research methods - sampling techniques.ppt
TRANSCRIPT
8/13/2019 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
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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
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RESEARCH METHODS: sampling
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
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RESEARCH METHODS: sampling
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
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RESEARCH METHODS: sampling
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
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RESEARCH METHODS: sampling
PROBABILITY SAMPLES
Types
Simple random
Systematic
Stratified
Cluster
Multistage
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RESEARCH METHODS: sampling
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
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RESEARCH METHODS: sampling
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
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RESEARCH METHODS: sampling
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
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RESEARCH METHODS: sampling
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