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OBJECTIVES

The learning objectives for random sampling are to understand its concept and importance in statistical analysis, identify different sampling methods (such as simple, stratified, and systematic), and demonstrate how to perform simple random sampling. Students should also learn to calculate sample size, evaluate the benefits of random sampling in reducing bias, and apply these techniques to research scenarios. Additionally, they should recognize the limitations of random sampling and explore ways to address challenges that may arise in real-world applications.

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

Lohr, S. L.

2021

Sampling: Design and Analysis

Chapman & Hall/CRC

Fowler, F. J.

2014

Survey Research Methods

SAGE Publications

Creswell, J. W., & Creswell, J. D.

2018

Research Design: Qualitative, Quantitative, and Mixed Methods Approaches

SAGE Publications

Thompson, S. K.

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

Sampling of Populations: Methods and Applications

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.

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