Survey Sampling Techniques

 Are Campus Businesses Struggling?

By loycedashingdetails |13 March 2025

Sampling refers to the process of selecting a subset of individuals or items from a larger population to gather information and make inferences about the entire population. Since it’s often impractical or impossible to collect data from an entire population, sampling allows researchers to work with a manageable group while still drawing accurate conclusions.

Types of Sampling Techniques

Sampling techniques are broadly classified into two categories: probability sampling and non-probability sampling.

A. Probability Sampling

In probability sampling, every member of the population has a known, non-zero chance of being selected. This ensures that the sample is more representative of the population, reducing bias.

1) Simple Random Sampling

• Every member of the population has an equal chance of being selected.

• Example: Selecting 100 students from a university by drawing names from a list.

2) Stratified Sampling

• The population is divided into subgroups (strata) based on specific characteristics (e.g., gender, age, income), and a sample is randomly taken from each stratum.

3)  Systematic Sampling

• A starting point is chosen at random, and every nth member of the population is selected.

4) Cluster Sampling

• The population is divided into clusters (e.g., neighborhoods or classrooms), and a random sample of clusters is selected. All individuals within the selected clusters .

B. Non-Probability Sampling

In non-probability sampling, not all members of the population have an equal chance of being selected. This can lead to sampling bias but is often quicker and more practical.

1) Convenience Sampling

• Participants are selected based on their availability and willingness to participate.

2) Purposive (Judgmental) Sampling

• Participants are selected based on the researcher’s judgment about who would provide the most useful

3) Snowball Sampling

• Existing participants recruit new participants, creating a chain-like sample.

4) Quota Sampling

• The population is divided into groups, and a fixed number of participants is selected from each group based on specific characteristics.

🎯 Sampling Techniques – How We Collected the Data

To ensure that the data accurately reflected the campus business environment, I used two key sampling techniques:

1. Stratified Sampling

Since campus businesses include various categories such as cafés, bookstores, and service providers, we divided the population into distinct strata (business types). I then randomly selected participants from each group to ensure all business types were proportionally represented. This approach provided a balanced and more accurate reflection of the campus business landscape.

✔️ Advantages of Stratified Sampling:

• Ensures that all groups within the population are represented.
• Improves the accuracy of results by reducing sampling error.
• Allows for meaningful comparisons between different business types.

2. Convenience Sampling

I also used convenience sampling by approaching business owners who were readily available during business hours. While this method helped me gather data quickly, combining it with stratified sampling ensured that the sample remained diverse and representative.

✔️ Advantages of Convenience Sampling:

• Quick and easy to implement, saving time and resources.

• Useful when a full list of the population is unavailable.

• Provides a practical way to gather initial insights.

💭In conclusion, using stratified sampling ensured a balanced representation of diverse campus businesses, while convenience sampling enabled rapid data collection. Together, they provided clear insights into student spending and business revenue trends. These findings can guide strategic improvements for campus businesses to better adapt to evolving market conditions more effectively.