RANDOM
SAMPLING
SANDRA
BENNY
I
ST M ED
Random Sampling/Simple Random
Sampling
This
is the most popular, basic method which is frequently assumed and misapplied.
In this every member has an equal and greater than 0 chance of being picked up
and selection of any individual does not influence the selection of any other.
The selection purely depends on chance.
Characteristics of Random Sampling
Equal
Probability
Each
member of the population has the same chance of being selected, ensuring
fairness (Lohr, 2021).
Unbiased
Selection
Eliminates
researcher bias by relying on randomization techniques (Fowler, 2014).
Representativeness
The sample is likely to represent the
population, allowing generalization of results (Creswell & Creswell, 2018).
Randomization
Methods
Techniques
like random number generators, lottery methods, and systematic approaches
ensure fairness (Thompson, 2012).
Independence
Each
selection is independent, meaning one choice does not affect others (Bryman,
2016).
Applicability
to Large Populations
Suitable for large datasets where direct
enumeration is impractical (Shadish, Cook, & Campbell, 2002).
Reduction
in Sampling Bias
Random sampling reduces systematic errors
(Teddlie & Yu, 2007).
Foundation
for Inferential Statistics
Provides a basis for hypothesis testing and
statistical inference (Field, 2018).
Efficiency
Requires less time and resources compared to a
complete population survey (Saunders, Lewis, & Thornhill, 2019).
Increased
Accuracy with Larger Samples
As sample size increases, random sampling
tends to yield more precise results (Babbie, 2020).
Advanatages
Fair
and Unbiased
Everyone has an equal chance of being chosen,
reducing favoritism.
Easy to understand
The
process is simple and straightforward.
Representative
It often provides a sample that reflects the
whole group.
Accurate Results
Reduces bias, leading to reliable data and
conclusions.
Equal Opportunity
Ensures all members of the group can be
selected.
Useful for Large Groups
Works well with big populations.
Disadvantages
Time-consuming and expensive
Random sampling can be time-consuming and
expensive, especially for large populations (American Psychological
Association, 2020, p. 86).
Difficulty in obtaining a representative
sample
It can
be challenging to obtain a representative sample, especially if the population
is diverse or dispersed (Cohen et al., 2013, p. 125).
Sampling frame errors
Errors in the sampling frame can lead to
biased samples (Kerlinger & Lee, 2000, p. 147).
Non-response bias
Non-response can lead to biased samples,
especially if the non-respondents differ significantly from the respondents
(Groves et al., 2013, p. 123).
Measurement errors
Measurement errors can occur due to various
factors, such as respondent fatigue or social desirability bias (Kish, 1965, p.
142).
Limited generalizability
Random sampling may not be generalizable to
other populations or contexts (Levy & Lemeshow, 2013, p. 345).
Difficulty in sampling rare populations
Random sampling may not be effective for
sampling rare populations, such as those with rare diseases (Murray, 1998, p.
23).
Ethical concerns
Random sampling may raise ethical concerns,
such as the potential for harm or exploitation of participants (Sieber & Tolich,
2013, p. 123).
Limited control over sampling process
Researchers may have limited control over the
sampling process, which can lead to biases or errors (Babbie, 2016, p. 145).
Difficulty
in achieving adequate sample size
Random
sampling may require large sample sizes to achieve adequate statistical power, which can be challenging to achieve
(Cohen, 1992, p. 156).
Assumptions in Random Sampling
Randomization
The sample is selected randomly from the
population, with every member having an equal chance of being selected
(American Psychological Association, 2020, p. 86).
Independence
Each member of the sample is selected
independently of the others (Cohen et al., 2013, p. 125).
Homogeneity
The
population is homogeneous, with no underlying subgroups that may affect the
sampling process (Kerlinger & Lee, 2000, p. 147).
No
sampling frame errors
The sampling frame accurately represents the
population, with no errors or biases (Levy & Lemeshow, 2013, p. 345).
No
non-response bias
The sample is representative of the
population, with no bias due to non-response (Groves et al., 2013, p. 123).
No
measurement errors
The data collected is accurate and reliable,
with no measurement errors (Kish, 1965, p. 142).
Normality
The data follows a normal distribution, with
no significant skewness or kurtosis (Tabachnick & Fidell, 2013, p. 123).
Equal
probability of selection
Every
member of the population has an equal probability of being selected (Murray,
1998, p. 23).
