Dr AB RAJAR # MMC/MPK

1. AB RAJAR
ASSOCIATE PROFESSOR.
COMMUNITY HEALTH SCIENCES.
MUHAMMAD MEDICAL COLLEGE.

2. DEFINITION:
• STATISTICS:
• It the collection,organization,summarization,presentation and
interpretation of data.
Statistic is a recorded data such as:
i. Number of road traffic accidents.
ii. The size of enrollment.
iii. The number of patients visiting a clinic.
Or the characteristics calculated for a data set:
i. Average household size.
ii. Prevalence of smoking.
iii. Infant mortality rate.
iv. Crude death rate.
v. Immunization coverage among children under 5 years.

3. DEFINITION
• BIO-STATISTICS:
 It is the branch of statistics that deals with the application of statistical
methods to the information related to health sciences.
It is the science which deals with development and application of the most
appropriate methods for the:
Collection of data.
Presentation of the collected data.
Analysis and interpretation of the results.
Making decisions on the basis of such analysis

4. AIMS/OBJECTIVES:
• AIMS/OBJECTIVES OF STUDYING BIO-STATISTICS:
1. Conduct of investigations.
2. Research management.
3. Learning to make correct inferences about a target population of
interest based on a sample data.
4. Ability to understand the right use of statistics by others and validity
of their claims.
5. Planning, implementation and evaluation of preventive, diagnostics
& therapeutic health program/project.
6. Evaluation of research proposals.

5. STATISTICS
DESCRIPTIVE INFERENTIAL
Estimation Hypothesis
Testing
Point
Estimate
Interval
Estimates ( CI )

6. • 1.DESCRIPTIVE STATISTICS:
 Methods of producing quantitative summaries of information in biological
sciences.
 It is the enumeration, organization and graphical presentation of data.
 Tabulation and Graphical presentation.
BRANCHES OF STATISTICS

7. • 2.INFERENTIAL STATISTICS:
Methods of making generalizations about a larger group based on information
about a sample of that group in biological sciences.
Primarily performed in two ways:
O Estimation
O Testing of hypothesis
BRANCHES OF STATISTICS

8. COMMON STATISTICAL TERMS.
• POPULATION:
• Whole collection of the units ( persons or objects ) having a common
observable characteristics.
• The complete collection of all elements(scores,people,measurements, and so
on) to be studied. The collection is complete in the sense that it includes all
subjects to be studied.

9. COMMON STATISTICAL TERMS.
• CENSUS:
• The collection of data from every member of the population.
• SAMPLE:
• A subset of the population, with all its inherent qualities.

10. Sample vs. Population
Population Sample

11. COMMON STATISTICAL TERMS.
•Parameter:
• A numerical measurement describing some characteristic of a
population.
Parameter

12. COMMON STATISTICAL TERMS.
•Statistic:
• A numerical measurement describing some characteristic of a sample.
• It is used as an estimate of a parameter of the population; and also
used to test hypothesis.
• It tend to differ from one sample to another.
sample
statistic

13. Symbols for Parameter & Statistic.

14. COMMON STATISTICAL TERMS.
• DATA:
• These are facts and figures to be collected,summarized,analyzed and
interpreted.
• A set of observations, usually obtained by measurement or counting.
• VARIABLES:
• A characteristic of a person, object or phenomenon that can take on different
values.
• E.g., Age,sex,hemoglobin,hight,weight, serum cholesterol level etc.

16. Sources of data
Records Surveys
Comprehensive Sample
Experiments

17. TYPES OF DATA:
DATA
Qualitative
Nominal Ordinal
Quantitative
Discrete Continuous

18. QUALITATIVE/Attribute
 QUALITATIVE { Descriptive/Categorical } DATA:
• These data do not have any amount or quantity,& consists of
classifying categories, which may be:
a) Dichotomous: i.e,it has only two mutually exclusive categories ( e.g,males
& females )
b) Multichotomous: i.e., Rhesus blood group, having four subgroups.( A,B,AB
& O ).
• Qualitative data may be:
1. Nominal Data.
2. Ordinal Data.

19. Nominal Data:
• The categories are only distinguished by names or labels and are
classified into unordered qualitative categories,e.g.
 Race,Religion,Country, Name of crops, Type of blood.
• There is no order to these categories.
• Sometimes numbers may also be used as names or identifiers of a
person’s status, category or attribute.E.g,
• Smoking status ( smokers v non-smokers -1 vs 2 )
• Telephone numbers ( referred to as nominal numbers ).
• Nominal data that falls into two groups are called dichotomous data
 e.g. male/ female, black/white, rural/ urban.

