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Solution Manual for Using Multivariate Statistics 7th Edition Barbara G. Tabachnick, Linda S. Fidell

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Solution Manual for Using Multivariate Statistics 7th Edition Barbara G. Tabachnick, Linda S. Fidell,

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Product Details:

  • ISBN-10 ‏ : ‎ 0134790545
  • ISBN-13 ‏ : ‎ 978-0134790541
  • Author:  Barbara G. Tabachnick, Linda S. Fidell

Using Multivariate Statistics, 7th Edition presents complex statistical procedures in a way that is maximally useful and accessible to researchers who may not be statisticians. The authors focus on the benefits and limitations of applying a technique to a data set – when, why, and how to do it. Only a limited knowledge of higher-level mathematics is assumed.

Students using this text will learn to conduct numerous types of multivariate statistical analyses; find the best technique to use; understand limitations to applications; and learn how to use SPSS and SAS syntax and output.

 

Table of Content:

  1. Chapter 1 Introduction
  2. Learning Objectives
  3. 1.1 Multivariate Statistics: Why?
  4. 1.1.1 The Domain of Multivariate Statistics: Numbers of IVs and DVs
  5. 1.1.2 Experimental and Nonexperimental Research
  6. 1.1.3 Computers and Multivariate Statistics
  7. 1.1.4 Garbage In, Roses Out?
  8. 1.2 Some Useful Definitions
  9. 1.2.1 Continuous, Discrete, and Dichotomous Data
  10. 1.2.2 Samples and Populations
  11. 1.2.3 Descriptive and Inferential Statistics
  12. 1.2.4 Orthogonality: Standard and Sequential Analyses
  13. 1.3 Linear Combinations of Variables
  14. 1.4 Number and Nature of Variables to Include
  15. 1.5 Statistical Power
  16. 1.6 Data Appropriate for Multivariate Statistics
  17. 1.6.1 The Data Matrix
  18. 1.6.2 The Correlation Matrix
  19. 1.6.3 The Variance–Covariance Matrix
  20. 1.6.4 The Sum-of-Squares and Cross-Products Matrix
  21. 1.6.5 Residuals
  22. 1.7 Organization of the Book
  23. Chapter 2 A Guide to Statistical Techniques Using the Book
  24. Learning Objectives
  25. 2.1 Research Questions and Associated Techniques
  26. 2.1.1 Degree of Relationship Among Variables
  27. 2.1.1.1 Bivariate r
  28. 2.1.1.2 Multiple R
  29. 2.1.1.3 Sequential R
  30. 2.1.1.4 Canonical R
  31. 2.1.1.5 Multiway Frequency Analysis
  32. 2.1.1.6 Multilevel Modeling
  33. 2.1.2 Significance of Group Differences
  34. 2.1.2.1 One-Way ANOVA and t Test
  35. 2.1.2.2 One-Way ANCOVA
  36. 2.1.2.3 Factorial ANOVA
  37. 2.1.2.4 Factorial ANCOVA
  38. 2.1.2.5 Hotelling’s T2
  39. 2.1.2.6 One-Way MANOVA
  40. 2.1.2.7 One-Way MANCOVA
  41. 2.1.2.8 Factorial MANOVA
  42. 2.1.2.9 Factorial MANCOVA
  43. 2.1.2.10 Profile Analysis of Repeated Measures
  44. 2.1.3 Prediction of Group Membership
  45. 2.1.3.1 One-Way Discriminant Analysis
  46. 2.1.3.2 Sequential One-Way Discriminant Analysis
  47. 2.1.3.3 Multiway Frequency Analysis (Logit)
  48. 2.1.3.4 Logistic Regression
  49. 2.1.3.5 Sequential Logistic Regression
  50. 2.1.3.6 Factorial Discriminant Analysis
  51. 2.1.3.7 Sequential Factorial Discriminant Analysis
  52. 2.1.4 Structure
  53. 2.1.4.1 Principal Components
  54. 2.1.4.2 Factor Analysis
  55. 2.1.4.3 Structural Equation Modeling
  56. 2.1.5 Time Course of Events
  57. 2.1.5.1 Survival/Failure Analysis
  58. 2.1.5.2 Time-Series Analysis
  59. 2.2 Some Further Comparisons
  60. 2.3 A Decision Tree
  61. 2.4 Technique Chapters
  62. 2.5 Preliminary Check of the Data
  63. Chapter 3 Review of Univariate and Bivariate Statistics
  64. Learning Objectives
  65. 3.1 Hypothesis Testing
  66. 3.1.1 One-Sample z Test as Prototype
  67. 3.1.2 Power
  68. 3.1.3 Extensions of the Model
  69. 3.1.4 Controversy Surrounding Significance Testing
  70. 3.2 Analysis of Variance
  71. 3.2.1 One-Way Between-Subjects ANOVA
  72. 3.2.2 Factorial Between-Subjects ANOVA
  73. 3.2.3 Within-Subjects ANOVA
  74. 3.2.4 Mixed Between-Within-Subjects ANOVA6
  75. 3.2.5 Design Complexity
  76. 3.2.5.1 Nesting
  77. 3.2.5.2 Latin-Square Designs
  78. 3.2.5.3 Unequal n and Nonorthogonality
  79. 3.2.5.4 Fixed and Random Effects
  80. 3.2.6 Specific Comparisons
  81. 3.2.6.1 Weighting Coefficients for Comparisons
  82. 3.2.6.2 Orthogonality of Weighting Coefficients
  83. 3.2.6.3 Obtained F for Comparisons
  84. 3.2.6.4 Critical F for Planned Comparisons
  85. 3.2.6.5 Critical F for Post Hoc Comparisons
  86. 3.3 Parameter Estimation
  87. 3.4 Effect Size
  88. 3.5 Bivariate Statistics: Correlation and Regression
  89. 3.5.1 Correlation
  90. 3.5.2 Regression
  91. 3.6 Chi-Square Analysis
  92. Chapter 4 Cleaning Up Your Act Screening Data Prior to Analysis
  93. Learning Objectives
  94. 4.1 Important Issues in Data Screening
  95. 4.1.1 Accuracy of Data File
  96. 4.1.2 Honest Correlations
  97. 4.1.2.1 Inflated Correlation
  98. 4.1.2.2 Deflated Correlation
  99. 4.1.3 Missing Data
  100. 4.1.3.1 Deleting Cases or Variables
  101. 4.1.3.2 Estimating Missing Data
  102. 4.1.3.3 Using a Missing Data Correlation Matrix
  103. 4.1.3.4 Treating Missing Data as Data
