human studies in architecture

1. Faculty of Engineering and Technology
Department of Architectural Engineering
ARC 323 : Human Studies in
Architecture
Fall 2018
Dr. Yasser Mahgoub
Lecture 7 - Data Analysis

2. Flow of Activities in Collecting Data
Identify the variable
Operationally define the variable
Locate data (measures,
observations, documents with
questions and scales)
Collect data on instruments
yielding numeric scores
Self-efficacy for learning from
others
Level of confidence that an
individual can learn something
by being taught by others
13 items on a self-efficacy
attitudinal scale from Bergin
(1989)
Scores of each item ranged from
0-10 with 10 being “completely
confident.”
Flow of Activities Example

3. Identify Data Options
Specify Types of Variables
• Independent - does not depend on another
• Dependent - value depends on another
• Intervening - prevent or alter a result or course of
events
• Control - influence or direct people's behavior or
the course of events.
• Moderating - less extreme, intense or violent.
• Confounding - cause surprise or confusion

6. Identify Data Options
Operationalize Variables
Operational Definition:
• The specification of how the variable will be
defined and measured:
–typically based on the literature review
–often found in reports under “definition of
terms”

7. Identify Data Options
Choose Types of Data Measures
• An instrument is a tool for measuring, observing, or
documenting quantitative data
• Types of Instruments
1. Performance Measures (e.g. test performance)
2. Attitudinal Measures (measures feelings toward
educational topics)
3. Behavioral Measures (observations of behavior)
4. Factual Measures (documents, records)

8. Record and Administer Data Collection
Locate or Develop an Instrument
• Develop your own instrument
• Locate an existing instrument
• Modify an existing instrument

9. Record and Administer Data Collection
Obtain Reliable and Valid Data
• Reliability: individual scores from an instrument
should be nearly the same or stable on repeated
administrations of the instrument
• Types of reliability
– Test-retest (scores are stable over time)
– Alternate forms (equivalence of two instruments)
– Alternate forms and test-retest
– Inter-rater reliability (similarity in observation of a behavior by
two or more individuals)
– internal consistency (consistent scores across the instrument

10. Record and Administer Data Collection
Obtain Reliable and Valid Data
• Validity: the ability to draw meaningful and
justifiable inferences from the scores about a sample
or a population
• Types of validity
– Content (representative of all possible questions that could
be asked)
– Criterion-referenced (scores are a predictor of an outcome
or criterion they are expected to predict
– Construct (determination of the significance, meaning,
purpose and use of the scores)

11. Record and Administer Data Collection
Develop Administrate Procedures for Data
Collection
• Develop standard written procedures for
administering an instrument
• Train researchers to collect observational data
• Obtain permission to collect and use public
documents
• Respect individuals and sites during data gathering

12. The Unit of Analysis
• Unit of analysis is the level (e.g. individual,
family, school, school district) the data will be
gathered.
• There may be different units of analysis

13. Amount of Data
• Determine amount of data needed to conduct
study
• Data sources, time periods, and number of
units sampled
• Involves sampling techniques

14. Accuracy and Reliability of Data
• Issues of data quality:
– validity, reliability and utility of measurement
• Reduction of error in measurement

15. Design Fit
• Statistical Conclusion Validity
• Utility (Efficiency/Generality)

16. Scales of Measurement
1. Nominal or Categorical
2. Ordinal
3. Interval
4. Ratio

17. Identify Data Options
Select Scales of Measurement
• Nominal (Categorical): categories that
describe traits or characteristics participants
can check
• Ordinal: participants rank order a
characteristic, trait or attribute

18. Identify Data Options
Select Scales of Measurement
• Interval: provides “continuous” response
possibilities to questions with assumed equal
distance
• Ratio: a scale with a true zero and equal
distances among units

19. Nominal or Categorical
• Classification according to presence or absence of qualities
• No information provided on order or magnitude of differences
• Because nominal scales have no quantitative properties, data
consist of frequencies only
– E.g., sex, race, religion, political party
Yes No
45 76

20. Ordinal
• Classification according to degree of quality present
• Distinguish between ordered relationships between
classes or characteristics, but no information about the
magnitude of difference
– E.g.,
tall > normal > short
first > second > third

21. Interval
• Addition of a meaningful unit of measure: equal size
interval
• Consistent and useful unit of measure allows the use of
basic arithmetic functions (addition, subtraction,
multiplication, division)
– E.g., Fahrenheit scale, shoe size
January 20
February 15
March 20
April 25
May 30
June 35
July 40
August 45
September 40
October 35
November 30
December 25

22. Ratio
• Addition of an absolute zero point to interval scale
• Zero implies total absence of the characteristic
• Ability to utilize ratio statements (2:1, 1:5)
– E.g., Height and weight Yes No
45 76

23. Bar Graphs
• Qualitative Data (NominalOrdinal)
• Width of the bars is constant
• Bars separated by constant distance
• Normally height of bar corresponds to frequency of category
• Concerns:
• Orientation (horiz vs. vertical)
• Grid lines
• Axes & Tickmarks
• Fill
• Order

24. Figure 1.
Prevalence of Eye Color
Frequency
Elements needed:
•Identification (Figure #)
•Title
•LabelsHeadings
Remember: Figure should read like a self-contained paragraph.
Eye Color

25. Figure 2.
Scores of First Exam
Frequency
95-99 94-90 89-85 84-80 79-75 74-70 69-65 64-60 59-55 54-50
Test Scores
Elements needed:
•Identification (Figure #)
•Title
•LabelsHeadings

26. Figure 3.
Scores of First Exam
Frequency
95-99 94-90 89-85 84-80 79-75 74-70 69-65 64-60 59-55 54-50
Test Scores
Elements needed:
•Identification (Figure #)
•Title
•LabelsHeadings
Remember: Figure should read like a self-contained paragraph.

27. 44%
29%
17%
10% Strongly Agree
Agree
Disagree
Strongly Disagree
Strongly
Agree
Agree
Disagree
Strongly
Disagree
S1
18
12
7
4
0
5
10
15
20
Series1
18
12
7
4
0
2
4
6
8
10
12
14
16
18
20
Strongly
Agree
Agree Disagree Strongly
Disagree
Series1
18
12
7
4
0
2
4
6
8
10
12
14
16
18
20
Strongly
Agree
Agree Disagree Strongly
Disagree
Series1

28. End