Figure 1
Simple Random Sampling
Table 1
Table on References
|
Author(s) |
Year |
Title |
Publisher |
||
|
2021 |
Sampling:
Design and Analysis |
Chapman
& Hall/CRC |
||
|
2014 |
Survey
Research Methods |
SAGE
Publications |
||
|
2018 |
Research
Design: Qualitative, Quantitative, and Mixed Methods Approaches |
SAGE
Publications |
||
|
2012 |
Research
Design: Qualitative, Quantitative, and Mixed Methods Approaches |
SAGE
Publications |
||
|
Bryman,
A. |
2016 |
Social
Research Methods |
Oxford
University Press |
||
|
Shadish,
W. R., Cook, T. D., & Campbell, D. T. |
2002 |
Experimental and Quasi-Experimental
Designs for Generalized Casual Inference |
Houghton
Mifflin |
||
|
Teddlie,
C., & Yu, F. |
2007 |
Mixed
Methods Sampling |
Wiley |
||
|
Field,
A. |
2018 |
Discovering
Statistics Using IBM SPSS Statistics |
SAGE
Publications |
||
|
Saunders,
M., Lewis, P., & Thornhill, A. |
2019 |
Research
Methods for Business Student |
Pearson
Education |
||
|
Babbie,
E. |
2020 |
The
Practice of Social Research |
Cengage
Learning |
||
|
American
Psychological Association (APA) |
2020 |
Publication
Manual of the American Psychological Association |
APA |
||
|
Cohen,
J. |
1992 |
Statistical
Power Analysis for the Behavioral Sciences |
Academic
Press |
||
|
Cohen,
L., Manion, L., & Morrison, K. |
2013 |
Research
Methods in Education |
Routledge |
||
|
Kerlinger,
F. N., & Lee, H. B. |
2000 |
Foundations
of Behavioral Research |
Wadsworth
Publishing |
||
|
Groves,
R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau,
R. |
2013 |
Survey
Methodology |
Wiley |
||
|
Kish, L |
1965 |
|
Wiley |
||
|
Levy,
P. S., & Lemeshow, S. |
2013 |
Sampling
of Populations: Methods and Applications |
Wiley |
||
|
Murray,
D. M. |
1998 |
Design
and Analysis of Group-Randomized Trials |
Oxford
University Press |
||
|
Sieber,
J. E., & Tolich, M. |
2013 |
Planning
Ethically Responsible Research |
SAGE
Publications |
||
|
Tabachnick,
B. G., & Fidell, L. S. |
2013 |
Using
Multivariate Statistics |
Pearson
Education |
References
American Psychological Association. (2020). Publication manual of the American
Psychological Association (7th ed.). Washington, DC: Author.
Babbie, E. (2020). The practice of social research (15th ed.). Cengage Learning.
Bryman, A. (2016). Social research methods (5th ed.). Oxford University Press.
Cohen, J. (1992). Statistical power analysis
for the behavioral sciences (2nd ed.). Academic Press.
Cohen, L., Manion, L., & Morrison,
K. (2013). Research methods in education
(8th ed.). Routledge.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative,
quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE
Publications.
Fowler, F. J. (2014). Survey research methods (5th ed.). SAGE Publications.
Groves, R. M., Fowler, F. J., Couper, M.
P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2013). Survey methodology (2nd ed.). Wiley.
Kerlinger, F. N., & Lee, H. B. (2000). Foundations of behavioral research (4th
ed.). Wadsworth Publishing.
Kish, L. (1965). Survey sampling. Wiley.
Levy, P. S., & Lemeshow, S. (2013). Sampling of populations: Methods and
applications(4th ed.). Wiley.
Lohr, S. L. (2021). Sampling: Design and analysis (3rd ed.). Chapman & Hall/CRC.
Murray, D. M. (1998). Design and analysis of group-randomized trials. Oxford University
Press.
Saunders, M., Lewis, P., & Thornhill, A.
(2019). Research methods for business
students (8th ed.). Pearson Education.
Shadish, W. R., Cook, T. D., & Campbell,
D. T. (2002). Experimental and
quasi-experimental designs for generalized causal inference. Houghton
Mifflin.
Sieber, J. E., & Tolich, M. (2013). Planning ethically responsible research
(2nd ed.). SAGE Publications.
Tabachnick, B. G., & Fidell, L. S.
(2013). Using multivariate statistics
(6th ed.). Pearson Education.
Teddlie, C., & Yu, F. (2007). Mixed methods sampling: A typology with examples. Journal of Mixed Methods Research, 1(1), 77–100.
Thompson, S. K. (2012). Sampling (3rd ed.). Wiley.
FLIP BOOK
CONCEPT MAP
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