**Survey Design**

In many studies in development there is a need to collect data from either households or individuals. If we wish to make statistical inferences we need to use sound statistical principles in our design. Since most surveys are considered observational data the data can be used to describe the communities. It is not appropriate to imply any cause and effect with observational data, although if we carefully design for comparing data from an intervention, it may be possible. If we use simple random sampling principles in selecting the survey units the estimates from these sample surveys have a broader applicability, in that they will avoid bias.

** Aims and objectives**

- To introduce survey concepts, some definitions and a simple overview
- To understand the basics of simple random sampling (SRS)
- To understand stratified sampling
- To develop research questions and link them to questions of interest on the survey
- To use objective methods to preparing wording of questions to suit your objectives
- To consider the report structure at the beginning to inform your questionnaire design
- To consider issues of resource use, and training of enumerators
- To use descriptive statistics to summarise the study
- To calculate and understand measures of reliability of estimates

## Introducing surveys with a simple overview

Here is a presentation that leads you through many aspects of survey design, make sure you understand the terminology used.

## Some definitions and notation

A** census** – this consists of collecting data on the whole population. In this case there is no sampling or estimation as the population parameters can be calculated. Many countries conduct a census once every five or ten years.

**Population** – this consist of all the units of interest. We distinguish between the **target population** which would be the whole aggregate population and the **sampled population**. For example the target population may be everyone (over 18 years old) in a State or district. However if we are conducting a phone survey the *sampled * population consists of those households with a phone. We aim to have the sampled population close to the target.

**Sample** – this is the units randomly selected to be in our sample. We use the sample to make estimates (such as means, totals or proportions from our response variables) with which to make inferences about the population.

**Sampling Frame** – this consists of an actual list of units within the population. So it could be a data frame of names and addresses, it could be a list of businesses, or it may be a map with houses marked on spatially. They should be extensive and up to date or current

**Sampling fraction (f)** – this is defined as n/N which is the ratio of the size of the sample to size of the population. **N** is the population size and **n** is the sample size.

**Response rate** – the percentage of completed questionnaires. There are some differences between types of surveys on how these are calculated. This is affected by the mode of the survey (face-to-face, online or telephone), the currency of the sampling frame, number of recalls, silent numbers and so on.

**Estimates** There are five characteristics of the population that are typically of interest:

- Mean
- Total
- A ratio of two totals or means
- Proportion of units in a defined class
- A percent of units in a defined class

## The basic principles of simple random sampling (SRS)

When we wish to undertake a simple representative survey, we should aim for simple random sampling. So this means that every individual within your target population has an equal change of being selected.

### What is stratified sampling?

### Developing research questions and the survey questionnaire

### Objective methods to preparing wording of questions to suit your objectives

### Consider the report structure at the beginning of the project to inform your questionnaire design

## Issues of resource use, and training of enumerators

## Descriptive statistics to summarise the study

## Calculating and understanding measures of reliability of your estimates

## For examining tests of association, see Module 4

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