1. Cmpe 589 Spring 2006

2. Measurement Theory Front-End –Design –Design Review and Inspection –Code –Code Inspections –Debug and Develop Test Cases  Integration

3. Measurement Theory Back-End –Formal Machine Testing –Early Customer Programs Rigorous Implementation –Assuming process formally documented –Adherence to process Data Collection and Testing Hypothesis

4. Measurement Theory Measurement Scales –Nominal- Category names, (no ordering), categories are mutually exclusive, jointly exhaustive –Ordinal- Categories are ordered If A > B then B is not equal to A If A > B and B > C then A > C Completely Satisfied Satisfied Neutral Somewhat Dissatisfied Completely Dissatisfied

5. Measurement Theory Interval –Constant distance between measurements (unit) –Arithmetic –16 degree’s Celsius vs. 80 degrees Celsius –Ratio- Absolute zero, interval scale

6. Basic Measurements Ratio- fraction Numerator and denominator are mutually exclusive categories (number of males/ number of females) Proportion- Numerator of fraction is part of the denominator N= a + b + c P = (a/(a+b+c)) 1= (a/n) + (b/n) + (c/n) Percentage- Proportion scaled to 100 percent of the denominator Total Population of 200 defects 15% were in requirements, 25% were coding errors, 50% were design errors, 10% were from other sources. Rate- Dynamic Changes –Formula in notes –Bell Curve

7. Basic Measurements Validity- the degree to which a value actually measures what you’re looking at. Reliability- Consistency of measurements on some individual using same places over time – Smaller the index the more reliable –Index of Variability (I.V.) = Standard Deviation / mean

8. Basic Measurements Validity- Construct validity- operation definition matches incorruptible concepts –Expert panel Criterion Validity- degree to which the measure covers range meanings of concept –Expert panel Content Validity- degree to which the measure covers range meanings of concept –Expert panel

9. Basic Measurements Measurement Errors Systematic Error- Validity Random Error- Reliability General Case M = T + S + E –M = Observed Measure –T = Actual Measure –S = Systematic Error –E = random error M = T +E Var(M) = var (T) + var (E) Reliability = var (T) /var (M) = [var(M) – var(E)]/ var(M) = 1 – [var(E)/var(M)]

10. Basic Measurements How to Assess Reliability Test/Retest Alternative Forms Split Halves Internal Consistency

11. Basic Measurements Correlation Cautions Correlation expects linear relationship Noisy data for outliers affect r or P adversely--(Spearman’s r) – non parametric version Large data range Correlation is not equal to causality

12. Basic Measurements Criteria for Causality –Cause precedes the effect (logic or time) –Two variables must be empirically or experimentally correlated –Third the relationship is not spurious relationship