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WELCOME
Dr. Subrata Kumer Sen
MBBS, MPH, BCS (Health)
Lecturer, Community Medicine
M Abdur Rahim Medical College
Dinajpur.
Statistics: The science of collecting, summarizing, and
analyzing data.
Biostatistics: The science of collecting, summarizing,
and analyzing biological data.
Medical Statistics: The science of collecting,
summarizing, and analyzing medical data.
Vital Statistics: Statistics that deals with vital events.
Vital events are births, marriages, divorces, separations,
and deaths.
Sources of data for vital events/ statistics:
• National level- Census, BBS
• City Corporation/ Municipalities for births &
deaths
• Kaji Office for marriages, divorces, separations
• Notification of infectious diseases for deaths
• Hospital records for births & deaths
• Disease registers for deaths etc.
Uses of Biostatistics:
• Collection of information in the best possible scientific way.
• Simplification of huge, complex sets of data.
• Helps in the formulation of hypothesis and amplification of
ideas.
• Identification of association between two or more variables
by testing hypothesis.
• Helps in interpretation and drawing conclusion by analyzing
data
• By summarizing and presenting, helps policy makers to make
decisions based on scientific evidence.
Data: Data is a set of values collected during any type of scientific
investigation.
Data Classification:
Based on Source of Data:
I. Primary: Data directly collected at the field level during a study
II.Secondary: collected from another study, census data, hospital records
III.Tertiary: from textbooks.
Based on Nature of Data:
• Qualitative/ Categorical/ Nominal: Age, Sex, economical status
• Quantitative/ Numerical:
a. Continuous: Cont. spectrum/range can be fractioned (e.g. height, weight,
temperature)
b. Discrete: No continuity, Complete #, no fraction (e.g. Parity, patient
number in hospital)
Variable: Any characteristic which differs for one individual or
object to another.
Types:
A. According to Relationship:
1. Independent Variable
2. Intervening Variable
3. Dependent Variable
4. Confounding variable
B. According to Nature/ Characteristic:
1. Qualitative
2. Quantitative: Continuous & Discrete.
Measurement of Scale:
A.Qualitative-
1. Nominal (Sex, Religion)
2. Ordinal (Educational level, Economical Status)
B. Quantitative-
1. Interval (Temperature)
2. Ratio (age, height, weight)
Methods of Data Collection:
*Surveying Study population
*Interviewing: Face to Face, Telephone, Internet etc
*Observation: Participant, Non-participant
*Written Questionnaire
*Electronic Data Reporting
*Focus Group Discussion
*Document Review
Data collection tool (instrument):
•Questionnaire
•Interview schedule
•Checklist
•Other electronic device
Data Presentation:
A.Tabulation/ Tabular Presentation:
1. Simple Table
2. Frequency Distribution Table
B. Graphical Presentation:
1. Qualitative:
a. Bar diagram - Simple Bar Diagram, Multiple Bar diagram,
Component Bar Diagram
b. Pie/ sector chart
c. Pictogram
d. Map/ spot diagram
2. Quantitative:
a. Histogram
b. Frequency polygon
c. Frequency Curve
d. Line chart
e. Scatter diagram
Characteristics of a table:
A table should have
i. Table number
ii. Title
iii. Body- a. Column b. Row
iv. Foot note/ Source (if any)
Biostatistics-C-2-Item-2pdf.pdf
Simple table
Frequency Distribution Table
Simple Bar Diagram
Multiple Bar diagram
Component Bar Diagram
Pie/ sector chart
Pictogram
Map/ spot diagram
Histogram
Frequency polygon
Line chart
Scatter diagram
Measures of Central Tendency:
Mean: Mean is the summation of all values of a
series divided by number of values.
Median: Median is the middle most value of a
data set while arranged in ascending or
descending order.
Mode: Mode is the most frequent and repeated
values observed in a data set.
Biostatistics-C-2-Item-2pdf.pdf
Biostatistics-C-2-Item-2pdf.pdf
Measures of Dispersion:
Range: Simply the difference between the largest and
smallest values in a set of data.
Mean Deviation: The mean deviation is an average of
absolute deviations of individual observations from the
central value of a series.
Standard Deviation: Standard deviation is the positive
square root of the mean-square deviations of the
observations from their arithmetic mean.
Population: A population is entire group of subjects or total
number of items about which information is desired.
Sample: Sample is a part of population describing the
characteristics of that population. The sample is drawn should
be representative of entire population.
Sampling: Sampling is the process of selection the sample
from a defined study population.
Sampling frame: When all the members or individuals of
population are listed by numbering then it is called sampling
frame.
