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Continuous Regression Analysis – Session 6

Chair of Business Administration esp. Information Management Prof. Dr. Wolfgang König Johann Wolfgang Goethe University. Continuous Regression Analysis – Session 6. Data Collection and Data Analysis in Information Systems Research Ph.D. Seminar Presentation Martin Wolf (09.05.2008)

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Continuous Regression Analysis – Session 6

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  1. Chair of Business Administration esp. Information Management Prof. Dr. Wolfgang König Johann Wolfgang Goethe University Continuous Regression Analysis – Session 6 Data Collection and Data Analysis in Information Systems Research Ph.D. Seminar Presentation Martin Wolf (09.05.2008) Supervisor: Dr. Oliver Hinz

  2. Agenda (Session 7) 09.05.2008 Slide 2/44

  3. Agenda (Session 7) 09.05.2008 Slide 3/44

  4. Part I: 1. Goals2. Assumptions 3. Exemplary Regression Analysis 4. Summary Agenda (Part I) 1. Goals of Regression Analysis 2. Underlying Assumptions 3. Exemplary Regression Analysis (SPSS) • Summary • Questions 09.05.2008 Slide 4/44

  5. Part I: 1. Goals 2. Assumptions 3. Exemplary Regression Analysis 4. Summary Agenda (Part I) 1.Goals of Regression Analysis 2. Underlying Assumptions 3.Exemplary Regression Analysis (SPSS) • Summary • Questions 09.05.2008 Slide 5/44

  6. Part I: 1. Goals 2. Assumptions 3. Exemplary Regression Analysis 4. Summary Goals of Regression Analysis • Examines the linear dependency between one (bivariate regression) or more (multiple regression) independent variable(s) and one dependent variable (explanatory approach) • Application of least squares method to minimize error between sample data and linear model • Domain of Interest: analysis of time series, prediction of causal relationships, root cause analysis (e.g. individual differences – computer skill) (regression function) 09.05.2008 Slide 6/44

  7. Part I: 1. Goals 2. Assumptions 3. Exemplary Regression Analysis 4. Summary Least Squares Method (Source: Skiera 2005) 09.05.2008 Slide 7/44

  8. Part I: 1. Goals 2. Assumptions 3. Exemplary Regression Analysis 4. Summary Regression Results • Regression coefficients • R²: Goodness of Fit • F-Ratio: Significance of the overall model • T-test: Significance of the regression coefficients 09.05.2008 Slide 8/44

  9. Part I: 1. Goals 2. Assumptions 3. Exemplary Regression Analysis 4. Summary Agenda (Part I) 1. Goals of a Regression Analysis 2.Underlying Assumptions 3.Exemplary Regression Analysis (SPSS) • Summary • Questions 09.05.2008 Slide 9/44

  10. Part I: 1. Goals 2. Assumptions 3. Exemplary Regression Analysis 4. Summary Underlying Assumptions (I) • Linear dependency between independent variables and dependent variable • Dependent and independent variables have to be provided at metric level (except dummy variables) • Independent variables have to be uncorrelated (nomulticollinearity)-> Collinearity Statistics, Tolerance >=0,1-> Correlation Matrix • Residuals have to be uncorrelated (noautocorrelation)-> Durbin-Watson-Coefficient ≈ 2 09.05.2008 Slide 10/44

  11. Part I: 1. Goals 2. Assumptions 3. Exemplary Regression Analysis 4. Summary Underlying Assumptions (II) • Residuals have to follow a normal distribution-> Kolmogorov-Smirnov Test-> Plots (normality, histogram)-> n>50 -> central limit theorem • No heteroscedasticity of the residuals-> e.g. White‘s general test for heteroscedasticity -> Plot (standardized residuals against stardardized predictors) • Data set has to represent a random sample • No outliers (check DFBETA, standard deviation as distance measure) 09.05.2008 Slide 11/44

  12. Part I: 1. Goals2. Assumptions 3. Exemplary Regression Analysis 4. Summary Agenda (Part I) 1. Goals of Regression Analysis 2. Underlying Assumptions 3.Exemplary Regression Analysis (SPSS) • Summary • Questions 09.05.2008 Slide 12/44

  13. Part I: 1. Goals2. Assumptions 3. Exemplary Regression Analysis 4. Summary Exemplary Regression Analysis • Example Data Set: Consequences of a reduction of work time per week from 40 to 38,5 hours within 80 industries in Baden-Wurttemberg (1985) • Research Question: How does a change in work time influence the employment? • Variables: 09.05.2008 Slide 13/44

