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Stevens J.P. — Intermediate Statistics: A Modern Approach
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Название: Intermediate Statistics: A Modern Approach
Автор: Stevens J.P.
Аннотация: James Stevens’ best-selling text is written for those who use, rather than develop, statistical techniques. Dr. Stevens focuses on a conceptual understanding of the material rather than on proving the results. Definitional formulas are used on small data sets to provide conceptual insight into what is being measured. The assumptions underlying each analysis are emphasized, and the reader is shown how to test the critical assumptions using SPSS or SAS. Printouts with annotations from SAS or SPSS show how to process the data for each analysis. The annotations highlight what the numbers mean and how to interpret the results. Numerical, conceptual, and computer exercises enhance understanding. Answers are provided for half of the exercises.
The book offers comprehensive coverage of one-way, power, and factorial analysis of variance, repeated measures analysis, simple and multiple regression, analysis of covariance, and HLM. Power analysis is an integral part of the book. A computer example of real data integrates many of the concepts. Highlights of the Third Edition include:*a new chapter on hierarchical linear modeling using HLM6;
*a CD containing all of the book’s data sets;
*new coverage of how to cross validate multiple regression results with SPSS and a new section on model selection (Ch. 6);
*more exercises in each chapter. Intended for intermediate statistics or statistics II courses taught in departments of psychology, education, business, and other social and behavioral sciences, a prerequisite of introductory statistics is required. An Instructor’s Solutions CD is available to adopters.
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Рубрика: Математика /
Статус предметного указателя: Готов указатель с номерами страниц
ed2k: ed2k stats
Издание: 3rd Edition
Год издания: 2007
Количество страниц: 460
Добавлена в каталог: 14.05.2008
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Предметный указатель
Actual alpha 57 323
Adelman, H. 230
Agresti, A. 230 275 282 361 391 392 393
Analysis of covariance (Ancova) 286
Analysis of covariance (ANCOVA), adjusted means 289—291
Analysis of covariance (ANCOVA), alternative analyses 305—306
Analysis of covariance (ANCOVA), assumptions 297 300
Analysis of covariance (ANCOVA), by multiple regression 297
Analysis of covariance (ANCOVA), choice of covariates 293
Analysis of covariance (ANCOVA), computer example 304—305
Analysis of covariance (ANCOVA), computer example with 2 covariates 312—314
Analysis of covariance (ANCOVA), covariate by treatment interaction 300
Analysis of covariance (ANCOVA), homogeneity of regression slopes 297—300 306 308
Analysis of covariance (ANCOVA), null hypothesis 294
Analysis of covariance (ANCOVA), numerical example 293—294
Analysis of covariance (ANCOVA), purposes 287—288
Analysis of covariance (ANCOVA), reduction of error variance 288 292—293
Analysis of variance (ANOVA) examples 45
Analysis of variance (ANOVA) examples, assumptions 56
Analysis of variance (ANOVA) examples, computer example on SAS and SPSS with Tukey procedure 62
Analysis of variance (ANOVA) examples, computer example with unequal variances and Games — Howell and Tamhane procedures 77 93
Analysis of variance (ANOVA) examples, expected mean squares 53
Analysis of variance (ANOVA) examples, F test 51
Analysis of variance (ANOVA) examples, linear model 55
Analysis of variance (ANOVA) examples, numerical example between group variation 49—50
Analysis of variance (ANOVA) examples, numerical example within group variation 50—51
Anderson , N.H. 303
Anscombe, F. 228 229
Aptitude by treatment interaction research (ATI) 124
ascii file 39—40
Baker, F 189
Balanced design 136
Barcikowski, R. 61 189
Becker, B. 110
Berry , J. 97
Bloom, B. 59
Bock, R.D. 57 182 212 390
Bonferroni inequality 80—81
Bonferroni inequality, improved Bonferroni type procedure 162—163
Bosker, R. 323 324 343
