2) Populasi, Sampel, Data, And Variabel

8/12/2019 2) Populasi, Sampel, Data, And Variabel

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Learning Objectives Subject 3:

To describe the definition of population and

sample

To explain the conditions required for a

representative sample

To explain several sampling methods

To describe types of data and variable


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POPULATION

GROUP OF OBJCETS

(peoples, animals, hospitalsor programs)

The prevalence of insulin retention among

DM patients study, DM patients should be the

population of the study


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TARGET POPULATIONPopulasi Sasaran

TO WHOM THE RESULT

WILL BE GENERALIZED

Target population is similar to the population

of the study


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SAMPLED POPULATION

Populasi Terjangkau

Where sample will be selected

DM Patiens who visited Sanglah

Hos ital an-Dec 2009

TARGET POPULATION

SAMPLED

POPULATION


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SAMPLE

Sample is part of the sampled population who

visited the Sanglah Hospitals during those periods

TARGET POPULATION

SAMPLED

POPULATION

SAMPLE


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Case 1

If we wish to conduct a study aboutthe effectiveness of Cefazolin to

prevent infection after hysterectomy at

Sanglah Hospital; so who is the target population

the sampled population the sample


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SAMPLING CRITERIONS

Kriteria Sampel

INCLUSION- represent the target population

EXCLUSION- contra indication- control for confounding variable- assure the quality of data

DROP OUT- occurrence of side effects

- incomplete data


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IlustrationStudy about the risk of PID in women using IUD

compare to non users

Inclusion criteria

Women at reproductive age (15-49 years)

MarriedExclusion criteria

Women with condition that contraindicate

IUD usage or laparoscopic surgery Imunodeficiency,Multipartner

Non cooperative

Drop Out

Refuse to be interviewed or examined


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CASE 2

Clinical trial regarding the effect of pro-biotic on the length of stay of acute diarrhea

patients with mild dehydration, among

infant patients at Sanglah hospital 2009

Define the sample criterions for

this study !


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Inclusion Criterions

Patient acute diarrhea with mild

dehydration

Age 0-1 year old

Hospitalize at January to

December 2009 at Sanglah Hospital


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Exclusion Criterions

Immune deficiency (confounding)

Malnutrition (confounding)

Allergy to pro-biotic (preventing

side effect)

Poor communication (assuring thequality of data)


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Drop Out Criterions

Withdrawal

Diverse side effect

Incomplete data


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Sampling Technique

Representativeness

Sample Size Sampling technique

The bigger the

more

representative

Proper method gives

representative

sample


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SAMPLESIZE

1. VARIABILITY

()2. RELIABILITY

() and ()

3. PRECISION ()

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POPULASI

SAMPEL


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SAMPLING TECHNIQUES

Simple R.S.Stratified S.

Systematic S.

Multi Stage S.Cluster S.

PPS

etc.tg

Purposive S.Convenient S.

Consecutive S.

Quota S.Snow Balling t.

etc.

NonrandomRandom


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When to choose a certain technique?

Technique Indication

Simple Random Probability of events in

Population is homogenous

Systematic Random Population is homogenous;If we wish the sample

distributed systematically

Stratified Random Probability of events in

population is heterogeneousCluster Sampling Group of people with the

same characteristic

(profession, geographic)


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SIMPLE RANDOMRandom Number Table

Choose the random table (digit)

Choose the page

Choose the first sample

Choose the next samples horizontal/diagonally

Digit: 1 digit: 1 digit population 2 digit: 2 digit population

3 digit: 3 digit population

4 digit: 4 digit populationIf we wish to select 13sample from125

population


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3 9 5 8 0 7 1 4 6 1 0 2 5 9 3 8 3 2 9 8 4 0 2 7

0 5 9 1 8 2 7 1 4 0 5 8 3 9 0 6 3 0 1 4 1 8 3 6

1 6 8 2 0 3 9 5 6 2 4 1 9 0 7 3 9 1 2 5 7 0 6 19 0 1 4 2 5 1 8 0 3 9 5 4 0 3 5 1 0 5 6 3 4 8 2

