8.processing

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8.processing

  1. 1. Processing &Analysis of dataD.A. Asir John Samuel, MPT (Neuro Paed), Lecturer, Alva’s college of Physiotherapy, Moodbidri Dr.Asir John Samuel (PT), Lecturer, ACP
  2. 2. Processing operations• Editing• Coding• Classification• Tabulation Dr.Asir John Samuel (PT), Lecturer, ACP
  3. 3. Editing• Process of examining the collected raw data• Editing is done to assure that data are accurate, consistent with other facts gathered, uniformly entered, as complete as possible• Field editing• Central editing Dr.Asir John Samuel (PT), Lecturer, ACP
  4. 4. Field editing• Review of reporting forms by the investigator for completing, translating or rewriting• Individual writing styles• On the very next day or on the next day• Not correct errors of omission by simply guessing Dr.Asir John Samuel (PT), Lecturer, ACP
  5. 5. Central editing• Take place when all forms or schedules have been completed and returned to fitness• Correct errors such as an entry in wrong place, wrong month, and the like• Respondent can be contacted for clarification• No bias Dr.Asir John Samuel (PT), Lecturer, ACP
  6. 6. Coding• Process of assigning numerals or other symbols to answers• Should be appropriate to research problem under consideration• Necessary for effective analysis• Extraction of data Dr.Asir John Samuel (PT), Lecturer, ACP
  7. 7. Classification• Large volume of raw data is reduced into homogeneous group• Arranging data in groups or classes on basis of common characteristics• Classification according to attributes• Classification according to class-intervals Dr.Asir John Samuel (PT), Lecturer, ACP
  8. 8. Tabulation• Arranging in concise and logical order• Summarising raw data and displaying in compact form• Orderly arrangement of data in columns and rows Dr.Asir John Samuel (PT), Lecturer, ACP
  9. 9. Tabulation is essential because of• Conserves space and reduces explanatory and descriptive statement to a minimum• Facilitates process of comparison• Facilitates summation of items and detection of errors and omissions• Basis for various statistical computations Dr.Asir John Samuel (PT), Lecturer, ACP
  10. 10. Problems in processing• Problem concerning “Don’t Know” responses• Use of percentages Dr.Asir John Samuel (PT), Lecturer, ACP
  11. 11. Problem concerning “Don’t Know” responses • When DK group is small, it is of little significance • In big group, it becomes mater of concern • Actually may not know the answer or • Researcher may fail in obtaining appropriate information (failure of questioning process) • Keep as a separate category in tabulation Dr.Asir John Samuel (PT), Lecturer, ACP
  12. 12. Use of percentages• 2/more percentages must not be averaged unless each is weighted by group size• Too large percentages should be avoided because difficult to understand and confuse• Hide base value• Real differences may not be correctly read• Can never exceed 100 percent and for decrease Dr.Asir John Samuel (PT), Lecturer, ACP
  13. 13. Statistics in Medical Research• Documentation of medical history of disease, their progression, variability b/w patient, association with age, gender, etc.• Efficacy of various types of therapy• Definition of normal range• Epidemiological studies Dr.Asir John Samuel (PT), Lecturer, ACP
  14. 14. Statistics in Medical Research• Study the effect of environment, socio- economic and seasonal factors• Provide assessment of state health in common, met and unmet needs• Success/failure of specific health programme• Promote health legislation• Evaluate total health programme of action Dr.Asir John Samuel (PT), Lecturer, ACP
  15. 15. Statistics in Medical Research - Limitation• Does not deal with individual fact• Conclusion are not exact• Can be misused• Common men cannot handle properly Dr.Asir John Samuel (PT), Lecturer, ACP
  16. 16. Normal distribution• Represented by a family of infinite curves defined uniquely by 2 parameter the mean and the SD of the population• The curve are always symmetrically bell shaped. The width of the curve is defined by population, SD Dr.Asir John Samuel (PT), Lecturer, ACP
  17. 17. Normal distribution• Mean, median and mode coincide• It extends from - ∞ to + ∞• Symmetrically about the mean• Approx 68% of distribution is within 1SD of mean (68.27%)- 95% - 2SD (1.96 SD)- 99% - 3SD (2.58 SD) Dr.Asir John Samuel (PT), Lecturer, ACP
  18. 18. Normal distribution• The total area under the curve is 1• The value of measure of skewness is zero. It is not skewed• The curve is asymptotic. It approaches but never touches baseline at extremes• The curve extends on the both sides -3σ distance on left to +3σ distance on the right Dr.Asir John Samuel (PT), Lecturer, ACP
