1. Copyright © 2010 Pearson Education, Inc. 22-1 Chapter Twenty-Two Structural Equation Modeling and Path Analysis

2. Copyright © 2010 Pearson Education, Inc. 22-2 Chapter Outline 1)Objectives 2)Overview 3)Basic Concepts in SEM i.Theory, Model and Path Diagram ii.Exogenous versus Endogenous Constructs iii.Dependence and Correlational Relationships iv.Model Fit Model Identification 4) Statistics Associated with SEM

3. Copyright © 2010 Pearson Education, Inc. 22-3 Chapter Outline 5)Conducting SEM 6)Define the Individual Constructs 7)Specify The Measurement Model i.Sample Size Requirements 8)Assess Measurement Model Reliability and Validity i.Assess Measurement Model Fit a.chi-square (χ 2 ) b.Absolute Fit Indices: Goodness-of-Fit c.Absolute Fit Indices: Badness-of-Fit d.Incremental Fit Indices e.Parsimony Fit Indices

4. Copyright © 2010 Pearson Education, Inc. 22-4 Chapter Outline ii.Measurement Model Reliability and Validity a. Reliability b.Discriminant Validity iii.Lack of Validity: Diagnosing Problems 9) Specify the Structural Model 10) Assess Structural Model Validity i.Assessing Fit ii.Comparison with Competing Models iii.Testing Hypothesized Relationships iv.Structural Model Diagnostics

5. Copyright © 2010 Pearson Education, Inc. 22-5 Chapter Outline 11)Draw Conclusions and Make Recommendations 12)Higher-Order Confirmatory Factor Analysis 13)Relationship of SEM to Other Multivariate Technique 14)Application of SEM: First-Order Factor Model 15)Application of SEM: Second-Order Factor Model 16)Path Analysis 17)Statistical Software

6. Copyright © 2010 Pearson Education, Inc. 22-6 Structural equation modeling (SEM), a procedure for estimating a series of dependence relationships among a set of concepts or constructs represented by multiple measured variables and incorporated into an integrated model. Structural Equation Modeling (SEM)

7. Copyright © 2010 Pearson Education, Inc. 22-7 1.Representation of constructs as unobservable or latent factors in dependence relationships. 2.Estimation of multiple and interrelated dependence relationships incorporated in an integrated model. 3.Incorporation of measurement error in an explicit manner. SEM can explicitly account for less than perfect reliability of the observed variables, providing analyses of attenuation and estimation bias due to measurement error. 4.Explanation of the covariance among the observed variables. Structural Equation Modeling: Distinctive Aspects

8. Copyright © 2010 Pearson Education, Inc. 22-8 Absolute fit indices These indices measure the overall goodness-of-fit or badness-of-fit for both the measurement and structural models. Average variance extracted A measure used to assess convergent and discriminant validity, which is defined as the variance in the indicators or observed variables that is explained by the latent construct. Chi-square difference statistic ( Δχ 2 ) A statistic used to compare two competing, nested SEM models. It is calculated as the difference between the models’ chi- square value. Its degrees of freedom equal the difference in the models’ degrees of freedom. Communality Communality is the variance of a measured variable that is explained by its construct. Statistics Associated with SEM

9. Copyright © 2010 Pearson Education, Inc. 22-9 Composite reliability (CR) It is defined as the total amount of true score variance in relation to the total score variance. Confirmatory factor analysis (CFA) A technique used to estimate the measurement model. It seeks to confirm if the number of factors (or constructs) and the loadings of observed (indicator) variables on them conform to what is expected on the basis of theory. Construct In SEM, a construct is a latent or unobservable concept that can be defined conceptually but that cannot be measured directly or without error. Also called a factor, a construct is measured by multiple indicators or observed variables. Statistics Associated with SEM

10. Copyright © 2010 Pearson Education, Inc. 22-10 Endogenous constructs An endogenous construct is the latent, multi-item equivalent of a dependent variable. It is determined by constructs or variables within the model and, thus, it is dependent on other constructs. Estimated covariance matrix Denoted by Σ k, it consists of the predicted covariances between all observed variables based on equations estimated in SEM. Exogenous construct An exogenous construct is the latent, multi-item equivalent of an independent variable in traditional multivariate analysis. An exogenous construct is determined by factors outside of the model and it cannot be explained by any other construct or variable in the model. Statistics Associated with SEM

