Chapter 1.3 Data Collection and Sampling Techniques
Jan 08, 2018
Chapter 1.3
Data Collection and Sampling Techniques
Data can be collected in a variety of ways.
One of the most common methods is through the use of surveys.
The three most common methods of conducting a survey are the telephone survey, the mailed questionnaire, and the personal interview.
Data Collection
Telephone Surveys
Benefits Cheaper than
personal interviews More candid about
opinions since there’s no face-to-face interactions
Disadvantages People who don’t
have phones , don’t answer, or have blocked numbers (under coverage and non-response
Tone of voice can influence response
Mailed SurveysBenefits
Cover a wider geographic area
Less expensive Respondents can
remain anonymous
Disadvantages Low number of
responses Inappropriate
answers to questions Lack of
understanding questions
Personal Interview
Benefits In-depth responses Body language
Disadvantages Most costly (training
of interviewers) Interviewers may be
biased
Researchers use samples to collect data and
information about a particular variable from a large population
Samples cannot be selected haphazardly because the information obtained may be biased
Unbiased samples give each subject in the population an equally likely chance of being selected
Statisticians use four basic methods of sampling: random, systematic, stratified, and cluster
Samples
Random samples are selected by using chance
methods or random numbers
Number each subject in the population and select as many as are needed for the sample.
Random number generators on computers or calculators are used to prevent bias
Random Sampling
Researchers obtain systematic samples by
numbering each subject of the population and then selecting every kth subject.
For example, suppose there were 1,000 people in a population and 50 were needed for the sample. 1000/50=20 , so every 20th subject would be selected.
However, the first subject (numbered between 1 and 20) would be selected at random
Arrangement of subjects must be done carefully
Systematic Sampling
Researchers obtain stratified samples by
dividing the population into groups (called strata) according to some characteristic important to the study, then sampling randomly from each group
For example, a high school might group students by grade and select a certain number of students from each grade
Stratified Sampling
In a cluster sample, the population is divided
into groups, called clusters by some means. The researcher randomly selects some of the
clusters and uses all members of the cluster as subjects in the sample
For example, suppose that a researcher wants to conduct a study about apartment dwellers in a certain city. If there are 10 apartment buildings, 2 may be selected and all its residents interviewed.
Cluster Sampling
In a convenience sample, researchers use
subjects that are convenient
For example, a researcher interviews subjects entering a mall to determine the nature of their visit and which stores they will be patronizing.
Convenience Sample
1. In Woodbridge Township’s school district, all
students from 3 schools are interviewed about their feelings on homework
2. Every 30th light bulb on an assembly line is checked for deficiency
3. Restaurant managers are selected using random numbers to determine annual salaries
4. Customers entering Walmart are asked about how frequently they shop there
Classify each sample as random, systematic,
stratified, cluster, or convenience
5. A sample of 100 voters from a particular town are selected using random numbers 6. Job satisfaction by racial group is being surveyed at a particular company. Employees are grouped by race, then 5 subjects from each group are surveyed
7. A study on hospital patients in NYC is conducted by interviewing all patients at 3 hospitals
Classify each sample as random, systematic,
stratified, cluster, or convienence
Pg. 13 #1-7
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