Data Analysis

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Data Analysis-Why? A. To study and examine data collected 1 Synthesis of data B. Identify community strengths C. Identify community health needs D. Determine need for further data collection: 1. find if research has been done. 2. Data gaps: determine need for further data collection. Make sure you can support data gaps with information from assessment. E. Look for trends/patterns; how often do you see a recurrent theme? F. Discovery of causative relationships: the R/T portion. I. Basic Steps of Data Analysis(4) 2 Categorize-e.g. by demographics, commonalities. E.g. intra/extra community for health and social services 3 Summarize 4 Compare 5 Inference/Interpretation II. Categorize Data 6 There are many ways to sort and categorize data e.g. demographically by age groups, by problem type 7 Geographic approaches may be used 8 Use of model; we are using the wheel from Neuman’s model. 9 Look for data convergence when categorizing-e.g. how many times do we see data converging in different categories? 10 Look for commonalties, health resources that are available. SEC, age, etc. III. Data Summary 11 Summary statements-summarize each table. 12 Summary statistics-put data into percentages and rates so that different areas/communities can be compared. Raw numbers will not work to compare different areas. 13 Graphic methods of data summary: 14 Remember that tables need concise summary data. P. 222, can put population statistics in graph. 15 Dependency Ratio: how many people in your community who can support the dependents. Calcuation on page 225. Should do for both census tracts. 16 Data summarization facilitates ease of reading and spotting trends/patterns in data IV. Summary Statistics 17 Rates-vital statistics 18 Percentages-population characteristics 19 Ratios-sex,

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