ADVANCED STATISTICS ASSIGNMENT -
AIMS - This unit covers the theoretical background, the performance, and the interpretation of range of statistical techniques intended for analysing data from psychological research.
LEARNING OUTCOMES - On completion of the unit, the student is expected to demonstrate:
1. Knowledge of advanced statistical analyses (including MANOVA, logistic regression, ANCOVA, log-linear analysis etc.);
2. Ability to select the appropriate test for the specific research design;
3. Knowledge of, and ability to screen for, the assumptions necessary for employment of different tests;
4. Ability to perform these tests using the SPSS software;
5. Ability to disseminate the results from the statistical tests in an appropriate written format.
Continual Assessment -
Task - Overview Independent completion of 5-10 short problems per week reacted to the lecture topic.
Aim: In the MSc programmes, we train you to become independent researchers and scholars. Knowledge of statistics is a crucial requirement both for your MSc thesis and for publishing research in peer-reviewed journals. An understanding of statistics is also fundamental for the ability of critically evaluating existing research and applying it in the real world. In the short tests that comprise this assignment, we test your knowledge of the fundamentals of null hypothesis significance testing, which is still the most common method of statistical inference in psychology. We also test whether you understand the basic concepts and operations underlying the most common statistical tests in Psychology and some very basic mathematical fundamentals that are necessary to understand these methods.
Task: Each week (out of a total of 10 weeks), we will cover an important topic in statistical awareness and competency. You will be expected to do the reading and watch the lecture videos before the lecture. During the last 30 minutes of our class meetings, you will be set a short activity based on the lecture content. These tasks will involve simple mathematical operations as required in statistics, writing null and experimental hypotheses, assessing statistical assumptions, conducting ANOVAs and t-tests, and reporting them appropriately. Word limits and other specifics for each task will be set during lessons.
Reading: Core materials: Fox (2015). Applied Regression Analysis and Generalised Linear Models (3rd Edition).
Learning Outcomes Assessed - This individual assignment contributes to the assessment of the following Intended Learning Outcomes:
- Understanding restrictions and assumptions for a range of tests
- Understanding of complex multi-factor designs
- Writing up results from statistical tests
Take-home assignment: Linear Mixed Models -
Aim - In the MSc programmes, we train you to become independent researchers and scholars. Knowledge of statistics is a crucial requirement both for your MSc thesis and for publishing research in peer-reviewed journals, but a deep understanding of what the statistics mean and how to interpret them is key to avoid erroneous findings. Additionally, the ability to use statistical software confidently is an extremely important skill. Advanced techniques such as multilevel linear modeling are becoming more and more popular in psychology. Familiarity with these techniques will be very important in performing and evaluating research in the future. In this assignment, we test your ability to perform complex multivariate and multilevel analyses using SPSS and report the results appropriately.
Task - Like the unit, this assignment consists of two parts: In the first part, you will reflect on the meaning of test statistics and how to interpret them. In the second part, you will perform, report, and interpret a series of statistical analyses, taking your conclusions from Part 1 into account,
Part 1: Critically evaluate the following statement from Benjamin et al. (2017):
The default P-value threshold for statistical significance for claims of new discoveries should be changed from 0.05 to 0.005.
Explain why the authors believe such a change is necessary at this point in time, and give several arguments for and against the proposal. Your statement should reflect your own position on the issue, and should indicate whether you are planning to use the 0.005 p-threshold for your own research (or apply it to existing literature). Your words should be completely your own, and must not overlap with either Benjamin et al. (2017), blog posts and internet commentary, or other students' work (although you may of course consult these sources and talk to your fellow students).
(1000 words maximum)
Part 2: Conduct and report the appropriate statistics for the data set you are given based on the scenario below as one would for an academic journal. Be sure to report the means, group sizes, and standard deviations of the discrete variables in a table and to make a plot of all the significant effects.
Scenario: A group of researchers (although the data are made up, this is based on a real study that was just published in Psychological Science: Joel, Teper, & MacDonald, 2014) wants to investigate how likely people are to agree to date unattractive people out of pity. In order to do this, they asked 40 heterosexual female participants (raters) to rate 20 male confederates (rated) of different attractiveness. The confederates were also present in the lab and were introduced as participants in the same study. Participants rated the confederates in terms of how likely they would be to go on a date with each man (on a scale from 0 = extremely unlikely to 100 = extremely likely). For half of the rated confederates, the confederate had left the room when participants gave the rating to the experimenter (absent condition). For the other half of the rated confederates, the confederates were present in the room and listening when the participants gave the rating to the experimenter (present condition). In order to see if attractiveness played a role, the researchers also obtained attractiveness ratings (from 0 = extremely unattractive to 10 = extremely attractive) for the 20 photographs from a different group of participants. Does the knowledge that the rated person will hear their rating (and perhaps be hurt) lead participants to give higher ratings? What role does the attractiveness of the rated person play? Does the pity effect disappear for very attractive people?
Instructions: Conduct and report the appropriate statistics using the data provided as one would for a Results section an academic journal. References are not necessary. Be sure to report the means, group sizes, and standard deviations of the discrete variables in a table and to make a plot of all the significant effects. Perform and report three analyses:
1. A standard Analysis of Variance with rating condition as a discrete predictor.
2. A standard multiple regression model with rating condition as a discrete predictor and attractiveness as a continuous predictor
3. A linear mixed model with rating condition as a discrete predictor and attractiveness as a continuous predictor and random intercepts for both participant and rated person (as we want to be able to generalise the results beyond the 40 raters and the 20 rated individuals).
Are the results of the three analyses similar? If not, explain (in non-technical terms) why not. Which analysis is more appropriate to the data? In layperson (non-academic) language describe the results and summarise the answers to the two following questions also given at the end of the scenario paragraph: Does the knowledge that the rated person will hear their rating (and perhaps be hurt) lead participants to give higher ratings? What role does the attractiveness of the rated person play? Does the pity effect disappear for very attractive people? Finally, calculate Bayes Factors for the ANOVA and the regression models and perform a power analysis based on the observed effect for these models. Explain how the results of these analyses affect your interpretation. (2000 words maximum)
Data: Each student will receive an individual data set. The data will be available in the file labelled Assignment2_yourname.csv on MyBU, where "yourname" is your surname. Please make sure that you analyse your correct data set, as a submission using an incorrect data set will be given a mark of 0%.
Reading: Core materials: Dienes, Z. (2008). Understanding Psychology as a Science. Basingstoke: Palgrave Macmillan Fox (2015). Applied Regression Analysis and Generalised Linear Models (3rd edition);
Learning Outcomes Assessed - This individual assignment contributes to the assessment of the following Intended Learning Outcomes:
1. Knowledge of advanced statistical analyses (including linear mixed models etc.);
2. Ability to critically evaluate the use and interpretation of a test for the specific research design;
3. Knowledge of, and ability to screen for, common errors made using inferential statistics and their causes
4. Ability to perform these tests using statistical software;
5. Ability to disseminate the results from the statistical tests in an appropriate written format.
Attachment:- Assignment Files.rar