20. Ordinal Data:
• When the categorical data can be placed in meaningful order on the
basis of their quality, it is known as ordinal data.
• In this the exact difference between the two groups cannot be
estimated e.g:
 Pain categorized as mild, moderate and severe.
• Similarly scoring of students categorized as:
 A (70% and above), B (60-69 %), C (50-59 %).
• In this the exact difference between the students placed in grade A
and B cannot be estimated.

21. QUANTITATIVE DATA
 QUANTITATIVE { Numerical} DATA:
• These have magnitude or numbers,& may be:
a) DISCRETE DATA: This is with whole numbers,e.g, Number of CVA patients.
b) CONTINOUS DATA: It is with fractions; the measurement is on continuous
scale. The interval b/w two values can be divided into indefinite number,e.g,
height or weight.
• Continuous data can be transformed into discrete;e.g, birth weight of infants
categorized into:
o Weight = 1500 gm.
o Weight > 1500 -- < 2500 gm.
o Weight = 2500 gm.
 Continuous data is Interval or Ratio type:

22. Continuous Data
 Interval:
• The categories are arranged in equally spaced units and there is no
absolute zero point e.g temperature where 0 ˚ C does not mean no
temperature but is equal to 32 ˚ F.
• In addition they have meaningful intervals between items, which are
usually measured quantities. For example on the Celsius scale the
difference between 100 ˚ C and 90 ˚ C is the same as the difference
between 50 ˚ C and 40 ˚ C.
• However because interval scales do not have an absolute zero, ratio
of scores are not meaningful e.g 100 ˚ C is not twice as hot as 50 ˚ C,
because 0 ˚ C does not indicate a complete absence of heat.

23. Continuous Data
 Ratio:
• This is an interval scale with a true zero point.
 Most biomedical variables form a ratio scale e.g.
oWeight in grams or pounds,
oTime in seconds or days,
oBlood pressure in millimeters of mercury,
o And pulse rate in beats per minute are all ratio scale data.

25. DEFINITION.
• A characteristic of a person, object or phenomenon that can take on
different values. E.g., age,sex,hemoglobin & cholesterol level.
• Simply variable is measurable characteristic that varies. It may change
from group to group, person to person, or even within one person
over time.
• Measurement is the assignment of numerals to objects or events
according to rules.
• Note: The values of the observations recorded for variables are
referred to as “data”.

26. Quantitative
•Discrete
•Continuous
Qualitative
•Ordinal
•Categorical

27. Types of variables.
• In order to conduct research, you must be able to identify your
variables. These are some of the most common types of variables in
a research project. There are six common variable types:
1. Dependent variables
2. Independent variables
3. Intervening variables
4. Moderator variables
5. Control variables
6. Extraneous variables

28. INDEPENDENT VARIABLES:
• The variables that are used to describe or measure the factors that
are assumed to cause or at least to influence the problem are called
the “INDEPENDENT VARIABLES.”
• Examples of Independent Variables:
oTeaching Method
oDiet Plan
oMedication
oGender
oAge
oTreatment Condition
oAchievement Score

29. DEPENDENT VARIABLES:
• The variable that is used to describe or measure the problem under study
is called the “DEPENDENT VARIABLE.”
• Examples of Dependent Variables:
o Attitudes
o Success in graduate school
o Homesickness of first year at college
o Success at controlling behavior
o Reduction of symptoms
o Achievement Score
o Time in 100 meter dash
*The independent and dependent variable depends on the research
question being asked.

30. In nutshell
• Independent variables dependent variable
EDUCATION
GENDER
AGE
COMPETENCE
EXPERIENCE
SALARY

31. Intervening variables
• Refer to abstract processes that are not directly observable but that
link the independent and dependent variables.
Intervening variables.
• Independent variables. Dependent variables.
TEACHING LANGUAGE
Performance of
a student

32. 32
Variable Name vs. Variable Values
• Variable name - properties of objects, events, and people that can
take on different values
• Hair color
• Gender
• Speed
• Goal orientation
• Self-esteem

33. 33
Variable Name vs. Variable Values
• Variable values: values of variable name
Variable Name Variable Value
Hair color Brown, blond, black, red
Gender/sex Male, female
Goal orientation Performance-approach,
Mastery-approach
Speed MPH
Self-esteem Score on survey computed,
low, medium, high

34. 34
Identifying IV and DV
• I want to determine if third grader’s math achievement significantly increases due
to the use of manipulatives.
• Is there a significant difference in amount of students’ dazing during a lecture
hour among the three different room arrangements?
• Is there a significant difference in work productivity in a glue factory when
working to a low tempo of music versus a high tempo of music?

