Data structure 
Populations and samples
A population is the entire collection of ‘things’ in which we are interested. A sample is a subset of a population. We wish to make an inference about a population of interest based on information obtained from a sample from that population. EXAMPLES:
_ You measure the profit/loss of 50 public hospitals in Victoria, randomly selected.
Points of interest:
_ Sales on 500 products from one company for the last 5 years are analysed.
Points of interest:

Cases and variables
Think about your data in terms of cases and variables.
_ A case is the unit about which you are taking measurements. E.g., a person, a business.
_ A variable is a measurement taken on each case.
E.g., age, score on test, grade-level, income.

Types of Data
The ways of organizing, displaying and analysing data depends on the type of data we are

_ Categorical Data (also called nominal or qualitative)
e.g. sex, race, type of business, postcode
Averages don’t make sense. Ordered categories are called ordinal data

_ Numerical Data (also called scale, interval and ratio)
e.g. income, test score, age, weight, temperature, time.
Averages make sense.
Note that we sometimes treat numerical data as categories. (e.g. three age groups.)

Response and explanatory variables

Response variable: measures the outcome of a study. Also called dependent variable.

Explanatory variable: attempts to explain the variation in the observed outcomes. Also called independent variables. Sometimes the response variable is called the dependent variable and the explanatory variables are called the independent variables.

The survey process

1. Planning a survey
State the objectives: In order to state the objectives we often need to ask questions such as:
_ What is the survey’s exact purpose?
_ What do we not know and want to know?
_ What inferences do we need to draw?
Begin by developing a specific list of information needs. Then write focused survey questions.

2. Design the sampling procedure
Identify the target population: Whom are we drawing conclusions about?
Select a sampling scheme: Examples: simple random sampling, stratified random sampling, systematic sampling, and cluster sampling.

3. Select a survey method
Decide how to collect the data: personal interviews, telephone interviews, mailed questionnaires, diaries, . . .

4. Develop the questionnaire
Write the questionnaire. Decide on the wording, types of questions, and other issues.

5. Pretest the questionnaire
Select a very small sample from the sampling frame. Conduct the survey and see what goes wrong. Correct any problems before carrying out the full-scale study.

6. Conduct the survey
Run the survey in an efficient and time effective manner.

7. Analyze the data
Gather the results and determine outcomes.