Biostatistics and Research Methodology
Theory: 3 hours/week | Tutorials: 1 hour/week | ECTS Units: 5
Learning outcomes
The aim of the course is to provide understanding of commonly employed statistical methods and to enable students to conduct statistical analyses of research data of the field of life sciences. In this context, after the completion of the course, students are expected to be familiar with:
- The purpose and the steps of the implementation of key statistical approaches
- The usefulness and elements of descriptive statistics
- The basic principles of probability theory and their application in statistical analyses
- Probability distributions
- Estimators of population parameters
- The concepts of inferential statistics and the use of tests (parametric and non-parametric) to draw conclusions related to population properties
- The proper interpretation of the results of statistical approaches
- Topics in Scientific Research Methodology, with applications in educational research.
Syllabus
- Introduction to statistics
Statistics and critical thinking
Data types
Collection of sample data
Tutorial: introduction to SPSS and sample data - Descriptive statistics
Frequency distributions for organizing and summarizing sample and population data
Histograms
Graphs that are informative and graphs that are misleading
Scatter plots, correlation and regression plots
Tutorial: Excel/SPSS – plots - Description, investigation and comparison of data
Position measures
Dispersion measures
Measures of position and boxplots
Tutorial: Excel/SPSS – boxplots - Probabilities
Basic concepts of probability
Addition rule and multiplication rule
Complements, bound probability and Bayes’ theorem
Complementary probabilities
Mortality, fertility and morbidity rates
Tutorial: SPSS – probabilities - Discrete probability distributions
Probability distributions
Binomial probability distribution
Poisson probability distributions
Tutorial: SPSS – binomial probability distribution - Continuous distributions: Normal distribution
Standard normal distribution
Applications of normal distribution
Sampling distribution and estimators
The central limit theorem
Assessment of normality
The normal distribution as an approximation to binomial distribution
Tutorial: SPSS – normal probability distribution - Population estimators
Estimating the proportion of a population
Estimation of the population mean
Estimation of the standard deviation and variance of a population
Bootstrap sampling
Tutorial: SPSS – bootstrapping - Scientific Research Methodology
Designing quantitative and qualitative Research.
The quantitative research questionnaire.
The research interview.
Qualitative and quantitative content analysis. - Hypothesis testing in inferential statistics
Basics of hypothesis testing
Hypothesis testing of ratios
Hypothesis testing of means
Hypothesis testing of standard deviations and variances
Tutorial: SPSS – hypothesis testing - Comparison of two samples with parametric methods (e.g. t-test of independent or dependent samples)
- Comparison of more than two samples with parametric methods (e.g. One-Way ANOVA), linear correlation, simple linear regression
- Non-parametric tests
Basic non-parametric hypothesis testing
Wilcoxon signed-rank testing for paired samples
Mann-Whitney U test and Wilcoxon sum-rank testing for two independent samples
Kruskal-Wallis test for three or more samples
Correlation of ranks
Tutorial: SPSS – non parametric testing - Demonstration and hands on parametric testing with SPSS and associated open access software
Descriptive statistics
Comparison of two samples (test condition checks and parametric methods)
Comparison of k samples (test condition checks and parametric methods)
Correlations
Simple linear regression
Tutorial: SPSS – parametric testing
Student performance evaluation
Performance in the course is assessed by written examinations during the examination period. The written examination includes: Solving of exercises, Statements about basic statistical concepts where the student must decide whether they are right or wrong, Questions that require short and precise answers, Decision trees where gaps need to be filled in.
Suggested bibliography
- Βιοστατιστική των Επιστημών Βιολογίας και Υγείας Triola M. Marc, Triola F. Mario, Roy Jason BROKEN HILL PUBLISHERS LTD 2021.
- Εφαρμοσμένη Στατιστική με Έμφαση στις Επιστήμες Υγείας, Α. Σαχλάς & Σ. Μπερσίμης, Εκδόσεις Τζιόλα 2017.
- Βιοστατιστική, Δ.Τριχόπουλος, Α. Τζώνου, Κ. Κατσουγιάννη, Εκδόσεις Παρισιάνου ΑΕ.
- Εφαρμοσμένη Στατιστική με χρήση του IBM SPSS Statistics 23: Με έμφαση στις Επιστήμες Υγείας, Α. Σαχλάς & Σ. Μπερσίμης, Εκδόσεις Τζιόλα 2017.
- Μέθοδοι Κοινωνικής Έρευνας, Alan Bryman, εκδόσεις Gutenberg 2017.
Teaching Material / E-class
Lecturers
Sotirios Vasileiadis
(Course Coordinator)
Leonidas Anthopoulos
Professor, Department of Business Administration, University of Thessaly
