TYPES OF DATA
Data can be
quantitative (numerical data) or
qualitatitive (non-numerical data).
Quantitative data can be
discrete (when it can only take certain values) or
continuous (can take any value in a certain range)
When spread of data is too big we often group the data in a frequency table using class intervals. Grouping data can result in loss of accuracy in both calculations & presentations.
Bivariate data are used when you measure 2 related things e.g. height & weight of children.
Categorical data is data sorted into categories (groups)
Ranked data have values that can be ranked (put into order)
Primary data - collected by person who is going to use it. Not yet been processed.
Advantages: known accuracy, know how obtained.
Disadvantages time consuming, expensive.
Secondary data - collected by someone else.
Advantages cheap & easy to obtain.
Disadvantages – might be out of date, have mistakes, don’t know how collected.
Experiments – one variable is controlled (explanatory or independent variable) while its effect on the other variable (response or dependent variable) is observed.
Surveys are useful if collecting personal data. Main survey methods are:
- Postal (advantage cheap; disadvantage poor response, limited data can be collected)
- Personal interviews (advantage good response; disadvantage expensive, interviewer can influence results)
- Telephone surveys (same as for personal interviews)
- Observations (advantage systematic; disadvantage results can depend on chance)
Population & Sampling
Population is the group you want to find out about (eg all girls in school; all cars in UK)
A Census is information about every member of the population.
A sample of data is collected from a part of the population in order to make conclusions about the whole population.
- Advantages: practical, cheaper, quicker than doing a census.
- Disadvantages: don’t have information about every member of the population. May not be representative of the population.
Always try to ensure sample is free from bias.
Types of sampling are:
· Simple random sampling – every member of the population has an equal chance of being selected. Advantage unbiased. Number the population then use calculator or random number tables to generate random numbers. Pick people with those numbers.
· Systematic sampling – divide population size by sample size eg 2000 ÷ 50 = 40. Randomly choose a start number between 1 & 40 say 3. Your sample will be the 3rd person on the list then every 40th person after that until you have 50 people. Should produce an unbiased sample unless there is some sort of pattern in the data.
· Stratified Sampling – split population into groups (strata) & choose a number from each group proportional to the size of the group in the population. Useful if you have easy to define categories eg age, gender.
· Cluster sampling – choose a random sample of…