Intro to Research Sampling

Intro to Research Sampling

• Surveys provide a method to gather information from a sample of people.
• Surveys can range from large statistical analysis to small qualitative analysis, or something between.
• To conduct a survey, you must first identify your population and the characteristics you are interested in, the identification process is called modelling the population.
• A population constitutes a complete set of things to be investigated.
• A census is a survey of a population.
• Interests are often centered on models of the population.

Types of Surveys

• Purely demographic surveys describe the population in terms of proportions and averages.
• Analytic surveys focus on proportions and averages that are different from expectations or between groups within a population.
• A census is a complete study of the population and 100% survey.
• Sample surveys take a sample of a population to make inferences.
• A population includes everyone in the group of people or things you are interested in.
• A sample is a selection of people or things from the population.
• Statistics use samples to infer the characteristics of the population.
• Sample analysis seeks to determine proportions of consumption or average spending amounts.

Sampling Techniques and Their Application

• Sampling helps gather information about the population when existing data is insufficient.
• Sampling is more efficient than asking the entire population because populations can be huge and census-taking requires significant effort and money.
• A random sample is ideal, but other sampling methods, like stratified and convenience sampling, are used.
• Stratified sampling uses existing information about the population.
• Convenience sampling aims to create approximately random sample more cheaply.
• Sampling methods are categorised into probability and non-probability sampling.
• Probability samples include simple random, systematic, stratified, and cluster samples.
• Non-probability samples include convenience, snowball, judgment, and quota samples.
• Simple random sampling involves randomly selecting population members and requires a sampling frame and a randomising device.
• Systematic sampling selects every Nth record from a list of population members.
• Systematic sampling approximates random sampling if there is no periodicity in the data.
• Stratified sampling involves dividing the population into groups (strata).
• Each stratum then undergoes simple random sampling.
• Cluster sampling involves selecting a random sample of clusters.
• Single-stage cluster samples include all elements within selected clusters.
• Multi-stage cluster samples involve randomly selecting a subset of elements within selected clusters.
• Stratified sampling divides population into distinct groups and samples randomly from each, yielding less error.
• Cluster sampling divides population into comparable groups and samples from some, which can reduce costs but increases error.
• Convenience samples involve interviewing people "at random" and is non-probabilistic.
• Convenience samples are most commonly used for pilot studies.
• A good selection procedure turns a convenience sample into a good approximation of a simple random sample, making basic statistics viable.
• Quota samples segment the population into mutually exclusive groups, similar to stratified sampling.
• Quota samples can ensure a specific demographic profile through quotas.
• Judgment samples select based on a researcher's judgment, which does not guarantee equal selection chances.
• Snowball samples use participant referrals to recruit more subjects.

Demographics and Population Description

• Demographics are characteristics of human populations and segments for identifying consumer markets.
• Commonly used demographics are gender, age, ethnic origin, income, disabilities, mobility, education, home ownership, employment status, and location
• Each sample from a given population is slightly different.
• It is better to be interested in the population rather than the sample itself.
• A music survey has 61% full-time students, further analysis may find that between 44.5% and 75.8% of the population are full-time students.
• The role of data analysis is descriptive and inferential.
• Generalizing from results equates to statistical inference.
• Descriptive methods describe patterns in data sets.
• Inferential methods infer findings about populations from sample results.
• Statistics helps to estimate population proportions and averages using a range of possibilities.

Describing a Population

• The proportion of males in a sample being, for example, 46% means the proportion of males is approximately 46%, however it may be slightly more or less.
• Predictions for proportions in a population should consider a 95% confidence interval.

Estimating Population Proportion

• To estimate a population proportion or average, statistical tools are used.
• These tools generate descriptive statistics and 95% confidence intervals to describe the population.

Bias

• A sample is biased if there are systematic differences from the population.
• A biased sample does not resemble its origin population and it creates incorrect predictions.
• Large but biased samples lead to precise but inaccurate predictions.
• Sources of bias include difficulty in identifying population members, non-response, poorly designed questionnaires, and poor sampling procedures like “self-selection" surveys.
• To check for bias, compare the sample to known population characteristics or previous studies.
• Sample proportions or means always lie within the confidence interval which is how Cls are constructed.
• For bias checks, see if the CI includes values that seem reasonable for the population.
• Unbiased samples do not necessarily have approximately 50% representation from any group.