  14. Part I: 1. Goals2. Assumptions 3. Exemplary Regression Analysis 4. Summary SPSS Syntax File * Compute Linear Regression, Save Standardized Residuals. * Calculate Durbin-Watson Coefficient (Check for autocorrelation). * Calculate Collinearity Statistics (Check for multicollinearity). * Generate P-P Diagramme (Check for heteroscedasticity). * Display Model Summary. REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT av85.10 /METHOD=ENTER uv85.10 stv85.10 azv /SAVE ZRESID /RESIDUALS DURBIN HIST(ZRESID) NORM(ZRESID) /SCATTERPLOT=(*ZRESID ,*ZPRED ). * Kolmogorov-Smirnov Test of Residuals. * (Check if residuals follow a normal distribution). NPAR TESTS /K-S(NORMAL)=ZRE_1 /MISSING ANALYSIS. 09.05.2008 Slide 14/44

  15. Part I: 1. Goals2. Assumptions 3. Exemplary Regression Analysis 4. Summary SPSS Output File 09.05.2008 Slide 15/44

  16. Part I: 1. Goals2. Assumptions 3. Exemplary Regression Analysis 4. Summary SPSS Output File 09.05.2008 Slide 16/44

  17. Part I: 1. Goals2. Assumptions 3. Exemplary Regression Analysis 4. Summary SPSS Output File 09.05.2008 Slide 17/44

  18. Part I: 1. Goals2. Assumptions 3. Exemplary Regression Analysis 4. Summary SPSS Output File 09.05.2008 Slide 18/44

  19. Part I: 1. Goals2. Assumptions 3. Exemplary Regression Analysis 4. Summary SPSS Output File 09.05.2008 Slide 19/44

  20. Part I: 1. Goals2. Assumptions 3. Exemplary Regression Analysis 4. Summary SPSS Output File 09.05.2008 Slide 20/44

  21. Part I: 1. Goals2. Assumptions 3. Exemplary Regression Analysis 4. Summary Agenda (Part I) 1. Goals of Regression Analysis 2. Underlying Assumptions 3.Exemplary Regression Analysis (SPSS) 4.Summary 09.05.2008 Slide 21/44

  22. Part I: 1. Goals2. Assumptions 3. Exemplary Regression Analysis 4. Summary Summary • Regression Analysis is a means of root cause analysis and prediction, if linear dependency can be assumed • Requires an extensive random sample for a significant model(at least independent variables * 5) • Strict assumptions have to be fullfilled 11.02.2008 Folie 22/44

  23. Part I: 1. Goals2. Assumptions 3. Exemplary Regression Analysis 4. Summary Literature • Cohen, Jacob; Cohen, Patricia; West, Stephen G.; Aiken, Leona S. (2003): Applied Multiple Regression/ Correlation Analysis for the Behavioral Sciences, 3rd Edition. Lawrence Erlbaum Associates, Publishers, New Jersey, USA. • Backhaus, Klaus; Erichson, Bernd; Plinke, Wulff; Weiber, Rolf (2003): Multivariate Analysemethoden, 10. Auflage. Springer Verlag, Berlin Heidelberg, Germany. • Chatterjee, Samprit; Hadi, Ali S.; Price, Bertram (2000): Regression Analysis by Example, Third Edition. John Wiley & Sons, Inc., New York, USA. • McClendon, MCKee J. (2002): Multiple Regression and Causal Analysis. Reissued by Waveland Press, Inc., Prospect Heights, Illinois,USA. 09.05.2008 Slide 23/44

  24. Part I: 1. Goals2. Assumptions 3. Exemplary Regression Analysis 4. Summary Literature • Brosius, Felix (2006): SPSS 14. Das mitp-Standartwerk. Redline GmbH, Heidelberg, Germany. • Schnell, Rainer; Hill, Paul B.; Esser, Elke (1999): Methoden der empirischen Sozialforschung, 6. Auflage. R. Oldenbourg Verlag, München, Germany. 09.05.2008 Slide 24/44

  25. Part I: 1. Goals2. Assumptions 3. Exemplary Regression Analysis 4. Summary Questions/Discussion ? 09.05.2008 Slide 25/44

  26. Agenda (Session 7) 09.05.2008 Slide 26/44

  27. Part II: 1. Background2. Research Question 3. Utilized Model 4. Results 5. Summary Agenda (Part II) 1. Background 2. Research Question 3. Utilized Model • Results • Summary (Pros and Cons) • Questions 09.05.2008 Slide 27/44

