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Arc 323 human studies in architecture fall 2018 lecture 6-data collection
1. Faculty of Engineering and Technology
Department of Architectural Engineering
ARC 323 : Human Studies in
Architecture
Fall 2018
Dr. Yasser Mahgoub
Lecture 6 - Data Collection
2. Key Ideas
1. Obtaining permissions for data collection
2. Selecting participants for data collection
3. Identifying data options
4. Recording and administering data collection
3. Procedures for Collecting Quantitative
Data
1. Obtain permissions
– identify the unit of analysis
–secure permissions
–obtain informed consent from participants
4. Obtaining Permissions
• Institutional or organizational (e.g. school
district)
• Site-specific (e.g. secondary school)
• Individual participants or parents
• Campus approval (e.g. university or college)
5. Procedures for Collecting Quantitative
Data
2. Select participants
–specify a population and sample
–use probability and non-probability
sampling
–choose a sample size
6. Select Participants
Choose a Sample Size
• Select a sample size as large as possible from
the individuals available
• Select a sufficient number of participants for
the statistical tests you will use (e.g. 15 per
group for experiments)
7. Procedures for Collecting Quantitative
Data
3. Identify data options
–specify variables
– operationalize variables
– select scales of measurement
– choose types of data measures
8. Procedures for Collecting Quantitative
Data
4. Record and administer data collection
–locate or develop an instrument
–obtain reliable and valid data
–develop administrative procedures for data
collection
10. Select Participants
Specify a Population and Sample
• A population is a group of individuals that
comprise the same characteristics
11. Select Participants
Specify a Population and Sample
• A sample is a sub-group of the target
population that the researcher plans to study
for the purpose about making generalizations
about the target population.
12. Sampling
• Aim of sampling is to equate unknown
characteristics that may influence
variation and to preserve the
representativeness of the sample
13. Populations and Samples
Sample
Target
Population
Sample
Population
-All teachers in high schools in one city
-College students in all community
colleges
-Adult educators n all schools
of education
Sample
-All high school biology teachers
-Students in one community
college
-Adult educators in 5 schools of
education in the Midwest
14. Two Classes of Sampling Techniques:
1. Non-probability Sampling
2. Probability Sampling
15. Select Participants: Use Probability and Non-
Probability Sampling
• Probability sampling is the selection of
individuals from the population so that they
are representative of the population
• Non-probability sampling is the selection of
participants because they are available,
convent, or represent some characteristic the
investigator wants to study.
17. Probability Sampling
• Common feature is that each unit in the
population has a known, nonzero
probability of being included in the
sample
18. Advantages of Probability Sampling
• Objective standards remove possibility of
unknown confounds
• Intent to remove bias in selection process
19. Types of Probability Samples
• Simple Random: selecting a sample from the
population so all in the population have an
equal chance of being selected
• Systematic: choosing every “nth” individual
or site in the population until the desired
sample size is achieved
20. Types of Probability Samples
• Stratified sampling: stratifying the population
on a characteristic (e.g. gender) than sampling
from each stratum.
• Multi-Stage Cluster Sampling: a sample
chosen in one or two stages because the
population is not easily identified or is large
23. B. Stratified Random Sample
Each member of a population is assigned
to a group or stratum, then random
sample is drawn from each stratum
(ensures levels represented)
24. C. Proportional Random Sample
Each member of a population is assigned
to a sub-group, then representative sample
is drawn from each group proportional to
population