Interval data is information that can be measured along a continuum, where there is equal, meaningful distance between each point on a scale. The number of right and wrong questions on a testДЄ website's bounce rate (percentages can be no less than 0 or great than 100)Дiscrete data is typically most appropriately visualized with a tally chart, pie chart, or bar graph, which is shown below.ДЄn example of a line graph representing quantitative dataД¬ontinuous data can be further broken down into two categories: interval data and ratio data. The number of outs a hitter makes in a baseball game The number of items eggs broken when you drop the carton While discrete data doesnвЂ™t have to be represented by whole numbers, there are limitations to how it can be expressed.

In reference to quantitative data, discrete data is information that can only take certain fixed values. Continuous data can be further divided into interval data and ratio data. There are two types of quantitative data: discrete and continuous. But these data types can be broken down into more specific categories, too. To facilitate the analysis of the data, all quantitative data collected during Stage 1 should be compiledВ into oneВ excel sheet.All quantitative data can be measured numerically, as shown above.

Collating and analysingВ quantitative data:

Lists and tables of outreach workers' activitiesДЈ.

Mapping of outreach workers' activities.

Lists and tables of admissionsВ and defaulters locations.

Other useful meansВ for mapping and analysing beneficiary locations and outreach worker activities are described in the following tools:

Information about villages in a catchment area: estimated population, main ethnicityД¬ollection templates are available in the Tools section.

Outreach activities within the catchment areas of the different SDUs: numbers of community health workers and volunteers operating out of the SDU and frequency of visits to individual villages.

Other useful information to collect might include: The findings from the analysis of the routine programme data or from qualitative interviewsВ may also reveal other informationВ that needs to be collected during this stage.ДЇor example, if the admissions by village in an SDU's catchment area reveal that fewer children are being admitted from villages located a long way from the SDU, during stage 1, enumerators could ask mothers present at the SDU what the approximate 'time-to-travel' is between the SDU and village. Collecting additionalВ quantitative data:ДЄdditionalВ information and data shouldВ also be collected during Stage 1 in order to build up a picture of why coverage may be low or high in a health district. Pull data from SDU routine programme data, enter into tally sheet ('Referral source') and enter totals into Data analysis tool.Дў. Tally of which sources referred SAM children to SDU: Pull data from SDU routine programme data, enter into tally sheet ('Home villages of admitted and defaulting cases') and enter totals into Data analysis tool. Pull data from SDU routine programme data, enter into tally sheets ('Length of stay before cure / default') and enter totals into Data analysis tool. Pull data from SDU routine programme data, enter into tally sheets ('MUAC at admission' and 'Last MUAC before cure / default') and enter totals into Data analysis tool.ДЇor each SDU and for health district in total, length of stay in programme: Programme discharge rates (percentage) per month and overall for period surveyed for discharges: Cured, Defaulter, Non-response and DeathДЇor each SDU and for health district in total, count of each MUAC measurement:

Pull from routine programme data at health district level and copy / paste into Data analysis tool. Data collection templates are available in the Tools section: Data typeДЇor each SDU and for health district in total: The key data required are listed in the table below. The data should be collectedВ for the duration of the period being assessed. During Stage 1 of the assessment, enumerators should go to all health centres or service delivery units (SDUs) in theВ health district to collect the data from health centre staff or, if the dataВ is already available, to fill any gaps in missing data. The SQUEAC assessmentВ methodology makes use of existing programme data to provide insights into where and why coverage of a programme might be low.

Collating and analysing quantitative data.

Collecting additional quantitative data.

Quantitative data analysis programs