Transcription
Unit 6:Analyzing and interpreting data“There’s a world of difference between truth and facts.Facts can obscure the truth.”- Maya AngelouBuilding Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data1
Myths Complex analysis and big words impresspeople. Analysis comes at the end when there is datato analyze. Qualitative analysis is easier than quantitativeanalysis Data have their own meaning Stating limitations weakens the evaluation Computer analysis is always easier and betterBuilding Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data2
Blind men and an elephant- Indian fableThings aren’t always what we think!Six blind men go to observe an elephant. One feels the side and thinks theelephant is like a wall. One feels the tusk and thinks the elephant is a like aspear. One touches the squirming trunk and thinks the elephant is like asnake. One feels the knee and thinks the elephant is like a tree. Onetouches the ear, and thinks the elephant is like a fan. One grasps the tail andthinks it is like a rope. They argue long and loud and though each was partlyin the right, all were in the wrong.For a detailed version of this fable see:http://www.wordinfo.info/words/index/info/view unit/1/?letter B&spage 3Building Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data3
Data analysis and interpretation Think about analysis EARLYStart with a planCode, enter, cleanAnalyzeInterpretReflect––––What did we learn?What conclusions can we draw?What are our recommendations?What are the limitations of our analysis?Building Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data4
Why do I need an analysis plan? To make sure the questions andyour data collection instrument willget the information you want Think about your “report” when youare designing your data collectioninstrumentsBuilding Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data5
Do you want to report the number of people who answeredeach question? how many people answered a, b, c, d? the percentage of respondents whoanswered a, b, c, d? the average number or score? the mid-point among a range of answers? a change in score between two points intime? how people compared? quotes and people’s own wordsBuilding Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data6
Common descriptive statistics Count (frequencies)PercentageMeanModeMedianRangeStandard deviationVarianceRankingBuilding Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data7
Key components of a data analysis plan Purpose of the evaluation Questions What you hope to learn from thequestion Analysis technique How data will be presentedBuilding Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data8
Getting your data ready Assign a unique identifier Organize and keep all forms(questionnaires, interviews,testimonials) Check for completeness andaccuracy Remove those that are incompleteor do not make senseBuilding Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data9
Data entry You can enter your data– By hand– By computerBuilding Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data10
Hand codingQuestion 1 : Do you smoke? (circle 1)YESNONo answer////////Building Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data11
Data entry by computer By Computer– Excel (spreadsheet)– Microsoft Access (database mngt)– Quantitative analysis: SPSS (statisticalsoftware)– Qualitative analysis: Epi info (CDC datamanagement and analysis program:www.cdc.gov/epiinfo); In ViVo, etc.Building Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data12
Data entry computer screenSmoking: 1 (YES) 2 (NO)Survey ID001002003004005Building Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting dataQ1 Do yousmoke11221Q2 Age241836482613
Dig deeper Did different groups show differentresults? Were there findings that surprisedyou? Are there things you don’tunderstand very well – further studyneeded?Building Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data14
nceUndecided/declined tocomment8(15% ofsmokers)33(60% ofsmokers)14(25% ofsmokers)Non-smokers(n 200)170(86% of nonsmokers)16(8% of nonsmokers)12(6% of nonsmokers)Total(N 255)178(70% of allrespondents)49(19% of allrespondents)26(11% of allrespondents)Currentsmokers(n 55)Building Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data15
Discussing limitationsWritten reports: Be explicit about your limitationsOral reports: Be prepared to discuss limitations Be honest about limitations Know the claims you cannot make– Do not claim causation without a trueexperimental design– Do not generalize to the population withoutrandom sample and quality administration(e.g., 60% response rate on a survey)Building Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data16
Analyzing qualitative data“Content analysis” steps:1. Transcribe data (if audio taped)2. Read transcripts3. Highlight quotes and note why important4. Code quotes according to margin notes5. Sort quotes into coded groups (themes)6. Interpret patterns in quotes7. Describe these patternsBuilding Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data17
Hand codingqualitative dataBuilding Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data18
Building Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting data19
Building Capacity in Evaluating OutcomesUnit 6: Analyzing and interpreting dataExample data set20
Unit 6: Analyzing and interpreting data 2 Myths Complex analysis and big words impress people. Analysis comes at the end when there is data to analyze. Qualitative analysis is easier than quantitative analysis Data have their own meaning Stating limitations weakens the e
