AQR TEST #1 REVIEW
• Sampling Methods
• Analyzing Data
• Ethical Principles in
Conducting Research
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
SIMPLE RANDOM SAMPLING
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.
STRATIFIED RANDOM SAMPLING
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.
CLUSTER SAMPLING
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.
CONVENIENCE SAMPLING
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
UNIVARIATE DATA – ONE VARIABLE
BIVARIATE DATA- TWO VARIABLES
SKEWED LEFT/SKEWED RIGHT
SYMMETRIC HISTOGRAM
CALCULATE THE CENTER OF DISTRIBUTION
(AVERAGE SALARY)
SOME ETHICAL GUIDELINES AND PRINCIPLES
Honesty
Objectivity
Informed Consent
Respect Intellectual Property
Protect Special Populations