The final Pecha-Kucha assignment is composed of three interconnected tasks. You need to pass all three in order to be successful in this assginment. There are three deadlines, one for each task:
TL;DR: Pick a data-set related to a topic in social-psychology, explore the data in detail and run regression analyses. That is your final project.
The goal of the final project is for you to use regression analysis to analyze a dataset related to a topic in social-psychology. Choose the data based on your interest, work you have done in other courses or work you intend to do in your capstone or living labs. Your objectives are the following:
The four primary deliverables for the final project are
In order for you to have the greatest chance of success with this project it is important that you choose a manageable dataset. This means that the data should be readily accessible and large enough that multiple relationships can be explored. Ideally, your dataset should have at least 100 observations and at least 10 variables (exceptions can be made but you must speak with me first). The response variable may be quantitative or categorical, and the data set should both quantitative and categorical variables that can be potential predictors.
Reusing datasets from class: You may not reuse datasets used in examples / homework in the class.
All analyses must be done in RStudio, and your final written report and analysis must be reproducible and professional. This means that you must create an R Markdown document, and include it together with other relevant resources (pdf/html, dataset, images etc…) in a zip file. It must be possible to reproduce your report exactly upon knitting. Whenever possible, you want to calculate values on the fly, avoid typing them in. You can find resources and ideas for topics further in this document below.
Your written report must be done using R Markdown.
Before you finalize your write up, make sure the printing of code chunks is off with the option echo = FALSE.
The mandatory components of the report will be posted below. You are free to add additional sections as necessary. There is no minimum page requirement; however, you must comprehensively address all of the aspects mentioned below.
Please be judicious in what you include in your final write-up.
The overall structure of your report should look as follows:
Introduction and data: This section includes an introduction to the project motivation, data, and research question. Describe the data and define key variables. What is the background to the data? Why have you chosen it? How is it related to social psychology? Why is worth exploring? What is your research question? In brief: what are some of the publications (academic, popular or other) saying about the topic?
Exploratory data analysis (EDA): Include exploratory data analysis (descriptive statistics), illustrating the distributions of some of your variables, any interesting joint distributions (using density heat-maps or density contour maps), scatter-plots showing the association between variables etc. All of the EDA won’t fit in the paper, so focus on the EDA for the response variable and a few other interesting variables and relationships.
Methodology: This section includes a brief description of your modeling process. Explain the reasoning for the type of models you experimented with, the predictor variables you considered for the model including any interactions and non-linear terms. Additionally, show how you arrived at the final model by describing the model selection process, any variable transformations (if needed), and any other relevant considerations that were part of the model fitting process.
Results: This is also where you will output the final model and include a brief discussion of the model assumptions, diagnostics, and any relevant model fit statistics. Additionally, interpret the key results from the model. The goal is not to interpret every single variable in the model but rather to show that you are proficient in answering your research question using the results from the analysis. Focus on the variables that help you answer the research question and that provide relevant context for the reader.
Discussion: This section is a conclusion and discussion. This will require a summary of what you have learned about your research question along with statistical arguments supporting your conclusions. Also, critique your own methods and provide suggestions for improving your analysis. Issues pertaining to the reliability and validity of your data and appropriateness of the statistical analysis should also be discussed here. Include 1 - 2 paragraphs on what you would do differently if you were able to start over with the project or what you would do next if you were going to continue work on the project should also be included.
The final write up will be neatly organized with clear section headers and appropriately sized figures with informative labels. All code, warnings, and messages are suppressed. Overall, the document would be presentable in a business or research setting.
Below you can find several research studies and data-sets, related to social psychology. The text below is quoted verbatim, from Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Sage publications. The datasets, zip file and other resources can be found in this shared drive.
Chamorro-Premuzic et al. (2008) measured students’ personality characteristics and asked them to rate how much they wanted these same characteristics in their lecturers. There is some evidence that students tend to pick courses of lecturers whom they perceive to be enthusiastic and good communicators. In a fascinating study, Tomas Chamorro-Premuzic and his colleagues (Chamorro-Premuzic, Furnham, Christopher, Garwood, & Martin, 2008) tested a slightly different hypothesis, which was that students tend to like lecturers who are like themselves. First of all, the authors measured students’ own personalities using a very well-established measure (the NEO-FFI) which gives rise to scores on five fundamental personality traits: Neuroticism, Extroversion, Openness to experience, Agreeableness and Conscientiousness. They also gave students a questionnaire that asked them to rate how much they wanted their lecturer to have each of a list of characteristics. For example, they would be given the description ‘warm: friendly, warm, sociable, cheerful, affectionate, outgoing’ and asked to rate how much they wanted to see this in a lecturer from −5 (they don’t want this characteristic at all) through 0 (the characteristic is not important) to +5 (I really want this characteristic in my lecturer). The characteristics on the questionnaire all related to personality characteristics measured by the NEO-FFI. As such, the authors had a measure of how much a student had each of the five core personality characteristics, but also a measure of how much they wanted to see those same characteristics in their lecturer. In doing so, Tomas and his colleagues could test whether, for instance, extroverted students want extrovert lecturers. An extract of the data from this study are in the file Chamorro-Premuzic.csv.
Here, you can see whether students’ personality characteristics predict the characteristics that they would like to see in their lecturers. Carry out five multiple regression analyses: the outcome variable in each of the five analyses is how much students want to see neuroticism, extroversion, openness to experience, agreeableness and conscientiousness. For each of these outcomes, force Age and Gender into the analysis in the first step of the hierarchy, then in the second block force in the five student personality traits (Neuroticism, Extroversion, Openness to experience, Agreeableness and Conscientiousness). For each analysis create a table of the results, and compare your results with Table 4 in the original article.
