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49 Cards in this Set
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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


SubSet

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 *“printout” 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 

NonParametric Methods

avoid labor as and repetitive calculations
Not based upon stringent assumptions Simpler to apply 