Sampling methods/Techniques:
a) Random/ Probability/ Non-purposive/ Unbiased
sampling:
i) Simple random sampling (e.g. Lottery)
ii) Systematic random sampling
iii) Stratified random sampling
iv) Multistage sampling
v) Multiphase sampling
vi) Cluster Sampling
b) Non- Probability sampling:
i) Convenience or Judgment sampling
ii) Quota sampling
iii) Snowball sampling
THANK YOU

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Biostatistics-C-2-Item-2pdf.pdf

  • 2. Dr. Subrata Kumer Sen MBBS, MPH, BCS (Health) Lecturer, Community Medicine M Abdur Rahim Medical College Dinajpur.
  • 3. Statistics: The science of collecting, summarizing, and analyzing data. Biostatistics: The science of collecting, summarizing, and analyzing biological data. Medical Statistics: The science of collecting, summarizing, and analyzing medical data. Vital Statistics: Statistics that deals with vital events. Vital events are births, marriages, divorces, separations, and deaths.
  • 4. Sources of data for vital events/ statistics: • National level- Census, BBS • City Corporation/ Municipalities for births & deaths • Kaji Office for marriages, divorces, separations • Notification of infectious diseases for deaths • Hospital records for births & deaths • Disease registers for deaths etc.
  • 5. Uses of Biostatistics: • Collection of information in the best possible scientific way. • Simplification of huge, complex sets of data. • Helps in the formulation of hypothesis and amplification of ideas. • Identification of association between two or more variables by testing hypothesis. • Helps in interpretation and drawing conclusion by analyzing data • By summarizing and presenting, helps policy makers to make decisions based on scientific evidence.
  • 6. Data: Data is a set of values collected during any type of scientific investigation. Data Classification: Based on Source of Data: I. Primary: Data directly collected at the field level during a study II.Secondary: collected from another study, census data, hospital records III.Tertiary: from textbooks. Based on Nature of Data: • Qualitative/ Categorical/ Nominal: Age, Sex, economical status • Quantitative/ Numerical: a. Continuous: Cont. spectrum/range can be fractioned (e.g. height, weight, temperature) b. Discrete: No continuity, Complete #, no fraction (e.g. Parity, patient number in hospital)
  • 7. Variable: Any characteristic which differs for one individual or object to another. Types: A. According to Relationship: 1. Independent Variable 2. Intervening Variable 3. Dependent Variable 4. Confounding variable B. According to Nature/ Characteristic: 1. Qualitative 2. Quantitative: Continuous & Discrete.
  • 8. Measurement of Scale: A.Qualitative- 1. Nominal (Sex, Religion) 2. Ordinal (Educational level, Economical Status) B. Quantitative- 1. Interval (Temperature) 2. Ratio (age, height, weight)
  • 9. Methods of Data Collection: *Surveying Study population *Interviewing: Face to Face, Telephone, Internet etc *Observation: Participant, Non-participant *Written Questionnaire *Electronic Data Reporting *Focus Group Discussion *Document Review
  • 10. Data collection tool (instrument): •Questionnaire •Interview schedule •Checklist •Other electronic device
  • 11. Data Presentation: A.Tabulation/ Tabular Presentation: 1. Simple Table 2. Frequency Distribution Table B. Graphical Presentation: 1. Qualitative: a. Bar diagram - Simple Bar Diagram, Multiple Bar diagram, Component Bar Diagram b. Pie/ sector chart c. Pictogram d. Map/ spot diagram
  • 12. 2. Quantitative: a. Histogram b. Frequency polygon c. Frequency Curve d. Line chart e. Scatter diagram
  • 13. Characteristics of a table: A table should have i. Table number ii. Title iii. Body- a. Column b. Row iv. Foot note/ Source (if any)
  • 27. Measures of Central Tendency: Mean: Mean is the summation of all values of a series divided by number of values. Median: Median is the middle most value of a data set while arranged in ascending or descending order. Mode: Mode is the most frequent and repeated values observed in a data set.
  • 30. Measures of Dispersion: Range: Simply the difference between the largest and smallest values in a set of data. Mean Deviation: The mean deviation is an average of absolute deviations of individual observations from the central value of a series. Standard Deviation: Standard deviation is the positive square root of the mean-square deviations of the observations from their arithmetic mean.
  • 31. Population: A population is entire group of subjects or total number of items about which information is desired. Sample: Sample is a part of population describing the characteristics of that population. The sample is drawn should be representative of entire population. Sampling: Sampling is the process of selection the sample from a defined study population. Sampling frame: When all the members or individuals of population are listed by numbering then it is called sampling frame.
  • 32. Sampling methods/Techniques: a) Random/ Probability/ Non-purposive/ Unbiased sampling: i) Simple random sampling (e.g. Lottery) ii) Systematic random sampling iii) Stratified random sampling iv) Multistage sampling v) Multiphase sampling vi) Cluster Sampling
  • 33. b) Non- Probability sampling: i) Convenience or Judgment sampling ii) Quota sampling iii) Snowball sampling