Original article: Chamorro-Premuzic, T., Furnham, A., Christopher, A. N., Garwood, J., & Martin, N. (2008). Birds of a feather: Students’ preferences for lecturers’ personalities as predicted by their own personality and learning approaches. Personality and Individual Differences, 44, 965–976.
In this study, researchers explored the relationship between aggression and several potential predicting factors in 666 children who had an older sibling. Variables measured were Parenting_Style (high score = bad parenting practices), Computer_Games (high score = more time spent playing computer games), Television (high score = more time spent watching television), Diet (high score = the child has a good diet low in additives), and Sibling_Aggression (high score = more aggression seen in their older sibling). Past research indicated that parenting style and sibling aggression were good predictors of the level of aggression in the younger child. All other variables were treated in an exploratory fashion. The data are in the file ChildAggression.csv. Explore and analyse the data with multiple regression.
Lacourse, Claes, and Villeneuve (2001) carried out a study to see whether a love of heavy metal could predict suicide risk. Questionnaires were used to measure several variables: suicide risk (yes or no), marital status of parents (together or divorced/separated), the extent to which the person’s mother and father were neglectful, self-estrangement/powerlessness (adolescents who have negative self-perceptions, are bored with life, etc.), social isolation (feelings of a lack of support), normlessness (beliefs that socially disapproved behaviours can be used to achieve certain goals), meaninglessness (doubting that school is relevant to gaining employment) and drug use. In addition, the authors measured liking of heavy metal; they included the sub-genres of classic (Black Sabbath, Iron Maiden), thrash metal (Slayer, Metallica), death/black metal (Obituary, Burzum) and gothic (Marilyn Manson). As well as liking, they measured behavioural manifestations of worshipping these bands (e.g., hanging posters, hanging out with other metal fans) and vicarious music listening (whether music was used when angry or to bring out aggressive moods). They used logistic regression to predict suicide risk from these predictors for males and females separately. The data for the female sample are in the file Lacourse et al. (2001) Females.csv. You need to carry out a logistic regression predicting Suicide_Risk from all of the predictors (forced entry).
To make your results easier to compare to the published results, enter the predictors in the same order as in Table 3 in the paper: Age, Marital_Status, Mother_Negligence, Father_Negligence, Self_Estrangement, Isolation, Normlessness, Meaninglessness, Drug_Use, Metal, Worshipping, Vicarious). Create a table of the results, and compare it to the results in the published paper. Does listening to heavy metal predict girls’ suicide? If not, what does?
Original article: Lacourse, E., Claes, M., & Villeneuve, M. (2001). Heavy metal music and adolescent suicidal risk. Journal of youth and adolescence, 30(3), 321-332.
Research has shown that lecturers are among the most stressed workers. A researcher wanted to know exactly what it was about being a lecturer that created this stress and subsequent burnout. She took 467 lecturers and administered several questionnaires to them that measured: Burnout (burnt out or not), Perceived Control (high score = low perceived control), Coping Style (high score = high ability to cope with stress), Stress from Teaching (high score = teaching creates a lot of stress for the person), Stress from Research (high score = research creates a lot of stress for the person) and Stress from Providing Pastoral Care (high score = providing pastoral care creates a lot of stress for the person). The outcome of interest was burnout, and Cooper, Sloan, and Williams’s (1988) model of stress indicates that perceived control and coping style are important predictors of this variable. The remaining predictors were measured to see the unique contribution of different aspects of a lecturer’s work to their burnout. Can you help her out by conducting a logistic regression to see which factors predict burnout? The data are in Burnout.csv.
Health psychologists studying HIV want to know the factors that influence condom use with a new partner (relationship less than 1 month old). The outcome measure was whether a condom was used (use: condom used = 1, not used = 0). The predictor variables were mainly scales from the Condom Attitude Scale (CAS) by Sacco, Levine, Reed, and Thompson (1991):
Previous research (Sacco, Rickman, Thompson, Levine, & Reed, 1993) has shown that gender, relationship safety and perceived risk predict condom use. Carry out an appropriate analysis to verify these previous findings, and to test whether selfcontrol, previous usage and sexual experience can predict any of the remaining variance in condom use. Try addressing the following:
condom.csv.Cosmetic surgery is on the increase at the moment. In the USA, there was a 1600% increase in cosmetic surgical and non-surgical treatments between 1992 and 2002, and in 2004, 65,000 people in the UK underwent privately and publicly funded operations (Kellett, Clarke, & McGill, 2008). With the increasing popularity of this surgery, many people are starting to question the motives of those who want to go under the knife. There are two main reasons to have cosmetic surgery: * to help a physical problem such as having breast reduction surgery to relieve backache; and * to change your external appearance, for example by having a face lift.
Related to this second point, there is even some case for arguing that cosmetic surgery could be performed as a psychological intervention: to improve self-esteem (Cook, Rosser, & Salmon, 2006; Kellett et al., 2008). The dataset Cosmetic Surgery.csv looks at the effects of cosmetic surgery on quality of life. The variables in the data file are:
There are loads of examples on how to do a Pecha-Kucha in an engaging manner. This presentation is particularly interesting, because it shows you how to transform your data into a story. But there are other useful videos to improve your presentation skills, such as this one.