Introduction to Biostatistics

1
Introduction to Biostatistics By Desta
Markos (MPH/ Epidemiology Biostatistics)
Wolaita Sodo University School of Public Health
Department of Epidemiology and Biostatistics
1
2
Learning Objectives

• Define Statistics and Biostatistics
• Identify the branches of statistics
• Enumerate the importance and limitations of
  statistics
• Define and Identify the different types of data
  and understand why we need to classifying
  variables
• Identify the different methods of data collection
  and criterion that we use to select a method of
  data collection
• Define a questionnaire, identify the different
  parts of a questionnaire and indicate the
  procedures to prepare a questionnaire

2
3
Introduction to biostatistics

• Statistics is the science of gaining information
  from data through
• Collection
• Organization
• Presentation
• Analysis and drawing conclusion (inferences) from
  data.
• Statistics is the summary of information (data)
  in a meaningful fashion, and its appropriate
  presentation.

3
4
Introduction biostatistics

• Biostatistics is the segment of statistics that
  deals with data arising from biological
  processes or medical experiments.
• When the data being analyzed are derived from the
  biological sciences and medicine, we use the
  term biostatistics.
• Has central role in medical investigations
• Concerned with interpretation of biological data
  the
• communication of information about data.

4
5
Branches of Statistics

• 1. Descriptive statistics
• refers to the different methods applied in order
  to organize, summarize and present data in a
  form which will make them easier to analyze and
  interpret.
• (tabulation, graphical presentation, computation o
  f
• averages as well as measures of variability).
• Ways of organizing and summarizing data.
• Helps to identify the general features and trends
  in a set of data and extracting useful
  information.

5
6
Branches of Biostatistics

• 2. Inferential statistics
• Is the process of generalizing or drawing
  conclusions about the target population on the
  basis of results obtained from a sample.
• The inferences are drawn from particular
  properties of sample to particular properties of
  population.
• Inferential statistics builds upon descriptive
  statistics.
• Example Principles of probability, estimation,
  hypothesis testing, etc. 6

7
Statistical Methods
Biostatistics
Descriptive Statistics
Inferential Statistics
collection organizing summarizing presenting
of data
making inferences hypothesis testing
determining relationship making the prediction
8
Uses of biostatistics

• Provide a way of organizing information
• Assessment of health status
• Health program evaluation
• Resource allocation
• Magnitude of association
• Strong vs weak association between exposure and
  outcome

9
Uses of biostatistics

• Assessing risk factors
• Cause effect relationship
• E.g. Evaluation of a new vaccine or drug
• How effective is the vaccine (drug)?
• Is the effect due to chance or some bias?
• Drawing of inferences
• Information from sample to population

10
Limitations of statistics

• It deals with only those subjects of inquiry that
  are capable of being quantitatively measured and
  numerically expressed.
• It deals with aggregates of facts and no
  importance is attached to individual items
• Statistical data are only approximately and not
  mathematically correct.

11
Variable

• Variable A variable is a characteristic of a pers
  on, object, or
• phenomenon that can take on different values.
• Any aspect of an individual or object that is
  measured (e.g. BP) or recorded (e.g. age, sex)
  and takes any value.
• There may be one variable in a study or many.
• Variables can be broadly classified into
• Categorical (or Qualitative) and
• Numerical variables(or Quantitative).

12
1. Categorical variable

• A variable which can not be measured in
  quantitative form but can only be sorted by name
  or categories
• Not able to be measured as we measure height or
  weight
• The notion of magnitude is absent or implicit.

13
Categorical variable is divided into two

• I) Nominal
• The simplest type of data, in which the values
  fall into un-ordered categories or classes
• Uses names, labels or symbols to assign each
  measurement.
• Examples Blood type, sex, race, marital status
• II) Ordinal
• The observations are classified into categories
  that can be ordered
• in an ascending series.
• Although non-numerical, can have a natural
  ordering
• The spaces or intervals b/n the categories are
  not necessarily equal.
• Examples Patient status, cancer stages,
  social class

14
2. Quantitative variable

• A variable that can be measured expressed
  numerically.
• Height, weight, of children, etc.
• Has the notion of magnitude.

or counted and
15
Quantitative variable is divided into two

• 1. Discrete when numbers represent actual
  measurable quantities rather than mere labels.
• Discrete data are restricted to taking only
  specified values often integers or counts that
  differ by fixed amounts.
• E.g. the number of episodes of diarrhoea a
  child has had in a year.
• Characterized by gaps or interruptions in the
  values.
• Both the order and magnitude of the values
  matter.
• The values are not just labels, but are actual mea
  surable quantities.

16
2. Continuous variable

• represent measurable quantities but are not
  restricted to taking on certain specific values
  i.e. fractional values are possible
• It can have an infinite number of possible values
  in any given interval.
• Both the magnitude and the order of the values
  matter
• Does not possess the gaps or interruptions
• E.g. Weight, Height, etc.

