Processing and Data Analysis

– An expository-active methodology that is easy to understand by the Master’s students is used.
– The exhibition will focus on the identification and understanding of theoretical concepts of Statistics with emphasis on the areas of Occupational Health and Environment.
– Presentation of different models of research studies in the master’s core areas (Observational Models – descriptive and analytical; Quasi-Experimental Models).
– Exposition of each method and statistical technique (principles, assumptions and algorithm) in the scope of both Descriptive Statistics and Inferential Statistics.
– Contact with data analysis software (steps and procedures): Excel; IBM SPSS Statistics.
– Manipulation of Databases (construction/projection) according to the study designs.
– Application of theoretical and practical knowledge of statistics, in the univariate, bivariate and multivariate scope with the application of the IBM SPSS Statistics Software.
– Extraction of results with IBM SPSS software and critical analysis.

The final evaluation will consist of carrying out an individual or group work that will address the following elements:
– Application of univariate, bivariate and multivariate analytical decisions.
– Data processing and analysis according to research proposals (theoretical-practical models – simulations)
– Interpretive and reflective analysis of the results according to the objectives of the study in the scope of occupational and environmental health.

Knowledge about analytical models in Statistics at the level of data analysis, in a specialized way, with application in the core areas of research in Environmental Health at the level of Occupational Health and Environment.
Skills in planning research strategies and analytical collection of data (qualitative and quantitative) according to the best practices with regard to the research process and in training, as a researcher, for decision making in the choice analysis of robust models of analysis and treatment of data.
Competences in terms of the use of specialized software in data processing; perform data analysis in its different aspects (descriptive and inferential) using univariate, bivariate and multivariate measures; in the distinction and choice of simple or multifactorial statistical research methodologies.

DESCRIPTIVE STATISTICS:
– Univariate and bivariate descriptive statistics measures.
– Introduction to the concepts of population, sampling methods and sample estimation.
STATISTICAL INFERENCE:
Introduction to statistical inference. The test of hypotheses; Probabilities and significance levels; Type I errors and type II errors.
Univariate/Bivariate models:
– Notions of probability (Simple, joint and conditional). Measures of occurrence; Relative and absolute measures; Impact measures.
– Parametric/nonparametric: t-Student tests (independent/paired) /Mann-Whitney/Wilcoxon, ANOVA to I Factor/Kruskal-Wallis/ Friedman;
Pearson/Spearman correlation.
Multivariate Models:
– For independent samples: Factorial ANOVA; ANCOVA; MANOVA
– For paired samples (longitudinal models): ANOVA repeated measures); MANOVA
– Single linear regression/Multiple linear/Categorial regression.
Application of previous models using specialized software.

NAO