Sampling Human Pop1

7/31/2019 Sampling Human Pop1

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Biostatistics

Bio life Statistics- from the Latin word status

meaning manner of standing or position

Science which deals with the collectionorganization, analysis and interpretationof data


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Biostatistics

Biostatistics- the scientific disciplineconcerned with the application ofstatistical methods to problems inbiology or medicine


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STATISTICS in singular sense:- is a body of methods in analyzing data

BRANCHES OF STATISTIC- Descriptive Statistics- Inferential Statistics


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Branches of Statistics

Descriptive Statistics

a set of statistics techniques whose mainobjective is to summarize and presentdata in a form that will make them easierto analyze and interpret


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Branches of StatisticsInferential Statistics

A branch of statistics concerned withmaking estimates, predictions,generalizations and conclusions abouta target population based oninformation from a sample

1. Estimation Point estimate Interval estimate lower limit, higher

limit2. H othesis testin


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Processes Involved inInference

1. Statement of an objective2. Data collection

-measurement-methods of data collection-sampling

3.Analysis and interpretation4.drawing inferences regarding the

population


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Statistics in Plural sense:-is a set of data,mass of information

USES of Statistics: P.H. Statistics- for planning and monitoring

of health status Vital statistics- data related to vital events

like birth. Health Statistics health status of an

individual or community Hospital or Clinic Statistics-e.g. # of beds:

drug distribution

Service statistics e.g family planning


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Role of Statistics in Research

Aids the researcher in:1. designing a research project2. How to process, organize, and

summarize research data3. Quantifying variability

4. In interpreting results and drawingvalid conclusions.


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Phenomenon of Variationthe tendency of a measurablecharacteristic to change

From one individual or one setting toanother; or

Within the same individual or setting atdifferent periods of time

Statistics is necessary to analyzevariability


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NATURE OF STATISTICAL DATA

1. Expressed numerically2. Treated as a mass or group of

observations3. Subject to variation


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CONSTANTS

-non-changing; applicable to the physicalsciences

-speed of light, #minutes in an hour


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VARIABLE -may vary or change-measurement or characteristics-things that changes within a category

-the value of which varies from oneindividual to within the same individualat different periods of time

- example:Age,gender


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Types of Variables1. Qualitative Variables- categories are merely expressed as

labels to distinguish one group formanother

- Numerical representation of thecategories are for labeling/coding andnot for comparison (greater or less)

- E.g sex, educational level, occupation,religion,place of residence,diseasestatus


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Types of Variables2. Quantitative Variables- values indicate a quality or amount and

can be expressed numerically

- Values can be arranged according tomagnitude- E.g. age, height, weight, #of students


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Levels of Measurements

1. Nominal A classificatory scale where the categories

are used as labels only; (does not represent

qty) Number or names which represent a set of

mutually exclusive and exhaustive classes towhich individuals or objects (attributes) may

be assignede.g. sex (male, female) race, blood grups,

seatbelts in car, psych diagnosis ( manic,depressive)


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2. OrdinalSame characteristics as for nominalscales with the additional feature thatthe classes can be ordered or ranked,however the distance between twocategories cannot be clearlyquantified.

e.g. psychosocial scales ( stronglydisagree, disagree, agree, stronglyagree), age groups ( infant, child,

teenager, adult)


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3. Interval same characteristics as for scales with

the additional feature that the distancesbetween all adjacent classes are equal

Conceptually, these scales are infinite,in that they have neither beginning norending

Zero point is arbitrary and does notmean absence of the characteristicse.g. Temp, calendar, time, IQ


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4. Ratiosame characteristics as for ordinalscales with the additional feature thatthe meaningful zero point exists.

e.g. weight, blood pressure, height,doctor visits


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Sampling

The act of studying or examining only asegment of the population to representthe whole. Whatever findings we get forthis segment of the population, wegeneralized to the total population.


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Advantages of Sampling

Cheaper- only a part of the population ison study

Faster- shorter time spent for datacollection and gathering

Better quality of information can becollected- the data collectors are trainedand supervised closely.


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More Comprehensive Data- Detailed

questions on a specific topic require alot of time to collect.

It is the only possible method for

destructive procedures- e.g. bloodsample


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Definition of Terms

Population- refers to the entire group ofindividuals or items of interest in thestudy

Target population- is the group fromwhich representative information isdesired and to which inferences will bemade.

Sampling population- population fromwhich a sample will actually be drawn


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Elementary unit or element- is an objector a person on which a measurement isactually taken or observation is made.

Sampling Unit- refers to the units whichare chosen in selecting the sample, and

maybe made up of non-overlappingcollection of elements or elementaryunits.

