PRELIM LEC 1 (1): INTRODUCTION TO BIOSTATISTICS Flashcards
INTRODUCTION TO BIOSTATISTICS
are the basic sciences of public health
Epidemiology and biostatistics
INTRODUCTION TO BIOSTATISTICS
is a branch of applied mathematics which deals with the collection, organization, presentation, analysis and interpretation of data.
Statistics
INTRODUCTION TO BIOSTATISTICS
is the application of statistics to problems in the biological sciences, health, and medicine
Biostatistics
INTRODUCTION TO BIOSTATISTICS
is the study of the distribution and determinants of health, disease, or injury in human populations and the application of this study to the control of health problems
Epidemiology
ROLE OF QUANTITATIVE METHODS IN PUBLIC HEALTH
Generate a hypothesis
Based on scientific rationale
Based on observations or anecdotal evidence
(not scientifically tested)
Based on results of prior studies
Examples of a hypothesis
The risk of developing lung cancer remains constant
in the last five years
The use of a cell phone is associated with developing
brain tumor
Vioxx increases the risk of heart disease
Address a public health question
ROLE OF QUANTITATIVE METHODS IN PUBLIC HEALTH
Survey study is used to estimate the extent of the disease in the population
Surveillance study is designed to monitor or detect specific diseases
Observational studies investigate association between an exposure and a disease outcome They rely on “natural” allocation of individuals to exposed or non-exposed groups
Experimental studies also investigate the association between an exposure, often therapeutic treatment, and disease outcome Individuals are “intentionally” placed into the treatment groups by the investigators
Conduct a study
is used to estimate the extent of the disease in the population
Survey study
is designed to monitor or detect specific diseases
Surveillance study
investigate association between an exposure and a disease outcome They rely on “natural” allocation of individuals to exposed or non-exposed groups
Observational studies
also investigate the association between an exposure, often therapeutic treatment, and disease outcome
Individuals are “intentionally” placed into the treatment groups by the investigators
Experimental studies
ROLE OF QUANTITATIVE METHODS IN PUBLIC HEALTH
Numerical facts, measurements, or observations obtained from an investigation to answer a question
Influences of temporal and seasonal trends on the reliability and accuracy of data
Examples: The number of lung cancer cases from 1960–2000 in the United States
The number of deaths from cardiovascular diseases in Whites and African Americans from 2000–2004
Collect data
ROLE OF QUANTITATIVE METHODS IN PUBLIC HEALTH
Descriptive statistical methods provide an exploratory assessment of the data from a study
Exploratory data analysis techniques
Organization and summarization of data
Tables Graphs Summary measures
Describe the observation/data
methods provide an exploratory assessment of the data from a study
Descriptive statistical
ROLE OF QUANTITATIVE METHODS IN PUBLIC HEALTH
Inferential statistical methods provide a confirmatory data analysis Generalize conclusions from data from part of a group (sample) to the whole group (population)
Assess the strength of the evidence Make comparisons Make predictions Ask more questions; suggest future research
Assess the strength of evidence for/against a hypothesis; evaluate the data
methods provide a confirmatory data analysis
Inferential statistical
ROLE OF QUANTITATIVE METHODS IN PUBLIC HEALTH
The study results will prove or disprove the hypothesis, or sometimes fall into a grey area of “unsure”
The study results appear in a peer-review publication and/or are disseminated to the public by other means
Consequently, the policy or action can range from developing specific regulatory programs to general personal behavioral changes
Recommend interventions or preventive programs
2 TYPES OF STATISTICS
Descriptive statistics
Inferential statistics
deals with the collection and presentation of data and collection of summarizing values to describe its group characteristics
Descriptive statistics
deals with predictions and inferences based on the analysis and interpretation of the results of the information gathered by the statistician
Inferential statistics
numerical characteristics or attribute associated with the population being studied
Variables
2 TYPES OF VARIABLES
Categorical or Qualitative Variables
Numerical - Valued or Quantitative Variables
example: Gender, Eye color, Blood Type, Civil Status, Socio Economic Status
Categorical or Qualitative Variables
Discrete - is a variable whose values are obtained by counting
Continuous - is a variable whose values are obtained by measuring such as temperature, distance, area, age, height
Numerical - Valued or Quantitative Variables
is a variable whose values are obtained by counting
Discrete
is a variable whose values are obtained by measuring such as temperature, distance, area, age, height
Continuous
4 SCALES OF MEASUREMENT
NOMINAL SCALE
ORDINAL SCALE
INTERVAL SCALE
RATIO SCALE
