bio-statistics, data types, sampling technique, errors in sampling, census study, observational study

1. BIOSTATISTICS
BIOSTATISTICS
Dr. noorah murad
B.D.S. D.D.S.
LECTURE 12
Part 1
Data collection

2. STATISTICS
COLLECTING
PRESENTING
INTERPRETING
A SET OF DADT (ANY TYPE OF DATD)
BIOSTATISTICS
COLLECTING
PRESENTING
INTERPRETING
A SET OF DATA THAT IS GENERATED IN CLINICAL MEDICINE,
BIOLOGY, PUBLIC HEALTH & OTHER HEALTH SCIENCES
INTRODUCTION

3. 1. To assess the state of oral health by define and quantifying
the diseases in the community.
2. To determine the availability and utilization of dental care
facilities.
3. To indicate the basic factors and causation of oral diseases.
4. To plan oral health measures.
5. To determine success or failure of specific oral health care
program.
6. For comparison and researches.
USES OF STATISTICS IN DENTISTRY

4. Constant data
 Never change
 e.g. number of body
parts
 days of the week
 not useful in statistics
variable data
 changing continuously
 e.g. temperature, age
height, weight
 greater value in
statistics
IN GENERAL THERE IS 2 TYPES OF
DATA

5. QUANTITATIVE
 Also called numerical data,
it is information about
quantities ,that is can be
measured and written down
with numbers
 Examples: height, no. of
people in household, no. of
cigarettes smoked per day
QUALITATIVE
 Is information about
qualities, information that
can not actually be
measured in numbers.
 It’s a categorical
measurement expressed by
categories
 Examples: gender race
religion, color of eye.
VARIABLE DATA
ATTRIBUTE THAT DESCRIBES PERSON, PLACE, THING, OR
PHENOMENON WHICH CAN TAKE DIFFERENT VALUES

6. nominal
 If there is no natural
order between
categories
 Examples: eye color
Blue, green, brown
Blood group
A, B, AB, O
ordinal
 If an ordering exists
 Examples: exam results
Excellent, very good
good
Socio-economic status
Low, middle, high
QUALITATIVE DATA
CLASSIFIED IN TO

7.  In general talking there are 2 types of data collection
1. Either by survey
2. Or by record.
In survey data collection method it is either
1. Comprehensive way (CENSUS STUDY)
2. Or sampling survey
METHODS OF DATA COLLECTION

8.  Studying all members of a population is difficult because it’s
time and money consuming.
 But the measurements made may be better and several types
of biases can be avoided.
 If sampling has been drawn it needs to be representative of
the population from which it is drawn and with an adequate
size.
 The actual sample selection can be accomplished in two basic
ways:
1. PURPOSIVE SELECTION. (researcher choose specific people)
2. RANDOM SELECTION.
TECHNIQUES OF SAMPLING

9. Since complete enumeration of the population is not feasible,
here a sample of unites is selected in such a way that all the
characteristics of the population is reflected in the sample
Sampling designs
 Simple random sampling
 Systematic random sampling
 Stratified sampling
 Cluster sampling
 Multiphase sampling
THE MOST COMMON SAMPLING
DESIGNS

10. SIMPLE RANDOM SAMPLING

11. SYSTEMATIC RANDOM SAMPLING

12. STRATIFIED SAMPLING

13. CLUSTER SAMPLING

14.  THE SAMPLING ERRORS: the error that arises in a data
collection process as a result of taking a sample from a
population because of:
1. Errors in sampling design.
2. The sample size is small.
 THE NON SAMPLING ERRORS: the errors that arise in a
data collection process as a result of factors other than
taking a sample.
1. Coverage error: non cooperative informant
2. Observational error: interviewer’s bias
3. Processing error: errors in statistical analysis.
ERRORS OF SAMPLING

15. SAMPLE SIZE: is the number of participants in a sample.
 Large sample can yield more accurate results.
 If we include very few subject in a study, the result can not
be generalized to the population (not representative
sample).
 If the study included more subject than required, more
individuals will be exposed to risk of intervention.
SAMPLE SIZE