Lecture11 research methods in architecture data analysis

Data Analysis

Educational Research by John W. Creswell. Copyright © 2002 by Pearson Education. All rights reserved.

Slide 1

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

•Sampling  Aim of sampling is to equate unknown

characteristics that may influence variation and to preserve the representativeness of the sample

Two Classes of Sampling Techniques: 1. Non-probability Sampling 2. Probability Sampling

1. Non-probability Sampling  Common feature is that subjective judgments

are used to determine the population that are contained in the sample.

A. Convenience sampling Select cases based on their availability for the study

B. Judgmental sampling Select cases based on some purpose (Most similar\dissimilar, Typical or Critical cases)

C. Systematic Sampling Select cases based on some predefined criteria (Interval sampling)

Every 4th

Advantages of Non-probability sampling  Fast, low effort\cost methods

 Useful in exploratory research

2. Probability Sampling  Common feature is that each unit in the

population has a known, nonzero probability of being included in the sample

A.Simple Random Sample Each member of the study population has an equal probability of being selected

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)

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

Advantages of Probability Sampling  Objective standards remove possibility of

unknown confounds  Intent to remove bias in selection process

4. Accuracy and Reliability of Data  Issues of data quality: validity, reliability and

utility of measurement  Reduction of error in measurement

5. Design Fit  Statistical Conclusion Validity

 Utility (Efficiency/Generality)

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

1. 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

37% Yes No 63%

2. 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

3. 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

20

February

15

March

20

April

25

May

30

June

35

July

40

August

45

September

40

October

35

November

30

December

25

January 7%

6%

February

4%

March

8%

6% 7%

10%

April May June

8%

Au gu st Se pt em be r O ct ob er No ve m be De r ce m be r

Ju ly

Ju ne

M

ay

11% Ap ril

Ja nu ar y Fe br ua ry M ar ch

50 45 40 35 30 25 20 15 10 5 0

January

July August

10% 12%

11%

September October November December

4. 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

Bar Graphs     

Qualitative Data (Nominal\Ordinal) 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

Figure 1.

Prevalence of Eye Color 8

Frequency

6

Blue Brown Green Black

4 2 0

Eye Color

Elements needed: •Identification (Figure #) •Title •Labels\Headings Remember: Figure should read like a self-contained paragraph.

Quantitative Data (Interval\Ratio)  Histogram (similar to Bar Graph)

• Okay to put breaks in axis where set of values omitted • Bar widths represent real limits • Therefore, touch • Keep bar widths constant

Figure 2.

Scores of First Exam 15

Frequency

10 5 0

95-99

94-90

89-85

84-80

79-75

74-70

69-65

Test Scores

Elements needed: •Identification (Figure #) •Title •Labels\Headings

64-60

59-55

54-50

Quantitative Data (Interval\Ratio)  Frequency Polygon

• Values represented as points above interval

Figure 3.

Scores of First Exam

Frequency

14 12 10 8 6 4 2 0 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 •Labels\Headings Remember: Figure should read like a self-contained paragraph.

10%

Strongly Agree

17%

44%

Agree Disagree Strongly Disagree

29%

20 18 16 14 12 10 8 6 4 2 0

18

12 Series1 7 4

Strongly Agree

20

18

15 12

10

7

5

Series1

4

Agree

12

Disagree

7

Strongly Disagree

4

S1 Strongly Disagree

18

Disagree

Strongly Agree

Agree

Strongly Agree

0

20 18 16 14 12 10 8 6 4 2 0

Agree

Disagree

Strongly Disagree

18

12 Series1 7 4

Strongly Agree

Agree

Disagree Strongly Disagree