33 Sampling Techniques and Data Collection Methods
33.1 Introduction
In this section we’ll cover some of the concepts involved in sampling, which determines how our data is collected within quantitative approaches. We’ll also briefly outline the main methods of data collection that you’ll encounter.
33.2 What is ‘sampling’?
Probability and non-probability sampling are two methods used in statistical surveys to select a subset of individuals from a larger population.
Probability sampling involves random selection, ensuring that every member of the population has a known, non-zero chance of being chosen. This method is useful for its ability to generalise findings to the whole population with a measurable margin of error.
Non-probability sampling, on the other hand, does not rely on random selection. Instead, elements of the population are chosen based on non-random criteria. While often easier and more cost-effective, non-probability sampling cannot reliably generalise to the population due to the potential for selection bias and lack of representativeness.
33.3 Probability sampling
Random sampling
Random sampling is a fundamental technique where each member of a population has an equal likelihood of being chosen.
This method is implemented by assigning numbers to each population element and employing a random number generator for sample selection.
The primary advantage of random sampling lies in its ability to reduce sampling bias significantly. It facilitates the application of statistical methods for result analysis, ensuring a representative sample of the population is obtained, which is crucial for the validity of research findings.
Stratified sampling
Stratified sampling is a method where the population is segmented into subgroups, or strata, based on shared characteristics, followed by random sampling within each stratum.
This process involves identifying relevant characteristics to form the strata, then dividing the population accordingly.
The key benefit of stratified sampling is its ability to guarantee that all subgroups in the population are represented. This leads to more precise and reliable results, as it addresses the potential for certain segments of the population to be underrepresented in the sample.
Cluster sampling
Cluster sampling is an efficient approach particularly suited for large, geographically dispersed populations.
In this method, the population is divided into clusters, and a random selection of these clusters is made. Subsequently, all members of the chosen clusters are included in the sample.
This technique is notably cost-effective and practical, especially when dealing with extensive populations. It simplifies the sampling process while still providing a comprehensive overview of the population by including all members of the selected clusters.
Systematic sampling
Systematic sampling is a straightforward method where every kth member of a population is selected following a randomly determined starting point.
This method is particularly efficient in terms of its ease of implementation and speed.
It’s most effective when the population does not have an order that could introduce bias into the sample.
The simplicity of systematic sampling makes it a popular choice, especially when a quick, yet representative, sample is needed from a large population.
33.4 Non-probability sampling
Convenience sampling
Convenience sampling is a method where individuals are selected based on their ease of accessibility and availability.
This approach is typically used when constraints such as time, budget, or limited access to a broader population are factors, and the research doesn’t require a representative sample.
However, a significant limitation of this method is the potential for biased results, which may not be generalizable to the larger population. This bias arises because the sample is not reflective of the population as a whole but rather of those who are most accessible.
Purposive sampling
Purposive sampling involves selecting individuals based on specific characteristics or qualities deemed relevant to the research question.
This method is particularly useful when the research aims to understand a specific subset of the population or requires participants with particular expertise or qualities.
When employing purposive sampling, it is crucial to give careful consideration to the criteria for selection. This careful planning is necessary to minimize bias and ensure that the sample accurately represents the subset of the population under study.
Quota sampling
Quota sampling is a technique where individuals are chosen to fulfill a pre-established quota relating to certain characteristics relevant to the research.
To conduct quota sampling, researchers first identify the characteristics important to their study. They then establish quotas for each characteristic and seek out individuals who meet these criteria until the quotas are filled.
The primary benefit of this method is its ability to ensure that all pertinent subgroups are represented in the sample, thereby providing a more comprehensive view of the population.
Snowball Sampling
Snowball sampling is a technique used particularly for hard-to-reach or hidden populations.
In this method, initial study subjects recruit future subjects from their acquaintances. The process begins with a small group of participants who meet the study’s criteria. These participants are then asked to refer others who also fit the criteria.
Snowball sampling is especially useful in studies where participants are part of a specific niche or are otherwise difficult to access, as it leverages personal networks to identify potential subjects.
33.5 Data collection methods
Surveys and questionnaires
Surveys and questionnaires are common tools in quantitative research, primarily used to collect extensive data from a large number of participants.
Their cost-effectiveness and ability to reach a broad audience make them particularly appealing.
However, when designing surveys and questionnaires, it is crucial to ensure that the questions are clear, unbiased, and structured in a way that minimises the burden on respondents. This careful design is essential to obtain accurate and reliable data that truly reflects the views and experiences of the participants.
Observations
In quantitative research, observations involve systematically recording behaviors or characteristics.
This method provides direct data from the field, which can often be more accurate than self-reported data.
However, observational research faces challenges such as observer bias, where the presence or expectations of the observer inadvertently influence the data collected.
Additionally, the Hawthorne effect, where subjects change their behavior because they are aware of being observed, can also impact the results.
Experiments
Experiments play a critical role in quantitative research, particularly in testing hypotheses under controlled conditions to establish causal relationships. The ability to control variables in an experimental setting allows for more definitive conclusions about cause and effect.
However, conducting experiments can be problematic, especially when they involve human participants. Ethical considerations are vital, ensuring that the research does not harm participants.
Secondary data analysis
Secondary data analysis involves the examination of existing data, originally collected by someone else, often for a different purpose. This approach is particularly cost-effective and time-efficient, especially when large datasets are available.
However, researchers may face challenges such as lack of control over the quality of the data. Also, the data may not perfectly align with your research question, requiring careful interpretation/consideration of the context in which the original data was collected.
Despite these challenges, secondary data analysis can provide valuable insights, particularly when primary data collection is not feasible.