  19. 19. Normal distribution - Uses• Construct confidence interval• Many statistical techniques makes an underlying assumption of normality• Distribution of sample means is normal• Normality is important in statistical inference Dr.Asir John Samuel (PT), Lecturer, ACP
  20. 20. Skewness• Measure of lack of symmetry in a distribution• Positive skewed- Right tail is longer- Mass of distribution is concentrated on left side- Distribution is said to be right skewed Dr.Asir John Samuel (PT), Lecturer, ACP
  21. 21. Negative skewed• Left tail is longer• Mass of distribution concentration on right side• Distribution is said to be left skewed• Value of skewness is 0 for normal distribution Dr.Asir John Samuel (PT), Lecturer, ACP
  22. 22. Kurtosis• Measure of degree of peakness in distribution• For normal distribution, value of kurtosis is 3• Leptokurtic – High peakness• Mesokurtic – normal• Platykurtic – Low peakness Dr.Asir John Samuel (PT), Lecturer, ACP
  23. 23. Descriptive statistics• Measures of location- Central tendency- Mean, median and mode• Measures of variation- Dispersion- Range, quartile, IQR, variance and SD Dr.Asir John Samuel (PT), Lecturer, ACP
  24. 24. Mean• Sum of all observation divided by total no. of observation Dr.Asir John Samuel (PT), Lecturer, ACP
  25. 25. Mean - merits• Well understood by most people• Computation of mean is easy• More stable• All items in a series are taken into account• Used in further statistical calculation• Good basis for comparison Dr.Asir John Samuel (PT), Lecturer, ACP
  26. 26. Mean - Demerits• Affected by extreme values• Cannot be computed by mere observation• Not suitable for skewed distribution• May not be an actual item• Not in qualitative data Dr.Asir John Samuel (PT), Lecturer, ACP
  27. 27. Median• Middle most observation when data is arranged in ascending/descending order of magnitude• Divides number into 2 halves such that no.of items below it is same as no.of items above Dr.Asir John Samuel (PT), Lecturer, ACP
  28. 28. Median Odd = n+1/2Even = n/2 + (n+1)/2 2 Dr.Asir John Samuel (PT), Lecturer, ACP
  29. 29. Median - Merits• Widely used measures of CD• Not influenced by extreme values• Can be determined if extremes are not known• Not a typical representation of series• Useful for skewed distribution Dr.Asir John Samuel (PT), Lecturer, ACP
  30. 30. Median - Demerits• When no. of items are small, median may not be representative• It is effected by frequency of neighboring items• Not a typical representation of series Dr.Asir John Samuel (PT), Lecturer, ACP
  31. 31. Mode• Most frequently occurring observation in data• If all values are different then no mode Dr.Asir John Samuel (PT), Lecturer, ACP
  32. 32. Mode - Merits• Can be computed by mere observation• Simple• Precise• Less time consuming• Less strain Dr.Asir John Samuel (PT), Lecturer, ACP
  33. 33. Mode - Demerits• Not an amenable to further algebraic treatment• Not rigidly defined• Affected by no. of frequency of items Dr.Asir John Samuel (PT), Lecturer, ACP
  34. 34. Measures of Dispersion (variation)• Range• Interquartile range• Variance• Standard Deviation Dr.Asir John Samuel (PT), Lecturer, ACP
  35. 35. Range• Difference between largest and smallest value Range = Largest no. – Smallest no. Dr.Asir John Samuel (PT), Lecturer, ACP
  36. 36. Quartile• Value that divide data into 4 equal parts when data is arranged in ascending order Q1 = (n+1/4)th ordered observation Q1 = [2(n+1)/4]th ordered observation Q3 = [3(n+1)/4]th ordered observation Dr.Asir John Samuel (PT), Lecturer, ACP
  37. 37. Interquartile range• Provides range which covers middlemost 50% of observation• Good measures of dispersion if there are extreme values IQR = Q3 – Q1 Dr.Asir John Samuel (PT), Lecturer, ACP
  38. 38. Variance• Sum of squares of difference of each observation from mean, divided by n-1 𝜀 𝑥−𝑥 2 Variance = 𝑛−1 Dr.Asir John Samuel (PT), Lecturer, ACP
  39. 39. Variance - Merits• Easy to calculate• Indicate the variability clearly• Most informative Dr.Asir John Samuel (PT), Lecturer, ACP
  40. 40. Variance - Demerits• Units of expression of variance is not the same Dr.Asir John Samuel (PT), Lecturer, ACP
  41. 41. Standard Deviation (SD)• Square root of variance 𝜀 𝑥−𝑥 2 SD = √ 𝑛−1 Dr.Asir John Samuel (PT), Lecturer, ACP
  42. 42. Standard Deviation - Merits• Most widely used• Used in calculating standard error Dr.Asir John Samuel (PT), Lecturer, ACP
  43. 43. Standard Deviation -Demerits• Lengthy process• Gives weightage to only extreme valves Dr.Asir John Samuel (PT), Lecturer, ACP

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