research methods - sampling techniques.ppt

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UNIVERSITY OF ARTS, BELGRADE

Masters program in management in culture

Course in social research methods

Sampling techniques

Slobodan Cvejic, PhD

Belgrade University, Faculty of Philosophy

[email protected]



RESEARCH METHODS: sampling

SAMPLE

The word is used in a variety of ways in scientific and everydaylanguage ( e.g. textile industry, reporter in the street, public opinion researcher )

Sample defined: a segment of population that provides reliable informationabout investigated features of that population; a subset of cases selected in

certain way to provide reliable conclusions on investigated topics Function of sample: to save time and money, and yet provide solid

grounds for improvement of knowledge and policy making

History of sampling: when statistics spread over sciences the need

appeared to extend data collection about different issues; Neymandefined how to conclude from a part to the whole; from mid 1900son the most of the statistics is about inference from sample topopulation; even some censuses done on large samples




NON-PROBABILITY SAMPLES Subjective judgement in selection

Chosen for convenience or on the basis of systematicallyemployed criteria

Types Convenience sample (most easily approached cases)

Most similar/most dissimilar sample (cases with similarfeatures)

Typical case sample (usefull cases, not extreme)

Critical case sample (cases that are key or essential)

Snowball sample (cases from first selection identify additionalcases to be selected) Quota sample (cases selected according to the population

proportions)

Subjective selection – external valididty and credibility at

risk




PROBABILITY SAMPLES

Probability: apriori and aposteriori, mathematical and

statistical – statistics deals with mass of cases, stohastic

probability established empiricaly, after the occasion

Relative frequences to probabilities ( e.g. throwing the dice ) Importance of statistical probability – allows for

inference from a part to the whole

In statistical experiment (a study) it is not important tohave equal or certain probabilities for outcomes it is

only important to know which those probabilites are




PROBABILITY SAMPLES

Each unit in the population has a known, nonzero probability ofbeing included in the sample

Random selection necessary to eliminate subjective judgements – either using a lottery procedure or using computer program to

generate random numbers Random does not mean arbitrary or haphazard – random

selection is very careful ensuring independence of selections

Completely random selection is an ideal – our goal is not to

avoid all possible risks of subjectivity, but to be able to measuretheir impact on inference

Sometimes we give certainty of selection to some cases, or makecorrections to probability post-hoc




PROBABILITY SAMPLES

Types

Simple random

Systematic

Stratified

Cluster

Multistage




3R RULE

3 principles for making a good probability sample Realism – adjusting goals, definitions and selection

procedures to realistic framework ( e.g. audience of a movie vs.cinema audience of the movie )

Randomization –

at least at one step selection should berandom in order to eliminate subjective judgments andincrease external validity and credibility ( e.g. choose cities ‘athand’, choose households in those cities systematicaly, but choose personsinside the households at random )

Reprezentativness –

population in minimizing mirror Target population – research population

Target sample – research sample




INFERENCE BASED ON

SAMPLE

SAMPLE

DESIGNSAMPLE

SELECTION

FEATURE

X

MEASUREMENT OF

FREQUENCES FOR

EACH VALUE OF XDISTRIBUTION

OF

FREQUENCES

S

A

M

P

LE

DISTRIBUTIONOF

PROBABILITY

P

O

P

UL

A

T

I

O

N

INFERENCE

EXPERIMENT

DESIGN

DEFINITION

OF OUTCOMES

DEFINITION

OF SAMPLING

FRAME

ASIGNING A

NUMBER TO

EACH OUTCOME

RANDOM

VARIABLE

X

DEFINITION

OF PROBAB.

FOR EACH

VALUE OF X




PRACTICAL SAMPLING DESIGN

TARGET POPULATION

To whome we want to

genralize findings

STUDY POPULATION

Operational definition of

target population &measurement instruments

SAMPLING DISTRIBUTION

The distribution of an estimator

computed from many samples

centered around its expected value

SAMPLE

The subset of subjects or units

for which data is obtained

Components and Sourcesof Total Error

NONSAMPLING BIAS

- Listing & Frame

- Nonresponse

- Measurement Error

SAMPLING BIAS

- Selection Bias

- Estimation Bias

SAMPLING VARIABILITY

- Sample Size

- Sample Homogeneity

T

O

T A

L

E

R

R O

R




POST-SAMPLING CHOICES

Weighting –

to adjust sample distributions to probabilities

Evaluation of nonresponse (potential for nonsampling bias) Plan to minimize nonresponse – follow up

Post-hoc solutions: forced face to face interviews with sample of

nonrespondents on restricted number of variables that are of utmostinterest;

e.g. Average score on test 94 out of 100 points; if response rate 90%,average score of 34 by nonrespondents needed to reverse conclusions, if rate80% - 64 points, if 75% - 70 points

FEMALE MALE

POPULATION 50% 50%

SAMPLE 80% 20%

WEIGHT 0.625 2.5

FINAL 80 * 0.625=50 20 * 2.5=50

research methods - sampling techniques.ppt

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