The document outlines different sampling methods used in research, including definitions of population, census, sample, and various sampling techniques like non-probabilistic (e.g., convenience, judgment, quotas, snowball) and probabilistic (e.g., simple random, systematic, stratified, cluster). It discusses sampling error and non-sampling error, criteria for selecting sample size, and presents two case studies with demographic quotas for samples from multiple countries. The document references foundational research methods from notable authors Malhotra and Hair Jr.

1. Sampling Daiane Umetsu Diego Pereira Jeferson L. Feuser

2. Population: Group of elements/cases that share common characteristics and which are relevant to the problem/research goal. Census: Complete set of elements/cases that compose the population. Sample: Group of elements/cases from population. The elements are selected according the selection criteria.
Malhotra (2012)
Hair Jr. (2010)
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3. Definition of the target population: Identify a target population that can provide the data. Determination of the sampling framework: Formal criteria of sample selection and organization of the qualified elements. Sample size: Determination of the number of necessary cases to obtain the data.
Malhotra (2012)
Hair Jr. (2010)
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4. Sampling error: Involves biases related with errors that occured in the elements selection process. Non-sampling error: Can occur at any research stage and involves the data accuracy. There is no procedure to evalute the impact of the error on the quality of collected data.
Malhotra (2012)
Hair Jr. (2010)
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5. Population: Marvel Super Heroes
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6. Non-probabilistic sampling: Convenience The elements are selected based in the convenience for the study. This technique is more used in the initial stages and also known as accidental sampling. The elements selection is performed by the researcher.
Malhotra (2012)
Hair Jr. (2010)
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7. Non-probabilistic sampling: Convenience
Criteria:
Same group
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8. Non-probabilistic sampling: Judgement The sample is choosen according with the researcher knowledge, that specific elements have conditions to contribute in a study.
Hair Jr. (2010)
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9. Non-probabilistic: Judgement
Criteria:
Intelligence
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10. Non-probabilistic sampling: Quotas Comprises a judgement technique performed in two stages. Categories are determined, the relevant characteristics and the proportion of the distribution in the population are fixed (e. g.: gender and age). After this, the elements are selected according with the researcher judgement.
Hair Jr. (2010)
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11. Non-probabilistic sampling: Quotas
Criteria:
Ability
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12. Non-probabilistic sampling: Snowball A specific group of elements is chosen to comprise the sample and these elements indicate other people from the same population. This technique is useful when a researcher needs to find reduced groups or when is difficult to reach specific populations.
Hair Jr. (2010)
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13. Non-probabilistic sampling: Snowball
Criteria:
Strike force
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14. Probabilistic sampling: Simple random The elements have the same possibility to be selected and the selection process is aleatory. The results are representative for the population and there is a sampling error.
Malhotra (2012)
Hair Jr. (2010)
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15. Probabilistic sampling: Simple random
Criteria:
Draw sample
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16. Probabilistic sampling: Systematic The population must be organized by natural criterion. The sample is determined using a sampling error. A selection range is calculated. An initial number is determined randomly and these elements are selected using the value obtained in the selection range stage.
Hair Jr. (2010)
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17. Probabilistic: Systematic
Criteria:
Selection range
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18. Probabilistic sampling: Stratified The population is separated in groups (e. g.: gender, age, stratum) and the sample elements are chosen considering these groups. The population must be divided in homogeneous clusters. Random samples are selected in each cluster. The cluster samples are combined in a big sample of whole population.
Hair Jr. (2010)
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19. Probabilistic sampling: Stratified
Stages:
-Separation by gender
-Draw sample
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20. Probabilistic sampling: Cluster Involves a division of the area to be surveyed in neighborhoods or households. The elements of these clusters (groups) are selected randomly or everybody are included in the sample.
Hair Jr. (2010)
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21. Probabilistic sampling: Cluster
Criteria:
-Elements of the same group
-Aleatory Selection
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22. Criteria for sample size determination When the variability of the characteristics of the population is high, the confidence level is high and the error sampling is low, the number of elements needs to be greater. In a non-probabilistic sampling, the determination is based in the researcher knowledge on the study object (theme).
Hair Jr. (2010)
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23. Project X: Probabilistic and aleatory sample – 6 countries Gender, Age, Stratum and Local Quotas
Gender
Proportion
Quota
Female
52,42%
1069
Male
47,58%
970
Age
Proportion
Quota
Less than 18
4,15%
85
18-24
19,25%
393
25-34
33,90%
691
35-44
22,97%
468
45-54
13,68%
279
55 or more
6,05%
123
Stratum
Proportion
Quota
A1
1,46%
30
A2
7,75%
158
B1
18,11%
369
B2
30,0%
611
C1
26,13%
533
C2
13,42%
274
D
2,85%
58
E
0,28%
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State
Proportion
Quota
São Paulo
32,64%
666
Rio de Janeiro
11,56%
236
Minas Gerais
8,32%
170
Bahia
5,00%
102
Paraná
4,74%
97
Rio Grande do Sul
4,73%
96
Pernambuco
2,95%
60
Santa Catarina
2,82%
58
Ceará
2,00%
41
Goiás
1,90%
39
Others
23,22%
474
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24. Project Y Non-probabilistic sample - 5 countries 3 brands evaluated in each country Sample quotas for each brand and region
Brand
Quotas
Region
Quotas
1
200
A
300
2
200
B
160
3
200
C
140
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25. References
HAIR JR., Joseph F. Fundamentos de pesquisa de marketing. Porto Alegre: Bookman, 2010.
MALHOTRA, Naresh K. Pesquisa de marketing: uma orientação aplicada. Porto Alegre: Bookman, 2012.
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