Using a statistics package to analyse survey data Module 2 Session 8

Objectives of this session You should be able to: Use a statistics package to produce tables and graphs of frequencies and proportions Pproduce tables of summary statistics Explain why weights are sometimes needed in analysing survey data Produce weighted tables of counts and other statistics

How to describe data well - review Look for oddities in the data and be prepared to adapt the summaries that you calculate Study the data as tables and graphs Use frequencies and percentages to summarize categorical variables Use averages and measures of variability to summarize numeric variables Identify any structure in the data and use it in producing your summaries

Look at the data The 2 types of variable are summarized in different ways

Analysis to meet objectives Simple objectives Not so simple objectives

Meeting simple objectives Summaries made with Instat – see practical 1

Answering more complicated objectives AND explaining some of the variability as shown in Module 1 These were also with Instat

Practical 1 Reviews the construction of tables Using a statistics package Particularly to look at percentages Because percentages have to be understood clearly to analyse multiple response data This practical also gives more practice In the use of a statistics package

Common complications when analysing survey data Common complications include: Missing values in survey data Weights are sometimes needed Because some observations represent more of the population than others Multiple response questions have to be processed These are all easier with an appropriate statistics package Here, as an example we introduce the need for weights

Introducing weights Suppose a sample of 2 farmers Farmer Yield A1 t/ha B2 t/ha What is the mean? Obviously it is (1 + 2)/2 = 1.5 t/ha! But…

Introducing weights - continued Suppose the same sample of 2 farmers FarmerArea YieldProduction A 5 ha1 t/ha5 tons B 0.5 ha2 t/ha1 ton Now what is the mean? It could still be (1 + 2)/2 = 1.5 t/ha Or it could be (5 + 1)/5.5 = 1.1 t/ha

But which is right? They are both right, but they answer different questions Take food security Are you interested in the farmer Or the production Or both If the farmer is the unit of interest Then there are 2 farmers The mean is 1.5 If the area is the unit of interest Then there are 5.5 ha And Farmer A is 10 times as important as farmer B So a weighted mean is produced

The weighted mean So if the area is of interest – then with FarmerArea Yield A 5 ha1 t/ha B 0.5 ha2 t/ha Weight each yield by the area it represents mean = (1*5 + 2*0.5)/5.5 = 1.1 Here the areas are the weights They are used when different observations represent different proportions of the population

Weights in the Tanzania agriculture survey The number of people in the population represented by each observation It was roughly a 1% sample, so the weights are about 100 The technical guide explains the calculations

Practical 2 Weights using a statistics package First the rice survey Weighting by the size of field Then the Tanzania agriculture survey Investigate ownership of radios By type of farming household

Possession of radio by type of farming Unweighted analysis Uses the observed numbers and percentages in the sample Look at livestock – but numbers small

Possession of radio by type of farming Weighted analysis The estimated numbers and percentages in the region of Tanzania Look at livestock now – what do you conclude?

Why such a large change with weighting? Examine the weights for these 2 groups Average weight = 60Average weight = 20 So estimated % with radio = 100*(42*20)/(10*60+42*20) = 59%

And always take care with small numbers Large sample overall But still a small sample of livestock-only farmers

Can you now? Use a statistics package to produce tables and graphs of frequencies and proportions Produce tables of summary statistics Explain why weights are sometimes needed in analysing survey data Produce weighted tables of counts and other statistics