Your first steps into statistics

1.1 Knowledge 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) Miranda Janssen, Msc member of the Dept. Methodology & Statistics at FHML. She has extensive experience in statistical education for bachelor students and in consultation about statistical methods for master and PhD students.

 
(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

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).