ways of expressing statistics on diagrams and graphs

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slide 1 ways of expressing statistics on diagrams and graphs lesson 15-16 writing we have already discussed how best to analyze data. the results of your data analysis tell you what is happening in your research. today, we will cover formats that effectively summarize data so that you can interpret the findings and begin to discover why your research is functioning as the results indicate. * learning objectives understand different ways to best summarize data choose the right table/graph for the right data interpret data to consider the programmatic relevance in this session, you will: * summarizing data tables simplest way to summarize data data are presented as absolute numbers or percentages charts and graphs visual representation of data data are presented as absolute numbers or percentages the two main ways of summarizing data are by using tables and charts or graphs. a table is the simplest way of summarizing …

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ables: frequency distribution set of categories with numerical counts year number of births 1900 61 1901 58 1902 75 let’s start with tables. most tables show a frequency distribution, which is a set of categories with numerical counts. here, you see the year as the category and the number of births as the numerical count. what should be added to this table to provide the reader with more information? note to facilitator: wait for a participant response before answering. answer – title answer – data source * tables percentage of births by decade between 1900 and 1929 source: u.s. census data, 1900–1929. year number of births (n) relative frequency (%) 1900–1909 35 27 1910–1919 46 34 1920–1929 51 39 total 132 100.0 to interpret this table, we should look at the relative frequencies. what do they tell us? we can see data across the three decades and what percentage of …

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. * bar chart comparing categories in this bar chart, we’re comparing the categories of data, which are the different sites. you see a comparison between sites by quarters and between quarters over time. * stacked bar chart represent components of whole & compare wholes number of months patients have been enrolled in hiv care number of months female and male patients have been enrolled in hiv care, by age group data source: aidsrelief program records january 2009 - 20011 a stacked bar chart is often used to represent components of a whole and compare the wholes (or multiple values). here, you see the number of months female and male patients have been enrolled in hiv care, by age group. by looking within each bar, you see the age breakdown by gender, and by looking at both bars together, you can compare the number of months enrolled for both males …

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always add up to 100. in this case, we used the chart to show the contribution of patients enrolled each quarter to the total enrollment for the year. for example, the first quarter contributed the largest percentage (59%) of enrolled patients. * interpreting data once we have transformed data into information by summarizing them with tables, graphs, or narrative, we need to interpret the data. that is, we need to consider the relevance of the findings to our program – the potential reasons for the findings – and possible next steps. in this process, we move from the ‘what’ is happening in our programs to the ‘why’ it is happening. * interpreting data adding meaning to information by making connections and comparisons and exploring causes and consequences data interpretation is the process of making sense of the information. it allows us to ask: what does this information tell me about …

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into the context of the research. supplementing the findings with expert opinion is a good way to do this. for example, talk to others with knowledge of the research or target population, who have in-depth knowledge about the subject matter, and get their opinions about possible causes. * interpretation – consider other data use routine service data to clarify questions calculate nurse-to-client ratio, review commodities data against client load, etc. use other data sources while it is important to consider other indicators in your analysis, remember – descriptive statistics do not show causality. in these cases, look at other data sources. * interpretation – other data sources situation analyses demographic and health surveys performance improvement data other data sources include: note to facilitator: read slide. * interpretation – conduct further research data gap conduct further research methodology depends on questions being asked and resources available once you review additional data, …

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slide 1 ways of expressing statistics on diagrams and graphs lesson 15-16 writing we have already discussed how best to analyze data. the results of your data analysis tell you what is happening in your research. today, we will cover formats that effectively summarize data so that you can interpret the findings and begin to discover why your research is functioning as the results indicate. * learning objectives understand different ways to best summarize data choose the right table/graph for the right data interpret data to consider the programmatic relevance in this session, you will: * summarizing data tables simplest way to summarize data data are presented as absolute numbers or percentages charts and graphs visual representation of data data …

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