  104. 4.1.3.5 Repeating Analyses With and Without Missing Data
  105. 4.1.3.6 Choosing Among Methods for Dealing With Missing Data
  106. 4.1.4 Outliers
  107. 4.1.4.1 Detecting Univariate and Multivariate Outliers
  108. 4.1.4.2 Describing Outliers
  109. 4.1.4.3 Reducing the Influence of Outliers
  110. 4.1.4.4 Outliers in a Solution
  111. 4.1.5 Normality, Linearity, and Homoscedasticity
  112. 4.1.5.1 Normality
  113. 4.1.5.2 Linearity
  114. 4.1.5.3 Homoscedasticity, Homogeneity of Variance, and Homogeneity of Variance–Covariance Matrices
  115. 4.1.6 Common Data Transformations
  116. 4.1.7 Multicollinearity and Singularity
  117. 4.1.8 A Checklist and Some Practical Recommendations
  118. 4.2 Complete Examples of Data Screening
  119. 4.2.1 Screening Ungrouped Data
  120. 4.2.1.1 Accuracy of Input, Missing Data, Distributions, and Univariate Outliers
  121. 4.2.1.2 Linearity and Homoscedasticity
  122. 4.2.1.3 Transformation
  123. 4.2.1.4 Detecting Multivariate Outliers
  124. 4.2.1.5 Variables Causing Cases to Be Outliers
  125. 4.2.1.6 Multicollinearity
  126. 4.2.2 Screening Grouped Data
  127. 4.2.2.1 Accuracy of Input, Missing Data, Distributions, Homogeneity of Variance, and Univariate Outliers
  128. 4.2.2.2 Linearity
  129. 4.2.2.3 Multivariate Outliers
  130. 4.2.2.4 Variables Causing Cases to be Outliers
  131. 4.2.2.5 Multicollinearity
  132. Chapter 5 Multiple Regression
  133. Learning Objectives
  134. 5.1 General Purpose and Description
  135. 5.2 Kinds of Research Questions
  136. 5.2.1 Degree of Relationship
  137. 5.2.2 Importance of IVs
  138. 5.2.3 Adding IVs
  139. 5.2.4 Changing IVs
  140. 5.2.5 Contingencies Among IVs
  141. 5.2.6 Comparing Sets of IVs
  142. 5.2.7 Predicting DV Scores for Members of a New Sample
  143. 5.2.8 Parameter Estimates
  144. 5.3 Limitations to Regression Analyses
  145. 5.3.1 Theoretical Issues
  146. 5.3.2 Practical Issues
  147. 5.3.2.1 Ratio of Cases to IVs
  148. 5.3.2.2 Absence of Outliers Among the IVs and on the DV
  149. 5.3.2.3 Absence of Multicollinearity and Singularity
  150. 5.3.2.4 Normality, Linearity, and Homoscedasticity of Residuals
  151. 5.3.2.5 Independence of Errors
  152. 5.3.2.6 Absence of Outliers in the Solution
  153. 5.4 Fundamental Equations for Multiple Regression
  154. 5.4.1 General Linear Equations
  155. 5.4.2 Matrix Equations
  156. 5.4.3 Computer Analyses of Small-Sample Example
  157. 5.5 Major Types of Multiple Regression
  158. 5.5.1 Standard Multiple Regression
  159. 5.5.2 Sequential Multiple Regression
  160. 5.5.3 Statistical (Stepwise) Regression
  161. 5.5.4 Choosing Among Regression Strategies
  162. 5.6 Some Important Issues
  163. 5.6.1 Importance of IVs
  164. 5.6.1.1 Standard Multiple Regression
  165. 5.6.1.2 Sequential or Statistical Regression
  166. 5.6.1.3 Commonality Analysis
  167. 5.6.1.4 Relative Importance Analysis
  168. 5.6.2 Statistical Inference
  169. 5.6.2.1 Test for Multiple R
  170. 5.6.2.2 Test of Regression Components
  171. 5.6.2.3 Test of Added Subset of IVs
  172. 5.6.2.4 Confidence Limits
  173. 5.6.2.5 Comparing Two Sets of Predictors
  174. 5.6.3 Adjustment of R2
  175. 5.6.4 Suppressor Variables
  176. 5.6.5 Regression Approach to ANOVA
  177. 5.6.6 Centering When Interactions and Powers of IVs are Included
  178. 5.6.7 Mediation in Causal Sequence
  179. 5.7 Complete Examples of Regression Analysis
  180. 5.7.1 Evaluation of Assumptions
  181. 5.7.1.1 Ratio of Cases to IVs
  182. 5.7.1.2 Normality, Linearity, Homoscedasticity, and Independence of Residuals
  183. 5.7.1.3 Outliers
  184. 5.7.1.4 Multicollinearity and Singularity
  185. 5.7.2 Standard Multiple Regression
  186. 5.7.3 Sequential Regression
  187. 5.7.4 Example of Standard Multiple Regression with Missing Values Multiply Imputed
  188. 5.8 Comparison of Programs
  189. 5.8.1 IBM SPSS Package
  190. 5.8.2 SAS System
  191. 5.8.3 SYSTAT System
  192. Chapter 6 Analysis of Covariance
  193. Learning Objectives
  194. 6.1 General Purpose and Description
  195. 6.2 Kinds of Research Questions
  196. 6.2.1 Main Effects of IVs
  197. 6.2.2 Interactions Among IVs
  198. 6.2.3 Specific Comparisons and Trend Analysis
  199. 6.2.4 Effects of Covariates
  200. 6.2.5 Effect Size
  201. 6.2.6 Parameter Estimates
  202. 6.3 Limitations to Analysis of Covariance
  203. 6.3.1 Theoretical Issues
  204. 6.3.2 Practical Issues
  205. 6.3.2.1 Unequal Sample Sizes, Missing Data, and Ratio of Cases to IVs
  206. 6.3.2.2 Absence of Outliers
  207. 6.3.2.3 Absence of Multicollinearity and Singularity
  208. 6.3.2.4 Normality of Sampling Distributions
  209. 6.3.2.5 Homogeneity of Variance
  210. 6.3.2.6 Linearity
  211. 6.3.2.7 Homogeneity of Regression
  212. 6.3.2.8 Reliability of Covariates
  213. 6.4 Fundamental Equations for Analysis of Covariance
  214. 6.4.1 Sums of Squares and Cross-Products
  215. 6.4.2 Significance Test and Effect Size
  216. 6.4.3 Computer Analyses of Small-Sample Example
  217. 6.5 Some Important Issues
  218. 6.5.1 Choosing Covariates
  219. 6.5.2 Evaluation of Covariates
  220. 6.5.3 Test for Homogeneity of Regression
  221. 6.5.4 Design Complexity
