Unit 1

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Teri

Cards (50)

  • Biostatistics - theory and application of statistical science to analyze public health problems. 
    • Bio: life
    • Statistics: collection, organization, analysis, and interpretation of numerical data
  • Uses of Biostatistics
    • compare two sets of data
    • compute the average, median, standard deviation
    • obtain a conclusion
    • find association between the two variables
    • find the correlation between the variables
    • give the results in a tabular or diagrammatic form
  • Areas of Biostatistics
    • Health Statistics - in public health or community health
    • Medical Statistics - in Medicine; includes defects, injury, disease, efficacy of drug, serum and line treatment
    • Vital Statistics - in population; study of vital events like births, marriages and deaths
  • Role of Statistics in Research 
    Aids the researcher in:
    1. Designing a research project 
    2. Processing, organizing  and summarizing research data 
    3. Quantifying variability
    4. Interpreting results and drawing valid conclusions
  • Why do we need statistics?
    • Variation 
    • Tendency of a measurable characteristic
    • change with respect to person, place & time
    • E.g. weight, age, height, etc.
    • Necessary to analyze variability in order to
    • Describe certain characteristics or make valid conclusions. 
  • Population
    • all subjects/ samples of interest
    • Result: Parameter
  • Samples
    • selected subjects/ samples of interest
    • Result: Statistics (estimate)
  • Parameter
    • the subject of interest
    • describes the whole population
  • Statistics
    • an estimate of a parameter
    • number describing a sample
  • Variables:
    • Characteristics of interest to be measured.
    • The value that varies from one individual to another or within the same individual to different periods of time.
    • Most of the time, variables are dependent on constants
  • Constant
    • Fixed characteristics.
    • Numbers that do not change.
    • Most of the time they are imposed by the researcher themselves.
  • POPULATION
    • Entire group of individuals or items of interest in the study.
    TARGET POPULATION
    • Group from which representative information is desired, and to which inferences will be made.
    SAMPLING POPULATION
    • Population from which a sample will actually be taken.
  • SAMPLING UNIT
    • Units chosen in selecting the sample, and may be made up of non-overlapping collection of elements.
  • ELEMENTARY UNIT OR ELEMENT
    • Person or object on which measurement is actually taken or an observation is made.
    • Example: to determine the prevalence of asthma in 1st year college student:
  • SAMPLING FRAME
    • Collection of sampling units.
    • Tool that allows for drawing a sample (ex: listing, spot maps, aerials photographs).
    • The availability of the sampling frame determines whether or not there is a gap between the target and sampling population.
  • SAMPLING ERROR
    • The difference between the value of the parameter (i.e. the true value) and estimates of these values based on different samples.
    • Deviation of sample values compared to true sample values
    • Parameter – statistics
  • Data
    • Raw material of statistical data
    • Figures or figures result from the process of counting
    • Figures from taking a measurement
    Examples:
    • Number of patients in the hospital
    • Number of laboratory tests to be analyzed
  • Variable
    • a characteristic that takes on different values in different persons, places, or things
    • For example: 
    • heart rate, 
    • the heights of adult males, 
    • the weights of preschool children, 
    • the ages of patients seen in a dental clinic
  • Quantitative - can be measured in the usual sense
    Qualitative - many characteristics are not capable of being measured. Some can be ordered (ordinal) and cannot be ordered (nominal)
  • Quantitative Variables
    1. Discrete
    2. characterized by gaps or interruptions in the values that it can assume. 
    3. whole numbers
    4. Continuous
    5. an assume any value within a specified relevant interval of values assumed by the variable. 
    6. usually decimal in reports
  • Types of Qualitative
    1. Nominal - As the name implies it consist of “naming” or classified into various mutually exclusive categories
    2. Ordinal - Whenever qualitative observation Can be ranked or ordered according to some criterion.
  • Statistical Methods
    • The collection, presentation, analysis, and interpretation of numerical data
    1. Collection of Data - the first step in data collection
    2. Presentation of Data - the mass data collected should be presented in a suitable form for further analysis
    3. Interpretation of Data - the final step is drawing a conclusion from the data collected
  • Sampling:
    • act of studying or examining only a segment of population (or sample) to represent the whole
    • Whatever findings we obtain from the sample, we generalize to the total population
  • Importance of Sampling
    • Cheaper and Faster
