experience assessing evidence in a rigorous way, 5) get experience with analyzing data using R, and 6) communicate what you have learned through this project. It is a semester project and not a "final project" because you will need to work on it throughout the second half of the semester, and not just in the final couple of weeks.

There is a lot of flexibility in how to approach this project (which can be both good and bad!). Ideally there is some issue that has piqued your interest either before or during the semester that you would like to explore. Here is what is required for the project:

1. Develop an overarching research question, which may or may not include testable hypotheses. What do you want to know? What is the goal of your project? This should be a succinct but detailed statement of a sentence or two.

2. Determine the data source you will use for your project. You can take one of two approaches:

Option 1: Locate between 10-20 empirical research articles on your topic. They must be based on original data, not a theory/review paper or meta-analysis, and they must be quantitative data, not qualitative data (i.e., they used statistical analyses).

Option 2: Locate a recent research article (since 2020) on your topic, and then extract 10-20 empirical research articles that were cited in the Introduction section of that paper (similar to what you did with the Kitayama & Park article). Again, the articles you extract must be based on original quantitative data. You will want to be sure that you select an initial article that has sufficient references in the Introduction section of the paper (this could be a challenge).

3. But what do you do with these articles once you have them? How do you turn the articles into data that you can analyze? The way to think about your project is like this: you have collected a bunch of articles and read them. Someone asks you, "hey, what do we know about [insert your topic]?" Your project should be designed to address this question, not only in terms of *what* we know on the topic but *how* we know it (e.g., the nature of the studies). For the *what* we know, you will want to primarily extract the p-values for the primary tests and the associated effect sizes. For the *how* we know it, you might include the sample size, country, race/ethnicity, gender, method (e.g., survey, experiment), design (e.g., cross-sectional, longitudinal), sample source (e.g., college students, clinic), and so on. Basically anything that stands out to you as interesting as you review your articles, and that seems to vary across the articles, is something that you can code for. The Ritchie article that we read earlier in the semester could be helpful for generating some ideas. There is no minimum or maximum number of categories to code for, but you should probably do at least 5 so that you have sufficient data to work with (in addition to p-values and effect sizes).

You will use this coding to create your own dataset. This dataset will have the articles as the rows, and the content coding categories as the columns, with values inserted for each. Once you have coded all of the articles for all of the categories, you have your dataset and you are ready to do some analysis!

4. All analyses are to be done using R. Your analyses should be designed to examine the research questions you outlined at the beginning of the project, which vary a lot in terms of their specificity. But, in general, you will want to ask some interesting questions about your data and conduct the associated tests. You will want to generate some questions that can be addressed with your data, and then determine which statistical tests you should do. In addition to these tests, you should conduct basic descriptive statistics (means, standard deviations, frequencies, etc.) of your dataset and include this information in your final paper (in text and/or in tables). All of the R code you should need for these analyses are included in Phase 3 of the tutorial. Of course, if you get stuck just ask! As part of the analysis you also need to include at least two plots. For example, boxplots are good for differences in group means and scatterplots for correlations. Or you could create a histogram showing the distribution of p values or effect sizes. But you can use any plot that works well with the data you have.

5. Write a paper reporting on your study and findings. This should be structured as a standard APA-style paper, with Introduction/Method/Results/Discussion/References sections, but will be much shorter than most published articles (1200-1500 words, ~5 double-spaced pages, not including tables/figures/references/appendixes).

But what to include in the paper?

The Introduction should set the stage for the topic. What are you studying and why? Provide a description of the key constructs you are focusing on. What kind of research has been done on this topic? What seem to be some major findings and conclusions? Remember, this is a brief paper! Your Introduction should be two pages at most. So just focus on summarizing why this topic is important and what has been generally found. This should then lead into your overall research questions that guided your project.

The Method section should include a description of your article search process (e.g., did you use Google Scholar, PsycInfo; what search terms did you use; how difficult was it to locate articles) and the articles you included in your analysis. The citations for these should go in your References section, but I would also make a table that includes them (because you might have some other references in your reference section as part of your Intro). Then you will want to have a full description of each of your coding categories: what they are, how they were defined, what the different levels/options were, etc.

The Results section is where you report on your analysis. Here you will report your summary descriptive statistics and any test that you ran. Use APA style for all of your reporting. You are free to use tables and figures as you see fit, but need at least two figures/plot. Save all of your R code that you use for all aspects of your analysis and include it at the end of the paper as an appendix (not included in the word count).

The Discussion section should include a brief overall summary of what you found. What do you conclude about this work? What do you determine is the quality of the articles that you read and coded? Then, write a little bit about limitations to your project or what you could have done differently.

Finally, the reference section should include full APA-style citations for all articles used in the project, either that you coded or used as references in other parts of the paper.

The paper should be approximately 1200-1500 words (~5 double-spaced pages, not including tables/figures/references/appendixes). You can use whatever font type and size you want (but be reasonable!).

6. As noted above, the paper must include at least two plots/figures created in R that are based on your project data.

Attachment:- Directed Research Project.rar