Biostatistics

General

• Code: 278-190106
• Semester: 1st
• Study Level: Undergraduate
• Course type: General Background
• Teaching and exams language: Greek
• Teaching Methods (Hours/Week): Theory (2) / Laboratory (1)
• ECTS Units: 3
• Course homepage: https://moodle.teithe.gr/course/index.php?categoryid=196
• Instructors: Minos Georgios
• Class Schedule:
  • Mon 08:00 - 09:00: Εργαστήριο Βιοστατιστικής
  • Tue 11:00 - 13:00: (Θ) Αμφιθέατρο Οικονόμου
  • Thu 09:00 - 10:00: Εργαστήριο Βιοστατιστικής
  • Thu 10:00 - 11:00: Εργαστήριο Βιοστατιστικής
  • Thu 11:00 - 12:00: Εργαστήριο Βιοστατιστικής
  • Thu 12:00 - 13:00: Εργαστήριο Βιοστατιστικής
  • Thu 13:00 - 14:00: Εργαστήριο Βιοστατιστικής

Course Contents

Theory

• Study design. Sample and population, sampling error, data collection, types of sampling and study design.
• Data collection and analysis. Data file format, data import, analysis, file management (Excel, SPSS), results management.
• Descriptive Statistical Analysis – Descriptive measures: Positional or Central tendency measures (Mean, Median, Mode. Percentiles, Quartiles), Measures of variance (Range, Variance, Standard deviation-Std., Standard error-S.E. OF-mean, Coefficient of variation, Interquartile Range-IQR, Skewness, Kurtosis), Measures of dispersion (Range, Variance, Standard deviation, Standard error-S.E. ofmean, Coefficient of variation, Interquartile range-IQR, Skewness, Kurtosis).
• Creating and editing of graphs. (Histogram, BarChart, Boxplot, Piechart, Scatterplot).
• Test of Normality – Graphical methods (Normal curve on Histogram, P-Pplots, Q-Qplots, Boxplot), Statistical tests (Kolmogorov-Smirnov, Shapiro-Wilk).
• Case control. Null hypotheses, degrees of freedom.
• Statistical analysis using Crosstabs. Chi-square test as a test of independence – Contingency coefficient (Phi & Cramer’s V).
• Use of the chi-square test for testing homogeneity (One sample Chi-Square test).
• Correlation analysis: parametric correlation of quantitative variables (Pearson’s r), Non-parametric correlation of quantitative & qualitative variables (Spearman’s rho, Kendall’stau-b).
• Statistical tests for comparison of means (t-test) – Comparison of a mean value against a predetermined numerical value (One sample t-test) – Comparison of means of two independent samples (Independent samples t-test) – Examination of differences between two means of correlated values – Paired samples (Paired Samples t-test).
• One-way analysis of variance (ANOVA).
• Two-factor analysis of variance (Two way ANOVA).
• Non-parametric Statistical tests for data comparison – Comparison for one sample (Wilcoxonsigned-rank) – Tests of two independent samples (Mann-WhitneyU, WilcoxonW) – Tests of two correlated samples (Sign, WilcoxonSigned-rank, McNemar) – Differences between several independent groups (Kruskal-Wallis H, Jonckheere-Terpstra).
• Analysis of Covariance (ANCOVA).
• Cronbach’s alpha reliability test.
• Exploratory Analysis – Principal Component Analysis (PCA).
• Linear Regression Analysis. Hierarchical Regression Analysis.
• Multivariate Analysis of Variance (MANOVA).

Laboratory

• Using a statistical program (SPSS, PSPP), the tests taught in the theory of the course are applied to health sciences data.

Educational Goals

The aim of the module is to enable students to understand the basic methodological issues related to applied research within the biomedical sciences field. Students are taught descriptive and inferential statistics, statistical measurements and techniques, research methodology and basic sample techniques and organizing field research in biomedical studies. Finally, they are taught how to present the research results through tables and charts.

General Skills

• Adapting to new situations.
• Decision-making.
• Working independently.
• Team work.
• Project planning and management.
• Respect for difference and multiculturalism.
• Showing social, professional and ethical responsibility and sensitivity to gender issues.
• Working in an interdisciplinary environment.
• Production of free, creative and inductive thinking.

Teaching Methods

• Face to face.

Use of ICT means

• Lectures with slides in PowerPoint (use of PC and projector).
• Use of videos and web applications in teaching.
• Posting of course material and communication with students on the online platforms E-class, Blackboard and Moodle.
• Use of computers and specialized software for statistical processing (PSPP, SPSS) in Laboratory Exercises.

Teaching Organization

Students Evaluation

Theory

• Written exams.

Laboratory

• Computer-based final exams using commercial statistical data analysis packages (SPSS, PSPP).