    • Better quality of information collected
    • More comprehensive data collected
    • Only possible method for destructive procedures
    • More “ethical” especially in intervention studies
    • Population entire group of individuals or items of interest in the study
    • Target Population Group from which representative information is desired, and to  which inferences will be made
    • Sampling Population population from which a sample will actually be taken
  • Ideally,
    TARGET POPULATION = SAMPLING POPULATION
  • TARGET POPULATION ≠ SAMPLING POPULATION
    • Unavailability of information for sampling purposes
    • Inaccessibility of the target population
    Example:
    • Research Objective To determine the prevalence of learning disabilities among children aged 7 to 12 years
    • Target Population - all children aged 7 to 12 years old
    • Sampling Population all school children aged 7 to 12 years old
  • Definition of Terms
    • Sampling Unit - Units chosen in selecting the sample, may be made up of non-overlapping collection of elements
    • Elementary Unit or element Person or object on which measurement is actually taken, or an observation is made
    • Sampling Frame
    • Collection of sampling units
    • Tool that allows for drawing a sample (e.g. listing, spot maps, aerials photographs)
    • The availability of the sampling frame determines whether or not there is a gap between the target and sampling population
    • Sampling Error
    • The difference between the value of the parameter (i.e. the true value) and estimates of this values based on different samples
    • Parameter- Statistics
  • Criteria of a Good Sampling Design
    • Sample should be representative of the population
    • Sample size should be adequate
    • Sampling procedure should be practical and feasible
    • Sampling design should be economical and efficient
  • Factors to Consider in Selecting/Developing the Sample Design
    • Nature of the variables
    • Population being study
    • Purpose for which the research undertaken
    • Availability of information relevant to sampling procedure itself
  • Judgment or Purposive Sampling
    • A sample is selected based on an expert’s subjective judgment or on some pre-specified criteria
    Example:
    • Selection of study areas based on:
    • Proximity
    • Level of cooperation of community members
    • Familiarity of investigators with community leaders
  • Accidental or Haphazard Sampling
    • Sample is selected based on whatever items come first or whoever is available
    Example:
    • “Person-on-the-street” sampling
    • “Ambush” interviews
  • Quota Sampling
    • Selection of items or individuals to include in the sample takes place until a pre-specified number (quota) is reached
    Example:
    • Patient satisfaction survey is conducted until the “required sample size” is completed
  • Snowball sampling
    • Frequently used when studying hidden populations (e.g. IVDU , PLWA)
    • Devised because of the difficulty producing the sampling frame and in identifying members of these populations
    Procedure:
    • The first person identified to be a member of the target population is interviewed / included in the study
    • He / She will be asked to identify the next person for inclusion in the study
    • And so on…
  • Simple Random Sampling
    • Most basic type of probability sampling design
    • Every element in the population has an equal chance of being included in the sample
    • Chronologically-numbered listing of the population is required as the sampling frame
    • Often used in studies involving a relatively small population, with readily available sampling frames
  • Systematic Sampling
    • Variation of simple random sampling
    • Involves calculating for the sampling interval (k)
    • Every kth unit is included in the sample
    • Appropriate sampling design when:
    • A sampling frame is not available
    • Sampling units are too numerous to list for purposes of simple random sampling
  • Stratified Random Sampling
    • Stratified Random Sampling
    • Involves dividing the population first into overlapping groups (or Strata)
    • Simple random sampling is then carried out within each stata
    • Appropriate sampling design to:
    • Ensure that sub-groups considered important in the research are adequately represented
    • Increase the precision of estimates of parameters being considered (more so when values of variables considered are heterogeneous but homogeneous within the stratum)
  • Cluster Sampling
    • Sampling design where the sampling units are clusters of elements
    • Sampling unit ≠ Elementary unit
    • Appropriate sampling design when:
    • Sampling frame for elements is not readily available
    • Cost consideration
  • Multi-stage Sampling
    • Appropriate for sample surveys that have a wide coverage
    • Advantages
    • Complete sampling frame is necessary only for the first stage of selection, while the sampling frame for succeeding stages of collection are confined to those selected as sample
    • Disadvantages
    • Increase in the variance estimate
    • Complexity of data analysis