SOW-PSB2RS20E
Data Analysis
Course infoSchedule
Course moduleSOW-PSB2RS20E
Credits (ECTS)4
CategoryBA (Bachelor)
Language of instructionDutch, English
Offered byRadboud University; Faculty of Social Sciences; Psychology;
Lecturer(s)
Lecturer		dr. J.L. Ellis
Other course modules lecturer
Lecturer		dr. S. Pieters
Other course modules lecturer
Examiner		dr. I.M. Rabeling-Keus
Other course modules lecturer
Coordinator		dr. I.M. Rabeling-Keus
Other course modules lecturer
Contactperson for the course		dr. I.M. Rabeling-Keus
Other course modules lecturer
Academic year2018
Period
PER4  (15/04/2019 to 12/07/2019)
Starting block
PER4
Course mode
full-time
Remarks-
Registration using OSIRISYes
Course open to students from other facultiesNo
Pre-registrationNo
Waiting listNo
Placement procedure-
Aims
Upon completion of the course:
  1. You use methods and techniques learned in Research Methods, Statistics 1, Statistics 2 and Psychometrics in an integrated and independent way.
  2. You apply these methods and techniques to genuine cases not previously known to you.
  3. You reflect on your own problem-solving ability by recognising what knowledge and skills you still lack, and quickly acquire them practically under your own power.
  4. You display an inquisitive attitude that leads you to in-depth analysis of your data. Your approach gives evidence of powers of abstraction, flexibility and perseverance.
Content
The Data Analysis course expands on the knowledge you have gained from the courses Research Methods, Statistics 1, Statistics 2 and Psychometrics. In this course you will increase your ability to solve problems in the area of methods and statistics by learning how to independently expand and deepen that knowledge. At the same time you will automatise the analytical skills you have learned so far. For example, you have done multiple regression analysis in Statistics 2, and it is possible that you now receive an assignment in hierarchical multiple regression analysis, which will be new to you. You will have to consult literature by yourself in order to learn it. Additionally, the prevailing data analyses in psychology (such as ANOVA, MRA, and factor analysis) will be treated, and attention will be paid to extensions of these analyses, such as HMRA and post-hoc tests. We will also discuss the interpretation of the analysis results in relation to questions and research methods, and the way they are reported.

Examination

    • Written examination with closed questions (25%)
    • PC examination, consisting of three cases (75%).

Note: Marks for partial examinations cannot be transferred to a subsequent academic year.
Prerequisites
• Admission to second year (B2)
• Passing mark for Statistics 1
• Passing mark for Statistics 2
• Knowledge of Psychometrics is assumed.

Required materials
Course guide
Ellis, J.L. Rabeling, I.M. (2019). Course guide Data Analysis. Nijmegen: Radboud University, School of Psychology and Artificial Intelligence.
Book
Ellis, J.L. Rabeling, I.M. (2018). Tips for Data Analysis. Nijmegen: Radboud University, Nijmegen, School of Psychology and Artificial Intelligence.
Book
Ellis, J.L. Rabeling, I.M. (2019). Cases Data Analysis. Nijmegen: Radboud University, School of Psychology and Artificial Intelligence.
Articles
Cohen, J. (1990). Things I have learned (so far). American Psychologist, 12, 1304-1312.
Articles
John, L.K., Loewenstein, G., & Prelec, D. (2012). Measuring the prevalence of questionable research practices with incentives for truth telling. Psychological Science, 23, 524 - 532. doi: 10.1177/0956797611430953.
Articles
Simmons, J.P., Nelson, L.D., & Simonsohn, U. (2011). False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant. Psychological Science, 22, 1359 - 1366. doi: 10.1177/0956797611417632.
Articles
Erceg-Hurn, D.M., & Mirosevich, V.M. (2008). Modern Robust Statistical Methods: An Easy Way to Maximize the Accuracy and Power of Your Research. American Psychologist, 63, 591-601. doi: 10.1037/0003-066X.63.7.591.
Articles
Ruxton, G.D. (2006). The unequal variance t-test is an underused alternative to Student’s t-test and the Mann–Whitney U test. Behavioral Ecology, 17, 688-690. doi: https://doi.org/10.1093/beheco/ark016

Instructional modes
Lecture

Practicals
Attendance MandatoryYes

Self-study assignments

Workgroup

General
assistance via Blackboard

Remark
Participating in the work groups is highly recommended. You can enrol for a work group via OSIRIS or Brightspace.

Tests
Multiple-choice and PC exam
Test weight1
Test typeExam
OpportunitiesBlock HERT, Block PER4