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Presenting information

Presenting information. Stages Tools Outputs. Information Cycle. What do we collect?. Timely Quality data. data sources & tools. How do we process it?. How do we use it?. Reliable Information. Data quality checks, Data analysis. Appropriate Information & Feedback. Information.

emery-flores
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Presenting information

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  1. Presenting information

  2. Stages • Tools • Outputs Information Cycle What do we collect? Timely Quality data data sources & tools How do we process it? How do we use it? Reliable Information Data quality checks, Data analysis Appropriate Information & Feedback Information How do we present it? Reports, tables & graphs

  3. Preparing for Presentationessential prerequisites • Correct • Complete • submission by all (most) reporting facilities • Consistent • data within normal ranges • clear definitions / standards • Timely

  4. Presenting Information Tabular:frequency distribution table Graphs:Histogram, Line diagrams, Scatter plot, Bar chart, Pie chart, population pyramids Numerical:  Measures of Typicality or Center: mode, median, mean  Measures of Variability (or Spread): range, variance, standard deviation  Measures of Shape: skewness, kurtosis • Proportions, rates, ratios Maps: geographical representation (GIS)

  5. Data Quantitative (Numbers) Qualitative(Characteristics) Discrete Continuous Types of data Discrete categories/ kinds counts measures

  6. Numerical Data Continuous – they are measurable • Examples: • Age of patients in years or months • Weight of newborn in grams Discrete – they are counted (possible values are distinct or separate) • Examples: • The size of a family expressed as the number of children • The number of days since the begining of a disease units of measurement

  7. Non-numerical Data qualitative description of categories of a characteristic Examples: • The gender of a patient is recorded as “male” or “female”; • The list of diagnoses in a health center

  8. Data Quantitative Qualitative Discrete Continuous Discrete Number of beds per HC Bed ocupation Addresses of patients Number of children Patient temperature in ºC Cost of a drug presciption Population of a village Age of patients in years Number of broken vials Mark with  in the blank spaces

  9. Tables Number of children per family in Maputo, 2005 Source: Statistics & Planning Directorate, 2005

  10. Tables • Beware information overload: • easy to produce – difficult to use • Ideally should contain: • Few rows • Few categories/columns • Useful for: • assess quality • trends over time • make comparisons • identify outliers, gaps

  11. GRAPHS(a visual representation of data) Advantages: • Information is instantly conveyed • Data are presented clearly and simply • Can expose relationships and patterns • Detect trends over time • Can be used to emphasise information

  12. Graph Elements Title – descriptive clinic name, what is graphed and the time period Y axis – must ALWAYS be labeled Y axis label X axis – label if appropriate Key or legend – used if more than one element graphed Y X Source: Notes: Scale – be appropriate

  13. Five rules for graphs • Never put too much information in the graph. KEEP IT SIMPLE. • Be careful about mixing different activities: stick to one group of people or diseases or services. • Label your graph: always have a clear heading, easily read labels on the axes, and a legend which explains each of the lines or bars. • Select scales that fit the entire graph on both axes. • Where possible, draw a target line or reference point to show where you are aiming at.

  14. Type of graphs Continuous data • histograms • line Graphs • scatter Graphs Discrete Data • bar graphs • pie charts

  15. Line graph Graph 2: PHC headcount under 5 years old, Manyara Clinic, 2001 • accurate, can show minute changes in the relationships between 2 major variables • displays trends over time • can be useful if more than one data item is used

  16. Bar graph versus Line graph which one is best?

  17. Line graph, with two dependent variables

  18. Line graph, for cumulative coverage

  19. Line graph, for cumulative coverage • Simple and effective monitoring tool • Used when targets are set for a year i.e. immunization, antenatal coverage, etc. • Each month, data is graphed individually and also added to the previous month • A target is set, a target line is drawn and progress is monitored with respect to the target line

  20. Graphs for sets of discrete data pie charts bar graphs

  21. Pie chart • best type of graph for showing the relative proportions of different categories to each other and to the whole • can be used when exact quantities are less important than the relative sizes of the parts • Works best with large discrepancies • Only for data that adds up to a total (100%)

  22. Bar graph, simple • displays data over time or can compare 2 or more different facilities / districts / regions / years

  23. Bar graph, stacked it displays the quantities, but it also shows the relative proportions of the categories to each other and to the whole BUT hard to estimate the value of the variables at the top

  24. Common faults with graphs • No title • No labels for the variables • No units of measurement (or incorrect units!) • No scale markings (or just too many!) • Inappropriate scale choice – data points should be evenly represented • Incorrect choice of independent (x-axis) and dependent (y-axis) variables • No legends when needed • Too high ink-to-data ratio (e.g. 3D graphs) Don’t trust the computer!

  25. Graphs- population pyramids they highlight the differences in age distribution between males and females as well as proportional age categories

  26. BADGRAPHS YOU SHOULD NOT BUILD!

  27. …gone fishing…

  28. and finally, some inspiration… GapMinder

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