6 0 1 5 1 0 2 9 9 0 2 1 4 8 3 7 5 6 3 8 0 3 7 1

7 2 9 8 5 9 3 6 1 2 5 3 0 1 4 8 0 3 9 1 6 8 4 1

4 6 1 9 2 7 4 3 3 0 1 2 9 0 1 5 8 5 1 4 6 5 0 39 0 1 3 9 5 1 8 1 5 3 8 4 0 2 7 7 4 2 5 9 1 3 8

5 1 3 7 4 0 1 2 9 5 0 6 6 1 4 8 2 9 5 7 2 4 8 5

2 5 0 1 7 1 3 9 4 9 2 0 1 3 5 0 6 2 9 4 1 5 7 0

8 3 2 0 3 4 5 0 2 8 6 4 7 2 9 1 5 8 2 7 8 0 5 29 3 6 1 3 0 6 2 5 8 9 3 2 0 5 7 1 0 6 4 3 8 5 9

0 5 7 2 9 4 7 1 0 5 7 2 9 0 3 1 0 4 7 5 8 0 3 7

6 8 5 1 8 5 0 3 8 5 0 1 0 6 3 1 4 7 9 2 5 0 3 8


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Sampling TechniqueSystematic Random Type: Liniar

Circular Select13samplesfrom125population

1. Construct the sampling frame

2. Calculate theINTERVAL (k): N/n3. Choose the 1stSample (RS):

Liniar: select from pop. no 1 to no interval Circular: select from pop. no.1 up to the last no

4. Choose the next samples by following certainrule


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25


1

2

34

5

6

7

89

10

11

.

.

.

.

.

.

125

2. Calculate INTERVAL (k):N/n 125 / 13 = 9,615...

3. Choose the 1stSample:

(Random Start)

Liniar: select no. of the populaton

from 1 to interval4. Select the next samples by following

2 = 6 + (1) x 9,615 = 15,615 -----> 16

3 = 6 + (2) x 9,615 = 25,230 ----->

4 = 6 + (3) x 9,615 = 34,845 -----> 35

And so on . . . . . . . . . .

R (i)= RS + (i-1)(I)


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1

2

34

5

6

7

89

10

11

.

.

.

92

.

.

125


2. Calculate INTERVAL (k):N/n 125 / 13 = 9,615...

3. Select the 1stsample (Random Start :)

Circular: choose the pop no. from 1

to the last

4. Choose the next samples as following

2 = 92 + 1 x 9,615 = 101,615 ----->

3 = 92 + 2 x 9,615 = 111,230 ----->

4 = 92 + 3 x 9,615 = 120,845 ----->Up to No. 125 . . . . Weve not got13 samples

111

121

----> Return to NO. 1

R (i)= RS + (i-1)(I)102

Equation


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R (i)= RS + (i-1)(I) - N

5 = 92 + (5-1)(9,615) - 125 = 5,460 -----------> 5

7 = 92 + (7-1)(9,615) - 125 = 24,690 ---------->

6 = 92 + (6-1)(9,615) - 125 = 15,075 ----------> 15

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And so on . . . . . . . . . . . . . . . . . . .

Note:

-RS = First Sample/random start-i = Interval

-N = Total no of population


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VARIABLES and DATA

VARIABLE DATA STATISTIC

Aspects that

observed/measured

SEX

Birth Weight

Nutritional status

Result of

observation/measure

ment

M, M, F, M, F,

1,5 2,0 3,5 4,0 3,0

good, poor,

very poor, good,

good

Result of analisis

60% laki

Mean BW= 2,8 kg

20% poor

20% very poor

status


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CLASIFICATION OF VARIABLES

QUALITY PENGUKURAN FUNCTION

CATEGORICAL

sex

occupation

NUMERIC

Birth weight

Age

NOMINALsex, occupation

ORDINAL

Nutritional status

DISCRETEParity

INTERVALtemperature

RATIOweight, height

Dependent

Independent

Intermediate

Confounding

Control

Random


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VACCINATION DISEASE

AGE

INDEPENDENT DEPENDENT

CONFOUNDING

NUTRITIONAL

STATUS

CONTROL

IMMUNITY

INTERMEDIATE

SEASON

RANDOM

RELATIONSHIP BETWEEN VARIABELS


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Documents

2) Populasi, Sampel, Data, And Variabel