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Dr.A.P. Kulkarni
Professor & Head
Department of Community Medicine
Rural Medical College, LONI
DATA MANAGEMENT
Becareful with data……!!
!!
Lesson objectives
At the end of the lesson, the participants
will be able to:
Enlist types of variables:
Reasons of knowing types of variables
?
Describe essentials of data editing
Common typesof variables
- /
- -
Nominal scale
In master chart in form of label, text
Example: Gender, Religion , ,
No natural order
No absolute zero
Descriptive statistics: Frequency (Count)
Proportion
Ordinal scale-
In master chart in form of label, text
Example: Severity of disease, Grading of
cancer,Apgar score, Grading of disability
, ,
Natural order exists
No absolute zero
Descriptive statistics: Frequency (Count)
Proportion
Interval scale
In master chart in form of numerical: /
Natural order exists
No absolute zero
Negative values exist
Example: Env.Temp Temp: Degrees F
Statistics: Mean, Median, Mode, SD
Ratio scale
In master chart in form of numerical /
Natural order exists
Absolute zeroYES
Negative values exist
Example: Height,Weight, Hb
Statistics: Mean, Median, Mode, SD
Summary
Characteristic Nominal Ordinal Interval Ratio
Value assigned Label Label Numerical Numerical
Meaningful
order
No Yes Yes Yes
Meaningful
interval in
values
No No Yes Yes
Meaningful
ratios in values
No No No Yes
Absolute zero No No No Yes
Possibility of
negative values
No No Yes No
Reasonsfor knowing scales
Data presentation
Statistics permissible
Choice of statistical test :
Stats
Statistics
permissible
Choice of
statistical test
Permissible statistics
Nominal Proportion, Chi-square
,
Ordinal Proportion,, ,
rank order correlation, non-parametric
analysis of variance
Interval Mean, Median and mode, Range
percentile,
, , , , percentile
rank order correlation, non-parametric
analysis of variance
Ratio All statistics permitted for interval scales
plus the following:
geometric mean, harmonic mean,
coefficient of variation, logarithms
Data Editing:
Check for
 Completeness
 Accuracy
 “Illegal entries”
 out-liers
 “Improbable
entries”
Completeness
Blanks
Nils
I don’t knows
Data Editing
 Accuracy
Sample checks
Standardization
Triangulation
Illegal entries
 Entry that should
not have been there
Inclusion criteria
violated
Improbable entries
Cause of death
“Rupture of uterus” in
male
4 living children to girl
aged 15
Outliers
Extreme values, Box
plot
Dataediting methods
Check for blanks, nils and Dont knows
Range check: Minimum & Maximum
Count check:Totals / sub-totals
Validation checks:Validation at entry stage
in MS-EXCEL
Use of Software
DataReduction
Tables, groups
K = 1+ (3.332 x Log 10 (n))
K Number of categories
N = number of observations
N = 200
Then K = 8.66= 9
Less than 5, more than 15 categories NOT advised
Data Reduction:
Descriptive Statistics
Rate, Ratio, Proportion
Rate: Numerator,
Denominator, Multiplier
Ratio: X & Y. How much
will be Y if X =(1,100,
1000)
Proportion: X /(X+Y)
Centering Constants:
Arithmetic Mean
Geometric Mean
Median
Measures of Variation
Range
Inter-quartile Range
Standard Deviation,
Variance
Percentile
95% CI
18
Thank You !

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11. data management

  • 1. Dr.A.P. Kulkarni Professor & Head Department of Community Medicine Rural Medical College, LONI DATA MANAGEMENT
  • 3. Lesson objectives At the end of the lesson, the participants will be able to: Enlist types of variables: Reasons of knowing types of variables ? Describe essentials of data editing
  • 5. - -
  • 6. Nominal scale In master chart in form of label, text Example: Gender, Religion , , No natural order No absolute zero Descriptive statistics: Frequency (Count) Proportion
  • 7. Ordinal scale- In master chart in form of label, text Example: Severity of disease, Grading of cancer,Apgar score, Grading of disability , , Natural order exists No absolute zero Descriptive statistics: Frequency (Count) Proportion
  • 8. Interval scale In master chart in form of numerical: / Natural order exists No absolute zero Negative values exist Example: Env.Temp Temp: Degrees F Statistics: Mean, Median, Mode, SD
  • 9. Ratio scale In master chart in form of numerical / Natural order exists Absolute zeroYES Negative values exist Example: Height,Weight, Hb Statistics: Mean, Median, Mode, SD
  • 10. Summary Characteristic Nominal Ordinal Interval Ratio Value assigned Label Label Numerical Numerical Meaningful order No Yes Yes Yes Meaningful interval in values No No Yes Yes Meaningful ratios in values No No No Yes Absolute zero No No No Yes Possibility of negative values No No Yes No
  • 11. Reasonsfor knowing scales Data presentation Statistics permissible Choice of statistical test :
  • 12. Stats Statistics permissible Choice of statistical test Permissible statistics Nominal Proportion, Chi-square , Ordinal Proportion,, , rank order correlation, non-parametric analysis of variance Interval Mean, Median and mode, Range percentile, , , , , percentile rank order correlation, non-parametric analysis of variance Ratio All statistics permitted for interval scales plus the following: geometric mean, harmonic mean, coefficient of variation, logarithms
  • 13. Data Editing: Check for  Completeness  Accuracy  “Illegal entries”  out-liers  “Improbable entries” Completeness Blanks Nils I don’t knows
  • 14. Data Editing  Accuracy Sample checks Standardization Triangulation Illegal entries  Entry that should not have been there Inclusion criteria violated Improbable entries Cause of death “Rupture of uterus” in male 4 living children to girl aged 15 Outliers Extreme values, Box plot
  • 15. Dataediting methods Check for blanks, nils and Dont knows Range check: Minimum & Maximum Count check:Totals / sub-totals Validation checks:Validation at entry stage in MS-EXCEL Use of Software
  • 16. DataReduction Tables, groups K = 1+ (3.332 x Log 10 (n)) K Number of categories N = number of observations N = 200 Then K = 8.66= 9 Less than 5, more than 15 categories NOT advised
  • 17. Data Reduction: Descriptive Statistics Rate, Ratio, Proportion Rate: Numerator, Denominator, Multiplier Ratio: X & Y. How much will be Y if X =(1,100, 1000) Proportion: X /(X+Y) Centering Constants: Arithmetic Mean Geometric Mean Median Measures of Variation Range Inter-quartile Range Standard Deviation, Variance Percentile 95% CI