  222. 6.5.4.1 Within-Subjects and Mixed Within-Between Designs
  223. 6.5.4.1.1 Same Covariate(s) for All Cells
  224. 6.5.4.1.2 Varying Covariate(s) Over Cells
  225. 6.5.4.2 Unequal Sample Sizes
  226. 6.5.4.3 Specific Comparisons and Trend Analysis
  227. 6.5.4.4 Effect Size
  228. 6.5.5 Alternatives to ANCOVA
  229. 6.6 Complete Example of Analysis of Covariance
  230. 6.6.1 Evaluation of Assumptions
  231. 6.6.1.1 Unequal n and Missing Data
  232. 6.6.1.2 Normality
  233. 6.6.1.3 Linearity
  234. 6.6.1.4 Outliers
  235. 6.6.1.5 Multicollinearity and Singularity
  236. 6.6.1.6 Homogeneity of Variance
  237. 6.6.1.7 Homogeneity of Regression
  238. 6.6.1.8 Reliability of Covariates
  239. 6.6.2 Analysis of Covariance
  240. 6.6.2.1 Main Analysis
  241. 6.6.2.2 Evaluation of Covariates
  242. 6.6.2.3 Homogeneity of Regression Run
  243. 6.7 Comparison of Programs
  244. 6.7.1 IBM SPSS Package
  245. 6.7.2 SAS System
  246. 6.7.3 SYSTAT System
  247. Chapter 7 Multivariate Analysis of Variance and Covariance
  248. Learning Objectives
  249. 7.1 General Purpose and Description
  250. 7.2 Kinds of Research Questions
  251. 7.2.1 Main Effects of IVs
  252. 7.2.2 Interactions Among IVs
  253. 7.2.3 Importance of DVs
  254. 7.2.4 Parameter Estimates
  255. 7.2.5 Specific Comparisons and Trend Analysis
  256. 7.2.6 Effect Size
  257. 7.2.7 Effects of Covariates
  258. 7.2.8 Repeated-Measures Analysis of Variance
  259. 7.3 Limitations to Multivariate Analysis of Variance and Covariance
  260. 7.3.1 Theoretical Issues
  261. 7.3.2 Practical Issues
  262. 7.3.2.1 Unequal Sample Sizes, Missing Data, and Power
  263. 7.3.2.2 Multivariate Normality
  264. 7.3.2.3 Absence of Outliers
  265. 7.3.2.4 Homogeneity of Variance–Covariance Matrices
  266. 7.3.2.5 Linearity
  267. 7.3.2.6 Homogeneity of Regression
  268. 7.3.2.7 Reliability of Covariates
  269. 7.3.2.8 Absence of Multicollinearity and Singularity
  270. 7.4 Fundamental Equations for Multivariate Analysis of Variance and Covariance
  271. 7.4.1 Multivariate Analysis of Variance
  272. 7.4.2 Computer Analyses of Small-Sample Example
  273. 7.4.3 Multivariate Analysis of Covariance
  274. 7.5 Some Important Issues
  275. 7.5.1 MANOVA Versus ANOVAs
  276. 7.5.2 Criteria for Statistical Inference
  277. 7.5.3 Assessing DVs
  278. 7.5.3.1 Univariate F
  279. 7.5.3.2 Roy–Bargmann Stepdown Analysis10
  280. 7.5.3.3 Using Discriminant Analysis
  281. 7.5.3.4 Choosing Among Strategies for Assessing DVs
  282. 7.5.4 Specific Comparisons and Trend Analysis
  283. 7.5.5 Design Complexity
  284. 7.5.5.1 Within-Subjects and Between-Within Designs
  285. 7.5.5.2 Unequal Sample Sizes
  286. 7.6 Complete Examples of Multivariate Analysis of Variance and Covariance
  287. 7.6.1 Evaluation of Assumptions
  288. 7.6.1.1 Unequal Sample Sizes and Missing Data
  289. 7.6.1.2 Multivariate Normality
  290. 7.6.1.3 Linearity
  291. 7.6.1.4 Outliers
  292. 7.6.1.5 Homogeneity of Variance–Covariance Matrices
  293. 7.6.1.6 Homogeneity of Regression
  294. 7.6.1.7 Reliability of Covariates
  295. 7.6.1.8 Multicollinearity and Singularity
  296. 7.6.2 Multivariate Analysis of Variance
  297. 7.6.3 Multivariate Analysis of Covariance
  298. 7.6.3.1 Assessing Covariates
  299. 7.6.3.2 Assessing DVs
  300. 7.7 Comparison of Programs
  301. 7.7.1 IBM SPSS Package
  302. 7.7.2 SAS System
  303. 7.7.3 SYSTAT System
  304. Chapter 8 Profile Analysis: The Multivariate Approach to Repeated Measures
  305. Learning Objectives
  306. 8.1 General Purpose and Description
  307. 8.2 Kinds of Research Questions
  308. 8.2.1 Parallelism of Profiles
  309. 8.2.2 Overall Difference Among Groups
  310. 8.2.3 Flatness of Profiles
  311. 8.2.4 Contrasts Following Profile Analysis
  312. 8.2.5 Parameter Estimates
  313. 8.2.6 Effect Size
  314. 8.3 Limitations to Profile Analysis
  315. 8.3.1 Theoretical Issues
  316. 8.3.2 Practical Issues
  317. 8.3.2.1 Sample Size, Missing Data, and Power
  318. 8.3.2.2 Multivariate Normality
  319. 8.3.2.3 Absence of Outliers
  320. 8.3.2.4 Homogeneity of Variance–Covariance Matrices
  321. 8.3.2.5 Linearity
  322. 8.3.2.6 Absence of Multicollinearity and Singularity
  323. 8.4 Fundamental Equations for Profile Analysis
  324. 8.4.1 Differences in Levels
  325. 8.4.2 Parallelism
  326. 8.4.3 Flatness
  327. 8.4.4 Computer Analyses of Small-Sample Example
  328. 8.5 Some Important Issues
  329. 8.5.1 Univariate Versus Multivariate Approach to Repeated Measures
  330. 8.5.2 Contrasts in Profile Analysis
  331. 8.5.2.1 Parallelism and Flatness Significant, Levels Not Significant (Simple-Effects Analysis)
  332. 8.5.2.2 Parallelism and Levels Significant, Flatness Not Significant (Simple-Effects Analysis)
  333. 8.5.2.3 Parallelism, Levels, and Flatness Significant (Interaction Contrasts)
  334. 8.5.2.4 Only Parallelism Significant
  335. 8.5.3 Doubly Multivariate Designs
  336. 8.5.4 Classifying Profiles
  337. 8.5.5 Imputation of Missing Values
  338. 8.6 Complete Examples of Profile Analysis
  339. 8.6.1 Profile Analysis of Subscales of the WISC
  340. 8.6.1.1 Evaluation of Assumptions
  341. 8.6.1.1.1 Unequal Sample Sizes and Missing Data