Sampling frame- collection of samplingunit


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Criteria of a good samplingdesign

The sample to be obtained should be arepresentative of the target population

The sample size should be adequate

Practicality and feasibility of the samplingprocedure. Should be sufficiently simple andstraightforward so that it can be carried outsubstantially as planned.

Economy and efficiency of the samplingdesigned- most information at the smallestcost.


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General / Basic types of samplingdesigns

A. Non - Probability Sampling Design-Probability of each member of thepopulation being selected as part ofthe sample cannot be determined

B. Probability Sampling Design eachmember of the population has aknown non-zero chance of beingselected as a sample e.g. P=1/n


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A. Non-Probability SamplingDesign

Judgmental or Purposive Accidental or Haphazard

Quota Sampling Snowball technique Convenience Sampling


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Judgmental or Purposive - Arepresentative sample of the

population is selected based on anexperts subjective judgment or somepre-specified criteriae.g. autism subject

- Accidental or Haphazard The sample ismade up of those come at hand or whois available e.g. ambush interview

- Quota Sampling Sample of a fixed size(quota) are obtained frompredetermined subdivisions of the

population ( e.g. religion research)


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Snowball Technique - The sample isobtained by a process whereby an

individual to be included is identified bya member who was previously included(e.g. Drug abuse)

Convenience

Study unit that are easilyaccessible are selected as samples (e.g.clinic studies)


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Advantages of Non-Probability Sampling Easier to execute The only possible means

Disadvantages of Non-probability

Sampling designs More likely to produce biased results No defined rules to compute for estimates Cannot compute reliability of estimates


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Simple Random Sampling The most basic type of sampling design.

Every element in the population has anequal chance of being included in thesample.

How to do an SRS? Make a numbered list of all the units in

the population from which you want todraw a sample

Decide on the size of the sample Select the required number of sampling

units using a lottery method a table ofrandom numbers or the RAN function ofa calculator


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Advantages of SRS Simple design Simple analysis

Disadvantages of SRS Not cost efficient because elementary

units maybe too widespread Requires a sampling frame listing all

elementary units of the population


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Systematic SamplingSamples are chosen at regular intervals

(for example every fifth) from thesampling frame.The researcher computes for the

sampling interval (k=N/n)


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Example Population Size (N) = 80 household;

Sample Size (n)= 10\ Sampling interval k = N/n = 80/10 = 8 Draw a number between 1-10 (inclusive).

The number drawn will be the startingpoint of the sampling.

The succeeding sample is the next kth

member of the population. Therefore, ifnumber 4 is picked, the every 8 th household will be included in the sample,

starting with household number 4 until 10


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Advantages of Systematic Sampling Less time consuming and easier to

perform Can be used even in the absence of thesampling frame

Sometimes can result in a morerepresentative sample a goodrepresentation or good distribution


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Disadvantages of Systematic Sampling Units are widely spread

Systematic bias (e.g in clinic attendance,systematic sampling with a samplinginterval of 7 days would be inappropriateas all the days would fall on the sameday of the week)


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Stratified Random Sampling

The population is first divided into non-overlapping groups called (stratum) strataand then a simple random sampling is donefor each stratum. (e.g. different level of highschool students).

Advantages of Stratified Random Sampling

Ensure subgroups are adequatelyrepresented Accurate estimates for each stratum can be

obtained Produces more reliable results


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Disadvantages of Stratified Random Sampling

May require a very large sample ifreliable estimates for each stratum arewanted

Cluster Sampling The selection of groups of study units(clusters) instead of the selection of studyunits individually(e.g. population maybe clustered perbarangay)


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Example: Nationwide survey of all the 15 regions

(stratified)Sample unit province- primary sampling unit(Simple Random)

1 urban and 1rural brgy secondary samplingunit (stratified)

One cluster of 35 household-tertiary samplingunit (cluster)

Choose the household elementary unit(Systematic)

Sampling design Is:4- stage stratifiedsystematic; cluster,simple random samplingdesign


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Advantages of Cluster & Multi-Stage Sampling

Cost efficient design Sampling frame for all elementary units not

required Sample easier to select

Disadvantages of Advantages of Cluster & Multi-Stage Sampling

More complicated design to implement More complicated analysis Need for bigger sample size to achieve sample

precision units are widely spread


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BIAS IN SAMPLING1. Non-response

Pre-test data collection tool Follow up Study non-respondents separately-

maybe they are more at risk Increase sample size


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BIAS IN SAMPLING

1. Studying volunteers only.2. Sampling of registered patients only3. Missing cases of short duration4. Seasonal Bias5. Tarmac Bias due to convenience

sampling


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Sampling Human Pop1

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