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49 Cards in this Set

  • Front
  • Back
Healthcare Investigation 5 stage process
Identifiying objectives
Planning
Data Collection
Analysis
Reporting
Methods of Statistics
Sample Surveys
Clinical Trials
Epidemiological studies
Population
any collection of individual items or units that are subject of investigation
Units
individual items
Variables
characteristics of a population that vary from individual to individual
Examples of Variables
Length, age, weight, temperature, number of heartbeates (numbers or values can be assigned to these examples
Sub-Set
information from a similiar group
Sample
info from a smaller group that represents the group as a whole
Sample
because it is rare to obtain measurments from a particular variable from all the units in its population, a sample is more practical for investigator to collect.
Observation
each unit in the sample provides a record, such as a measurment.
Sampling Unit
a collection with specified deminsions
Observation
the number of objects or items counted in a sampling unit
Identfying population under investigation is essential in formulating a
"null hypothesis"
Key to GOOD sampling
formulate the aims of the study
decide what analysis is required to satisy these aims
decide what data are required to facillitate the analysis
collect the data required by the survey
Good Sampling
sequence is crucial point
info can be obtained through practice records
cross checking records maybe required to validate the assessment
Target Population
the number of registered patience within the practice being studied.
Study Population
consist of all patients who could actually be selected to form the sample
Sample Designs
Simple Random Sampling
Systematic Sampling
Stratified Sampling
Quota Sampling
Cluster Sampling
Designs applied to sampling from finite populations
Symple, Systematic, and Stratified
Sampling used when not possible/practicable to enumerate every member of the study population.
Quota and Cluster Sampling
Simple Random Sampling
subset of a statistical population in which each member of the subset has an equal probability of being chosen
Observer Bias
personal prejudice as to which items should be selected for measurement
Systematic Sampling
similar to random sampling when choosing the first subject of sample then every subsequent 10th or 20th patient is chosen to cover the entire range of the population
Stratified Sampling
affective when the population comprises a number of subgroups that are thought to have an effect on the data being collected such as male and female
Approaches to Stratified Sampling
Equal Allocation
and
Proportional Allocation
Forms of statistics in Health Care
o Patients registered at a GP practice or outpatient clinic
Hospital measurements and records of temperature, blood pressure, and pulse rate
Data collected from various surveys, censuses, and clinical trials
•Impossible to imagine life without statistics
Statistics is used in two senses
One: Collections of quantitative information, and methods of handling that sort of data
Statistics is used in two senses
Two: Drawing of inferences about large groups on the basis of observations made on smaller ones
Statistics
Ways of organizing, summarizing and describing quantifiable data, and methods of drawing inferences and generalizing upon them.
Limitations of statistics
•Describe data
•Designed experiments
•And test hunches about relationships things/events of interest
•Tool that helps acceptance or rejection of the hunches within recognized degree of confidence
•Statistics never prove anything
•Statistics only indicates the likelihood of results being product of chance
Scientific Calculator
calculates mean and standard deviation from single input is INDISPENSABLE
Computers
*Undertake any analysis you ask if it, but can’t provide intelligent reasoning
*Don’t know if test used is appropriate for that kind of data you collected
*“print-out” of analysis can be confusing without understanding the underlying principles.
Two ways of obtaining random numbers
*By using calculators or pocket computers to generate random numbers
*Random number tables
Systematic sampling
•Possible to generate biased or unrepresentative sample
•Works well if patients in the population are listed in chronological order
Subgroups
are called strata
Stratum (layer)
a collection of individuals or sampling units that are as alike as possible
Equal Allocation
results in an equal number per stratum
Proportional Allocation
sample sizes from each stratum reflect the sizes of those in the population
Quota sampling
simple random sample is not chosen from each stratum – instead – sample is obtained by using the most accessible patients, as long as they represent the identified subgroups
Quota sampling
The accessible individuals may not be representative of the study populations – like people at work, students in class, etc.
Cluster sampling
involves dividing the population into subgroups called clusters each cluster must include all the various characteristics that the population might contain
Cluster sampling
•Idea is not to have a homogeneous group
•Commonly used when population covers an area that can be divided by region
•Small number of clusters are selected at random
•Key problem is choosing appropriate clusters
Stratification
used when it’s known that the response of interest is related to some factor (age or sex)
Statistics
measures that describe a variable of a sample
Parameters
hypothetical population of all observations that could be made during the observation period
Descriptive Statistics
•Used to organize to summarize and describe measures of a sample
•No predictions or inferences are made
Inferential Statistics
used to infer or predict population parameters from sample measures
*Done by inductive reasoning based on the mathematical theory of probability
Parametric Methods
the oldest, most often used by Statisticians, not always appropriate for analyzing Medical Data
-Make strict assumptions that may not always hold true
Non-Parametric Methods
avoid labor as and repetitive calculations
-Not based upon stringent assumptions
-Simpler to apply