11. Copyright © 2010 Pearson Education, Inc. 22-11 First-order factor model Covariances between observed variables are explained with a single latent factor or construct layer. Incremental fit indices These measures assess how well a model specified by the researcher fits relative to some alternative baseline model. Typically, the baseline model is a null model in which all observed variables are unrelated to each other. Measurement error It is the degree to which the observed variables do not describe the latent constructs of interest in SEM. Statistics Associated with SEM

12. Copyright © 2010 Pearson Education, Inc. 22-12 Measurement model The first of two models estimated in SEM. It represents the theory that specifies the observed variables for each construct and permits the assessment of construct validity. Modification index An index calculated for each possible relationship that is not freely estimated but is fixed. The index shows the improvement in the overall model χ 2 if that path was freely estimated. Nested model A model is nested within another model if it has the same number of constructs and variables and can be derived from the other model by altering relationships, as by adding or deleting relationships. Statistics Associated with SEM

13. Copyright © 2010 Pearson Education, Inc. 22-13 Nonrecursive model A structural model that contains feedback loops or dual dependencies. Parsimony fit indices The parsimony fit indices are designed to assess fit in relation to model complexity and are useful in evaluating competing models. These are goodness-of-fit measures and can be improved by a better fit or by a simpler, less complex model that estimates fewer parameters. Parsimony ratio Is calculated as the ratio of degrees of freedom used by the model to the total degrees of freedom available. Statistics Associated with SEM

14. Copyright © 2010 Pearson Education, Inc. 22-14 Path analysis A special case of SEM with only single indicators for each of the variables in the causal model. In other words, path analysis is SEM with a structural model, but no measurement model. Path diagram A graphical representation of a model showing the complete set of relationships amongst the constructs. Dependence relationships are portrayed by straight arrows and correlational relationships by curved arrows. Residuals In SEM, the residuals are the differences between the observed and estimated covariance matrices. Recursive model A structural model that does not contain any feedback loops or dual dependencies. Statistics Associated with SEM

15. Copyright © 2010 Pearson Education, Inc. 22-15 Sample covariance matrix Denoted by S, it consists of the variances and covariances for the observed variables. Second-order factor model There are two levels or layers. A second-order latent construct causes multiple first-order latent constructs, which in turn cause the observed variables. Thus, the first-order constructs now act as indicators or observed variables for the second order factor. Squared multiple correlations Similar to communality, these values denote the extent to which an observed variable’s variance is explained by a latent construct or factor. Statistics Associated with SEM

16. Copyright © 2010 Pearson Education, Inc. 22-16 Standardized residuals Used as a diagnostic measure of model fit, these are residuals, each divided by its standard error. Structural error Structural error is the same as an error term in regression analysis. In the case of completely standardized estimates, squared multiple correlation is equal to 1 – the structural error. Structural model The second of two models estimated in SEM. It represents the theory that specifies how the constructs are related to each other, often with multiple dependence relationships. Statistics Associated with SEM

17. Copyright © 2010 Pearson Education, Inc. 22-17 Structural relationship Dependence relationship between an endogenous construct and another exogenous or endogenous construct. Unidimensionality A notion that a set of observed variables represent only one underlying construct. All cross-loadings are zero. Statistics Associated with SEM

18. Copyright © 2010 Pearson Education, Inc. 22-18 Exogenous and Endogenous Constructs Exogenous constructs are the latent, multi-item equivalent of independent variables. They use a variate (linear combination) of measures to represent the construct, which acts as an independent variable in the model. Multiple measured variables (X) represent the exogenous constructs (ξ). Endogenous constructs are the latent, multi-item equivalent to dependent variables. These constructs are theoretically determined by factors within the model. Multiple measured variables (Y) represent the endogenous constructs (η).

19. Copyright © 2010 Pearson Education, Inc. 22-19 Models can be represented visually with a path diagram. Dependence relationships are represented with single- headed straight arrows. Correlational (covariance) relationships are represented with two-headed curved arrows. SEM Models

20. Copyright © 2010 Pearson Education, Inc. 22-20 (a) Dependence Relationship Exogenous Construct: C 1 Endogenous Construct: C 2 X1X1 X2X2 X3X3 Y1Y1 Y2Y2 Y3Y3 Dependence and Correlational Relationships in SEM Fig. 22.1

21. Copyright © 2010 Pearson Education, Inc. 22-21 Exogenous Construct: C 1 Exogenous Construct: C 2 X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 Dependence and Correlational Relationships in SEM Fig. 22.1 Cont.

