It is very important to consider the sample design in the analysis and modelling of complex survey data. It permits the production of correct results, which can be used for public political decision making and evaluation. The main objective of this dissertation is to give information about the importance of the use of the techniques for complex survey data. The methodologies for complex survey data analysis can be divided in two different approaches. The first is based on incorporating weights and design effects in the fitting of usual statistical models, such as contingency tables, regression, etc. This approach is called aggregated approach. The other approach, called disaggregated approach, modifies the model attempting to incorporate the complex population structure and/or design effects, for example using hierarchical (or multilevel) linear models. The data analysed in this dissertation were collected by the Brazilian National System of Basic Education Assessment (SAEB), in 1999. This survey applies an exam and asks social-economic-demographic information about more than 200.000 students, schools and teachers. The SAEB 99 sample were selected by a complex survey design, considering stratification and conglomeration, with multiples steps. There is not any problem in estimation of descriptive statistics, such as means, correlation and regression coefficients, provided that we correctly use the sample weights to expand the data. An example will be presented to verify the importance of the use of the sample weights. The theoretical aspects of the techniques for the estimation of regression model parameters and their variances will be presented. The design effect, confidence intervals, significance tests, and SUDAAN characteristics will also be discussed. The application of these techniques will be presented. It will be also conducted a study of the determinants of the student proficiency. It still will be presented and analysed the consequences of the non- consideration of the sample design in the estimation of parameters and their variances, for SAEB 99 data. The results will be educationally interpreted.