INDE 6335 ENGINEERING ADMINISTRATION SURVEY DESIGN Dr. Christopher A. Chung Dept. of Industrial Engineering

AGENDA l Item Remainder Analysis l Multiple Linear Regression

DESIGNING THE SURVEY MAJOR STEPS Define Constructs Design Scale Pilot Test Administration Item Analysis Validation & Analysis

PILOT TEST l Comprehension l Test reliability n Item remainder analysis...

ITEM ANALYSIS l How well does each item relate to the construct l Tools n Item remainder correlation n Cronbach Alpha l Resources n Excel

ITEM REMAINDER COEFFICIENT l How well does each item correlate with the sum of the remaining items l Pearson correlation coefficient

CRONBACH ALPHA l Measure of internal consistency of the construct l Function n Item intercorrelation (item remainder coefficient) n Number of items in construct l If alpha is high n Items reflect a single construct l 0.70

alpha = k k -1 s - s s T T I X s T 2 = Total variance of the sum of the items s I 2 = Variance of individual items k = Number of items CRONBACH ALPHA FORMULA

DETERMINING WHICH ITEMS TO KEEP l Use item remainder coefficient and Cronbach Alpha to identify items to keep l Want to raise alpha l Retain as many items as possible l Discard items with low correlation l Remaining items are used for constructs

MULTIPLE REGRESSION ANALYSIS l Determine relationship between n Multiple input variables n Single output variable l Input - Independent variables n Decision n Amount of effort spent on each type of resource l Output - dependent variable n Results n Level of success of the resulting implementation

MULTIPLE REGRESSION ANALYSIS STEPS l Select independent and dependent variables l Data transformation l Choosing among regression models l Check model for potential violations of assumptions

SELECT INDEPENDENT AND DEPENDENT VARIABLES l Constructs l Colinearity n Don't want independent variables to correlate with each other n Want to separate out the predictive effects of each variable l Correlation matrix n.9 serious problem

DATA TRANSFORMATION l Non-linear data may be transformed into linear data n log n ln n Equation Transformed data is fit

CHOOSING AMONG REGRESSION MODELS l Determine which variables are significant

CHECK MODEL FOR VIOLATION OF ASSUMPTIONS l Residual analysis l Non-normality n Normal Scores Plot l Heteroscedasticity n Look for patterns

CHECKING FOR NORMALITY Normal Scores Plot Should Be Linear Residual Convert y est to Normal Z Score Value 0

HETEROSCEDASTICITY Do Not Want Non-Constant Variation Y est error

REGRESSION RESULTS l Intercept n Artificial construct for xi=0 l Coefficients n Contribution to effect from each variable l Standard Deviation n How much variability is in the data l Significance n Coefficient divided by SD n Is the l R squared…

REGRESSION RESULTS l Percent of variation in the data that can be explained l Number between 0.0 and 1.0 l High r squared values support hypotheses and validate survey l For a controlled experiment r squared of 0.90 is good l For survey type analysis r squared of 0.30 is good

USING EXCEL Multiple Regression