A study note on research population and sample in dissertation project works (information from perplexity.ai)
My question 1: "What are research methods topics of (1) research population and (2) sample, including sample size) are all about with reference to qualitative research and quantitative research? Please provide some illustrative examples in business management and housing studies in your explanation."
The reply: "Research methods topics of (1) research population and (2) sample (including sample size) are fundamental concepts in both qualitative and quantitative research. Their understanding and application differ somewhat depending on the research approach, but both are crucial for ensuring valid, reliable, and generalizable findings.
1. Research Population
The research population refers to the entire group of individuals, objects, or events that share common characteristics and are the main focus of the study. It is the complete set from which researchers want to draw conclusions48.
In qualitative research, the population is often defined more narrowly, focusing on individuals who have specific experiences or characteristics relevant to the research question. The goal is to gain deep understanding rather than generalization.
In quantitative research, the population is usually larger and more precisely defined, as the aim is often to generalize findings to this broader group through statistical inference.
2. Sample and Sample Size
A sample is a subset of the population selected for actual study. Sampling allows researchers to collect data more feasibly while aiming to represent the population accurately148.
In qualitative research, samples are typically smaller and purposively selected to provide rich, detailed information. Sampling techniques include purposive, convenience, snowball, and theoretical sampling, focusing on participants with relevant knowledge or experience6.
In quantitative research, samples tend to be larger and often randomly selected to enable statistical generalization. Sample size is critical and depends on factors such as population size, desired precision, expected effect size, statistical power, and resource availability23.
Sample Size Considerations in Quantitative Research
For small populations (less than 500), a larger proportion (around 50%) may be sampled.
For large populations (greater than 5,000), smaller percentages (17%-27%) suffice.
For very large populations (over 250,000), sample sizes typically range between 1,060 and 1,840 to maintain statistical power2.
Sample size also depends on the complexity of the research model, number of variables, and analysis method3.
Sample Size in Qualitative Research
Sample sizes are generally smaller and determined by data saturation-the point at which no new information emerges.
Sampling controls based on population demographics ensure the sample represents the population adequately110.
Summary Table: Population and Sample in Qualitative vs Quantitative Research
In conclusion, population defines the entire group of interest, while the sample is a manageable subset studied to infer about the population. Qualitative research emphasizes depth and purposive sampling with smaller samples, whereas quantitative research emphasizes representativeness and statistical validity with larger, often random samples. Both approaches require thoughtful consideration of these concepts to ensure meaningful and credible research outcomes in fields like business management and housing studies."
My question 2: "What are the common challenges faced when selecting a sample size for research in business management?"
The reply: "Common challenges faced when selecting a sample size for research in business management include the following:
Balancing Statistical Power and Practical Feasibility: Selecting a sample size that is too small can lead to low statistical power, increasing the risk of Type II errors (failing to detect a true effect). Conversely, a very large sample size may be costly and time-consuming to collect and analyze. Researchers must find a balance between achieving adequate power and managing resources effectively1.
Budget and Resource Constraints: Limited funding and time often restrict the size of the sample that can be realistically obtained. This challenge forces researchers to optimize their sampling strategy to maximize precision while minimizing costs14.
Population Heterogeneity: Business populations are rarely homogeneous; variability within subgroups (e.g., departments, regions, customer segments) affects the sample size needed to achieve reliable results. Oversampling or stratifying specific subgroups may be necessary, complicating sample size determination1.
Estimating Effect Size: Determining the expected effect size before data collection is difficult but crucial for calculating sample size. Overestimating or underestimating effect size can lead to inappropriate sample sizes. Researchers often rely on pilot studies or literature reviews to inform these estimates1.
Nonresponse and Response Bias: Nonresponse from selected participants reduces the effective sample size and may introduce bias if nonrespondents differ systematically from respondents. Anticipating nonresponse and oversampling or using follow-up strategies is necessary to maintain representativeness125.
Sampling Frame Limitations: Obtaining a complete and accurate list of the population (sampling frame) is often challenging in business contexts. An incomplete or outdated sampling frame can lead to selection bias and affect the validity of the sample size and representativeness257.
Choosing the Appropriate Sampling Method: The method of sampling (random, stratified, convenience) impacts how sample size should be calculated and the representativeness of the sample. Researchers must align the sampling method with research objectives and population characteristics5.
Sampling Error and Statistical Assumptions: Sample size calculations depend on assumptions such as normality and homogeneity of variance. Violations of these assumptions may require adjustments to sample size to maintain validity15.
Ethical Considerations: Ensuring informed consent and protecting participant privacy can limit access to certain populations, affecting sample size and composition5.
Time Constraints: Tight project timelines may limit the ability to recruit a sufficiently large or representative sample, forcing compromises on sample size2.
These challenges highlight the complexity of determining an appropriate sample size in business management research, requiring a careful balance between statistical requirements and practical limitations to ensure valid, reliable, and actionable results.
Illustrative Example in Business Management
A company conducting employee satisfaction research may want a sample size large enough to detect differences between departments (heterogeneous subgroups) but may face budget and time constraints limiting the number of participants. They might use stratified sampling to ensure representation across departments and oversample to account for expected nonresponse, balancing cost and statistical power125.
By addressing these challenges through careful planning, pilot testing, and appropriate sampling strategies, business management researchers can improve the accuracy and reliability of their findings."
A collection of blog notes on using chatgpt for research purpose.
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