  342. 8.6.1.1.2 Multivariate Normality
  343. 8.6.1.1.3 Linearity
  344. 8.6.1.1.4 Outliers
  345. 8.6.1.1.5 Homogeneity of Variance–Covariance Matrices
  346. 8.6.1.1.6 Multicollinearity and Singularity
  347. 8.6.1.2 Profile Analysis
  348. 8.6.2 Doubly Multivariate Analysis of Reaction Time
  349. 8.6.2.1 Evaluation of Assumptions
  350. 8.6.2.1.1 Unequal Sample Sizes, Missing Data, Multivariate Normality, and Linearity
  351. 8.6.2.1.2 Outliers
  352. 8.6.2.1.3 Homogeneity of Variance–Covariance Matrices
  353. 8.6.2.1.4 Homogeneity of Regression
  354. 8.6.2.1.5 Reliability of DVs
  355. 8.6.2.1.6 Multicollinearity and Singularity
  356. 8.6.2.2 Doubly Multivariate Analysis of Slope and Intercept
  357. 8.7 Comparison of Programs
  358. 8.7.1 IBM SPSS Package
  359. 8.7.2 SAS System
  360. 8.7.3 SYSTAT System
  361. Chapter 9 Discriminant Analysis
  362. Learning Objectives
  363. 9.1 General Purpose and Description
  364. 9.2 Kinds of Research Questions
  365. 9.2.1 Significance of Prediction
  366. 9.2.2 Number of Significant Discriminant Functions
  367. 9.2.3 Dimensions of Discrimination
  368. 9.2.4 Classification Functions
  369. 9.2.5 Adequacy of Classification
  370. 9.2.6 Effect Size
  371. 9.2.7 Importance of Predictor Variables
  372. 9.2.8 Significance of Prediction with Covariates
  373. 9.2.9 Estimation of Group Means
  374. 9.3 Limitations to Discriminant Analysis
  375. 9.3.1 Theoretical Issues
  376. 9.3.2 Practical Issues
  377. 9.3.2.1 Unequal Sample Sizes, Missing Data, and Power
  378. 9.3.2.2 Multivariate Normality
  379. 9.3.2.3 Absence of Outliers
  380. 9.3.2.4 Homogeneity of Variance–Covariance Matrices
  381. 9.3.2.5 Linearity
  382. 9.3.2.6 Absence of Multicollinearity and Singularity
  383. 9.4 Fundamental Equations for Discriminant Analysis
  384. 9.4.1 Derivation and Test of Discriminant Functions
  385. 9.4.2 Classification
  386. 9.4.3 Computer Analyses of Small-Sample Example
  387. 9.5 Types of Discriminant Analyses
  388. 9.5.1 Direct Discriminant Analysis
  389. 9.5.2 Sequential Discriminant Analysis
  390. 9.5.3 Stepwise (Statistical) Discriminant Analysis
  391. 9.6 Some Important Issues
  392. 9.6.1 Statistical Inference
  393. 9.6.1.1 Criteria for Overall Statistical Significance
  394. 9.6.1.2 Stepping Methods
  395. 9.6.2 Number of Discriminant Functions
  396. 9.6.3 Interpreting Discriminant Functions
  397. 9.6.3.1 Discriminant Function Plots
  398. 9.6.3.2 Structure Matrix of Loadings
  399. 9.6.4 Evaluating Predictor Variables
  400. 9.6.5 Effect Size
  401. 9.6.6 Design Complexity: Factorial Designs
  402. 9.6.7 Use of Classification Procedures
  403. 9.6.7.1 Cross-Validation and New Cases
  404. 9.6.7.2 Jackknifed Classification
  405. 9.6.7.3 Evaluating Improvement in Classification
  406. 9.7 Complete Example of Discriminant Analysis
  407. 9.7.1 Evaluation of Assumptions
  408. 9.7.1.1 Unequal Sample Sizes and Missing Data
  409. 9.7.1.2 Multivariate Normality
  410. 9.7.1.3 Linearity
  411. 9.7.1.4 Outliers
  412. 9.7.1.5 Homogeneity of Variance–Covariance Matrices
  413. 9.7.1.6 Multicollinearity and Singularity
  414. 9.7.2 Direct Discriminant Analysis
  415. 9.8 Comparison of Programs
  416. 9.8.1 IBM SPSS Package
  417. 9.8.2 SAS System
  418. 9.8.3 SYSTAT System
  419. Chapter 10 Logistic Regression
  420. Learning Objectives
  421. 10.1 General Purpose and Description
  422. 10.2 Kinds of Research Questions
  423. 10.2.1 Prediction of Group Membership or Outcome
  424. 10.2.2 Importance of Predictors
  425. 10.2.3 Interactions Among Predictors
  426. 10.2.4 Parameter Estimates
  427. 10.2.5 Classification of Cases
  428. 10.2.6 Significance of Prediction with Covariates
  429. 10.2.7 Effect Size
  430. 10.3 Limitations to Logistic Regression Analysis
  431. 10.3.1 Theoretical Issues
  432. 10.3.2 Practical Issues
  433. 10.3.2.1 Ratio of Cases to Variables
  434. 10.3.2.2 Adequacy of Expected Frequencies and Power
  435. 10.3.2.3 Linearity in the Logit
  436. 10.3.2.4 Absence of Multicollinearity
  437. 10.3.2.5 Absence of Outliers in the Solution
  438. 10.3.2.6 Independence of Errors
  439. 10.4 Fundamental Equations for Logistic Regression
  440. 10.4.1 Testing and Interpreting Coefficients
  441. 10.4.2 Goodness of Fit
  442. 10.4.3 Comparing Models
  443. 10.4.4 Interpretation and Analysis of Residuals
  444. 10.4.5 Computer Analyses of Small-Sample Example
  445. 10.5 Types of Logistic Regression
  446. 10.5.1 Direct Logistic Regression
  447. 10.5.2 Sequential Logistic Regression
  448. 10.5.3 Statistical (Stepwise) Logistic Regression
  449. 10.5.4 Probit and Other Analyses
  450. 10.6 Some Important Issues
  451. 10.6.1 Statistical Inference
  452. 10.6.1.1 Assessing Goodness of Fit of Models
  453. 10.6.1.1.1 Constant-Only versus Full Model
  454. 10.6.1.1.2 Comparison with a Perfect (Hypothetical) Model
  455. 10.6.1.1.3 Deciles of Risk
  456. 10.6.1.2 Tests of Individual PREDICTORS
  457. 10.6.2 Effect Sizes
  458. 10.6.2.1 Effect Size for a Model
  459. 10.6.2.2 Effect Sizes for Predictors
  460. 10.6.3 Interpretation of Coefficients Using Odds
  461. 10.6.4 Coding Outcome and Predictor Categories