22. Copyright © 2010 Pearson Education, Inc. 22-22 Conducting SEM Steps in SEM Step 1: Define the Individual Constructs Step 2: Specify the Measurement Model Step 3: Assess Measurement Model Reliability and Validity Step 4: Specify the Structural Model Step 5: Assess Structural Model Validity Step 6: Draw Conclusions and Make Recommendations

23. Copyright © 2010 Pearson Education, Inc. 22-23 The Process for Structural Equation Modeling Define the Individual Constructs Develop and Specify the Measurement Model Assess Measurement Model Reliability and Validity Specify the Structural Model Assess Structural Model Validity Draw Conclusions and Make Recommendations Measurement Model Valid? Refine Measures and Design a New Study YES NO Structural Model Valid? YES Refine Model and Test with New Data No Fig. 22.2

24. Copyright © 2010 Pearson Education, Inc. 22-24 Path Diagram of a Simple Measurement Model C1ξ1C1ξ1 C2ξ2C2ξ2 X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 λ x1,1 λ X2,1 λ X4,2 λ X5,2 λ x6,2 Φ 21 δ 4 δ 5 δ 6 δ 1 δ 2 δ 3 λ х3,1 Fig. 22.3

25. Copyright © 2010 Pearson Education, Inc. 22-25 In Figure 22.3, ξ 1 represents the latent construct C 1, ξ 2 represents the latent construct C 2, x 1 - x 6 represent the measured variables, λ x1,1 - λ χ6,2 represent the relationships between the latent constructs and the respective measured items (i.e; factor loadings), and δ 1 - δ 6 represent the errors. A Simple Measurement Model

26. Copyright © 2010 Pearson Education, Inc. 22-26 1.Absolute fit measures overall goodness- or badness-of-fit for both the structural and measurement models. This type of measure does not make any comparison to a specified null model (incremental fit measure) or adjusts for the number of parameters in the estimated model (parsimonious fit measure). 2.Incremental fit measures goodness-of-fit that compares the current model to a specified “null” (independence) model to determine the degree of improvement over the null model. 3.Parsimonious fit measures goodness-of-fit representing the degree of model fit per estimated coefficient. This measure attempts to correct for any “overfitting” of the model and evaluates the parsimony of the model compared to the goodness-of-fit. Types of Fit Measures

27. Copyright © 2010 Pearson Education, Inc. 22-27 A Classification of Fit Measures Absolute Fit Incremental Parsimony Indices Fit Indices Fit Indices Goodness-of-Fit Badness-of-Fit Goodness-of-Fit NFI NNFI CFI TLI RNI Goodness-of-Fit  PGFI  PNFI GFI AGFI x 2 RMSR SRMR RMSEA Fit Measures Fig. 22.4

28. Copyright © 2010 Pearson Education, Inc. 22-28 Assessing Model Fit: Chi-square

29. Copyright © 2010 Pearson Education, Inc. 22-29 Multiple fit indices should be used to assess a model’s goodness of fit. They should include: The χ 2 value and the associated df Two absolute fit indices (GFI, AGFI, RMSEA, or SRMR) One goodness-of-fit index (GFI, AGFI) One badness-of-fit index (RMSR, SRMR, RMSEA) One incremental fit index (CFI, TLI, NFI, NNFI, RNI) One parsimony fit index for models of different complexities (PGFI, PNFI) Assessing Model Fit: Fit Indexes

30. Copyright © 2010 Pearson Education, Inc. 22-30 Where CR=Composite reliability λ=completely standardized factor loading δ=Error variance p=number of indicators or observed variables CR = (∑ λ i ) 2 (∑λ i ) 2 + (∑δ i ) p i=1 p Composite Reliability (CR)

31. Copyright © 2010 Pearson Education, Inc. 22-31 p 2 Σ λ ί i=1 AVE = p 2 p Σ λ ί + Σ δ i i=1 i=1 Where AVE=Average variance extracted λ=Completely Standardized factor loading δ=Error variance p =number of indicators or observed variables Average Variance Extracted (AVE)

