Analysing & interpreting data.ppt
- 1. Analyzing and interpreting data
- 2. Things aren’t always what we think! Six blind men go to observe an elephant. One feels the side and thinks the elephant is like a wall. One feels the tusk and thinks the elephant is a like a spear. One touches the squirming trunk and thinks the elephant is like a snake. One feels the knee and thinks the elephant is like a tree. One touches the ear, and thinks the elephant is like a fan. One grasps the tail and thinks it is like a rope. They argue long and loud and though each was partly in 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=3 Blind men and an elephant - Indian fable
- 3. Data analysis and interpretation • Think about analysis EARLY • Start with a plan • Code, enter, clean • Analyze • Interpret • Reflect –What did we learn? –What conclusions can we draw? –What are our recommendations? –What are the limitations of our analysis?
- 5. Why do we need an analysis plan? • To make sure that the questions and data collection instrument will get the information, what we want • Think about “report” when are designing data collection instruments
- 6. Preparing Data for Analysis
- 7. Analysis Analysis is a process, which enters into research in one form or another form the very beginging . it consist of two large steps :- gathering data and analysis of these data .
- 8. Key components of a data analysis plan • Purpose of the evaluation • Questions • What you hope to learn from the question • Analysis technique • How data will be presented
- 9. STEPS IN PROCESSING OF DATA identifying the data structure editing the data classifying the data transcription of data coding tabulation of data
- 10. Identifying the data structure It is a dynamic collection of related variables and can be convinently represented and state the preliminary relation ship between variables /groups of variables
- 11. Editing the data Data is editing in the data instrument . it is a processes of checking to detect and correct of errors , means remove of incompleteness or inconsistency in the data . check by 3 steps :- completeness accuracy uniformity
- 12. Classification of data 1. according to attributes :- descriptive ( such as honesty , literacy ) and numerical ( weight, height ) . descriptive cannot be measure in quantitative study . 2. according to class interval :- statistics of variables , which classify basic of class interval ( income , production , age ).
- 13. Transcription of data long worksheets , sorting cards or sorting strips could be used by the researcher to manually transcript the responces .
- 14. Data Coding
- 15. Data Coding
- 16. Hand coding Question 1 : Do you smoke? (circle 1) YES NO No answer // ///// /
- 17. Data entry by computer • By Computer – Excel (spreadsheet) – Microsoft Access (database mngt) – Quantitative analysis: SPSS (statistical software) – Qualitative analysis: Epi info (CDC data management and analysis program: www.cdc.gov/epiinfo); In ViVo, etc.
- 18. Data entry computer screen Survey ID Q1 Do you smoke Q2 Age 001 1 24 002 1 18 003 2 36 004 2 48 005 1 26 Smoking: 1 (YES) 2 (NO)
- 20. Describing the data Descriptive statistics are used to describe the basic features of data and to provide simple summaries about the sample and the measures used in the study .
- 21. Common descriptive statistics • Count (frequencies) • Percentage • Mean • Mode • Median • Range • Standard deviation • Ranking
- 22. Drawing the inferences of data It helps in drawing inferences from the data . eg. finding the diferences , relationship, and association between two more variables . inferential stistical tests are Z-test , t- test , ANOVA , chi -squire test etc.
- 23. Interpretation of the data It refers to the critical examination of the analyzed study results to draw inferences and conclusion . it involves a search for their meaning in relation to the research problem, objectives , conceptual framework and hypotheses.
- 24. Strategies for effective interpretations interpretations must be made in light of research problem , objectives,conceptual framework , hypotheses, and assumptions . critical examination of the each elements . careful consideration and recognition of the limitition . each part , aspect and segment of the analysed result must receive close attentions, so that misrepresentation can be avoided.
- 25. Comparison of range ,mean ,median of pretest and post test knowledge score n=30 knowledge score Range Mean Median Pretest 6-20 11.5 11.83 Posttest 17-29 23.3 24.1
- 28. Hypothesis testing • State the Null and Alternate hypotheses in – sentences and - equation form • State the significance level e.g α = 0.05 • State the number of tails( one or two) and the test statistic ( z,t) • For two sample indicate independent or paired
- 29. The Non-Zero Null Hypothesis 1.Usually the hypothesized difference is zero, That is the difference between the mean of the sample and expected value is zero µ = M
- 30. Hypothesis testing • When do you do paired t test test? Does the average difference between the two measures of the sample contradicct null hypothesis • When do you independent t test? Does the difference between average responses for sample one and sample two contradict the null hypothesis?
- 31. Discussing limitations Written reports: • Be explicit about your limitations Oral reports: • Be prepared to discuss limitations • Be honest about limitations • Know the claims you cannot make – Do not claim causation without a true experimental design – Do not generalize to the population without random sample and quality administration (e.g., <60% response rate on a survey)
- 32. Analyzing qualitative data “Content analysis” steps: 1. Transcribe data (if audio taped) 2. Read transcripts 3. Highlight quotes and note why important 4. Code quotes according to margin notes 5. Sort quotes into coded groups (themes) 6. Interpret patterns in quotes 7. Describe these patterns
- 35. Example data set