  462. 10.6.5 Number and Type of Outcome Categories
  463. 10.6.6 Classification of Cases
  464. 10.6.7 Hierarchical and Nonhierarchical Analysis
  465. 10.6.8 Importance of Predictors
  466. 10.6.9 Logistic Regression for Matched Groups
  467. 10.7 Complete Examples of Logistic Regression
  468. 10.7.1 Evaluation of Limitations
  469. 10.7.1.1 Ratio of Cases to Variables and Missing Data
  470. 10.7.1.2 Multicollinearity
  471. 10.7.1.3 Outliers in the Solution
  472. 10.7.2 Direct Logistic Regression with Two-Category Outcome and Continuous Predictors
  473. 10.7.2.1 Limitation: Linearity in the Logit
  474. 10.7.2.2 Direct Logistic Regression with Two-Category Outcome
  475. 10.7.3 Sequential Logistic Regression with Three Categories of Outcome
  476. 10.7.3.1 Limitations of Multinomial Logistic Regression
  477. 10.7.3.1.1 Adequacy of Expected Frequencies
  478. 10.7.3.1.2 Linearity in the Logit
  479. 10.7.3.2 Sequential Multinomial Logistic Regression
  480. 10.8 Comparison of Programs
  481. 10.8.1 IBM SPSS Package
  482. 10.8.2 SAS System
  483. 10.8.3 SYSTAT System
  484. Chapter 11 Survival/Failure Analysis
  485. Learning Objectives
  486. 11.1 General Purpose and Description
  487. 11.2 Kinds of Research Questions
  488. 11.2.1 Proportions Surviving at Various Times
  489. 11.2.2 Group Differences in Survival
  490. 11.2.3 Survival Time with Covariates
  491. 11.2.3.1 Treatment Effects
  492. 11.2.3.2 Importance of Covariates
  493. 11.2.3.3 Parameter Estimates
  494. 11.2.3.4 Contingencies Among Covariates
  495. 11.2.3.5 Effect Size and Power
  496. 11.3 Limitations to Survival Analysis
  497. 11.3.1 Theoretical Issues
  498. 11.3.2 Practical Issues
  499. 11.3.2.1 Sample Size and Missing Data
  500. 11.3.2.2 Normality of Sampling Distributions, Linearity, and Homoscedas­ticity
  501. 11.3.2.3 Absence of Outliers
  502. 11.3.2.4 Differences Between Withdrawn and Remaining Cases
  503. 11.3.2.5 Change in Survival Conditions over Time
  504. 11.3.2.6 Proportionality of Hazards
  505. 11.3.2.7 Absence of Multicollinearity
  506. 11.4 Fundamental Equations for Survival Analysis
  507. 11.4.1 Life Tables
  508. 11.4.2 Standard Error of Cumulative Proportion Surviving
  509. 11.4.3 Hazard and Density Functions
  510. 11.4.4 Plot of Life Tables
  511. 11.4.5 Test for Group Differences
  512. 11.4.6 Computer Analyses of Small-Sample Example
  513. 11.5 Types of Survival Analyses
  514. 11.5.1 Actuarial and Product-Limit Life Tables and Survivor Functions
  515. 11.5.2 Prediction of Group Survival Times from Covariates
  516. 11.5.2.1 Direct, Sequential, and Statistical Analysis
  517. 11.5.2.2 Cox Proportional-Hazards Model
  518. 11.5.2.3 Accelerated Failure-Time Models
  519. 11.5.2.4 Choosing a Method
  520. 11.6 Some Important Issues
  521. 11.6.1 Proportionality of Hazards
  522. 11.6.2 Censored Data
  523. 11.6.2.1 Right-Censored Data
  524. 11.6.2.2 Other Forms of Censoring
  525. 11.6.3 Effect Size and Power
  526. 11.6.4 Statistical Criteria
  527. 11.6.4.1 Test Statistics for Group Differences in Survival Functions
  528. 11.6.4.2 Test Statistics for Prediction From Covariates
  529. 11.6.5 Predicting Survival Rate
  530. 11.6.5.1 Regression Coefficients (Parameter Estimates)
  531. 11.6.5.2 Hazard Ratios
  532. 11.6.5.3 Expected Survival Rates
  533. 11.7 Complete Example of Survival Analysis
  534. 11.7.1 Evaluation of Assumptions
  535. 11.7.1.1 Accuracy of Input, Adequacy of Sample Size, Missing Data, and Distributions
  536. 11.7.1.2 Outliers
  537. 11.7.1.3 Differences Between Withdrawn and Remaining Cases
  538. 11.7.1.4 Change in Survival Experience over Time
  539. 11.7.1.5 Proportionality of Hazards
  540. 11.7.1.6 Multicollinearity
  541. 11.7.2 Cox Regression Survival Analysis
  542. 11.7.2.1 Effect of Drug Treatment
  543. 11.7.2.2 Evaluation of Other Covariates
  544. 11.8 Comparison of Programs
  545. 11.8.1 SAS System
  546. 11.8.2 IBM SPSS Package
  547. 11.8.3 SYSTAT System
  548. Chapter 12 Canonical Correlation
  549. Learning Objectives
  550. 12.1 General Purpose and Description
  551. 12.2 Kinds of Research Questions
  552. 12.2.1 Number of Canonical Variate Pairs
  553. 12.2.2 Interpretation of Canonical Variates
  554. 12.2.3 Importance of Canonical Variates and Predictors
  555. 12.2.4 Canonical Variate Scores
  556. 12.3 Limitations
  557. 12.3.1 Theoretical Limitations1
  558. 12.3.2 Practical Issues
  559. 12.3.2.1 Ratio of Cases to IVs
  560. 12.3.2.2 Normality, Linearity, and Homoscedasticity
  561. 12.3.2.3 Missing Data
  562. 12.3.2.4 Absence of Outliers
  563. 12.3.2.5 Absence of Multicollinearity and Singularity
  564. 12.4 Fundamental Equations for Canonical Correlation
  565. 12.4.1 Eigenvalues and Eigenvectors
  566. 12.4.2 Matrix Equations
  567. 12.4.3 Proportions of Variance Extracted
  568. 12.4.4 Computer Analyses of Small-Sample Example
  569. 12.5 Some Important Issues
  570. 12.5.1 Importance of Canonical Variates
  571. 12.5.2 Interpretation of Canonical Variates
  572. 12.6 Complete Example of Canonical Correlation
  573. 12.6.1 Evaluation of Assumptions
  574. 12.6.1.1 Missing Data