32. Copyright © 2010 Pearson Education, Inc. 22-32 Path Diagram of a Simple Structural Model Construct 1: C 1 ξ 1 Construct 2:C 2 η 1 X1X1 X2X2 X3X3 Y1Y1 Y2Y2 Y3Y3 γ 1,1 λ x1,1 λ x2,1 λ y1,1 λ y2,1 δ1δ1 δ2δ2 δ3δ3 ε1ε1 ε2ε2 ε3ε3 λ y3,1 λ x3,1 Fig. 22.5

33. Copyright © 2010 Pearson Education, Inc. 22-33 Δχ 2 Δdf = χ 2 df(M1) - χ 2 df(M2) and Δdf = df(M1) –df(M2) Chi-Square Difference Test

34. Copyright © 2010 Pearson Education, Inc. 22-34 First-Order Model of IUIPC COLCON AWA X1X1 X2X2 X3X3 X4X4 X5X5 X6X6 X7X7 X8X8 X9X9 X 10 Φ 2,1 Ф 3,2 Ф 3,1 Fig. 22.6

35. Copyright © 2010 Pearson Education, Inc. 22-35 Second-Order Model of IUIPC IUIPC Layer 2 COLCONAWA Layer 1 X1X1 X3X3 X4X4 X2X2 X5X5 X6X6 X7X7 X 10 X8X8 X9X9 X 1,1 X 2,1 X 3,1 Legend: First-Order Factors: COL = Collection CON = Control AWA = Awareness Second-Order Factor : IUIPC = Internet Users Information Privacy Concerns Fig. 22.7

36. Copyright © 2010 Pearson Education, Inc. 22-36 Measurement Model for TAM PUPE INT PU1PU2PU3PE1PE2PE3INT1 INT2INT3 Φ 2,1 Ф 3,2 Ф 3,1 Fig. 22.8

37. Copyright © 2010 Pearson Education, Inc. 22-37 Structural Model for TAM Perceived Usefulness PU Perceived Ease Of Use PE Intention to Use INT 0.46 0.28 0.40 0.60 Fig. 22.9

38. Copyright © 2010 Pearson Education, Inc. 22-38 TAM: Means, Standard Deviations, and Correlations Table 22.1

39. Copyright © 2010 Pearson Education, Inc. 22-39 TAM: Results of Measurement Model Table 22.2

40. Copyright © 2010 Pearson Education, Inc. 22-40 x1x2 x3 x4 X5 X6X7 X8 X9 x10 X11x12x13x14X16X15X17x18x19x20x21x22x23 x28 x24 X29 TANG ξ 1 REL ξ 2 RESP ξ 3 ASS4 ξ 4 EMP ξ 5 ATT ξ 6 SAT ξ 7 PAT ξ 8 δ1δ1 δ2δ2 δ3δ3 δ4δ4 δ5δ5 δ6δ6 δ7δ7 δ8δ8 δ9δ9 δ 10 δ 11 δ 12 δ 13 δ 14 δ 15 δ 16 δ 17 δ 18 δ 19 δ 20 δ 21 δ 22 δ 23 δ 24 δ 25 δ 26 δ 27 δ 28 δ 29 δ 30 Banking Application: Measurement Model x25x26x27x30 Fig. 22.10

41. Copyright © 2010 Pearson Education, Inc. 22-41 Service Quality TANG REL RESP ASSU Patronage Intention Service Attitude Service Satisfaction EMP TANG = tangibility; REL= reliability; RESP = responsiveness; ASSU = assurance; EMP = empathy Banking Application: Structure Model Fig. 22.11

42. Copyright © 2010 Pearson Education, Inc. 22-42 When it comes to…. My Perception of My Bank’s Service. TANG1: Modern equipments TANG2: Visual appeal of physical facilities TANG3: Neat, professional appearance of employees TANG4: Visual appeal of materials associated with the service REL1: Keeping a promise by a certain time REL2: Performing service right the first time REL3: Providing service at the promised time REL4: Telling customers the exact time the service will be performed Loadings Measurement Error 0.71 0.49 0.80 0.36 0.76 0.42 0.72 0.48 0.79 0.37 0.83 0.31 0.91 0.18 0.81 0.34 Psychometric Properties of Measurement Model Table 22.3

