Ch. 5 continued: Measuring Variables and Sampling 9.25.2012
Exam 1
• Will share class statistics next class– (Brian will enter grades in BbLearn right now)– Mean on multiple choice = 83%
• Discuss short answer questions
• Any questions about multiple choice?
Quick Review
• Scales of measurement• Reliability• Validity• Appropriate use/interpretation of reliability
and validity information
Sampling• Very important part of research methodology
Let’s establish some vocabulary:
• Population- full set of elements that exist
• Sample- a set of elements taken from the population
• Element- the basic unit of sampling
More Lingo
• Sampling method
• Representative sample
• Equal probability of selection method – (EPSEM)
Statistics vs. Parameters• Statistic• Parameter
• Sampling Error: Difference between the sample values and the “true” population value
• illustration
• Inferential Statistics: goal is to draw conclusions (inferences) about population based on sample statistics
Even More Lingo
• Census
• Response rate: % of people selected to be in the sample who actually participate in the study
• What if our response rate is really low?
Will the Lingo Never End?
• Biased Sample: A non-representative sample
Hopefully you have:• Proximal similarity: generalization to people,
places, settings, and contexts similar to those described in the study
Simple Random Sampling
• popular and most basic type of random sampling
• Think slips of paper in a hat
• With replacement• Without replacement – preferred
Stratified Random Sampling
• Divide the population into mutually exclusive groups (strata)
• Then select a random sample from each group
• Stratification variable
• Proportional vs. Disproportional
Cluster Random Sampling
• Cluster- collective type of unit that includes multiple elements (people)
• Examples?– Schools, classes, families– clusters are randomly selected
Systematic Sampling
1. Determine the sampling interval (k)2. Randomly select an element between 1 and k3. Select every kth element.
• Sampling interval- The population size divided by the desired sample size– symbolized by the letter k
example
• Population N=100 • Desired sample n =10• k = Population N/sample n = 100/10 = 10• Step 1 select element between 1 and 10– we randomly select 7
• Now select every 10th (k) element• Sample= 7,17,27,37,47,57,67,77,87,97
Warning for Systematic Sampling
• Periodicity - problem if there is a cyclical pattern in the population from which you’re sampling
Example:• If I have lists of classes organized by student
grade (highest to lowest) and the length of each class list is = to k.
• Might always be selecting the A or F students.
Nonrandom Sampling
• Weaker method (less representative of population)
• But sometimes necessary for practical reasons
• Four types– Convenience sampling– Quota sampling– Purposive sampling– Snowball sampling
Convenience Sampling
• Use of people who are readily available, volunteer, or are easily recruited for inclusion in a sample
Quota Sampling
• Researcher sets quotas– numbers of the kind of people wanted in the
sample
• Then locates (via convenience sample) the numbers of people to meet the quotas
Snowball Sampling
• When research question requires individuals who are hard to find
• Example: researching HIV+ white females
• Start with small group who meet criteria• They spread the word
– “snowball effect”
Random Selection vs. Random Assignment
• Random Selection: select participants for study– Purpose: create a representative sample
• Random assignment: place participants in experimental conditions – Purpose: create equivalent groups for use in an
experiment