  575. 12.6.1.2 Normality, Linearity, and Homoscedasticity
  576. 12.6.1.3 Outliers
  577. 12.6.1.4 Multicollinearity and Singularity
  578. 12.6.2 Canonical Correlation
  579. 12.7 Comparison of Programs
  580. 12.7.1 SAS System
  581. 12.7.2 IBM SPSS Package
  582. 12.7.3 SYSTAT System
  583. Chapter 13 Principal Components and Factor Analysis
  584. Learning Objectives
  585. 13.1 General Purpose and Description
  586. 13.2 Kinds of Research Questions
  587. 13.2.1 Number of Factors
  588. 13.2.2 Nature of Factors
  589. 13.2.3 Importance of Solutions and Factors
  590. 13.2.4 Testing Theory in FA
  591. 13.2.5 Estimating Scores on Factors
  592. 13.3 Limitations
  593. 13.3.1 Theoretical Issues
  594. 13.3.2 Practical Issues
  595. 13.3.2.1 Sample Size and Missing Data
  596. 13.3.2.2 Normality
  597. 13.3.2.3 Linearity
  598. 13.3.2.4 Absence of Outliers Among Cases
  599. 13.3.2.5 Absence of Multicollinearity and Singularity
  600. 13.3.2.6 Factorability of R
  601. 13.3.2.7 Absence of Outliers Among Variables
  602. 13.4 Fundamental Equations for Factor Analysis
  603. 13.4.1 Extraction
  604. 13.4.2 Orthogonal Rotation
  605. 13.4.3 Communalities, Variance, and Covariance
  606. 13.4.4 Factor Scores
  607. 13.4.5 Oblique Rotation
  608. 13.4.6 Computer Analyses of Small-Sample Example
  609. 13.5 Major Types of Factor Analyses
  610. 13.5.1 Factor Extraction Techniques
  611. 13.5.1.1 PCA Versus FA
  612. 13.5.1.2 Principal Components
  613. 13.5.1.3 Principal Factors
  614. 13.5.1.4 Image Factor Extraction
  615. 13.5.1.5 Maximum Likelihood Factor Extraction
  616. 13.5.1.6 Unweighted Least Squares Factoring
  617. 13.5.1.7 Generalized (Weighted) Least Squares Factoring
  618. 13.5.1.8 Alpha Factoring
  619. 13.5.2 Rotation
  620. 13.5.2.1 Orthogonal Rotation
  621. 13.5.2.2 Oblique Rotation
  622. 13.5.2.3 Geometric Interpretation
  623. 13.5.3 Some Practical Recommendations
  624. 13.6 Some Important Issues
  625. 13.6.1 Estimates of Communalities
  626. 13.6.2 Adequacy of Extraction and Number of Factors
  627. 13.6.3 Adequacy of Rotation and Simple Structure
  628. 13.6.4 Importance and Internal Consistency of Factors
  629. 13.6.5 Interpretation of Factors
  630. 13.6.6 Factor Scores
  631. 13.6.7 Comparisons Among Solutions and Groups
  632. 13.7 Complete Example of FA
  633. 13.7.1 Evaluation of Limitations
  634. 13.7.1.1 Sample Size and Missing Data
  635. 13.7.1.2 Normality
  636. 13.7.1.3 Linearity
  637. 13.7.1.4 Outliers
  638. 13.7.1.5 Multicollinearity and Singularity
  639. 13.7.1.6 Factorability of R
  640. 13.7.1.7 Outliers Among Variables
  641. 13.7.2 Principal Factors Extraction with Varimax Rotation
  642. 13.8 Comparison of Programs
  643. 13.8.1 IBM SPSS Package
  644. 13.8.2 SAS System
  645. 13.8.3 SYSTAT System
  646. Chapter 14 Structural Equation Modeling
  647. Learning Objectives
  648. 14.1 General Purpose and Description
  649. 14.2 Kinds of Research Questions
  650. 14.2.1 Adequacy of the Model
  651. 14.2.2 Testing Theory
  652. 14.2.3 Amount of Variance in the Variables Accounted for by the Factors
  653. 14.2.4 Reliability of the Indicators
  654. 14.2.5 Parameter Estimates
  655. 14.2.6 Intervening Variables
  656. 14.2.7 Group Differences
  657. 14.2.8 Longitudinal Differences
  658. 14.2.9 Multilevel Modeling
  659. 14.2.10 Latent Class Analysis
  660. 14.3 Limitations to Structural Equation Modeling
  661. 14.3.1 Theoretical Issues
  662. 14.3.2 Practical Issues
  663. 14.3.2.1 Sample Size and Missing Data
  664. 14.3.2.2 Multivariate Normality and Outliers
  665. 14.3.2.3 Linearity
  666. 14.3.2.4 Absence of Multicollinearity and Singularity
  667. 14.3.2.5 Residuals
  668. 14.4 Fundamental Equations for Structural Equations Modeling
  669. 14.4.1 Covariance Algebra
  670. 14.4.2 Model Hypotheses
  671. 14.4.3 Model Specification
  672. 14.4.4 Model Estimation
  673. 14.4.5 Model Evaluation
  674. 14.4.6 Computer Analysis of Small-Sample Example
  675. 14.5 Some Important Issues
  676. 14.5.1 Model Identification
  677. 14.5.2 Estimation Techniques
  678. 14.5.2.1 Estimation Methods and Sample Size
  679. 14.5.2.2 Estimation Methods and Nonnormality
  680. 14.5.2.3 Estimation Methods and Dependence
  681. 14.5.2.4 Some Recommendations for Choice of Estimation Method
  682. 14.5.3 Assessing the Fit of the Model
  683. 14.5.3.1 Comparative Fit Indices
  684. 14.5.3.2 Absolute Fit Index
  685. 14.5.3.3 Indices of Proportion of Variance Accounted FOR
  686. 14.5.3.4 Degree of Parsimony Fit Indices
  687. 14.5.3.5 Residual-Based Fit Indices
  688. 14.5.3.6 Choosing among Fit Indices
  689. 14.5.4 Model Modification
  690. 14.5.4.1 Chi-Square Difference Test
  691. 14.5.4.2 Lagrange Multiplier (LM) Test
  692. 14.5.4.3 Wald Test
  693. 14.5.4.4 Some Caveats and Hints on Model Modification
  694. 14.5.5 Reliability and Proportion of Variance
  695. 14.5.6 Discrete and Ordinal Data
  696. 14.5.7 Multiple Group Models
  697. 14.5.8 Mean and Covariance Structure Models
  698. 14.6 Complete Examples of Structural Equation Modeling Analysis
  699. 14.6.1 Confirmatory Factor Analysis of the WISC
  700. 14.6.1.1 Model Specification for CFA