  28. Part II: 1. Background 2. Research Question 3. Utilized Model 4. Results 5. Summary Agenda (Part II) 1. Background 2. Research Question 3.Utilized Model • Results • Summary (Pros andCons) • Questions 09.05.2008 Slide 28/35

  29. Part II: 1. Background 2. Research Question 3. Utilized Model 4. Results 5. Summary Background • Introduction of a vessel traffic service (VTS) for the lower Mississippi in late 1977 in order to prevent rammings and collisions of vessels • VTS is an example of a Decision Support System (DSS) • Literature: utilization surrogate of success, only measured as dichotomous variable, no consistent results 09.05.2008 Slide 29/35

  30. Part II: 1. Background2. Research Question 3. Utilized Model 4. Results 5. Summary Agenda (Part II) 1. Background 2.Research Question 3.Utilized Model • Results • Summary (Pros andCons) • Questions 09.05.2008 Slide 30/44

  31. Part II: 1. Background 2. Research Question 3. Utilized Model 4. Results 5. Summary Research Question Is there a linear causalrelationshipbetween DSS Usageand System Performance(lessvesselaccidents)? 09.05.2008 Slide 31/44

  32. Part II: 1. Background2. Research Question 3. Utilized Model 4. Results 5. Summary Agenda (Part II) 1. Background 2. Research Question 3.Utilized Model • Results • Summary (Pros andCons) • Questions 09.05.2008 Slide 32/44

  33. Part II: 1. Background2. Research Question 3. Utilized Model 4. Results 5. Summary Utilization as an Intervening Variable Backward Linkages Forward Linkages (Source: Trice and Treacy 1988) 09.05.2008 Slide 33/44

  34. Part II: 1. Background2. Research Question 3. Utilized Model 4. Results 5. Summary Utilized Linear Regression Model 09.05.2008 Slide 34/44

  35. Part II: 1. Background2. Research Question 3. Utilized Model 4. Results 5. Summary Agenda (Part II) 1. Background 2. Research Question 3.Utilized Model • Results • Summary (Pros andCons) • Questions 09.05.2008 Slide 35/44

  36. Part II: 1. Background2. Research Question 3. Utilized Model 4. Results 5. Summary Model Summary ** p<0,01; * p<0.05 09.05.2008 Slide 36/44

  37. Part II: 1. Background2. Research Question 3. Utilized Model 4. Results 5. Summary Results • Significant negative correlation of DSS utilization, length of DSS Use with objective performance criterion (number of vessel accidents) 09.05.2008 Slide 37/44

  38. Part II: 1. Background2. Research Question 3. Utilized Model 4. Results 5. Summary Agenda (Part II) 1. Background 2. Research Question 3.Utilized Model • Results • Summary (Pros andCons) • Questions 09.05.2008 Slide 38/44

  39. Part II: 1. Background2. Research Question 3. Utilized Model 4. Results 5. Summary Pros • Objective justification of DSS introduction(IT is an enabler) • Utilization of a broad model • Relatively high fit of the model • High significance of the model 09.05.2008 Slide 39/44

  40. Part II: 1. Background2. Research Question 3. Utilized Model 4. Results 5. Summary Cons • No exact specification of the used dimensions of the coefficients (-> standardized coefficients) • Peak utilization was aggregated for DSS usage • No specification how weather indicator was derived • Assumptions were not addressed • Momentum already showed decreasing trend 09.05.2008 Slide 40/44

  41. Part II: 1. Background2. Research Question 3. Utilized Model 4. Results 5. Summary Literature • Blanc and Kozar (1990): An Empirical Investigation of the Relationship Between DSS Usage and System Performance: A Case Study of a Navigation Support System. In: MISQ, 14(3), pp. 263-277. 09.05.2008 Slide 41/44

  42. Part II: 1. Background2. Research Question 3. Utilized Model 4. Results 5. Summary Agenda (Part II) 1. Background 2. Research Question 3.Utilized Model • Results • Summary (Pros andCons) • Questions 09.05.2008 Slide 42/44

  43. Part II: 1. Background2. Research Question 3. Utilized Model 4. Results 5. Summary Questions/Discussion ? 09.05.2008 Slide 43/44

  44. Part II: 1. Background2. Research Question 3. Utilized Model 4. Results 5. Summary Thank you very much for your attention! 09.05.2008 Slide 44/44

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