43. Copyright © 2010 Pearson Education, Inc. 22-43 When it comes to…. My Perception of My Bank’s Service. RESP1: Keeping a promise by a certain time RESP2: Willingness to always help customers RESP3: Responding to customer requests despite being busy ASSU1: Employees instilling confidence in customers ASSU2: Customers’ safety feelings in transactions (e.g. physical, financial, emotional, etc.) ASSU3: Consistent courtesy to customers ASSU4: Employees’ knowledge to answer customer questions Loadings Measurement Error 0.73 0.47 0.89 0.21 0.81 0.35 0.71 0.49 0.80 0.36 0.86 0.26 Psychometric Properties of Measurement Model Table 22.3 Cont.

44. Copyright © 2010 Pearson Education, Inc. 22-44 When it comes to…. My Perception of My Bank’s Service. EMP1: Giving customers individual attention EMP2: Dealing with customers with care EMP3: Having customers’ best interests at heart EMP4: Understanding specific needs of customers Overall attitude toward your bank (items reverse coded): ATT1: Favorable 1---2---3---4---5---6---7 Unfavorable ATT2: Good 1---2---3---4---5---6---7 Bad ATT3: Positive 1---2---3---4---5---6---7 Negative ATT4: Pleasant 1---2---3---4---5---6---7 Unpleasant SAT1: I believe I am satisfied with my bank’s services SAT2: Overall, I am pleased with my bank’s services Loadings Measurement Error 0.80 0.37 0.84 0.29 0.87 0.24 0.95 0.10 0.93 0.14 Psychometric Properties of Measurement Model Table 22.3 Cont.

45. Copyright © 2010 Pearson Education, Inc. 22-45 When it comes to…. My Perception of My Bank’s Service. SAT3: Using services from my bank is usually a satisfying experience SAT4: My feelings toward my bank’s services can best be characterized as PAT1: The next time my friend needs the services of a bank I will recommend my bank PAT2: I have no regrets of having patronized my bank in the past PAT3: I will continue to patronize the services of my bank in the future Loadings Measurement Error 0.88 0.23 0.92 0.15 0.88 0.22 0.89 0.20 0.88 0.22 Table 22.3 Cont. Psychometric Properties of Measurement Model

46. Copyright © 2010 Pearson Education, Inc. 22-46 ---------------------------------------------- Degrees of Freedom = 377 Minimum Fit Function Chi-Square = 767.77 (P = 0.0) Chi-Square for Independence Model with 435 Degrees of Freedom = 7780.15 Root Mean Square Error of Approximation (RMSEA) = 0.064 Standardized RMR = 0.041 Normed Fit Index (NFI) = 0.90 Non-Normed Fit Index (NNFI) = 0.94 Comparative Fit Index (CFI) = 0.95 ---------------------------------------------- Table 22.4 Goodness of Fit Statistics Measurement Model

47. Copyright © 2010 Pearson Education, Inc. 22-47 Measurement Model: Construct Reliability, Average Variance Extracted & Correlation Matrix Construct Reliability Average Variance Extracted Correlation Matrix 1 2 3 4 5 6 7 8 1. TANG 0.84 0.560.75 2. REL 0.900.700.77 0.84 3. RESP 0.850.660.65 0.76 0.81 4. ASSU 0.870.630.73 0.80 0.92 0.80 5. EMP 0.910.710.69 0.75 0.85 0.90 0.85 6. ATT 0.970.900.42 0.46 0.52 0.54 0.58 0.95 7. SAT 0.850.830.53 0.56 0.66 0.67 0.69 0.72 0.91 8. PAT 0.920.780.50 0.55 0.57 0.62 0.62 0.66 0.89 0.89  TANG = tangibles; REL = reliability; RESP = responsiveness; ASSU = assurance; EMP = empathy; ATT = attitude; SAT = satisfaction; PAT = patronage.  Value on the diagonal of the correlation matrix is the square root of AVE. Table 22.5