  701. 14.6.1.2 Evaluation of Assumptions for CFA
  702. 14.6.1.2.1 Sample Size and Missing Data
  703. 14.6.1.2.2 Normality and Linearity
  704. 14.6.1.2.3 Outliers
  705. 14.6.1.2.4 Multicollinearity and Singularity
  706. 14.6.1.2.5 Residuals
  707. 14.6.1.3 CFA Model Estimation and Preliminary Evaluation
  708. 14.6.1.4 Model Modification
  709. The Hypothesized Model
  710. Assumptions
  711. Model Estimation
  712. 14.6.2 SEM of Health Data
  713. 14.6.2.1 SEM Model Specification
  714. 14.6.2.2 Evaluation of Assumptions for SEM
  715. 14.6.2.2.1 Sample Size and Missing Data
  716. 14.6.2.2.2 Normality and Linearity
  717. 14.6.2.2.3 Outliers
  718. 14.6.2.2.4 Multicollinearity and Singularity
  719. 14.6.2.2.5 Adequacy of Covariances
  720. 14.6.2.2.6 Residuals
  721. 14.6.2.3 SEM Model Estimation and Preliminary Evaluation
  722. 14.6.2.4 Model Modification
  723. The Hypothesized Model
  724. Assumptions
  725. Model Estimation
  726. Direct Effects
  727. Indirect Effects
  728. 14.7 Comparison of Programs
  729. 14.7.1 EQS
  730. 14.7.2 LISREL
  731. 14.7.3 AMOS
  732. 14.7.4 SAS System
  733. Chapter 15 Multilevel Linear Modeling
  734. Learning Objectives
  735. 15.1 General Purpose and Description
  736. 15.2 Kinds of Research Questions
  737. 15.2.1 Group Differences in Means
  738. 15.2.2 Group Differences in Slopes
  739. 15.2.3 Cross-Level Interactions
  740. 15.2.4 Meta-Analysis
  741. 15.2.5 Relative Strength of Predictors at Various Levels
  742. 15.2.6 Individual and Group Structure
  743. 15.2.7 Effect Size
  744. 15.2.8 Path Analysis at Individual and Group Levels
  745. 15.2.9 Analysis of Longitudinal Data
  746. 15.2.10 Multilevel Logistic Regression
  747. 15.2.11 Multiple Response Analysis
  748. 15.3 Limitations to Multilevel Linear Modeling
  749. 15.3.1 Theoretical Issues
  750. 15.3.2 Practical Issues
  751. 15.3.2.1 Sample Size, Unequal-n, and Missing Data
  752. 15.3.2.2 Independence of Errors
  753. 15.3.2.3 Absence of Multicollinearity and Singularity
  754. 15.4 Fundamental Equations
  755. 15.4.1 Intercepts-Only Model
  756. 15.4.1.1 The Intercepts-Only Model: Level-1 Equation
  757. 15.4.1.2 The Intercepts-Only Model: Level-2 Equation
  758. 15.4.1.3 Computer Analyses of Intercepts-Only Model
  759. 15.4.2 Model with a First-Level Predictor
  760. 15.4.2.1 Level-1 Equation for a Model With a Level-1 Predictor
  761. 15.4.2.2 Level-2 Equations for a Model With a Level-1 Predictor
  762. 15.4.2.3 Computer Analysis of a Model With a Level-1 Predictor
  763. 15.4.3 Model with Predictors at First and Second Levels
  764. 15.4.3.1 Level-1 Equation for Model with Predictors at Both Levels
  765. 15.4.3.2 Level-2 Equations for Model with Predictors at Both Levels
  766. 15.4.3.3 Computer Analyses of Model With Predictors at First and Second Levels
  767. 15.5 Types of MLM
  768. 15.5.1 Repeated Measures
  769. 15.5.2 Higher-Order MLM
  770. 15.5.3 Latent Variables
  771. 15.5.4 Nonnormal Outcome Variables
  772. 15.5.5 Multiple Response Models
  773. 15.6 Some Important Issues
  774. 15.6.1 Intraclass Correlation
  775. 15.6.2 Centering Predictors and Changes in Their Interpretations
  776. 15.6.3 Interactions
  777. 15.6.4 Random and Fixed Intercepts and Slopes
  778. 15.6.5 Statistical Inference
  779. 15.6.5.1 Assessing Models
  780. 15.6.5.2 Tests of Individual Effects
  781. 15.6.6 Effect Size
  782. 15.6.7 Estimation Techniques and Convergence Problems
  783. 15.6.8 Exploratory Model Building
  784. 15.7 Complete Example of MLM
  785. 15.7.1 Evaluation of Assumptions
  786. 15.7.1.1 Sample Sizes, Missing Data, and Distributions
  787. 15.7.1.2 Outliers
  788. 15.7.1.3 Multicollinearity and Singularity
  789. 15.7.1.4 Independence of Errors: Intraclass Correlations
  790. 15.7.2 Multilevel Modeling
  791. Hypothesized Model
  792. Assumptions
  793. Multilevel Modeling
  794. 15.8 Comparison of Programs
  795. 15.8.1 SAS System
  796. 15.8.2 IBM SPSS Package
  797. 15.8.3 HLM Program
  798. 15.8.4 MLwiN Program
  799. 15.8.5 SYSTAT System
  800. Chapter 16 Multiway Frequency Analysis
  801. Learning Objectives
  802. 16.1 General Purpose and Description
  803. 16.2 Kinds of Research Questions
  804. 16.2.1 Associations Among Variables
  805. 16.2.2 Effect on a Dependent Variable
  806. 16.2.3 Parameter Estimates
  807. 16.2.4 Importance of Effects
  808. 16.2.5 Effect Size
  809. 16.2.6 Specific Comparisons and Trend Analysis
  810. 16.3 Limitations to Multiway Frequency Analysis
  811. 16.3.1 Theoretical Issues
  812. 16.3.2 Practical Issues
  813. 16.3.2.1 Independence
  814. 16.3.2.2 Ratio of Cases to Variables
  815. 16.3.2.3 Adequacy of Expected Frequencies
  816. 16.3.2.4 Absence of Outliers in the Solution
  817. 16.4 Fundamental Equations for Multiway Frequency Analysis
  818. 16.4.1 Screening for Effects
  819. 16.4.1.1 Total Effect
  820. 16.4.1.2 First-Order Effects
  821. 16.4.1.3 Second-Order Effects
  822. 16.4.1.4 Third-Order Effect
  823. 16.4.2 Modeling
  824. 16.4.3 Evaluation and Interpretation
  825. 16.4.3.1 Residuals
  826. 16.4.3.2 Parameter Estimates
  827. 16.4.4 Computer Analyses of Small-Sample Example
