Standard courses on statistical analysis assume that survey data arise from a simple random sample of the target population. Little attention is given to characteristics often associated with survey data, including missing data, unequal probabilities of selection, stratified multistage sample designs, and measurement errors. Most standard statistical procedures in software packages commonly used for data analysis (e.g. SAS, SPSS, and Stata) do not allow the analyst to take these properties of survey data into account unless specialized survey procedures are used. Failure to do so can have an important impact on the results of all types of analyses, ranging from simple descriptive statistics to estimates of parameters of multivariate models.

This course provides an introduction to specialized software procedures that have been developed for the analysis of complex sample survey data including testing for between-group differences in means and proportions, regression analysis, logistic regression and multilevel modeling. We will also consider the consequences of non-response and missing data on survey analysis and methods for dealing with missing data. Specialized procedures for survey data analysis from the Stata systems for data management and analysis will be used to develop course examples and exercises

DURATION

10 days

WHO SHOULD ATTEND?

The course does not require rigorous training in mathematics; however, proficiency in basic mathematics, including algebra and functions, is essential. Survey sampling methods and a basic understanding of sampling concepts such as stratification, cluster sampling and weighting is required. Participants should also have familiarity with basic statistical concepts, including point estimates, sampling variance, confidence intervals, p-values, the maximum likelihood method of estimation and simple linear and logistic regression models.

TOPICS TO BE COVERED

Survey estimation and inference for complex designs
Sampling error calculation models; ultimate clusters
Sampling error estimation for descriptive statistics
Replication Methods for Variance Estimation
Estimation and inference for special statistics (percentiles, indices)
Methods for Categorical Data
Linear Regression Analysis
Logistic Regression Analysis
Multinomial, ordinal logistic regression
Poisson and negative binomial regression
Survival analysis and event history analysis
Multiple imputation inference for survey data
Multi-level models for complex sample survey data.

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For further inquiries, please contact us on Tel: +254 715 077 817, +254 (020) 211 3814.

Email [email protected]

ACCOMMODATION

Accommodation is arranged upon request. For reservations contact the Training Officer.

Email: [email protected].

Mob: +254 715 077 817

Tel: 020 211 3814

PAYMENT

Payment should be transferred to IRES account through bank on or before C.O.B16th April 2018.

Send proof of payment to [email protected]

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Indepth Research Services (IRES) is a global institutional capacity building, technical and management consultancy firm with its head office in Nairobi, Kenya.

IRES offers technical and management consultancy services as well as education and training services designed for individuals’ and groups from organizations working in public, not for profit and private sectors.

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