Bounds, W. 182 317 318 323
Box, G.P. 24 25 168 188 207 208 209 214
Breen, L. 189
Brown, M.B. 73 389
Browne, W. 358
Bryan , T. 174 285 314 317
Bryant, J.L. 285 314 317
Bryk, A.S. 59 303 322 324 325 327 328 329 337 338 339 341 342 343
Burstein, L. 321
Carlson, J. 144
Central limit theorem 57
Chase, C. 21 160 161 329
Cheong, Y.F. 325 329 337 338
Circularity 187
Cochran, W.G. 58 165 287 319
Coggeshall, P. 390
Cohen, J. 3 76 108 109 113 116 118 166 167 236 258
Cohen, P. 76 258
Collier, R. 189
Compact disk 39—40
Compound symmetry 187
Congdon, R. 325 329 337 338
Conservative 58 68
contrast 71 82
Cook, R.D. 47 227 240 246 261 262 263 264 266 273 277
Copenhaver, M.D. 162
Cormier, W. 182
Counterbalancing 185 213
Cradler, J. 146
Cronbach, L. 111 124 150 172
Crowder, R. 255
Crystal, G. 230
Dance, K. 124 315
Daniels, R. 125 145
data files 21 23
Dataset editing 25—28
Davenport , J. 59 323
Davidson, M.L. 189
de Leeuw, J. 323 324 326 342 343 358
Delaney, H.D. 353
Dizney, H. 234
Draper, D. 358
Draper, N. 232 249 250 254
Dudycha, A. 263 265 273
Dummy coding (group membership) 267 270
Dunnett procedure 68 92
Dunnett, C.W. 68 69 92 98 99
Effect size, factorial ANOVA 168
Effect size, one way ANOVA 168
Effect size, t test 168
Elashoff, J. 203 204 217 287 303
Empathy model data 355
Epsilon, Greenhouse — Geisser 215
Epsilon, Huynh — Feldt 189
Eta squared 78
Excel (spreadsheet program) 23
Expected mean squares 53
Factorial analysis of variance, advantages 124
Factorial analysis of variance, balanced design 136
Factorial analysis of variance, four way 160—161
Factorial analysis of variance, interpretation of effects 146
Factorial analysis of variance, numerical example for two way 127—133
Factorial analysis of variance, on SAS and SPSS 153
Factorial analysis of variance, three way 144—157
Factorial analysis of variance, two computer examples 138—142
Factorial analysis of variance, unbalanced design 136—137
Feldt, L. 187 189 215 217
Feshbach, S. 230
Fixed effects 169
Fixed factor 169—170
Forsythe, A.B. 73
Frane, J. 164
Games, P. 41 73 74 77 93
Geisser, S. 188 204 215 217
Glasnapp, D. 222 304
Glass, G. 57 60 79 102 182
Goldstein, H. 358
Goodwin, D. 146
Greenhouse, S. 188 204 215 217
Gromen, L. 234
Hagen, E. 307
Hand, D.J. 141
Harmonic mean 69
Harrington, S. 113
Hayes, T. 189
Hays, W. 76 80 85
Hayter, A. 68
Herzberg, P.A. 239 254 258
Heterogeneous variances and unequal group sizes 73
Hierarchical Linear Modeling (HLM), adding predictors 340 348
Hierarchical Linear Modeling (HLM), data analysis of 329
Hierarchical Linear Modeling (HLM), datasets 330—331
Hierarchical Linear Modeling (HLM), empathy model data 355
Hierarchical Linear Modeling (HLM), estimating parameters 335—338
Hierarchical Linear Modeling (HLM), evaluating efficacy 351
Hierarchical Linear Modeling (HLM), MDM file 331—335
Hierarchical Linear Modeling (HLM), multilevel data, single-level analysis 323
Hierarchical Linear Modeling (HLM), multilevel model, formulation of 325
Hierarchical Linear Modeling (HLM), two-level example 329
Hierarchical Linear Modeling (HLM), two-level model, formulation of 325—328
Hierarchical Linear Modeling (HLM), two-level unconditional model 335
Higher order designs see “Factorial ANOVA”
HLM software output 338—339 344—345 347—348 356—357
HLM6 329
Hoaglin, D. 261
Holland, B.S. 162
Holm, S. 162 163
Homogeneity of variance assumption statistical tests for 58
Hopkins, K. 60 79 102 182
Howell, J.K. 73 74 77 93
Hox, J.J. 323 324 328 348 359
Huberty, C.J. 68 238
Huck, S. 182 305 307 314 317 318
Huitema, B. 287 293 304 308 310
Huynh, H. 187 189 215 217
Hyman, R. 173
Importing datasets 23
Independence of observations 59
Influential points 227
Interaction, disordinal 125
Interaction, ordinal 125
Intraclass correlation 59 323—324
Jennings, E. 305 314
Johnson, P.O. 145
Johnson, R. 164
Johnson—Neyman technique 287 303 308
Jones, L.V. 40 95 153 246 390
Judd, C. 61 321 322
Kaiser, M. 189
Kenny, D. 61 321 322
Keppel, G. 183 185
Kerlinger, F. 76
Keselman, H.J. 189 197 201
Keselman, J.C. 197 201
Kirk, R. 76 318
Kreft, I. 321 323 324 326 342 343 358
Lepine, D. 187
level of significance 47
Levin, J. 97 98
Liberal 58
Light, R. 109
