Day 2 wrap up

Objectives We learn many tests today Day 2 wrap up Objectives We learn many tests today Tomorrow we will see how DHS/MICS and SMART apply them And what international experts say And we will pracice more

Day 2 wrap up Data quality scores Failing a test does not mean that the data that failed the test are of no use. It means that we should treat the failing data with a degree of caution. A simple overall or summary measure of data quality is the proportions of tests that were not good quality. Like many summary measures this approach will hide details and will usually not be very useful The SMART overall data quality score is a grade for each individual score and a weighted grade for the total score Different test Different standards Data are assessed for their quality by applying each and every appropriate test to the data. criteria to judge that data are of good Failing a test is a warning to proceed with caution. It does not condemn the data as useless. We expect most datasets to fail one or more tests.

TEST Routine Data Age Z scores MUAC Missing data YES Legal Values Digit preference Age heaping NA Sex ratio Age Ratio Age structure Flags NO Normality Dispersion SD Many tests can be apply to routine data Don´t apply flags or normality tests to routine data. In routine data your population is not a normal population but just a small part of the total population: the sick population. The group of sick people may not follow a normal distribution and may have extreme values of anthropometry that should not be flagged

Question 1 Graphical methods are often less reliable than numerical summaries and should be avoided True or false? Notes for trainers: 5

Answer False We have seen many example of the importance of histograms and when statistical test might lead us to wrong conclusions. Always look at graphical methods 6

Question 2 Height is recorded to the nearest cm and digit preference happens because measures tend to round up to the closest 10 cm. For example 88cm to 90cm True or false? 7

Answer False Height is measured to the closest 0.1 cm. Measurements in nutritional anthropometry surveys are usually taken and recorded to one decimal place The rounding often happens because measurers ignore the decimal points. For example 85.4 cm is recorded as 85 cm 8

Question 3 To compare the sex ratio in a survey against the sex ration in the population we can use a T-Student test True or false? 9

Answer False T-student is used to compare a quantitative variable with a mean to a qualitative dichotomic variables. For example Height in cm vs having hypertension (yes/no) X-squared is used to compare to qualitative dichotomic variables. Males and females in a survey vs males and females in the populations. Smoking (yes/no) vs Hypertension (yes/no) 10

Question 4 SMART surveys always use SMART flags True or false? Notes for trainers: 11

Answer False SMART flags were developed by the SMART team but SMART methodologies allows to use WHO or SMART flags, you need to read the report to see which one were included. Often WHO flags are used in SMART surveys 12

Question 5 We do not have clear guidance on what levels of SD are acceptable since a high value can be due to errors or to heterogeneity in the population True or false? 13

Answer True 14

Question 6 The dispersion index can be used to detect pockets of malnutrition in some clusters True or false? Notes for trainers: 15

Answer True 16

Question 7 A normal distribution has mean=0 and SD=1 True or false? 17

Answer False The mean of a normal distribution is not zero. A Normal Distribution has two parameters μ(mean) and σ(standard deviation) represented by N(μ,σ)N(μ,σ). A Standard Normal Distribution is defined by us as a normal distribution ofμ=0μ=0 and σ=1σ=1 i.e. N(0,1) 18

Question 8 I am going to use all the tests we learned today True or false? Notes for trainers: 19

Answer True 20