17
Types of Variable
Qualitative or categorical
Quantitative measurement
Nominal (not ordered) e.g. ethnic group
Continuous (real-valued) e.g. height
Ordinal (ordered)
Discrete (count data) e.g. of admissions
e.g. response to
treatment
18
Depending on scales of measurement we have

• All measurements are not the same.
• Measuring weight e.g. 40kg
• Measuring the status of a patient on scale
  improved, stable, not improved.
• There are four types of scales of measurement.

19
1. Nominal scale

• The simplest type of data, in which the values fal
  l into un- ordered categories or classes
• Uses names, labels or symbols to assign each
  measurement.
• Categories of the variable that are exhaustive and
  mutually exclusive
• They are the lowest level of measurements
• Examples Blood type, sex, race, marital status

20
Example of nominal Scale

• Race/Ethnicity
• Black
• White
• Latino
• Other

• The numbers have NO meaning
• They are labels only

21

• If nominal data take only two possible values,
  they are called
• dichotomous or binary.
• E.g. sex is dichotomous (male or female).
• Yes/no questions
• E.g., cured from TB at 6 months of Rx
• With nominal scale data the obvious and
  intuitive descriptive summary measure is the
  proportion or percentage of subjects who exhibit
  the attribute.

22
2. Ordinal scale

• Assigns each measurement to one of a limited
  number of categories that are ranked in terms of
  order.
• Although non-numerical, can be considered to have
  a natural
• ordering
• Examples Patient status, severity of an illnes
  s may be categorized as mild, moderate or severe.

23
Example of ordinal scale

• The numbers have LIMITED meaning 4gt3gt2gt1 is all
  we know apart from their utility as labels

• Pain level
• None
• Mild
• Moderate
• Severe

24
3. Interval scale

• Measured on a continuum
• Differences between any two numbers on a scale are
  of known size.

25
4. Ratio scale - Measurement begins at a true
zero point and the scale has equal
space. - Examples Height, weight, BP, etc.
26
Nominal
Interval
Ordinal
Ratio
Degree of precision in measuring
27
Exercises

• Give the correct scales of measurement for each
  variable
• Blood group
• Temperature (Celsius)
• Hair colour
• Job satisfaction index (1-5)
• Number of heart attacks
• Calendar year
• Serum uric acid (mg/100ml)
• Number of accidents in a 3 - year period
• Number of cases of each reportable disease
  reported by a health worker
• The average weight gain of 6 1-year old dogs with
• a special diet supplement was 950 grams last
  month
• Ethnic group

28
Definition of terms

• Census Complete enumeration of the population
• Sampling The technique of selecting
  representative portion of the entire
• population
• Data
• Numbers which can be measurements or can be
  obtained by counting
• The raw material for statistics
• Can be obtained from
• Routinely kept records , Surveys , Counting
• Experiments , Reports

• Observation

29
29
Sources of Data

• Primary sources of data it needs the involvement
  of the researcher himself. Census and sample
  survey are sources of primary types of data.
• Secondary sources of data In this case data were
  obtained from already collected sources like
  newspaper, magazines, CSA, DHS, hospital records
  and existing data like
• Mortality reports
• Morbidity reports
• Epidemic reports
• Reports of laboratory utilization (including
  laboratory test results)30

30
Techniques of Primary Data collection

• Data collection is a crucial stage in the
  planning and
• implementation of a study
• If the data collection has been superficial,
  biased or incomplete, data analysis becomes
  difficult, and the research report will be of
  poor quality.
• Therefore, we should concentrate all possible
  efforts on developing appropriate tools, and
  should test them several times.

31
31
Techniques of Primary Data collection

• Observation
• is a technique that involves systematically
  selecting, watching and recording behavior and
  characteristics of living beings, objects or
  phenomena.
• It can be undertaken in different ways
• Participant observation The observer takes part
  in the situation he or she observes.
• Non-participant observation The observer watches
  the situation,

32
openly or concealed, but does not participate.
32
Techniques of Primary Data collection
? Observations can give additional, more accurate
information on behavior of people than
interviews or questionnaires ? Observations can
also be made on objects ? For example, the
presence or absence of a latrine and its state of
cleanliness may be observed. ? observation
would be the major research technique
33
Techniques of Primary Data collection

• Interview (face-to-face)
• Is a data-collection technique that involves oral
  questioning of respondents, either individually
  or as a group.
• Answers to the questions posed during an
  interview can be recorded by
• writing them down (either during the interview
  itself or immediately after the interview) or
• by tape-recording the responses, or by a
  combination of both.