48. Copyright © 2010 Pearson Education, Inc. 22-48 --------------------------------------------------------------------------------- Degrees of Freedom = 396 Minimum Fit Function Chi-Square = 817.16 (P = 0.0) Chi-Square for Independence Model with 435 Degrees of Freedom = 7780.15 Root Mean Square Error of Approximation (RMSEA) = 0.065 Standardized RMR = 0.096 Normed Fit Index (NFI) = 0.89 Non-Normed Fit Index (NNFI) = 0.94 Comparative Fit Index (CFI) = 0.94 --------------------------------------------------------------------------------------------------------------- Table 22.6 Goodness of Fit Statistics (Structural Model)

49. Copyright © 2010 Pearson Education, Inc. 22-49 Banking Application: Structural Coefficients Dimensions of Service Quality Second Order T-values Loading Estimates TANG   0.82 fixed to 1 REL   0.85 13.15 RESP   0.93 13.37 ASSU   0.98 16.45 EMP   0.93 15.18 Table 22.7

50. Copyright © 2010 Pearson Education, Inc. 22-50 SQ  ATT   0.60 10.25 SQ  SAT   0.45 8.25 ATT  SAT   0.47 8.91 ATT  PAT   0.03 0.48 SAT  PAT   0.88 13.75 Consequences of Service Quality Structural Coefficient Estimates T-Values Table 22.7 Cont. Banking Application: Structural Coefficients

51. Copyright © 2010 Pearson Education, Inc. 22-51 Path analysis A special case of SEM with only single indicators for each of the variables in the causal model. In other words, path analysis is SEM with a structural model, but no measurement model. Path Analysis

52. Copyright © 2010 Pearson Education, Inc. 22-52 Diagram for Path Analysis B C A X 1 Attitude X 2 Emotion Y 1 Purchase Intention Fig. 22.12

53. Copyright © 2010 Pearson Education, Inc. 22-53 Figure 22.12 portrays a simple model with two exogenous constructs X 1 and X 2 causally related to the endogenous construct Y 1. The correlational path A is X 1 correlated with X 2. Path B is the effect of X 1 predicting Y 1, and path C shows the effect of X 2 predicting Y 1. The value for Y 1 can be modeled as: Y 1 = b 1 X 1 + b 2 X 2 Note that this is similar to a regression equation. The direct and indirect paths in our model can now be identified. Direct Paths Indirect Paths A=X 1 to X 2 B=X 1 to Y 1 C=X 2 to Y 1 AC = X 1 to Y 1 (Via X 2 ) AB = X 2 to Y 1 (Via X 1 ) Path Analysis: Calculating Structural Coefficients

54. Copyright © 2010 Pearson Education, Inc. 22-54 Path Analysis: Calculating Structural Coefficients The unique correlations among the three constructs can be shown to be composed of direct and indirect paths as follows: Corr x1, x2 = A Corr x1, y1 = B + AC Corr x2, y1 = C + AB The correlation of X 1 and X 2 is simply equal to A. The Correlation of X 1 and Y 1 ( Corr x1,y1 ) can be represented by two paths:B and AC. B represents the direct path from X 1 to Y 1. AC is a compound path that follows the curved arrow from X 1 to X 2 and then to Y 1. Similarly, the correlation of X 2 and Y 1 can be shown to consist of two causal paths: C and AB.

55. Copyright © 2010 Pearson Education, Inc. 22-55 Bivariate Correlations X 1 X 2 Y 1 X 1 1.0 X 2.40 1.0 Y 1.50.60 1.0 Path Analysis

56. Copyright © 2010 Pearson Education, Inc. 22-56 Correlations as Compound Paths Corr X1, X2 = A Corr X1,Y1 = B+AC Corr X2,Y1 = C+AB Path Analysis

57. Copyright © 2010 Pearson Education, Inc. 22-57.40 = A.50 = B+AC.60 = C+AB Substituting A =.40.50 = B+.40C.60 = C+.40B Solving for B and C B =.310 C =.476 Path Analysis: Calculating Structural Coefficients

58. Copyright © 2010 Pearson Education, Inc. 22-58 Statistical Software LISREL, LInear Structural RELations AMOS, Analysis of Moment Structures, is an added module to SPSS CALIS is offered by SAS EQS, an abbreviation for equations Mplus is another software Selection of a specific computer program should be based on availability and user’s preference.

59. Copyright © 2010 Pearson Education, Inc. 22-59

60. Copyright © 2010 Pearson Education, Inc. 22-60 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. Copyright © 2010 Pearson Education, Inc.