  828. 16.5 Some Important Issues
  829. 16.5.1 Hierarchical and Nonhierarchical Models
  830. 16.5.2 Statistical Criteria
  831. 16.5.2.1 Tests of Models
  832. 16.5.2.2 Tests of Individual Effects
  833. 16.5.3 Strategies for Choosing a Model
  834. 16.5.3.1 IBM SPSS HILOGLINEAR (Hierarchical)
  835. 16.5.3.2 IBM SPSS GENLOG (General Log-Linear)
  836. 16.5.3.3 SAS CATMOD and IBM SPSS LOGLINEAR (General Log-Linear)
  837. 16.6 Complete Example of Multiway Frequency Analysis
  838. 16.6.1 Evaluation of Assumptions: Adequacy of Expected Frequencies
  839. 16.6.2 Hierarchical Log-Linear Analysis
  840. 16.6.2.1 Preliminary Model Screening
  841. 16.6.2.2 Stepwise Model Selection
  842. 16.6.2.3 Adequacy of Fit
  843. 16.6.2.4 Interpretation of the Selected Model
  844. 16.7 Comparison of Programs
  845. 16.7.1 IBM SPSS Package
  846. 16.7.2 SAS System
  847. 16.7.3 SYSTAT System
  848. Chapter 17 Time-Series Analysis
  849. Learning Objectives
  850. 17.1 General Purpose and Description
  851. 17.2 Kinds of Research Questions
  852. 17.2.1 Pattern of Autocorrelation
  853. 17.2.2 Seasonal Cycles and Trends
  854. 17.2.3 Forecasting
  855. 17.2.4 Effect of an Intervention
  856. 17.2.5 Comparing Time Series
  857. 17.2.6 Time Series with Covariates
  858. 17.2.7 Effect Size and Power
  859. 17.3 Assumptions of Time-Series Analysis
  860. 17.3.1 Theoretical Issues
  861. 17.3.2 Practical Issues
  862. 17.3.2.1 Normality of Distributions of Residuals
  863. 17.3.2.2 Homogeneity of Variance and Zero Mean of Residuals
  864. 17.3.2.3 Independence of Residuals
  865. 17.3.2.4 Absence of Outliers
  866. 17.3.2.5 Sample Size and Missing Data
  867. 17.4 Fundamental Equations for Time-Series ARIMA Models
  868. 17.4.1 Identification of ARIMA (p, d, q) Models
  869. 17.4.1.1 Trend Components, d: Making the Process Stationary
  870. 17.4.1.2 Auto-Regressive Components
  871. 17.4.1.3 Moving Average Components
  872. 17.4.1.4 Mixed Models
  873. 17.4.1.5 ACFs and PACFs
  874. 17.4.2 Estimating Model Parameters
  875. 17.4.3 Diagnosing a Model
  876. 17.4.4 Computer Analysis of Small-Sample Time-Series Example
  877. 17.5 Types of Time-Series Analyses
  878. 17.5.1 Models with Seasonal Components
  879. 17.5.2 Models with Interventions
  880. 17.5.2.1 Abrupt, Permanent Effects
  881. 17.5.2.2 Abrupt, Temporary Effects
  882. 17.5.2.3 Gradual, Permanent Effects
  883. 17.5.2.4 Models with Multiple Interventions
  884. 17.5.3 Adding Continuous Variables
  885. 17.6 Some Important Issues
  886. 17.6.1 Patterns of ACFs and PACFs
  887. 17.6.2 Effect Size
  888. 17.6.3 Forecasting
  889. 17.6.4 Statistical Methods for Comparing Two Models
  890. 17.7 Complete Examples of Time-Series Analysis
  891. 17.7.1 Time-Series Analysis of Introduction of Seat Belt Law
  892. 17.7.1.1 Evaluation of Assumptions
  893. 17.7.1.1.1 Normality of Sampling Distributions
  894. 17.7.1.1.2 Homogeneity of Variance
  895. 17.7.1.1.3 Outliers
  896. 17.7.1.2 Baseline Model Identification and Estimation
  897. 17.7.1.3 Baseline Model Diagnosis
  898. 17.7.1.4 Intervention Analysis
  899. 17.7.1.4.1 Model Diagnosis
  900. 17.7.1.4.2 Model Interpretation
  901. 17.7.2. Time-Series Analysis of Introduction of a Dashboard to an Educational Computer Game
  902. 17.7.2.1 Evaluation of Assumptions
  903. 17.7.2.1.1 Normality of Sampling Distributions and Homogeneity of Variance
  904. 17.7.2.1.2 Outliers
  905. 17.7.2.2 Baseline Model Identification and Diagnosis
  906. 17.7.2.3 Intervention Analysis
  907. 17.7.2.3.1 Model Diagnosis
  908. 17.7.2.3.2 Model Interpretation
  909. 17.8 Comparison of Programs
  910. 17.8.1 IBM SPSS Package
  911. 17.8.2 SAS System
  912. 17.8.3 SYSTAT System
  913. Chapter 18 An Overview of the General Linear Model
  914. Learning Objectives
  915. 18.1 Linearity and the General Linear Model
  916. 18.2 Bivariate to Multivariate Statistics and Overview of Techniques
  917. 18.2.1 Bivariate Form
  918. 18.2.2 Simple Multivariate Form
  919. 18.2.3 Full Multivariate Form
  920. 18.3 Alternative Research Strategies
  921. Appendix A A Skimpy Introduction to Matrix Algebra
  922. A.1 The Trace of a Matrix
  923. A.2 Addition or Subtraction of a Constant to a Matrix
  924. A.3 Multiplication or Division of a Matrix by a Constant
  925. A.4 Addition and Subtraction of Two Matrices
  926. A.5 Multiplication, Transposes, and Square Roots of Matrices
  927. A.6 Matrix “Division” (Inverses and Determinants)
  928. A.7 Eigenvalues and Eigenvectors: Procedures for Consolidating Variance from a Matrix
  929. Appendix B Research Designs for Complete Examples
  930. B.1 Women’s Health and Drug Study
  931. Method
  932. B.2 Sexual Attraction Study
  933. Method
  934. B.3 Learning Disabilities Data Bank
  935. B.4 Reaction Time to Identify Figures
  936. B.5 Field Studies of Noise-Induced Sleep Disturbance
  937. B.6 Clinical Trial for Primary Biliary Cirrhosis
  938. B.7 Impact of Seat Belt Law
  939. B.8 The Selene Online Educational Game
  940. Appendix C Statistical Tables
  941. References
  942. Index

 

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