# Coverage estimate calculation - Final stage - Help again

This question was posted the Coverage assessment forum area and has 14 replies.

### Géraldine LE CUZIAT

ACF

Normal user

14 Dec 2011, 18:56

### Mark Myatt

Consultant Epidemiologist

Frequent user

15 Dec 2011, 10:39

```
scale .frameControls.likeN -variable likeN -from 1 -to 96
```

change the 96 to anything you like and recompile using a tclkit.
If there is a demand for a larger limit then I will make the change and compile for Windows and OS-X.
In the mean time I suggest that you use the hand calculation method as outline in this section of the SQUEAC handbook.
If you post the data here we could go through it as a "worked example".### Saul Guerrero

Director of Nutrition

Technical expert

16 Dec 2011, 12:45

### Mark Myatt

Consultant Epidemiologist

Frequent user

16 Dec 2011, 16:58

### Saul Guerrero

Director of Nutrition

Technical expert

16 Dec 2011, 17:06

### Lio

CMAM Advisor

Technical expert

19 Dec 2011, 10:59

### Mark Myatt

Consultant Epidemiologist

Frequent user

19 Dec 2011, 18:19

### Mark Myatt

Consultant Epidemiologist

Frequent user

20 Dec 2011, 09:51

```
Low prevalence = Low case numbers
High prevalence + Low coverage = Low case numbers
```

can be difficult to distinguish from each other and wishful thinking can cloud judgement so that evidence of low coverage is ignored (there is an example of this in the case-studies in the SQUEAC handbook).
The sample size prompting this question is four or five times larger than required. This suggests a large miscalculation on the part of the investigator. Everyone makes mistakes. It would be interesting to hear Géraldine's take on this.
A sample size such as *n*= 96 is conventional for coverage surveys of child survival interventions (e.g. EPI surveys are design to have an effective sample size, after accounting for expected design effects, of

*n*= 96). I am not in favour in increasing the sample size limit in the BayesSQUEAC software since SQUEAC is supposed to be a low resource method. A sample size of much about

*n*= 50 will usually be a waste of resources (I have done a few SQUEACs and cannot recall ever going above that). I have, however, increased this to

*n*= 192 at Saul's request.

**A note on sample size and precision :**A larger sample size will improve precision but not as much as you might think. Moving (e.g.) from a sample sise of 100 to a sample size of 200 does

**double precision (or half the with of the credible or confidence interval). Instead, precision increase with the square root of sample size. This means that the doubling the sample size increases precision by about 1.4 times (i.e. the square root of 2). Moving (e.g.) from a sample size of 100 to a sample size of 1000 improves precision by only about 3.2 times (i.e. the square root of 10).**

*not*### Mark Myatt

Consultant Epidemiologist

Frequent user

20 Dec 2011, 10:28

### Ernest Guevarra

Katilingban

Technical expert

10 Jan 2012, 15:44

### Mark Myatt

Consultant Epidemiologist

Frequent user

11 Jan 2012, 16:39

### Mark Myatt

Consultant Epidemiologist

Frequent user

12 Jan 2012, 12:07

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