34
Techniques of Primary Data collection

• Administered written questionnaire is a data
  collection tool in which written questions are
  presented that are to be answered by the
  respondents in written form.
• It can be administered in different ways, such as
  by
• Sending questionnaires by mail with clear
  instructions on how to answer the questions and
  asking for mailed responses
• Gathering all or part of the respondents in one
  place at one time, giving oral or written
  instructions, and letting the respondents fill
  out the questionnaires
• Hand-delivering questionnaires to respondents and
  collecting them later

35
35
Techniques of Primary Data collection

• Focus group discussions
• It allows a group of 8 - 12 informants to freely
  discuss a
• certain subject with the guidance of a
  facilitator or reporter
• In-depth interview
• It is a conversion between the researcher and the
  respondent about the research area or topic.
• It is designed to allow the respondent to tell
  their story in their own way
• Issues covered in detail respondent leads the
• interviews/sets the agenda no fixed order 36

36
Types of questions

• Depending on how questions are asked and recorded
• we can distinguish two major possibilities
• 1. Open-ended questions (allowing for completely
  open as well as partially categorized answers).
• It permit free responses which should be recorded
  in the respondents' own words.

37
Types of questions

• Such questions are useful for obtaining in-depth
  information on
• facts with which the researcher is not very
  familiar,
• opinions, attitudes and suggestions of
  informants, or
• sensitive issues.

38
Types of questions

• Example
• 'What is your opinion on the services provided in
  the ANC?'
• (Explain why.)
• 'What do you think are the reasons some
  adolescents in this
• area start using drugs?
• 'What would you do if you noticed that your
  daughter (school girl) had a relationship with a
  teacher?'

39
Types of questions

• Advantage of open-ended questions
• Allow you to probe more deeply into issues of
  interest being raised.
• Information provided in the respondents' own words
  might be useful
• Risks of completely open-ended questions
• A big risk is incomplete recording of all
  relevant issues covered in
• the discussion.
• Analysis is time-consuming and requires experience
  otherwise important data may be lost.

40
Types of questions

• 2. Closed questions have a list of possible
  options or answers from which the respondents
  must choose.
• Closed questions are most commonly used for
  background variables such as age, marital status
  or education, although in the case of age and
  education you may also take the exact values and
  categorize them during data analysis

41
Types of questions

• Example
• Women who have induced abortion should be severel
  y punished."

42
Types of questions

• Advantages of closed ended questions
• It saves time
• Comparing responses of different groups, or of
  the same group
• over time, becomes easier.
• Risks of closed ended questions
• In case of illiterate respondents, bias will be
  introduce
• Many choices can be confusing
• Can't tell if respondent misinterpreted the
  question
• Fine distinctions may be lost

43
Steps in designing questionnaire
Step 1 Content Step 2 Formulating questions
Step 3 Sequencing the questions Step 4
Formatting the questionnaire Step 5
Translation Step 6 pre-test
44
Steps in designing questionnaire

• 1. Content Take your objectives and variables as
  a starting point
• Decide what questions will be needed to measure
  or to define your variables and reach your
  objectives.
• Formulating questions Formulate one or more
  questions that will provide the information
  needed for each variable.
• Check whether each question measures one thing at
  a time.
• Avoid leading questions.
• Ask sensitive questions in a socially acceptable
  way.
• Take care that questions are specific and precise
  enough that different respondents do not
  interpret them differently.

45
Cont

• For example, the question, ''How large an
  interval would you and your husband prefer
  between two successive births?'' would better be
  divided into two questions because husband and
  wife may have different opinions on the
  preferred interval.
• A question is leading if it suggests a certain
  answer. For example, the question, ''Do you
  agree that the district health team should visit
  each health center monthly?'' hardly leaves room
  for no or for other options.
• Better would be Do you think that district
  health teams should visit each health center? If
  yes, how often?

46
Steps in designing questionnaire

• 3. Sequencing the questions Design your
  interview schedule or questionnaire to be
  'informant friendly"
• Arrange questions in logical sequence
• Group questions by topic, and place a few sentence
  s of transition between topics

47
Cont

• Pose more sensitive questions as late as possible
  in the interview (e.g., questions pertaining to
  income, sexual behavior, or diseases with stigma
  attached to them, etc.
• Use simple everyday language.

48
Cont"d

• 4. Formatting the questionnaire
• When you finalize your questionnaire, be sure
  that
• A separate, introductory page is attached to each
  questionnaire

49
Steps in designing questionnaire

• explaining the purpose of the study
• requesting the informant's consent to be
  interviewed
• assuring confidentiality of the data obtained.
• Each questionnaire has a heading and space to
  insert the number,
• date and location of the interview
• You may add the name of the interviewer, to facili
  tate quality
• control.

50
Steps

• 5. Translation
• If interview will be conducted in one or more
  local languages, the questionnaire has to be
  translated to standardize the way questions will
  be asked.
• After having it translated you should have it
  retranslated into the original language. You can
  then compare the two versions for differences
  and make a decision concerning the final phrasing
  of difficult concepts.
• 6. Pre-test
• Include thank you after the last question

51
(No Transcript)