Sex, Nationality
NOMINAL SCALE
ordered but differences between values are not important
e.g., Likert scales, rank on a scale of 1..5 your degree of satisfaction
e.g., pain ratings
ORDINAL SCALE
ordered, constant scale, but no natural zero
e.g., temperature (C,F)
INTERVAL SCALE
ordered, constant scale, natural zero
e.g., height, weight, age, length
RATIO SCALE
is defined as groups of people, animals, places, things or ideas to which any conclusions based on characteristics of a sample will be applied
Population
subgroup of the population
Sample
SLOVIN’S FORMULA:
n= N
_______
1+N(e)2
SLOVIN’S FORMULA
where:
n – _______
N – population
1 – constant
e – sampling error
Sample
SLOVIN’S FORMULA
where:
n – sample
N – _______
1 – constant
e – sampling error
Population
SLOVIN’S FORMULA
where:
n – sample
N – population
1 – _______
e – sampling error
Constant
SLOVIN’S FORMULA
where:
n – sample
N – population
1 – constant
e – ________
sampling error
STAGES IN THE SELECTION OF A SAMPLE
- Define the target population
- Select a sampling frame
- Determine id a probability or nonprobability sampling method will be chosed
- Plan procedure for selecting sampling units
- Determine sample size
- Select actual sampling units
- Conduct fieldwork
2 TYPES OF SAMPLING TECHNIQUES
PROBABILITY SAMPLING
NON-PROBABILITY SAMPLING
the sample is a proportion (a certain percent) of the population and such sample is selected from the population by means of some systematic way in which every element of the population has a chance of being included in the sample
o Numerical - Valued or Quantitative Variables Discrete - is a variable whose values are obtained by counting
Continuous - is a variable whose values are obtained by measuring such as temperature, distance, area, age, height
SCALES OF MEASUREMENT
A. Nominal Scale Sex, Nationality
B. Ordinal Scale ordered but differences between values are not important
e.g., Likert scales, rank on a scale of 1..5 your degree of satisfaction
e.g., pain ratings
C. Interval Scale ordered, constant scale, but no natural zero e.g., temperature (C,F)
D. Ratio Scale ordered, constant scale, natural zero e.g., height, weight, age, length
SAMPLING TECHNIQUE
Population o is defined as groups of people, animals, places, things or ideas to which any conclusions based on characteristics of a sample will be applied
Sample o subgroup of the population
SLOVIN’S FORMULA: n= _____N_____ 1 + N(e)2
where: n – sample N – population 1 – constant e – sampling error
STAGES IN THE SELECTION OF A SAMPLE
1. Define the target population 2. Select a sampling frame 3. Determine id a probability or nonprobability sampling method will be chosed
4. Plan procedure for selecting sampling units 5. Determine sample size 6. Select actual sampling units 7. Conduct fieldwork
Types of Sampling Techniques
1. Probability Sampling the sample is a proportion (a certain percent) of the population and such sample is selected from the population by
Randomization is a feature of the selection process rather that an assumption about the structure of the population
More complex, time consuming and more costly
Probability sampling
The sample is not a proportion of the
population and there is no system in selecting the sample. The selection depends upon the situation.
No assurance is given that each item has a chance of being included as a sample
There is an assumption that there is an even distribution of characteristics within the population, believing that any sample would be representative
Non-probability sampling
4 EXAMPLES OF PROBABILITY SAMPLING
Simple Random Samping
Stratified Random Sampling
Systematic Sampling
Cluster Sampling
Lottery Method
This is the most popular and simplest method
Simple random Sampling
the population is split into non - overlapping groups (“strata”), then simple random sampling is done on each group to form a sample
Stratified random sampling
This method is widely employed because of its ease and convenience.
A frequently used method of sampling when a complete list of the population is available It is also called Quasi - Random Sampling
Systematic Sampling
When the geograpical area where the study is too big and the target population is too large
Cluster sampling
2 EXAMPLES OF NON-PROBABILITY SAMPLING
Convenience sampling
Purposive sampling
no system of selection but only those whom the researcher or interviewer meet by chance are include the sample.
process of picking out people in the most convenient and fastest way to immediately get their reactions to a certain hot and controversial issue
not representative of target population because sample are selected if they can be accessed easily and conveniently.
Advantage: easy to use Disadvantage: bias is present
it could deliver accurate resultwhen the population is homogeneous
Convenience sampling
the respondents are chosen based on their knowledge of the information desired.
Purposive sampling
specified number of persons of certain types are include in the sample.
quota sampling
sample is taken based on certain judgements about the overall population
Judgment sampling
