Course

Your first steps into statistics

Online introduction to statistical methods for data analysis.

Looking for a course that will help you to gain knowledge and skills needed to critically interpret scientific literature? To design, analyse and interpret your own scientific data and to be able to report the findings in a clear and concise manner? This is it!

The focus of this course is on statistical concepts and techniques that play a crucial role in summarizing and describing observed variables and relationships between variables, as well as generalizing the results for a larger group of people than the observed group. The course consists of three themes:

  1. The first theme summarizes the observed data.
  2. The second theme introduces the testing concept.
  3. The third theme pertains to various basic statistical techniques that are used to analyse observed data.

Ample opportunities for peer exchange, peer review and individual consultations with the two course leaders will enable you to understand and employ your first statistical analyses. A variety of sessions on core concepts such as sampling, hypothesis testing and statistical test are offered during this four-week course (including study hours 20-30 hours per week).

The course is intended for PhD students who wish to extend their knowledge and skills related to statistics and data analysis. The course is not intended for researchers with extensive knowledge and experience in statistical analyses.

Overview of topics and objectives

1.1 Knowlegde and insight
After completing this course, the student will have knowledge of and insight into:

  • Descriptive statistics (including frequency, average, median, standard deviation, interquartile range, histogram, boxplot, cross-classified table, and scatterplot).
  • The principles of inferential statistics, such as population distribution, sample distribution, sampling distribution, central limit theorem, hypothesis testing, p-value, and confidence interval.
  • The basic principles and concepts of elementary statistical techniques (including t-test, chi-square test, and simple linear regression).
  • The differences and similarities between various basic techniques (such as a t-test and simple linear regression).

1.2 Application knowledge and insight
After completing the course, the student can:

  • Carry out simple descriptive statistics using the SPSS statistical package.
  • Carry out a simple test (t-test, Chi-square test, simple linear regression) using the SPSS statistical package.
  • Make a justifiable choice from a number of elementary analysis techniques in order to answer the research question(s).

1.3 Making judgements
After completing the course, the student:

  • Is able to adequately interpret the results of learned statistical analyses in view of the research question and, in doing so, provide critical comments.

 

Contact staff
Both course leaders have been involved in a wide range of research projects in health care.
(1) Dr. Shahab Jolani is an assistant professor at the department of Methodology & Statistics. He has a PhD in Statistics. He has extensive experience in research and consultation about statistical methods, and in statistical education for bachelor and PhD students.
 s.jolani[at]maastrichtuniversity[dot]nl

(2) Dr. Sil Aarts is an assistant professor at The Living Lab in Aging and Long-term Care, part of the department of Health Services Research. She has a PhD in general practice and has, through the years, accumulated a lot of experience in statistical analyses. 
s​.​aarts​@​​maastricht​university​.​nl
+31(0)43 3881731

Course content (per week)

Week 1
Part 1: typology of variables, histogram, boxplot, indicators for central tendencies and dispersion, scatter plot, and measures of association for two variables (theme 1).
Part 2: simple linear regression, quality of a regression line, relationship between simple linear regression, and correlation coefficient – as a descriptive technique only (theme 3).

Week 2
Part 1: simple linear regression with a dichotomous independent variable, cross-classified table analysis, and effect sizes (OR and RR) – as a descriptive technique only (theme 3).
Part 2: construction and interpretation of a confidence interval, sampling distribution, central limit theorem, and one sample problem (theme 2).

Week 3
Part 1: null and alternative hypothesis, one sample t-test, p-value, two-sided testing (one-sided briefly), type I and type II errors (theme 2).
Part 2: two sample test for paired observations, comparison of more than two groups, and correction methods for multiple tests such as Bonferroni correction (theme 2).

Week 4
Part 1: Chi-Square test and assumptions (themes 2 and 3).
Part 2: statistical testing of regression coefficients for simple linear regression, and linear regression assumptions (themes 2 and 3).

Contact: aioonderwijs[at]maastrichtuniversity[dot]nl
* Please note: when you subscribe, you will receive a response from the aioonderwijs emailbox after 17 September 2019.

The course is paid by FHML if you have an employment contract or registration as PhD candidate at one of the FHML Schools/Institutes. Please provide your FHML ID number and UM email address in the subscription form.

Course fee:  PhD candidates (Promovendi) of FHML, MaCSBIO, M4I and MERLN: no fee(*)
Master students: no fee (*)
Other: €500,00
(*) PhD candidates and other participants are given preference. If some spots are still available, then Master students can apply.

Outline of course weeks

Day

Task

Monday

  • Students watch opening weekvideo
  • Students watch online clips, study syllabus & make assignments

Tuesday

Online session for 2 hours at 15.00

Wednesday

Students watch online clips, study syllabus & make assignments

Thursday

Online session for 2 hours at 15.00 (for one group this session will be held on Friday at 15:00)

Friday

Students watch closing weekvideo

* All weeks are similar in lay-out. All times are local times in Europe/Amsterdam time.
Investment participants: 20-30 hours per week