Sampling Methods for Research Analysis
Explore different sampling methods such as simple random, stratified random, cluster, and systematic sampling, along with ethical considerations in research. Learn how each method is applied and its significance in research data analysis.
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Presentation Transcript
Sampling Methods Analyzing Data Ethical Principles in Conducting Research AQR TEST #1 REVIEW
SAMPLING METHODS Simple Random Stratified Random Cluster Systematic Census Convenience
SIMPLE RANDOM SAMPLING Each individual is chosen entirely by chance and each member of the population has an equal chance of being included in the sample. Example: Number the desired population and and use a random number generator to select participants
STRATIFIED RANDOM SAMPLING A stratified sample is obtained by taking random samples from each stratum or sub-group of a population Example: Suppose a farmer wishes to work out the average milk yield of each cow type in his herd which consists of Ayrshire, Friesian, Galloway and Jersey cows. He could divide up his herd into the four sub-groups and take samples from these.
CLUSTER SAMPLING The entire population is divided into groups, or clusters, and a random sample of these clusters are selected. All observations in the selected clusters are included in the sample. Example: Suppose that the Department of Agriculture wishes to investigate the use of pesticides by farmers in England. A cluster sample could be taken by identifying the different counties in England as clusters. A sample of these counties (clusters) would then be chosen at random, so all farmers in those counties selected would be included in the sample.
SYSTEMATIC SAMPLING In a systematic sample, the elements of the population are put into a list and then every kth element in the list is chosen (systematically) for inclusion in the sample. Example: If the population of study contained 2,000 students at a high school and the researcher wanted a sample of 100 students, the students would be put into list form and then every 20th student would be selected for inclusion in the sample. To ensure against any possible human bias in this method, the researcher should select the first individual at random.
CENSUS In a census, all of the desired population participates in the study. Example: A teacher wants know if her students prefer tests on Fridays or Mondays. She distributes a survey to all of her students.
CONVENIENCE SAMPLING Convenience sampling is a non-probability sampling technique where subjects are selected because of their convenient accessibility and proximity to the researcher. Example: Choosing the closet five people from a class or choosing the first five names from the list of patients.
ANALYZING DATA Categorical Symmetric Quantitative Skewed Left Univariate Skewed Right Bivariate Center of Distribution Histogram
QUANTITATIVE VS. CATEGORICAL DATA Quantitative: Categorical: Data that is numerical. Data that fits into categories. Examples: age, GPA, annual income Examples: gender, shirt color
CALCULATE THE CENTER OF DISTRIBUTION (AVERAGE SALARY)
SOME ETHICAL GUIDELINES AND PRINCIPLES Honesty Objectivity Informed Consent Respect Intellectual Property Protect Special Populations