35. 35
Identifying IV and DV
• Is there a significant difference in weight loss between those on a diet and those
exercising?
• Does highest degree earned (GED, HS,college) affect social awareness?
• Is there a significant difference in cognitive development among infants born less
than 4 pounds, 4.1-6 pounds, and 6.1-8 pounds?
• Do elementary school boys and girls differ in their needs for social interaction?

36. 36
Identifying IV and DV
• A researcher has developed a new aid for teaching 7th grade students about
electric circuits. The researcher wants to know whether students’ knowledge
of electric circuits increases more using the aid if they
(a) explore it individually without instruction,
(b) are given written instructions about it, or
(c ) watch a demonstration of how it works.
The researcher administers a pre- and post-test to assess students’ learning.

37. 37
Identifying IV and DV’S
• A company wants to investigate the influence of three types of
training programs (coworker, consultant, self) on employees’ job
performance three months after the programs have been completed.

38. 38
Categorical vs. Continuous Variables
• Categorical variables
• Take on a small set of possible values
• Typically qualitative
• Also called “qualitative” or “discrete” variables
• Examples:
Variable Category
Gender Male, female
Political party Republican, Democrat, Independent
Ethnicity African American, White, Asian, Hispanic
Parenting style Passive, Authoritarian, Authoritative

39. 39
Discrete vs. Continuous Variables
• Continuous variables
• Always quantitative
• Measurement
• Less to more of something
• Able to put on a continuum and quantify (assign meaningful # to it)
• Examples:
Variable Scale (Less to more)
Age Years
Temperature FO or CO
Running time Minutes
Heart rate Beats per minute
Distance Miles, yards, meters

40. Moderator variables
• Affect the relationship between the independent and dependent
variables by modifying the effect of the intervening variable(s).

41. Extraneous variables
• Independent variables that are not related to the purpose of the
study, but may affect the dependent variable are termed as
extraneous variables.
• Intelligence may as well affect the social studies achievement, but
since it is not related to the purpose of the study undertaken by the
researcher, it will be termed as an extraneous variable.
• Whatever effect is noticed on dependent variable as a result of
extraneous variable(s) is technically described as an ‘experimental
error’.

42. Control variables
• One important characteristic of a good research design is to minimize
the influence or effect of extraneous/inappropriate variable(s).
• The technical term ‘control’ is used when we design the study
minimizing the effects of extraneous independent variables. In
experimental researches, the term ‘control’ is used to refer to
restrain/free experimental conditions.

43. How are variables measured
• Variables are not measured at one specific level only. whether a
variable will be measured one way or another depends very much on
how it is conceptualized and on what type of indicators have been
used during measurement. The same variable can be measured in a
various way.(Sarantakos,2005)
• This is to say that measurement can be done in various level.

44. Levels of measurement
• Nominal level:
• This is the simplest , and the lowest type of measurement.
• When measuring using a nominal scale, one simply names or categorizes
responses. Gender, handedness, favorite color, and religion are examples of
variables measured on a nominal scale.
• The essential point about nominal scales is that they do not imply any
ordering among the responses.
• For example, when classifying people according to their favorite color, there
is no sense in which green is placed "ahead of" blue. Responses are merely
categorized.
• Nominal scales embody the lowest level of measurement.

45. Levels of measurement
• Ordinal level:
• A researcher wishing to measure consumers' satisfaction with their
governance system might ask them to specify their feelings as either "very
dissatisfied," "somewhat dissatisfied," "somewhat satisfied," or "very
satisfied."
• The items in this scale are ordered, ranging from least to most satisfied.
• This is what distinguishes ordinal from nominal scales. Unlike nominal
scales, ordinal scales allow comparisons of the degree to which two subjects
possess the dependent variable.
• For example, our satisfaction ordering makes it meaningful to assert that
one person is more satisfied than another with their governance system.
Such an assertion reflects the first person's use of a verbal label that comes
later in the list than the label chosen by the second person.

46. Levels of measurement
• Interval level:
• Interval scales are numerical scales in which intervals have the same
interpretation throughout.
• As an example, consider the Fahrenheit scale of temperature. The
difference between 30 degrees and 40 degrees represents the same
temperature difference as the difference between 80 degrees and 90
degrees. This is because each 10-degree interval has the same
physical meaning (in terms of the kinetic energy of molecules)

47. Levels of measurement
• Ratio level:
• The ratio scale of measurement is the most informative scale. It is an
interval scale with the additional property that its zero position indicates
the absence of the quantity being measured. You can think of a ratio scale
as the three earlier scales rolled up in one.
• Like a nominal scale, it provides a name or category for each object (the
numbers serve as labels). Like an ordinal scale, the objects are ordered (in
terms of the ordering of the numbers).
• Like an interval scale, the same difference at two places on the scale has
the same meaning.
• And in addition, the same ratio at two places on the scale also carries the
same meaning.