Analysis of Complex Sample Survey Data using Stata


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We are proud to offer this course in a variety of training formats to suit your needs. We use the highest quality learning facilities to make sure your experience is as comfortable as possible. Our face to face calendar allows you to choose any classroom course of your choice to be delivered at any venue of your choice - offering you the ultimate in convenience and value for money.


July 2024

Code Date Duration Location Fee Action
ACS001 15 Jul 2024 - 26 Jul 2024 10 days Nairobi, Kenya KES 159,000 | $2,200 Register
ACS001 22 Jul 2024 - 2 Aug 2024 10 days Kisumu, Kenya KES 165,000 | $2,200 Register
ACS001 29 Jul 2024 - 9 Aug 2024 10 days Mombasa, Kenya KES 182,000 | $2,200 Register
ACS001 15 Jul 2024 - 26 Jul 2024 10 days Kampala, Uganda $3,300 Register
ACS001 15 Jul 2024 - 26 Jul 2024 10 days Accra, Ghana $4,400 Register
ACS001 22 Jul 2024 - 2 Aug 2024 10 days Johannesburg, South Africa $4,400 Register
ACS001 29 Jul 2024 - 9 Aug 2024 10 days Zanzibar, Tanzania $3,300 Register
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July 2024

Date
Duration
Location
Fee
Action
15 Jul - 26 Jul
10 days
Nairobi
KES 159,000 | $2,200
22 Jul - 2 Aug
10 days
Kisumu
KES 165,000 | $2,200
29 Jul - 9 Aug
10 days
Mombasa
KES 182,000 | $2,200
15 Jul - 26 Jul
10 days
Kampala
- | $3,300
15 Jul - 26 Jul
10 days
Accra
- | $4,400
22 Jul - 2 Aug
10 days
Johannesburg
- | $4,400
29 Jul - 9 Aug
10 days
Zanzibar
- | $3,300
I Want To See More Dates...
I Want To See More Dates...

INTRODUCTION

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.


Course Level:

Course Objectives

  • Enhance Survey estimation and inference for complex designs
  • Determine Sampling error calculation models; ultimate clusters
  • Sampling error estimation for descriptive statistics
  • Replication Methods for Variance Estimation

Course Outline

  • Survey estimation and inference for complex designs
    • Complex sample designs, survey estimation and inference
    • Multi-stage designs, stratification, cluster sampling, weighting, item missing data, finite population corrections
    • Models and assumptions for inference from complex sample survey data
    • Sampling distributions, confidence intervals
    • Design effects.
  • 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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Course Administration Details:

ACCREDITATION

Upon successful completion of this training, participants will be issued with an Indepth Research Institute (IRES) certificate certified by the National Industrial Training Authority (NITA).

TRAINING VENUE

The training will be held at IRES Training Centre. The course fee covers the course tuition, training materials, two break refreshments and lunch.

All participants will additionally cater for their, travel expenses, visa application, insurance, and other personal expenses.

ACCOMMODATION AND AIRPORT PICKUP

Accommodation and airport pickup are arranged upon request. For reservations contact the Training Officer.

Email:[email protected].  

Mob: +254 715 077 817/+250789621067

TAILOR- MADE

This training can also be customized to suit the needs of your institution upon request. You can have it delivered in our IRES Training Centre or at a convenient location.

For further inquiries, please contact us on Tel: +254 715 077 817/+250789621067.

Mob: +254 792516000+254 792516010 ,or mail [email protected]/[email protected]

PAYMENT

Payment should be transferred to IRES account through bank on or before start of the course

Send proof of payment to [email protected]/[email protected]


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# Job Title Organisation Country
1 PRINCIPAL HUMAN RESOURCE PLANNING AND DEVELOPMENT OFFICER Ministry of Labour, National Human Resource Planning and Development Department Kenya
2 PRINCIPAL HUMAN RESOURCE PLANNING AND DEVELOPMENT OFFICER Ministry of Labour, National Human Resource Planning and Development Department Kenya
3 PRINCIPAL HUMAN RESOURCE PLANNING AND DEVELOPMENT OFFICER Ministry of Labour, National Human Resource Planning and Development Department Kenya
4 PRINCIPAL HUMAN RESOURCE PLANNING AND DEVELOPMENT OFFICER Ministry of Labour, National Human Resource Planning and Development Department Kenya
5 PRINCIPAL HUMAN RESOURCE PLANNING AND DEVELOPMENT OFFICER Ministry of Labour, National Human Resource Planning and Development Department Kenya

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