Lindzey, G. 246 390
Littell, R.C. 358
Locus of control 16
Longford, N.T. 358
Lord, F.M. 236 303
Lotus 1-2-3 (spreadsheet program) 23
Main effects 128 130
Mallows, C.L. 238 240 247 263 272 278
Mandeville, C.K 189
Marwit, S. 145
Maxwell, S.E. 193 194 201 353
McCabe, G. 250
McCormick, C. 97
McLean, R.A. 305 307 314
Measures of association 75
Mendoza, J. 189
Merging files 28
Miller, G. 97
Milliken, G.A. 358
Missing data 31
Moore, D. 250
Morrison, D.F. 239 240 241 244 250 259 261 262 264 282
Multiple regression, ANOVA as a special case of regression analysis 265
Multiple regression, computer examples 239
Multiple regression, controlling order with SAS and SPSS 257
Multiple regression, examples of 230
Multiple regression, Mallow’s 238
Multiple regression, mathematical maximization procedure 231
Multiple regression, MAXR (from SAS) 246
Multiple regression, model selection procedures, multicollinearity 234
Multiple regression, model selection procedures, multiple correlation 231 233
Multiple regression, model selection procedures, substantive knowledge 236
Multiple regression, model selection procedures, variance inflation factor 235
Multiple regression, model validation, adjusted 254
Multiple regression, model validation, data splitting 239 252—253
Multiple regression, model validation, Press statistic 283
Multiple regression, order of predictors 255
Multiple regression, positive bias of 258
Multiple regression, preselection of predictors 257
Multiple regression, sample size (for a reliable prediction equation) 258
Multiple regression, sequential procedures, backward selection 237
Multiple regression, sequential procedures, forward selection 237
Multiple regression, stepwise 237
Multivariate analysis of variance (Manova) 89
Murray, R.M. 69
Myers, J. 144 175 185 195 198 293 294 295 297 298 301
Myers, R. 233 235 238 277 283
Neufeld, R. 124
Neumann, G. 145
Neyman, J. 285 287 300 308 311 314 315
Normality 9 187
Notebook computer 16
Novick, M. 236
Novince, L. 315
Nunnally, J. 231 257
Omega squared 76
Orthogonal comparisons 83
Outliers 12
Outliers in regression analysis 260
Outliers, detecting 13
Outliers, effect on correlation 14
Output navigator (SPSS) 31
Overall alpha 197
Overall, J. 138
O’Brien, R. 189
O’Grady, K. 76 79 96
p values 67—68
Park, C. 263 265 273
Partial correlation 237
Partial eta squared 116
Paulson, A.S. 285 314 317
Peckham, P. 57
Pedhazur, E. 76 144 304
Pillimer, D. 109
Planned comparisons 79—87 212
Planned comparisons on SAS and SPSS 87
Planned comparisons, test statistic 84—85
Platykurtosis 57
Plewis, I. 358
Poggio, J. 222 304
Porter, A. 304
Power on SPSS MANOVA 116
Power, a priori determination of sample size 111
Power, factors dependent on 106
Power, post hoc estimation of 111
Power, ways of improving 115
Presley, M. 97
Pukulski, J. 172
Random factor 169—170
Rasbash, J. 358
Raudenbush, S.W. 322 324 325 327 328 329 337 338 339 341 342 343 358
Regression, multiple see “Multiple regression”
Regression,simple 219—225
Reichardt, C.S. 303
Repeated measures analysis, advantages and disadvantages 184—185
Repeated measures analysis, advantages and disadvantages, single group, univariate approach 186
Repeated measures analysis, assumptions 187
Repeated measures analysis, computer analysis on SAS and SPSS 190
Repeated measures analysis, one between and one within (trend analysis) 194
Repeated measures analysis, one between and two within 203—208 210
Repeated measures analysis, planned comparisons 212
Repeated measures analysis, SPSS syntax setup for Helmert contrasts 212
Repeated measures analysis, totally within designs 209 211
Repeated measures analysis, univariate and multivariate approaches compared 189
Residual plots 250
Robey, R. 189
Robust 57
Rogan, J. 69 189
Rogosa, D. 300
Rosenthal, R. 79
Rosnow, R. 79
Rounet, H. 187
Sample variance 2
Sanders, J. 57
SAS (selected printouts), ANCOVA 295—296
SAS (selected printouts), MAXR regression 248
SAS (selected printouts), one between and two within repeated measures 206
SAS (selected printouts), one way ANOVA 64
SAS (selected printouts), planned comparisons 91
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