Solid Science: Research Methods

This course is offered through Coursera — you can add it to your Accredible profile to organize your learning, find others learning the same thing and to showcase evidence of your learning on your CV with Accredible's export features.

Course Date: 01 September 2014 to 13 October 2014 (6 weeks)

Price: free

Course Summary

Discover the principles of solid scientific methods in the behavioral and social sciences. Join us and learn to separate sloppy science from solid research!

Estimated Workload: 4-7 hours/week

Course Instructors

Annemarie Zand Scholten

I currently divide my time between managing the reorganization of the premaster programs for Social Sciences (SS) and Child Development and Educational Sciences (CDES) and lecturing at the department of CDES at the University of Amsterdam. My research focuses on quantitative measurement in psychology. I'm interested in the possibilities of combining representational measurement theory and psychometric latent variable modeling to ascertain the measurement level of psychological properties and the risk of inferential error when performing parametric tests. I've recently developed an interest in Learning Analytics research. I'm interested to investigate what aspects of automated feedback about learners' online activity can increase study performance and motivation.

The premaster programs include Political Sciences, Sociology (7 master tracks), Child Development and Educational Sciences (4 master tracks). The project encompasses development of a joint methods and statistics curriculum, a didactical model centered around a new digital learning environment, a cost-effective financial plan, student recruitment and a more efficient administrative work flow, all in strong collaboration with the HvA.

Courses I currently teach include Methods and Statistics in Educational Sciences in the CDES research master on multivariate statistics, 'Onderzoeksmethodologie' in the premaster program, an introductory course on research methods, and 'Toegepaste Methodenleer en Statistiek' or TMLS, a second year bachelor course on research methods and statistics. I also used to coordinate the 'OnderzoeksPracticum', a research project course. 

Other professional activities include work for the Society for Mathematical Psychology. I helped organize the 2009 conference and created an maintained the society and conference websites for some time. Currently, I'm involved in setting up the Women of MathPsych workgroup. I'm also an associate editor for the Netherlands Journal of Psychology. 

Course Description

Can we still put our trust in the social and behavioural sciences? Cases of social scientists exposed as frauds keep turning up and many disciplines are under fire for their failure to replicate key results. No wonder the integrity of our field is being questioned; sloppy science is starting to seem the norm rather than the exception!

As social scientist Daniel Kahneman suggests, it is time for the social sciences to clean house. We will try to answer his call with a series of courses that explain the scientific principles of research and how methodology and statistics can help to ensure that research is solid. We will explain the basics and put them into context by showing you how things can go horribly wrong when methods and statistics are abused. And we will teach you how to recognize these questionable research practices - after the fact - in published articles.

This first course, Solid Science: Research Methods (in the Social and Behavioral Sciences), will cover the fundamental principles of science, some history and philosophy of science, research designs, measurement, sampling and ethics. This basic material will lay the groundwork for the more technical stuff in subsequent courses. The course is comparable to a university level introductory course on quantitative research methods in the social sciences, but has a strong focus on research integrity. We will use examples from sociology, political sciences, educational sciences, communication sciences and psychology.

Please note that this course will focus on quantitative methods, qualitative methods will be treated in a separate course.


Will I get a Statement of Accomplishment after completing this class?
Yes. Students who successfully complete the class will receive a Statement of Accomplishment signed by the instructor.

What resources will I need for this class?
All you need is an Internet connection, some time and motivation. We'll do everything in our power to help you meet the last requirement, the first two are up to you!


Week 1: Origins of the scientific method
  • non-scientific and scientific ways to gain knowledge, types of scientific claims 
  • history of the scientific method (classical period, enlightenment, modern science) 
  • philosophical considerations: ontology and epistemology 
  • approaches to science (qualitative versus quantitative) 
  • warm-up assignments (not graded)
Week 2: The scientific method
  • the empirical cycle and testing hypotheses 
  • scientific criteria and causality 
  • threats to internal validity 
  • variables of interest and disinterest 
  • small assignments (graded)
Week 3: Research designs
  • true experiments, manipulation and randomization 
  • experimental, quasi-experimental, correlational designs 
  • factorial and repeated measures designs 
  • matching and ecological validity 
  • small assignments & paper on week 1 & 2 due (graded)
Week 4: Measurement
  • variables and operationalizations 
  • measurement levels and types, validity and reliability 
  • surveys, questionnaires and tests, items and scales 
  • other forms of measurement 
  • small assignments (graded)
Week 5: Sampling
  • threats to external validity 
  • random and non-random sampling 
  • random sampling methods 
  • sampling bias and error, sample size 
  • assignments & paper on on week 3 & 4 due (graded)
Week 6: Practice, ethics & integrity
  • research protocols and data management 
  • ethics towards participants and research integrity 
  • review and publication process 
  • questionable research practices
  • small assignments (graded)
Week 7: Study week
  • time to catch up and study for the final exam
  • time to ask your final questions
  • time to work on last paper
Week 8: Exam week
  • paper on week 5 & 6 due (graded), final exam (graded) and course evaluation


Course length
  • 8 weeks (6 weeks lectures, 1 week to catch up and study, 1 exam week)
Lecture format
  • per week: on average 10 lecture videos (4-8 min.) and one interview (10-20 min.) 
  • per video: 1 or 2 quiz questions to assess your understanding (not graded) 
Assignment format
  • 1 to 3 small graded assignments per week (e.g. a 10 item multiple-choice test or a small peer-graded writing assignment or contribution to the discussion forum) 
  • 1 larger paper assignment per two weeks, 3 in total (e.g. a paper reviewing the internal validity of a particular research study of around 800-1000 words) 
Exam format
  • the final exam will consist of multiple-choice questions

Suggested Reading

Additional readings will be suggested during the course. These suggestions are optional and will always refer to freely available material.

Course Workload

4-7 hours/week

Review course:

Please sign in to review this course.

Similar Courses

{{ }} {{ }}


{{course.start_date | date:'MMM d'}} — {{ course.end_date | date:'MMM d'}}   ({{ course.time_until_course_starts }} ,   length: {{ course.length_in_weeks }} weeks) Self-paced — no deadlines    
${{ course.price }} p/mfree


Course Activity & Community

Be the first Accredible user to join this course!

uploaded {{ feed_item.model.caption || feed_item.model.url || feed_item.model.file_file_name }} for the course {{ }} — {{ feed_item.time_ago }}

{{ }} {{ comment.text | truncate: (comment.length || comment_display_length) }}   read more hide

{{ comment.time_ago }}

started the course {{ }} — {{ feed_item.time_ago }}
followed {{ }} — {{ feed_item.time_ago }}
followed thier friend {{ }} — {{ feed_item.time_ago }}
{{ feed_item.model.text }} (on the course {{ }}) — {{ feed_item.time_ago }}

{{ }} {{ comment.text | truncate: (comment.length || comment_display_length) }}   read more hide

{{ comment.time_ago }}